Computer Science and Convergence: CSA 2011 & WCC 2011 Proceedings

Lecture Notes in Electrical Engineering Volume 114

For further volumes: http://www.springer.com/series/7818

James J. (Jong Hyuk) Park
Han-Chieh Chao Mohammad S. Obaidat Jongsung Kim •

Editors

Computer Science and Convergence CSA 2011 & WCC 2011 Proceedings

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Prof. James J. (Jong Hyuk) Park Department of Computer Science and Engineering Seoul National University of Science & Technology Gongreung 2-dong 172 Seoul 139-742, Republic of South Korea e-mail: [email protected] Prof. Han-Chieh Chao Institute of Computer Science & Information Engineering and Department of Electronic Engineering National Ilan University 1 Sec. 1, Shen-Lung Rd. 260, I-Lan, Taiwan e-mail: [email protected]

ISSN 1876-1100 ISBN 978-94-007-2791-5 DOI 10.1007/978-94-007-2792-2

Prof. Mohammad S. Obaidat Department of Computer Science & Software Engineering Monmouth University W. Long Branch NJ USA e-mail: [email protected] Prof. Jongsung Kim Department of e-Business Kyungnam University Masan Changwon Gyeongnam Republic of South Korea e-mail: [email protected]

e-ISSN 1876-1119 e-ISBN 978-94-007-2792-2

Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2011943213  Springer Science+Business Media B.V. 2012 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Message from the CSA 2011 General Chairs

Welcome to the 3rd International Conference on Computer Science and its Applications (CSA 2011), held in the Jeju Island, Korea. CSA 2011 is the next event in a series of highly successful International Conference on Computer Science and its Applications, previously held as CSA 2008 (Australia, October, 2008) and CSA 2009 (Jeju, December, 2009). The CSA 2011 will be the most comprehensive conference focused on the various aspects of advances in computer science and its applications. The CSA 2011 will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of CSA. We would like to sincerely thank the following speakers who kindly accepted our invitations, and, in this way, helped to meet the objectives of the conference: • • • •

Mohammad S. Obaidat, Monmouth University, USA Habib F. Rashvand, University of Warwick, UK Han-Chieh Chao, National Ilan University, Taiwan Hai Jin, Huazhong University of Science and Technology (HUST), China

We sincerely thank the CSA 2011 honorary chiar, Makoto Takizawa and also thank the CSA 2011 steering chairs, James J. (Jong Hyuk) Park, Han-Chieh Chao and Mohammad S. Obaidat. It would have been impossible to organize our program without the hard work of all our chairs and committees. Finally, we would like to thank the DCIT 2011, HNCA 2011, MIST 2011, CC 2011 and MCM 2011 workshop chairs for contributions that have added variety to the topics represented. Sang-Soo Yeo and Victor Leung CSA 2011 General Chairs

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Message from the CSA 2011 Program Chairs

On behalf of the CSA 2011 organizing committee, it is our pleasure to welcome you to the 3rd International Conference on Computer Science and its Applications (CSA 2011). The success of a conference is mainly determined by the quality of its technical program. This year’s program will live up to high expectations due to the careful selection by the Program Committee. They have spent long hours in putting together an excellent program and deserve a big applaud. The conference received 133 submissions, and all were reviewed by the Program Committee. In the review process, we assigned at least three Program Committee members to each paper. After careful deliberation and peer-reviews, we selected 42 papers for presentation and inclusion of the conference proceedings, whose acceptance rate is around 31.5%. There are many people who contributed to the success of CSA 2011. We would like to thank the many authors from around the world for submitting their papers. We are deeply grateful to the Program Committee for their hard work and enthusiasm that each paper received a thorough and fair review. Finally, we would like to thank all the participants for their contribution to the conference. Jongsung Kim, Isaac Woungang, Ray (Yueh-Min) Huang, Irfan Awan, Vincenzo Loia CSA 2011 Program Chairs

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CSA 2011 Organization

Honorary Chair Makoto Takizawa, Seikei University, Japan Steering Chairs James J. (Jong Hyuk) Park, SeoulTech, Korea Han-Chieh Chao, National Ilan University, Taiwan Mohammad S. Obaidat, Monmouth University, USA General Chairs Sang-Soo Yeo, Mokwon University, Korea Victor Leung, University of British Columbia, Canada Program Chairs Jongsung Kim, Kyungnam University, Korea Isaac Woungang, Ryerson University, Canada Ray (Yueh-Min) Huang, National Cheng-Kung University, Taiwan Irfan Awan, University of Bradford, UK Vincenzo Loia, University of Salerno, Italy Workshop Chairs Young-Sik Jeong, Wonkwang University, Korea Damien Sauveron, University of Limoges, France Der-Jiunn Deng, National Changhua University of Education, Taiwan Jalal Al Muhtadi, King Saud University, Saudi Arabia International Advisory Board Hamid R. Arabnia, The University of Georgia, USA Doo-soon Park, SoonChunHyang University, Korea Hsiao-Hwa Chen, Sun Yat-Sen University, Taiwan Philip S. Yu, University of Illinois at Chicago, USA Yi Pan, Georgia State University, USA ix

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CSA 2011 Organization

Salim Hariri, University of Arizona, USA Leonard Barolli, Fukuoka Institute of Technology, Japan Jiankun Hu, RMIT University, Australia Shu-Ching Chen, Florida International University, USA Lei Li, Hose University, Japan Kevin (Jar-Ferr) Yang, National Cheng Kung University, Taiwan Local Arrangement Chairs Sang Yep Nam, Kookje College, Korea Deok Gyu Lee, ETRI, Korea Taeshik Shon, Samsung Electronics, Korea Publicity Chairs David Taniar, Monash University, Australia Joel Rodrigues, University of Beira Interior, Portugal Markus Aleksy, ABB Corporate Research Center, Germany Chengcui Zhang, The University of Alabama at Birmingham, USA Senol Z. Erdogan, Maltepe University, Turkey Jose A. Onieva, University of Malaga, Spain Seong O. Hwang, Hongik University, Korea Sang Oh ParK, Chungang University, Korea Finance & Registration Chair Changhoon Lee, Hanshin University, Korea Registration and Finance Chair Changhoon Lee, Hanshin University, Korea Web and system management chair Yang Sun Lee, Chosun University, Korea Jaeik Cho, Korea University, Korea

Track Chairs Track 1. Mobile and ubiquitous computing Deok Gyu Lee, ETRI, Korea Sanjay Kumar Dhurandher, University of Delhi, India Hoon Choi, Chungnam National University, Korea Track 2. Dependable, reliable and autonomic computing Kyusuk Han, KAIST, Korea Deqing Zou, Huazhong University of Science & Technology, China Jiman Hong, Soongsil University, Korea

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Track 3. Security and trust management Namsu Chang, Sejong Cyber University, Korea Ahmad AlMulhem, King Fahd University of Petroleum & Minerals, Saudi Arabia Isaac Woungang, Ryerson University, Toronto, Canada Track 4. Graphics, multimedia systems and visual computing Chih-Lin Hu, National Central University, Taiwan Liang Zhou, Institute of Media Technology, Germany Lei Ye, University of Wollongong, Australia Track 5. Networking and communications Naveen Chilamkurti, La Trobe University, Australia Yuh-Shyan Chen, National Taipei University, Taiwan Yang Sun Lee, Chosun University, Korea Track 6. Database and data mining Takahiro Hara, Osaka University, Japan Richi Nayak, Queensland University of Technology, Australia Taeshik Shon, Ajou University, Korea Track 7. Game and software engineering Shu-Ching Chen, Florida International University, USA Ashkan Sami, Shiraz University, Iran Roman Neruda, Academy of Sciences of the Czech Republic, Czech Republic Track 8. Grid, cloud and scalable computing Christophe Cerin, Universite de Paris Nord, Frence Jen-Wen Ding, National Kaohsiung University of Applied Sciences, Taiwan Yeong-Deok Kim, Woosong University, Korea Track 9. Embedded system and software Chin-Fu Kuo, the National Kaohsiung University, Taiwan Jian-Jia Chen, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland Qing Tan, Athabasca University, Canada Chin Feng Lai, National Ilan University, Taiwan Track 10. Artificial intelligence Vincenzo Loia, University of Salerno, Italy Yo-Ping Huang, National Taipei University of Technology, Taiwan Simone Ludwig, North Dakota State University, USA Alex Kuo, School of Health Information Science, University of Victoria, Canada Ruey-Maw Chen, National Chin-Yi University of Technology, Taiwan

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CSA 2011 Organization

Track 11. Distributed and parallel algorithms Ching-Hsien Hsu, Chung Hua University, Taiwan Jemal H. Abbawajy, Deakin University, Austrailia Constantinos Mourlas, University of Athens, Greece Track 12. Web and internet computing Ruay-Shiung Chang, National Dong Hwa University, Taiwan SangKeun Lee, Korea University, Korea Guandong Xu, Victoria University, Australia Track 13. IT policy and business management Hangbae Chang, Daejin University, Korea Andrew Kusiak, The University of Iowa, USA Ying Jiang, School of Information Management, Wuhan University, China Program Committee Track 1. Mobile and ubiquitous computing Andry Rakotonirainy, Queensland University of Technology, Australia David Taniar, Monash University, Australia Evi Syukur, University of New South Wales, Australia Jan Newmarch, BoxHill Institute, Australia Sudip Misra,Indian Institute of Technology, Kharagpur, India G. V. Singh,Jawaharlal Nehru University, New Delhi, India R. K. Pateriya, Maulana Azad National Institute of Technology, Bhopal, India Vijeta Mehra, University of Delhi, India M. Dominguez-Morales, University of Seville, SPAIN Taek Jin Kwon, Telchordia Technologies, USA Kae-Won Choi, SeoulTech, Korea Min Choi, Department of Computer Engineering, Wonkwang University, Korea Seung-Ho Lim, Hankuk University of Foreign Studies, Korea Nak-Seon Seong, Electronics and Telecommunications Research Institute, Korea Kilhung Lee, Seoul National University of Science and technology, Korea Hyo Hyun Choi, Department of Computer Science, Inha Technical College, Korea Track 2. Dependable, reliable and autonomic computing Masakazu Soshi, Hiroshima City University, Japan Gregorio Martinez, University of Murcia, Spain Jianxun Liu, Hunan University of Science and Technology, China Wei Chen, Nanjing University of Posts and Telecommunication, China Dennis Pfisterer, University of Luebeck, Germany Bo Zhao, Wuhan University, China Fei Yan, Wuhan University, China

CSA 2011 Organization

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Weizhong Qiang, Huazhong University of Science and Technology, China Lansheng Han, Huazhong University of Science and Technology, China Neal N. Xiong, Georgia State University, USA Yuan Luo, Shanghai Jiaotong University, China Chuliang Weng, Shanghai Jiaotong University, China Konidahla M. Divyan, Singapore Chan Yeob Yeun, KUSTAR, UAE Dang Nguyen Duc, Samsung Electronics, Korea Hyeran Mun, ETRI, Korea Track 3. Security and trust management Dieter Gollmann, TUHH, Germany Danfeng Yao, Rutgers University, USA Claudio Ardagna, University of Milan, Italy Jordi Forne, Technical University of Catalonia, Spain Jose Onieva, University of Malaga, Spain Willy Susilo, University of Wollongong, Australia Aljosa Pasic, Atos Origin, Spain Stefanos Gritzalis, University of the Aegean, Greece Jan de Meer, Brandenburg Technical University, Germany Seog Chung Seo, Korea University, Korea Young In Cho, Korea University, Korea HeeSeok Kim, Korea University, Korea Tae Hyun Kim, Korea University, Korea Taek-Young Youn, ETRI, Korea Chang Han Kim, Semyung University, Korea Young-Ho Park, Sejong Cyber University, Korea Zubair Baig, King Fahd University of Petroleum and Minerals, Saudi Arabia El-Sayed El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia Mohammed Houssaini Sqalli, King Fahd University of Petroleum and Minerals, Saudi Arabia Sami Zhioua, King Fahd University of Petroleum and Minerals, Saudi Arabia Talal Mousa Alkharobi, King Fahd University of Petroleum and Minerals, Saudi Arabia Min Zhang, University of Nottingham, UK Soltan Alharbi, University of Victoria, Canada Peter Herrmann, NTNU, Norway Sjouke Mauw, University of Luxemburg, LU Stig F. Mjoelsnes, NTNU, Norway Stefanos Gritzalis, University of the Aegean, Greece Ketil Stoelen, SINTEF, Norway Christian Damsgaard Jensen, DTU, Denmark Jean-Marc Seigneur, University of Geneva, Switzerland

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CSA 2011 Organization

Track 4. Graphics, multimedia systems and visual computing Atul Sajjanhar, Deakin University, Australia Lin Liu, University of South Australia, Australia Tanveer Zia, Charles Sturt University, Australia Xiaofei Liao, Huazhong University of Science and Technology, China Wei-Shinn Ku, Auburn University, USA Chuan-Ming Liu, National Taipei University of Technology, Taiwan Yeong-Sheng Chen, National Taipei University of Education, Taiwan Yao-Nan Lien, National Chengchi University, Taiwan Chih-Herng Ke, National Quemoy University, Taiwan Jiun-Long Huang, National Chiao Tung University, Taiwan Wei-Guang Teng, National Cheng Kung University, Taiwan Honggang Wang, University of Massachusetts, USA Rongbo Zhu, South-Central University for Nationalities, China Chin-Feng Lai, National Ilan University, Taiwan Kai Lin, Dalian University of Technology, China Sherali Zeadally, University of the District of Columbia, USA Dalei Wu, University of Nebraska-Lincoln, USA Xingang Liu, Yonsei University, Korea Track 5. Networking and communications Lain-Chyr Hwang, I-Shou University, Taiwan Ilsun You, Korean Bible University, Korea Rongxing Lu, University of Waterloo, Canada Chang Wu Yu, Chung Hua University, Taiwan ROC Tzung-Shi Chen, National University of Tainan, Taiwan ROC Chih-Yung Chang, Tamkang University, Taiwan ROC Chih-Shun Hsu, Shih Hsin University, Taiwan ROC Ling-Jyh Chen, Academia Sinica, Taiwan ROC Zeadally Sherali, University of the District of Columbia, USA Wang Yu, University of North Carolina at Charlotte, USA Cliff Zou, University of Central Florida, USA Hongyi Wu, University of Louisiana at Lafayette, USA Damla Turgut, University of Central Florida, USA Track 6. Database and data mining Shichao Zhang, University of Technology, Australia Chunping Li, Tsinghua University, China Qingyuan Bai, Fuzhou University, China Yasuhiko Morimoto, Hiroshima University, Japan Young-Ho Park, Sookmyung Women’s University, Korea Sang-Won Lee, Sungkyunkwan University, Korea Masashi Toyoda, University of Tokyo, Japan Jun Miyazaki, NAIST, Japan Yasuhiko Morimoto, Hiroshima University, Japan

CSA 2011 Organization

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Toshiyuki Amagasa, Tsukuba University, Japan Sanjay Madria, Missouri University of Science and Technology, USA Anirban Mondal, Indraprastha Institute of Information Technology, Delhi, India Sourav Bhowmick, Nanyang Technological University, Singapore Ramamohanarao Kotagiri, The University of Melbourne, Australia Hua Wang, University of Southern Queensland, Australia Raymond Lau, City University of Hong Kong, Hong Kong Sharma Chakravarthy, The University of Texas at Arlington, USA Simeon Simoff, University of Western Sydney, Australia Debajyoti Mukhopadhyay, Balaji Institute of Telecom & Management Pune, India Navneet Goyal, Department of Computer Science & Information Systems BITS Pilani Rajasthan, India Shlomo Geva, Queensland University of Technology, Australia Longbing Cao, University of Technology Sydney, Australia Track 7. Game and software engineering Son Bao Pham, Vietnam National University Hanoi, Vietnam Mei-Ling Shyu, University of Miami, USA Min Chen, University of Montana, USA Lin Lin, American National Standards Institute, USA Kasturi Chatterjee, hi5 Networks, Inc., USA Choochart Haruechaiyasak, NECTEC, Thailand Hongli Luo, Indiana University - Purdue University Fort Wayne, USA Jianhua Yan, Mintec Inc., USA Zhenyu Yang, Florida International University, USA Chengcui Zhang, University of Alabama at Birmingham, USA David LO, Singapore Management University, Singapore Masao Ohira, Nara Institute of Science and Technology, Japan Farhad Daneshgar, The University of New South Wales, Australia Tomoharu NAKASHIMA, Osaka Prefecture University, Japan Apostolos Ampatzoglou, Aristotle University of Thessaloniki, Greece Mazeiar Salehie, Lero—The Irish Software Engineering Center, Ireland Track 8. Grid, cloud and scalable computing Chao-Tung Yang, Tunghai University, Taiwan Keqiu Li, Dalian University of Technology, China Ramin Yahyapour, University of Dortmund, Germany Yangwoo Kim, Dongguk University, Korea Jong-Hyouk Lee, IMARA Team, INRIA, France Rajkumar Buyya, University of Melbourne, Australia Andrzej M. Goscinski, Deakin University, Geelong, Australia Uwe Schwiegelshohn, TU Dortmund University, Germany Jehn-Ruey Jiang, National Central University, Taiwan Dimitrios Lekkas, University of the Aegean, Greece Sebastien Goasguen, Clemson University, USA

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CSA 2011 Organization

Namsoo Chang, Sejong Cyber University, Korea SeungTaek Ryoo, Hanshin University, Korea Hae Young Lee, Electronics and Telecommunications Research Institute, Korea Dong Kyoo Kim, Convergence Research Department, Electronics and Telecommunications Research Institute, Korea Dongho Kim, Halla University, Korea Yong-hee Lee, Halla University, Korea Hyunsung Kim, Kyungil University, Korea Namje Park, Jeju National University, Korea Track 9 . Embedded system and software Li-Pin Chang, National Chiao-Tung University, Taiwan Hiroyuki Tomiyama, Nagoya University, Japan Jun Wu, National Pingtung Institute of Commerce, Taiwan Shengquan Wang, University of Michigan Dearborn, USA Jian Li, Shanghai Jiaotong University, China Zili Shao, The Hong Kong Polytechnic University, Hong Kong Liang-Teh Lee, Tatung University, Taiwan Jen-Wei Hsieh, National Taiwan University of Science and Technology, Taiwan Yung-Feng Lu, National Tai-Chung, Institute of Technology, Taiwan Yuan-Tse Yu, National Kaohsiung Normal University, Taiwan Yashu Chen, National Taiwan University of Science and Technology, Taiwan Yuan-Hao Chang, National Taipei University of Technology, Taiwan Track 10. Artificial intelligence Babak Esfandiari, Carleton University, Canada Shu Chen Cheng, Southern Taiwan University, Taiwan Frode Eika Sandnes, Oslo University College, Norway Chuan-Kang Ting, National Chung Cheng University, Taiwan Antonina Dattolo, University of Udine, Italy Hakan Duman, British Telecom, UK Hani Hagras, University of Essex, UK Tzung-Pei Hong, National University of Kaohsiung, Taiwan Chang-Shing Lee, National University of Tainan, Taiwan Ahmad Lotfi, Nottingham Trent University, UK Marek Reformat, University of Alberta, Canada Thanos Vasilakos, University of Western acedonia, Greece Mario Vento, University of Salerno, Italy Yue-Shan Chang, National Taipei University, Taiwan Yen-Lin Chen, National Taipei University of Technology, Taiwan Hung-Chi Chu, Chaoyang University of Technology, Taiwan Shang-Lin Hsieh, Tatung University, Taiwan Li-Jen Kao, Hwa Hsia Institute of Technology, Taiwan Tsang-Long Pao, Tatung University, Taiwan Chwan-Lu Tseng, National Taipei University of Technology, Taiwan

CSA 2011 Organization

Shun-Yuan Wang, National Taipei University of Technology, Taiwan Don-Lin Yang, Feng Chia University, Taiwan Morris Chang, Iowa State University, USA Frode Eika Sandnes, Oslo University College, Norway Chang-Tien Lu, Virginia Tech, USA Jinglong Wu, Okayama University, Japan David Bell, Brunel University, UK Babak Esfandiari, Carleton University, Canada Kevin Grant, University of Lethbridge, Canada Wei Jin, North Dakota State University, USA Brigitte Krenn, Austrian Research Institute for Artificial Intelligence, Austria Jen Li, North Dakota State University, USA Ken Nygard, North Dakota State University, USA Julian Padget, University of Bath, UK Omer Rana, Cardiff University, UK Alireza Sadeghian, Ryerson University, Canada Track 11. Distributed and parallel algorithms Paolo Trunfio, University of Calabria, Italy Farhad Arbab, CWI, The Netherlands Kuan-Ching Li, Providence University, Taiwan Qishi Wu, University of Memphis, USA Jen-Wei Hsieh, National Taiwan University of Techonolgy, Taiwan Huaqun Guo, Institute for Infocomm Research, Singapore Massimo Cafaro, University of Salento, Italy Stefan Fischer, University of Luebeck, Germany Ragib Hasan, Johns Hopkins University, USA Mukaddim Pathan, CSIRO, AUSTRALIA Bahman Javadi, Melbourne University, AUSTRALIA Abdelkarim Erradi, Qatar University, Qatar Ekow Otoo, University of Witwatersrand, South Africa Sameer Bataineh, United Arab Emirates University, UAE Mustafa Mat Deris, UTHM, Malaysia Lionel Eyraud-Dubois, INRIA, France Chuan Wu, University of Hong Kong, China Sun-Yuan Hsieh, National Cheng Kung University, Taiwan Daniel Grosu, Wayne State University, USA Hae-Duck Joshua Jeong, Korean Bible University, Korea Xin Yuan, Florida State University, USA Cho-Chin Lin, National Yilan University, Taiwan Raj Kettimuthu, Argonne National Laboratory, USA Victor Malyshkin, ICMMG, Russia

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CSA 2011 Organization

Track 12. Web and internet computing Chia-Hung Yeh , National Sun Yat-sen University, Taiwan Yuan-Cheng Lai, National Taiwan University of Science and Technology, Taiwan Guan-Ling Lee, National Dong Hwa University, Taiwan Yao-Chung Chang, National Taitung University, Taiwan Kuei-Ping Shih, Tamkang University, Taiwan Hazalina Hashim, Open University Malaysia, Malaysia Guanling Lee, National Dong Hwa University, Taiwan Chia-Min Wu, National Dong Hwa University, Taiwan Jui-Feng Yeh, National Chia-Yi University, Taiwan Hiroshi Esaki, University of Tokyo, Japan Daisuke Umehara, Kyoto University, Japan Huiping Guo, Cal State U, Los Angeles, USA Ilmi Yoon, Cal State U, San Francisco, USA Kyong-Ho Lee, Yonsei University, Korea Jae-Ho Choi, Yonsei University, Korea SungJin Choi, KT, Korea SungSuk Kim, Seokyeong University, Korea Hoh Peter In, Korea University, Korea Jaewoo Kang, Korea University, Korea Taek-Hun Kim, Korea University, Korea Juejie Yao, Peking University, China Toshiyuki AMAGASA, Tsukuba University, Japan Lin Li, Wuhan University of Technology, China Irene Garrigos, Universidad de Alicante, Spain Xiaohui Tao, University of Southern Queensland, Australia Zhenglu Yang, University of Tokyo, Japan JIanwei Zhang, Kyoto Sangyo University, Japan Yu Zong, University of Science and Technology of China, China Track 13. IT policy and business management Yang Shen, Wuhan University, P.R. China Han Xu, InfoQ, P.R. China Alton Chua Yeow Kuan, Nanyang Technological University, Singapore Margaret Tan, Nanyang Technological University, Singapore Theng Yin Leng, Nanyang Technological University, Singapore Qing Tan, Athabasca University, Canada Chin-Chen Chang, Computer Science Feng Chia University, Taiwan Johnnes Arreymbi, University of East London, UK Chia-Chen Lin, Providence University, Taiwan Neeli Prasad, Aalborg University, Denmark Jiqiang Lu, University of London, UK Theng Yin Leng, Nanyang Technological University, Singapore Benting Wan, Jiangxi University of Finance and. Economics, China Chu-Hsing Lin, Tunghai University, Taiwan

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Margaret Tan, Nanyang Technological University, Singapore Alton Chua Yeow Kuan, Nanyang Technological University, Singapore Ernesto Cuadros-Vargas, San Pablo Catholic University, Peru Dong Seong Kim, Duke University, USA Jeng-Shyang Pan, National Kaohsiung University of Applied Sciences, Taiwan Seong-Moo Yoo, The University of Alabama in Huntsville, USA Chien-Liang(Jonathan) Liu, University of Florida, USA Stefanos Gritzalis, University of the Aegean, Greece

Message from the WCC 2011 General Chairs

As the General Chairs of the 2011 FTRA World Convergence Conference (FTRA WCC 2011), we have the pleasure of welcoming you to this conference, which will be held at the beautiful Jeju Island, Korea. FTRA WCC 2011 is the next event in a series of highly successful World Convergence Conference, previously held as FTRA WCC 2010 (Gwangju, December 2010). The conference is aimed at addressing key themes on ‘‘Technologies and Services for Converged environments’’. We would like to thank the founding steering chair, James J. (Jong Hyuk) Park. Finally, we sincerely thank all our chairs and committees. Without their hard work, the success of WCC 2011 would not have been possible. Mohammad S. Obaidat, Jin Kwak, Laurence T. Yang, Changhoon Lee, Rajkumar Buyya, Han-Chieh Chao, Wanlei Zhou, Sangjin Lee, Leonard Barolli, Young-Sik Jeong, Sang-Soo Yeo, Jianhua Ma, Hangbae Chang, Dong Yong Kwak, Hai Jin, Jungduk Kim WCC 2011 General Chairs

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Message from the WCC 2011 Program Chairs

It is with great pleasure that we welcome you to the 2011 FTRA World Convergence Conference (FTRA WCC 2011), held on December 12–15, 2011 at Jeju, Korea. In WCC 2011, we received lots of paper submissions which have undergone blind review by at least three reviewers of technical program committee that consists of leading researchers around the globe. Without their hard work, achieving such a high quality of proceedings would have been impossible. We take this opportunity to thank them for their great support and cooperation. Finally, we would like to thank all the authors and participants for their contributions to the conference. Jongsung Kim, Hamid R. Arabnia, Naveen Chilamkurti, Sajid Hussain, Taeshik Shon, Dong-Seong Kim, Ching-Hsien Hsu, Nazim AGOULMINE, Ketan Mane, Yang Sun Lee, Dong Kyoo Kim, Seungmin Rho, Hangbae Chang WCC 2011 Program Chairs

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WCC 2011 Organization

Steering Chair James J. (Jong Hyuk) Park, Seoul National University of Science and Technology, Korea General / General-Vice Chairs Mohammad S. Obaidat, Monmouth University, USA Jin Kwak, Soonchunhyang University, Korea Laurence T. Yang, St Francis Xavier University, Canada Changhoon Lee, Hanshin University, Korea Rajkumar Buyya, University of Melbourne, Australia Han-Chieh Chao, National Ilan University, Taiwan Wanlei Zhou, Deakin University, Australia Sangjin Lee, Korea University, Korea Leonard Barolli , Fukuoka Institute of Technology, Japan Young-Sik Jeong , Wonkwang University, Korea Sang-Soo Yeo, Mokwon University, Korea Jianhua Ma, Hosei University, Japan Hangbae Chang, Daejin University, Korea Dong Yong Kwak, ETRI, Korea Hai Jin, Huazhong University of Science and Technology, China Jungduk Kim, Chung-Ang University, Korea Program Chairs Jongsung Kim, Kyungnam University, Korea Hamid R. Arabnia, University of Georgia, USA Naveen Chilamkurti, La Trobe University, Australia Sajid Hussain, Fisk University, USA Taeshik Shon, Ajou University, Korea Dong-Seong Kim, Duke University, USA Ching-Hsien Hsu, Chung Hua University, Taiwan Nazim AGOULMINE, University of Evry Val d’Essonne, France xxv

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Ketan Mane, University of North Carolina, USA Yang Sun Lee, Chosun University, Korea Dong Kyoo Kim, ETRI, Korea Seungmin Rho, Korea University, Korea Hangbae Chang, Daejin University, Korea Publicity Chairs Jin Kwak, Soonchunhyang University, Korea Sang Oh Park, Chungang University, Korea

WCC 2011 Organization

Message from the DCIT 2011 Chairs (CSA 2011 Workshop)

It is our great pleasure to welcome you to the 1st International Workshop on Digital Convergence, Information and Technology (DCIT-2011), which will be held in conjunction with the 3-rd International Conference on Computer Science and its Applications (CSA-2011) in Jeju, Korea, from December 12 to December 15, 2011. Digital Convergence and Information Technology of today are going through a rapid evolution. Different kinds of Technology and Learning, Science with different characteristics are emerging and they are integrating in Different Content and technology. Also known as digital convergence, the combining of several industries computers, communications, consumer electronics, entertainment, and mass media through various devices exchange data in digital form. Why it is important: Convergence has led to electronic products that perform multiple functions, such as TVs with internet access, cell phones that are also digital cameras, and a refrigerator that allows you to send email. The progress of electronic integration and wireless communications is going to pave the way to offer people the access to the wireless networks on the fly, based on which all electronic devices will be able to exchange the information with each other whenever necessary. Also, ubiquitous computing is an emerging field of research for computing paradigms. This emergence is the natural result of research and technological advances mainly in Digital Convergence and Information Technology, mobile computing, sensor networks, ad-hoc networks, IT service and application, Digital Convergence Policy , Telemedicine, Cloud Computing, Smart learning and application, Robotics, 3D animation and Multimedia application, Biometrics and System, GPS and GIS, Bioinformatics and agent technologies. The aim of this international workshop is to present the innovative research and technologies as well as developments related to Digital Convergence and Information Technology, mobile computing, Cloud Computing, Smart learning and

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Message from the DCIT 2011 Chairs

application, Image Processing, GPS and GIS and their applications. The organizing committee of DCIT-2011 selected 10 papers for presentation in the workshop. The organization of an International Workshop requires the support and help of many people. First, we would like to thank all the authors for submitting their papers, the Program Committee Members, and the reviewers. We would like to give special thanks to Prof. Young-Sik Jeong, Wonkwang University, Korea as leading Workshops General and Program Chair of DCIT2011 for their encouragement and support to hold our workshop in conjunction with DCIT-2011. Finally, we would like to thank the Local Arrangement Chairs for the local arrangement of the workshop. We hope you will enjoy the workshop and have a great time in Jeju, Korea. Bonghwa Hong, Hwayoung Jeong, Seongsoo Cho DCIT 2011 Chairs

DCIT 2011 Organization

Workshop Organizer Bonghwa Hong, Kyunghee Cyber University, Seoul, Korea Program Chairs Hwa-Young Jeong, Kyunghee University, Seoul, Korea Seong-soo Cho, Kwangwoon University, Seoul, Korea Program Committee Members Won-bong Lee, Kyung Hee Cyber University, Korea Dong-Keon Kim, Kyung Hee University, Korea Un-ho Choi, Seoul National University, Korea Hye-jung Jung, Pyeongtaek University, Korea Byeong Kug Yoon, Kyunghee Cyber University, Korea Jin-Mook Kim, SunMoon University, Korea Juyeon Jo, University of Nevada, USA Jungsun (Sunny) Kim, Texas Tech University, USA Sun Dan Ning, Shandong Institute of Business and Technology, China Andrew Thompson, Kyung Hee University, Humanitas College, Korea Frelyn Pagente, Speechpower Inc. Philippines HaeJong Joo, Dongguk University, Korea Heagil Choi, Kyunghee Cyber University, Korea JangMook Kang, Dongguk University, Korea

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Message from the HNCA 2011 Chairs (CSA 2011 Workshop)

It is our great pleasure to welcome you to the 1st International Workshop on Heterogeneous Networks, Computing and Applications (HNCA-2011), which will be held in conjunction with the 3rd International Conference on Computer Science and its Applications (CSA-2011) in Jeju, Korea, from December 12 to December 15, 2011. Networks of today are going through a rapid evolution. Different kinds of networks with different characteristics are emerging and they are integrating in heterogeneous networks. For these reasons, there are many interconnection problems which may occur at different levels in the hardware and software design of communicating entities and communication networks. These kinds of networks need to manage an increasing usage demand, provide support for a significant number of services, guarantee their QoS, and optimize the utilization of network resources. The progress of electronic integration and wireless communications is going to pave the way to offer people the access to the wireless networks on the fly, based on which all electronic devices will be able to exchange the information with each other whenever necessary. Also, ubiquitous computing is an emerging field of research for computing paradigms. This emergence is the natural result of research and technological advances mainly in wireless communications, mobile computing, sensor networks, ad-hoc networks, P2P networks and agent technologies. The aim of this international workshop is to present the innovative research and technologies as well as developments related to heterogenous networking, wireless communications, mobile computing and their applications. The organizing committee of HNCA-2011 selected sixpapers for presentation in the workshop. The organization of an International Workshop requires the support and help of many people. First, we would like to thank all the authors for submitting their papers, the Program Committee Members, and the reviewers.

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Message from the HNCA 2011 Chairs

We would like to gice special thanks to Prof. Young-Sik Jeong, Wonkwang University, Korea as leading Workshops Co-Chair of CSA-2011 for his encourangement and support to held our workshop in conjunction with CSA-2011. Finally, we would like to thank the Local Arrangement Chairs for the local arrangement of the workshop. We hope you will enjoy the workshop and have a great time in Jeju, Korea. Leonard Barolli, Makoto Takizawa, Makoto Ikeda, Tomoya Enokido HNCA 2011 Chairs

HNCA 2011 Organization

Workshop Organizers Leonard Barolli, Fukuoka Institute of Technology, Japan Makoto Takizawa, Seikei University, Japan Program Chairs Makoto Ikeda, Fukuoka Institute of Technology, Japan Tomoya Enokido, Rissho University, Japan Program Committee Members Jiro Iwashige, Fukuoka Institute of Technology, Japan Arjan Durresi, Indiana University Purdue University Indianapolis (IUPUI), USA Hiroshi Maeda, Fukuoka Institute of Technology, Japan Fatos Xhafa, Technical University of Catalonia, Spain Joan Arnedo Moreno, Open University of Catalonia, Spain Akio Koyama, Yamagata University, Japan Toshiaki Matsunaga, Fukuoka Institute of Technology, Japan Vladi Kolici, Tirana Polytechnic University, Albania Kazunori Uchida, Fukuoka Institute of Technology, Japan Valbona Barolli, Seikei University, Japan Toshihiko Wakahara, Fukuoka Institute of Technology, Japan Rozeta Miho, Tirana Polytechnic University, Albania Koki Watanabe, Fukuoka Institute of Technology, Japan Tao Yang, Fukuoka Institute of Technology, Japan

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Message from the MIST 2011 Chair (CSA 2011 Workshop)

Welcome to the First International Workshop on Multimedia and Information Security Technology (MIST 2011). The MIST 2011 will be held in conjunction with the Third International Conference on Computer Science and its Applications in Jeju, Korea. The MIST 2011 is a comprehensive workshop focused on the various aspects of information security including multimedia. The MIST 2011 will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the areas of multimedia and information security as well as related issues in innovative multimedia technology and public policy. In this year, there were large number of good papers submitted to the MIST 2011. All the submitted papers had undergone rigorous review process by the program committee members and some external reviewers. Only four papers were accepted for presentation and publication in the Conference proceedings. For organizing an international workshop, there were the support and help of many people. First, we would like to thank all the authors for submitting their outstanding papers. Also, we thank the program committee members and reviewers who carefully evaluated the submitted papers. We hope that the MIST 2011 will provide you with opportunity to interact, share ideas with, and learn from the other researchers working in the fields of multimedia and information security. We also encourage you to continue to participate in future editions of MIST workshop in order to contribute to the growth of this community. Thank you. Hae-Yeoun Lee MIST 2011 Chair

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MIST 2011 Organization

Workshop Organizer Hae-Yeoun Lee, Kumoh National Institute of Technology, Korea Workshop Committee Heung-Kyu Lee, Korea Advanced Institute of Science and Technology (KAIST), Korea Yongjian Hu, South China University of Technology, China Kyung-Su Kim, KT Network R&D Laboratory, Korea Min-Jeong Lee, Korea Advanced Institute of Science and Technology (KAIST), Korea Program Committee Members Hae-Yeoun Lee, Kumoh National Institute of Technology, Korea Yongjian Hu, South China University of Technology, China Hyungshin Kim, Chungnam National University, Korea Kyung-Su Kim, KT Network R&D Laboratory, Korea Dong-Hyuck Im, KT Network R&D Laboratory, Korea

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Message from the CC 2011 Chairs (CSA 2011 Workshop)

It is our pleasure to welcome to the 2011 International Workshop on Computing and Communications (CC-11). The MIST 2011 will be held in conjunction with the Third International Conference on Computer Science and its Applications in Jeju, Korea. Recently, advances in computing and communication technologies are creating exciting new paradigms and are emerging as disciplines to provide intelligent computing and communication services all the time, everywhere, such as ubiquitous/pervasive computing and communication, smart/Intelligent computing and communication, green computing and communication, and so on. This emergence is a natural outcome of research and technological advances in wireless networks, computer communication, mobile computing and distributed computing. And its explosive impact on our daily life can be compared to that of computer over the past century. So, we must realize computing and communications are an important base and trend for present and future information technologies. The primary goal of this workshop is to provide researchers and academic communities around the world with the highest quality articles while reporting the state-of-the-art research results and scientific findings. We would like to take this opportunity to thank all the authors and participants for your contributions to make CC 2011 a great success. Changhoon Lee, Ching-Hsien Hsu CC 2011 Chairs

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CC 2011 Organization

Workshop Organizers Changhoon Lee, Hanshin University, Korea Ching-Hsien Hsu, Chung Hua University, Taiwan Program Committee Members Jung-Sik Cho, Chung-Ang University, Korea Hoojin Lee, Hansung University, Korea Eun-Young Lee, Dongduk Women’s University, Seoul, Korea

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Message from the MCM 2011 Chairs (CSA 2011 Workshop)

The 2011 International Workshop on Mobile platform, Consumer electronics and Multimedia services (MCM-11) is a merged workshop of the following three workshops: • International Workshop on Mobile Platform, Computing and Applications (MPCA 2011) • The 2011 International Workshop on Consumer Electronics and Digital Convergence (CEDC-11) • The 2011 International Workshop on Web and Multimedia Services (WMS-11) The workshop MCM-11 will comprehensively provide an interdisciplinary opportunity for academic and industry professionals to discuss the latest issues, progress, projects, research cooperation, and new idea in the areas of Mobile platform, Consumer electronics and Multimedia services. On behalf of the CSA-11, we would like to welcome all the delegates and their guests to the workshop MCM-11. We hope that you will enjoy the workshop and find it fruitful. Sang-Soo Yeo, Wen-Chung Kao, Sang Oh Park, Won Woo Ro, James J. (Jong Hyuk) Park, Young-Sik Jeong, Seung-Ho Lim, Min Choi, Seungmin Rho, Sethuraman Panchanathan MCM 2011 Chairs

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MCM 2011 Organization

Workshop Organizers Sang-Soo Yeo, Mokwon University, Korea (Chair) Wen-Chung Kao, NTNU, Taiwan (Co-Chair) Sang Oh Park, Chung-Ang University, Korea Won Woo Ro, Yonsei University, Korea James J. (Jong Hyuk) Park, Seoul National University of Science and Technology, Korea Young-Sik Jeong, Wonkwang University, Korea Seung-Ho Lim, Hankuk University of Foreign Studies, Korea Min Choi, Wonkwang University, Korea Seungmin Rho, Korea University, Korea Sethuraman Panchanathan, Arizona State University, USA Program Committee Members Davide Proserpio, Carlos III University, Spain Zhaocheng Wang, Tsinghua University, China, Yueh-Min Huang, National Cheng Kung University Jong-Ok Kim, Korea University, Korea Tonghong Li, University of Madrid, Spain Da-Wei Chang, National Cheng Kung University, Taiwan Yawgeng A.Chau, Yuan-Ze University, Taiwan Youngmo Han, Hanyang Cyber University, Korea Haidi Ibrahim, Universiti Sains Malaysia, Malaysia Yonghong Kuo, Xidian University, China Zi-Wei Zheng, Ningbo University, China Juan Carlos Lopez, University of Castilla-La Mancha, Spain Omer ileri, Technology and Innovation Funding Directorate, Turkey Thomas Wook Choi, Hankuk University of Foreign Studies, Korea Chan Yeun Yeob, Khalifa University of Science Technology and Research, UAE Hyohyun Choi, Inha Technical College, Korea

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MCM 2011 Organization

Rodrigo Roman Castro, University of Malaga, Spain Yong Lee, ChungJu University, Korea Ting-Wei Hou, National Cheng-Kung University, Taiwan Paul S. Fisher, Winston-Salem State University, USA Stefan Mozar, CCM Consulting, Germany Mohammad. A. Karim, Old Dominion University, USA Dong Keun Kim, Sangmyung University, Korea Raed Mesleh, Jacobs University Bremen, German G. Fernández-Escribano, University of Castilla-La Mancha, Spain Mohammed Golam Sarwer, University of Windsor, Canada Samuel H. Russ, University of South Alabama, USA Giancarlo Iannizzotto, University of Messina, Italy Liu Wei, Samsung Electronics Jiakun Liu, University of Pensilvenia Horry Hojung Nam, University of California, San Diego Youngjae Kim, Oak Ridge Research Center Namgi Kim, Kyonggi University Wonjae Lee, ETRI Young-Gab Kim, Korea University, Korea Marco Bertini, Universita di Firenze, Italy Stephan Kopf, University of Mannheim, Germany

Contents

Part I

Computer Science and its Applications

A Large-Scale Terrain Rendering Method With GPU-Based Geometry Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zihou Ge and Wenhui Li

3

Cots Sparse Matrix Utilization in Distribution Power Flow Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dino Ablakovic, Izudin Dzafic and Hans-Theo Neisius

11

A New Graph-Based Algorithm for Persian Text Summarization . . . . Hassan Shakeri, Saeedeh Gholamrezazadeh, Mohsen Amini Salehi and Fatemeh Ghadamyari The Factors Affect User Behaviour in a Picture-Based User Authentication System: PixelPin . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Min, Brendan Ryan and Sarah Atkinson Analysis of Smartphone-Based Location Information . . . . . . . . . . . . . Dohyun Kim, Jewan Bang and Sangjin Lee

21

31

43

Dynamic Visualization for Sensing Information Based on Mobile Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Su-Hyun Yang, Eun-Ha Song and Young-Sik Jeong

55

Model Driven Real-Time Power System Application Development Using Just-In-Time Compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Izudin Dzafic, Priyanka Mohapatra and Nenad Lecek

67

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Contents

An Efficient Distribution Sensitive Privacy for Real-Time Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arijit Ukil, Jaydip Sen and Sumit Ghosh DeSPoT: A Method for the Development and Specification of Policies for Trust Negotiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tormod Håvaldsrud, Birger Møller-Pedersen, Bjørnar Solhaug and Ketil Stølen Extended NIST Prime Family for Efficient Modular Reduction. . . . . . Young In Cho, Nam Su Chang, Chang Han Kim and Seokhie Hong

81

93

105

High Attack Cost: Hash Based RFID Tag Mutual Authentication Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jung-Sik Cho and James J. (Jong Hyuk) Park

113

Energy-Efficient Multi-Granularity Resource Reservations for Multimedia Services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joohyung Sun and Hyeonjoong Cho

121

Single Camera Vehicle Detection Using Edges and Bag-of-Features. . . Syed Jahanzeb Hussain Pirzada, Ehsan Ul Haq and Hyunchul Shin

135

A New BiGaussian Edge Filter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ehsan Ul Haq, Syed Jahanzeb Hussain Pirzada and Hyunchu Shin

145

Self-Healing in Smart Distribution Networks Using Data Exchange Model and Reliable Multicast Protocol. . . . . . . . . . . . . . . . . . . . . . . . I. Dzafic, N. Lecek and T. Donlagic

155

Applying Event-Based Framework to Support Power System Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nenad Lecek and Izudin Dzafic

167

Utilizing Converged Networks for Green Network Operations . . . . . . Xiaodong Yang A Ring and Clustering Routing Scheme Based on Data Field in Sensor Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenwen Dai, Kaihua Xu, James J. (Jong Hyuk) Park, Naixue Xiong and Yuhua Liu

183

193

Contents

A Case Study on Cooperative Real-time Quality Monitoring Service Utilizing Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . Hangbae Chang, Jonggu Kang, Yanghoon Kim, Jaehwan Lim and Youngsub Na High Availability Archive for Time-Based Data . . . . . . . . . . . . . . . . . Tobias Haag, Thomas Meier and Izudin Dzafic A Multivariate Spatial Outlier Detection Method Based on Semantic Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatemeh Azam and Ahmad Baraani-Dastjerdi Cohesion Between Size of Commit and Type of Commit. . . . . . . . . . . Maryam Marzban, Zahra Khoshmanesh and Ashkan Sami

xlix

203

211

221

231

Software Defect Prediction Using Transitive Dependencies on Software Dependency Graph. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Javad Kamyabi, Farzaneh Maleki and Ashkan Sami

241

Test Case Generation for Classes in Objects-Oriented Programming Using Grammatical Evolution . . . . . . . . . . . . . . . . . . . Jirawat Chaiareerat, Peraphon Sophatsathit and Chidchanok Lursinsap

251

An Efficient Access Control Scheme for Online Gaming Server . . . . . Hye-Young Kim

259

Performance Evaluation of a New Scheduling Algorithm in Data Grid Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Mansouri, Gh. Dastghaibyfard and A. Horri

269

A Novel Dynamic Replication Algorithm in Hierarchical Data Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Mansouri and Gh. Dastghaibyfard

281

Intelligent Application Migration within a Self-Provisioned Hybrid Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei-Jen Wang, Yo-Ming Lo, Shao-Jui Chen and Yue-Shan Chang

295

Multi-Band OFDM and OFDM Simulation Software Using MATLABÒ Graphical User Interface . . . . . . . . . . . . . . . . . . . . Roslina Mohamad, Wan Mohd Hafeez, Wan Mohd Salleh and Nuzli Mohdmad Anas

305

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Contents

A Comparison on PSO Variants in Calculating HEPWM Switching Angles for a Cascaded H-Bridge Multilevel Inverter. . . . . . . . . . . . . . Norkharziana Mohd Nayan, Naziha Ahmad Azli and Shahrin Md. Ayob

315

A Hybrid Artificial Potential Field: Genetic Algorithm Approach to Mobile Robot Path Planning in Dynamic Environments . . . . . . . . . Yanping Liu and Kamal K. Bharadwaj

325

Energy-Constrained Scheduling for Weakly-Hard Real-Time Tasks on Multiprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yeonhwa Kong and Hyeonjoong Cho

335

A New Software Process Model: The Insect Model . . . . . . . . . . . . . . . Mahmood Vahedi Moghaddam and Mehdi Zand Akbari

349

Improving Recommendations in Tag-Based Systems with Spectral Clustering of Tag Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rong Pan, Guandong Xu and Peter Dolog

355

Event-Centric Test Case Scripting Method for SOA Execution Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Youngkon Lee

365

bQoS (Business QoS) Parameters for SOA Quality Rating . . . . . . . . . Youngkon Lee

375

Business-Centric Test Assertion Model for SOA . . . . . . . . . . . . . . . . . Youngkon Lee

385

A Classification of Model-Driven Approaches to Develop Semantic Web Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alaeddin Kalantari, Suhaimi Ibrahim and Hamed Taherdoost A Conceptual Model Explaining IT/IS Usage Behavior . . . . . . . . . . . . Sabine Hoesch and Siegfried Voessner Offering a Conceptual Model of Technology Acceptance in e-Banking Based on the Customers’ Satisfaction and Trust. . . . . . . Alireza Dehghan, Parham Arjomand, Amirahmed Nayyeri and Masoud Tabatabaey

395

405

415

Contents

Part II

li

Computer Convergence

Architecture for Secure SCADA System Based on IF-MAP Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuan Deng, Lin Yang, Linru Ma and Yuan Cao

427

Research on the Model of the Survivable Storage System (SSS) and Its Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao-yuan Yang, Jing-jing Wang and Xiao-feng Chen

437

New Improved Impossible Differential Attack on Reduced-Round AES-128 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ya Liu, Dawu Gu, Zhiqiang Liu, Wei Li and Weihao Kong

453

Smart Space Model for Facilitating Dynamic Service Binding. . . . . . . Changgyu Bak, Euna Choi, Jung-Won Lee and Nammee Moon Functionality Evaluation of Ubiquitous Service Ontology for Dynamic Service Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . Meeyeon Lee, Jung-Won Lee, Nammee Moon and Seung Soo Park OO Divide and Conquer Pattern Suitable for Parallel, Grid and Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manouchehr ZadahmadJafarlou, Bahman Arasteh and Parisa YousefzadehFard A Dynamic and Reliable Failure Detection and Failure Recovery Services in the Grid Systems. . . . . . . . . . . . . . . . . . . . . . . . Bahman arasteh, Manouchehr ZadahmadJafarlou and Mohammad Javad Hosseini Vehicle Positioning Technology Using Infra-based Laser Scanner Sensors for Autonomous Driving Service . . . . . . . . . . . . . . . . . . . . . . Kyoungwook Min and Jeongdan Choi Security Manpower Scheduling for Smart Airports . . . . . . . . . . . . . . Sangwon Seo, Sanggyun Choi and Chulung Lee A Study on Static Analysis Model of Mobile Application for Privacy Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seil Kim, Jae Ik Cho, Hee Won Myeong and Dong Hoon Lee

463

475

487

497

511

519

529

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Part III

Contents

Digital Convergence Information Technology

China’s Industrial Policy: Focusing on the . . . . . . . . . . . Won-bong Lee

543

A Dual CPU Based Fault Tolerance Technique for Manufacturing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HwaYoung Jeong and BongHwa Hong

553

A Computational Clustering of Korean Classical Texts: Focusing on the Gogocheonbyeon Passage of Sugungga . . . . . . . . . . . . Woonho Choi and Dong Keon Kim

561

The Survey of Quality Model for Software and System . . . . . . . . . . . Hye-Jeong Jeong and Suck-Joo Hong

569

Improvement of Retinex Algorithm for Backlight Image Efficiency . . . Seongsoo Cho, Bhanu Shrestha, Hae-Jong Joo and Bonghwa Hong

579

A User Authentication Method for M2M Environments . . . . . . . . . . . Jin-Mook Kim, Hwa-Young Jeong and Bong-Hwa Hong

589

Test Driven Development of Model Transformation with Reusable Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jong-won Ko and Young-jae Song An Advanced E-learning System Using Web Services . . . . . . . . . . . . . Hae-Gill Choi, Jungsun Kim and JuYeon Jo A Study of Mobile Architecture Web Design for Virtual Ecology Learning System Using LED (Light-Emitting Diodes) Lights in Smart-Phone Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jang-Mook Kang, Bong-Hwa Hong, You-Jin Song and Jae-sang Cha A Study on the Tag Performance Test for International Standards Using RFID Emulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hae-Gill Choi, Sang-Soo Kim, Moon-Taek Cho, Hae-Jong Joo and Euy-Soo Lee

597

607

613

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Contents

Part IV

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Heterogenous Networks, Computing and Applications

Simulation and Performance Analysis of Ad-hoc Networks Using NS-2 and NS-3 Simulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . Makoto Ikeda, Elis Kulla, Masahiro Hiyama and Leonard Barolli A Comparison Study for Different Settings of Crossover and Mutation Rates Using WMN-GA Simulation System . . . . . . . . . . Evjola Spaho, Tetsuya Oda, Admir Barolli, Fatos Xhafa, Leonard Barolli and Makoto Takizawa A Survey on MANET Testbeds and Mobility Models . . . . . . . . . . . . . Elis Kulla, Makoto Ikeda, Leonard Barolli, Fatos Xhafa and Jiro Iwashige Evaluation of Wireless Sensor Networks for Multi Mobile Events Using Different Topologies, Protocols and Radio Propagation Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Yang, Leonard Barolli, Jiro Iwashige, Arjan Durresi and Fatos Xhafa

635

643

651

659

Trustworthiness-Based Broadcast Protocols in Peer-to-Peer Overlay Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ailixier Aikebaier, Tomoya Enokido and Makoto Takizawa

669

Energy Efficient Distributed Systems for Storage-Based Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takuro Inoue, Ailixier Aikebaier, Tomoya Enokido and Makoto Takizawa

677

Part V

Multimedia and Information Security Technology

Forged Region Detection for Scanned Images. . . . . . . . . . . . . . . . . . . Chang-Hee Choi, Min-Jeong Lee, Dai-Kyung Hyun and Heung-Kyu Lee

687

Camcorder Identification for Heavily Compressed Low Resolution Videos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dai-Kyung Hyun, Chang-Hee Choi and Heung-Kyu Lee

695

Block-based Image Authentication Algorithm Using Reversible Watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dong-Gyu Yeo and Hae-Yeoun Lee

703

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Contents

Digital Forgery Detection Scheme Incorporating Imaging Device Characteristics Using Wiener Filter . . . . . . . . . . . . . . . . . . . . Jae Youn Shim and Seong-Whan Kim

Part VI

713

Computing and Communications

Dynamic Multi-channel Multi-path Routing Protocol for Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jih-Ching Chiu, Kai-Ming Yang, Yao-Chin Huang and Ming-Shiou Wu

725

Speed and Surge Control for an Unmanned Aircraft Vehicle with Turbojet Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min-Seok Jie and Beyong-In Jung

735

Influence of G729 Speech Coding on Automatic Speaker Recognition in VoIP Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . Dalila Yessad, Abderrahmane Amrouche, Mohamed Debyeche and Nabil Ramou Scheduling DAGs with Parallel Tasks in Multi-Clusters Based on Parallel Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Silvio Luiz Stanzani and Líria Matsumoto Sato Tomographic Reconstruction of a Multi-Attenuation Phantom by Means of Ultrasonic Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohd Hafiz Fazalul Rahiman, Ruzairi Abdul Rahim and Herlina Abdul Rahim A Healthcare Cloud Computing Strategic Planning Model . . . . . . . . . Kuo Mu-Hsing

Part VII

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Mobile Platform, Consumer Electronics and Multimedia Services

Multimedia Data Placement and Retrieval for Mobile Platform . . . . . Seung-Ho Lim A Platform-Independent Smartphone Application Development Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Choi

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Energy-Efficient Scheduling for Mobile Sensor Networks with Renewable Energy Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kae Won Choi

795

Issues and Challenges in Determining Quality of Service Requirements in Wired, Wireless and Mobile Networks . . . . . . . . . . . P. Calduwel Newton and L. Arockiam

805

Implementation of the River Monitoring System Using Embedded RTU Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dae-Sik Ko

813

A Study on Message Batch Verification Scheme Using Bloom Filter in VANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Su-Hyun Kim and Im-Yeong Lee

821

Computing Text-to-Text Semantic Relatedness Based on Building and Analyzing Enriched Concept Graph . . . . . . . . . . . . . Zoleikha Jahanbakhsh Nagadeh, Fariborz Mahmoudi and Amir Hossein Jadidinejad Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part I

Computer Science and its Applications

A Large-Scale Terrain Rendering Method With GPU-Based Geometry Compression Zihou Ge and Wenhui Li

Abstract We present a large-scale terrain rendering method, which is based on restricted quadtree meshes. The error metric method corresponds to parallel computing requires, and processes the error metric and triangulation by GPU, to improve the rending efficiency of image hardware, and save the running time of CPU. For large terrain data, terrain data are compressed and stored in special method, and dynamically transmitted to graphic hardware according to rendering demand, and decoded by GPU too. Our compression method significantly reduces bandwidth requirements that is the bottleneck of terrain rendering.



Large-scale terrain rendering Keywords GPU-based meshes Nested error metric Geometry compression







Restricted quadtree

1 Introduction Real-time terrain rendering is an important area of computer graphics, which is used for flight simulators, computer games, virtual reality and visualization. With the development of technology of remote sensing, the size and resolution of digital Z. Ge (&)  W. Li College of Computer Science and Technology, JiLin University, Changchun, Jilin, China e-mail: [email protected] W. Li e-mail: [email protected] Z. Ge Aeronautical Engineering department, Aeronautical University of Air Force, Changchun, Jilin, China

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_1, Ó Springer Science+Business Media B.V. 2012

3

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Z. Ge and W. Li

elevation models (DEMs) has quickly increased. Today, spatial resolutions as high as 1 m are available for some areas, and raw data amount is even more than 100 GB. As the data is too large to fit into main memory, recently terrain rendering algorithm mainly utilize out-of-core method to save tiled data set in hard disk, preload required data to main memory and send data to GPU for rendering, which rendering process involves frequent disk access and large amount of data transmission. So, terrain rendering performance is limited by bandwidth restrictions rather than computational or rendering power. From this observation, several compression schemes for DEMs have been proposed, for instance, based on wavelet decomposition [1], space-filling curves [2], or tiled quadtrees [3]. And the geometric clipmap approach proposed by Losasso and Hoppe [4], which compressed large DEMs to a regular height map pyramid. It should be noted that the aforementioned approaches utilize CPU-based decoding of the compressed data streams. As a consequence, their throughput is limited by the computational power and memory bandwidth of the CPU as well as the bandwidth of the graphics bus. In this paper, we present geometry compression method for large-scale terrain rendering method, which dynamically transmit compressing data to graphic hardware. The domain was first tiled and computed into a nested geometric space error map and stored into displaying memory with terrain map. In order to obtain the seamless connection, the edge errors of two connecting tiles are consistent with each other. At run time, screen space error is projected from geometric error, and compare with error threshold according to view-point, to select terrain grid points by GPU. At last, triangle strip is constructed according to RQT method. The error metric method corresponds to parallel computing requires, and processes the error metric and triangulation by GPU, to improve the rending efficiency of image hardware, and save the running time of CPU.

2 Related Work The restricted constraint of restricted quadtree is that neighboring regions must be within one level of each other in the quadtree hierarchy. It was first presented by Von Herzen, and was applied for terrain visualization by Lindstrom in [5, 6]. Pajarola proposed an geometric error metric computing method with RQT In [7]. Error values of vertexes in triangle grid T are propagated and maximized along the dependency graph. The maximum error ensures that every vertex conform to error saturation and obtain accurate object-space geometric error. Then, Lindstrom presented an error metric algorithm, which is based on the accurate object-space geometric error of [7] and nested sphere hierarchy of [8], to transform the nested object-space geometric error into nested screen-space error for terrain simplification. This algorithm ensures both object-space geometric errors and screen-space errors are saturated. The same method also can be seen in paper [9], and so on.

A Large-Scale Terrain Rendering Method With GPU-Based Geometry Compression

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Moreover, Today’s GPUs are able to sustain speeds of hundreds of millions of triangles per second. So, terrain rending algorithms mostly utilizes the performance of GPU. References [10, 11] partition the terrain into square patches tessellated at different resolutions, [4] directly loads compressed terrain data into graphics memory, and [12] still provides a seamless connecting method between patches. Those algorithms use batch technology of CPU and cache of graphics memory, largely improve rending efficiency. But [10] only computes static error, [11] largely simplifies error, and [4] even takes the distance to view point for error metric. So, it is still difficulty to take quick, GPU-based, optimized terrain rending effect. As scanned DEMs are typically much larger than the main memory available on desktop computers, data layout schemes on external storage devices [11, 13] in combination with visibility-based and speculative pre-fetching strategies as well as GPU occlusion queries have been developed [14]. Common to all these approaches is the goal to reduce both the amount of data to be transferred from disk and the number of seek operations to be performed. [15] provided quantitative evidence that a circle-shaped pre-fetching region is superior to a fan-shaped region, as it only requires slightly more memory but does not considerably increase bandwidth requirements, yet it allows fast changes of the viewing direction. It should be noted that although the aforementioned approaches can achieve similar or even better compression rates than our method, CPU-based decoding of the compressed data streams is inherent to all of them. As a consequence, their throughput is limited by the computational power and memory bandwidth of the CPU as well as the bandwidth of the graphics bus.

3 GPU-Based Error Metric Rendering Method On the basis of paper [13], our method computes saturated error of each vertex in terrain grid patch, and compose error map, which is loaded into graphic memory with terrain elevation map. Then we project geometric error to view-dependent screen space error of each vertex, and select vertexes to compose triangle strip by GPU according to the screen space error.

3.1 Error Metric Based on GPU In [13], by defining nested geometric error and nested sphere hierarchy, we obtain activeðiÞ , qðei ; Bi ; eÞ [ s , ðvei þ ri Þ2 [ di2

ð1Þ

The above expression involves only six additions and five multiplications, and corresponds to parallel computing requires. Therefore, error metric and active vertexes can be computed by GPU.

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(vεi + ri) 2 cos θ εi

Warping & Adding

di

2

Comparing

ri

Fig. 1 GPU-based error metric and LOD selecting

For massive terrain data, we partition the terrain mesh into equal square tiles (size of 257 9 257), which elevation data are saved by terms of array. In preprocessing stage, we compute the nested geometric error of each vertex of every tile according to Eq. 2 during pre-processing, and saved by array too. Moreover, in order to project nested screen space error, we compute a nested bounding sphere radius array according to Eq. 1 for all tiles. During rendering, load DEM, geometric error, bounding sphere radius data of required tiles into display memory first according to view point parameter. At runtime, we take view point (e) and view angle(h) into account, Eq. 1 turn to: activeðiÞ , ðvei þ ri Þ2 cos h [ di2

ð2Þ

The process is shown in Fig. 1. Therefore, we compare one by one to get the LOD active vertexes according to the view point, and construct the triangle strip by means of RQT to render quickly.

3.2 Geometry Compression Our geometry compression method works on restricted quadtree meshes [10]. Mesh construction and compression is performed in a pre-processing step: Firstly, we tile the entire DEM. into square regions of 257 9 257 samples. Then we compute nested geometry error of each node, and organize the tile data in such a way that arrange each quad node according to nested geometry error and form an array, the front data have larger error. Because array is sorted as nested error, so when a node is loaded into memory, all of its ancestors must be in memory as well. The memory layout for any given node is shown in Fig. 2.

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Fig. 2 Node order sorted by nested geometry error

After a tile is partitioned, the tile’s height values can be quantized. Using a uniform quantization at k bits, which can be computed as k ¼ max f dlog2 ðhmax  hmin Þe ; 0g where hmin and hmax denote the tile’s minimum and maximum height value. In all of our experiments, the smaller tile, the smaller number of bits k. But too small tile causes lower rendering efficiency, so in our method, we partition the terrain mesh into 257 9 257 tiles. Normally k is smaller than 16, and height can be stored in 16 bits. The same as height value, geometric error of tile nodes can be quantized too. Because ei \ hmax - hmin, so bit number of geometric error is smaller than that of height value, and geometric error can be stored in 16 bits too. Because our tile data are stored in special order as Fig. 2, thus we should store x/y-coordinates with height value for decoding. In our method, the x/y-coordinates can be stored in 8 bits. As a result, all data of a node use 48 bits, and data decoding can be performed entirely on the GPU using geometry shades as well as bit and integer arithmetic and rendering as the method of Sect. 3.1. The propose of our method is that data can be transmitted to display hardware as require, and save bandwidth from CPU to GPU, which is just the bottleneck of terrain rendering nowadays.

4 Results We used a 2.8 GHz Pentium Dual-Core E7400 PC, with 2G DDR2 of RAM, GeForce 9800GT graphics with 1G of graphics RAM, and SATA 500G disk. For all results, a data set over the Puget Sound area in Washington was used, which is

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Fig. 3 Puget sound area, generated with our method (a), and its triangle construction (b)

Fig. 4 Examination result

made up of 16,384 9 16,384 vertices at 10 m horizontal and 0.1 m vertical resolution, and the texture data used pseudo-color. Our application is implemented by VC and OpenGL, and the window size is in all cases 1,024 9 768 pixels with 1 pixel screen space error thresholds. Application image is as Fig. 3, and the result of 1,000 frames is shown in Fig. 4. The frame rates are between 60 and 120 fps, and the number of triangles is 150,000 in average. Results from our analyses suggest that our method largely reduce the triangles number, and frame rates reached the real-time requirement of our travels. In summary, a GPU-based dynamic error metric and geometry compression technique for large, textured terrain rendering is presented here. By this method, the works of error metric computing is transferred from CPU to GPU without any rendering quantity reduction, which sufficiently utilizes the computing ability of GPU while leaving the CPU free to work on other important tasks. In order to obtain the seamless connection, we also keep the error consistency of two

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connecting tiles. The result meets our rendering requirement. But, our method increases about 10% sizes of out-of-core files, which restricts the rendering efficiency in a certain extent. For the future, a method to improve loading speed of terrain files is necessary for the purpose of increase farther rendering speed.

References 1. Gobbetti E, Marton F, Cignoni P, DI Benedetto M, Ganovelli F (2006) C-BDAM— compressed batched dynamic adaptive meshes for terrain rendering. Comput Graphics Forum 25(3):333–342 2. Hoppe H (1998) Smooth view-dependent level-of-detail control and its application to terrain rendering. In: Proceedings of IEEE visualization’98 [C], New Caledonia, pp 135–142 3. Gerstner T, Rumpf M, Weikard U (2000) Error indicators for multilevel visualization and computing on nested grids. Comput Graphics 24(3):363–373 4. Losasso F, Hoppe H (2004) Geometry clipmaps: terrain rendering using nested regular grids. ACM Trans Graphics 23(3):769–776 5. Koller D, Lindstrom P, Ribarsky W, Hodges LF, Faust N, Turner G (1995) Virtual GIS: a real-time 3D geographic information system. In: Proceedings visualization 95, IEEE, Computer Society Press, Los Alamitos, California, pp 94–100 6. Lindstrom P, Koller D, Ribarsky W, Hodges LF, Faust N, Turner GA (1996) Real-time, continuous level of detail rendering of height fields. In: Proceedings SIGGRAPH 96, ACM SIGGRAPH, pp 109–118 7. Pajarola R (1998) Large scale terrain visualization using the restricted quadtree triangulation. In: Proceedings IEEE visualization, pp 19–26 8. Thomas gerstner: multiresolution visualization and compression of global topographic data (1999) Geoinformatica, 2002, To appear. Available as SFB 256 Report 29, University of Bonn 9. Bao X, Pajarola R, Shafae M (2004) SMART: an efficient technique for massive terrain visualization from out-of-core. In: Proceedings vision, modeling and visualization (VMV), pp 413–420 10. Ulrich T (2000) Rendering massive terrains using chunked level of detail. In: Super-size-it! Scaling up to massive virtualworlds (ACM SIGGRAPH Tutorial Notes), ACM SIGGRAPH 11. Cignoni P, Ganovelli F, Gobbetti E, Marton F, Ponchio F, Scopigno R (2003) BDAMbatched dynamic adaptive meshes for high performance terrain visualization. In: Proceedings EUROGRAPHICS, pp 505–514 12. Livny Y, Kogan Z, El-Sana J (2007) Seamless patches for GPU-based terrain rendering. In: Proceedings of WSCG, pp 201–208 13. Lindstrom P, Pascucci V (2002) Terrain simplification simplified: a general framework for view-dependent out-of-core visualization. IEEE Trans Visual Comput Graphics 8(3):239–254 14. Correa WT, Klosowski JT, Silva CT (2003) Visibility-based prefetching for interactive outof-core rendering. In: Proceedings of IEEE symposium on parallel and large-data visualization and graphics, pp 1–8 15. Ng C-M, Nguyen C-T, Tran D-N, Tan T-S, Yeow S-W (2005) Analyzing pre-fetching in large-scale visual simulation. In: Proceedings of computer graphics international, pp 100–107

Cots Sparse Matrix Utilization in Distribution Power Flow Applications Dino Ablakovic, Izudin Dzafic and Hans-Theo Neisius

Abstract Sparse matrix is used in many modern industrial applications, to provide low cost and high performance solution. Unlike a number of previous works in the field of distribution networks, which analyzed the sparse matrix calculations as integral part of its calculation engine, this paper presents the considerations and analysis for the utilization of commercial of-the-shelf sparse matrix solvers (COTS SMS). Analysis is given for the parallel direct sparse solver utilized in unbalanced and unsymmetrical Distribution System Power Flow solution, which maximizes parallelization on multi-core processors. Optimal matrix population algorithm and container class from the power network object model is proposed for the fast population, search and access to the matrix elements. Optimal use cases of symmetrical and unsymmetrical matrices for the given method are analyzed. Test results for the Current Injection—Power Flow algorithm on a Three-Phase large scale system are presented which show the performance scales for symmetric and unsymmetrical matrices over a scope of system sizes and for different network topologies. Keywords Power flow

 Sparse matrix  Distribution network  Three-phase

D. Ablakovic (&)  I. Dzafic  H.-T. Neisius Siemens AG, Humboldtstrasse 59, 90459 Nuernberg, Germany e-mail: [email protected] I. Dzafic e-mail: [email protected] H.-T. Neisius e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_2,  Springer Science+Business Media B.V. 2012

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1 Introduction Distribution Power Networks have changed immensely in recent years, becoming larger and more complex every day. Distribution generation increased the problem complexity by far. Networks became much larger with the need for interconnection between different networks on distribution and medium voltage level. Three-Phase unbalanced and unsymmetrical networks, typical for the USA, require three-phase analysis. All of this requires better, and more performant DSPF application. Advanced DSPF solutions can utilize sparse matrix as the system impedance/ admittance matrix depending on the methods used. Introduced in 1960s to the power systems calculation [1], sparse matrix utilization evolved to present times as the crucial element of DSPF. Nodal matrix method, Newton–Raphson and FastDecoupled DSPF algorithms in all different variations make a great use of sparse matrix. However, almost all previous DSPF solutions utilize sparse matrix internally, and all operations such as factorization and eventually forward backward substitution are deeply integrated into the DSPF algorithm. It has long been realized that DSPF parallelization with different technologies and hardware can provide significant performance improvements [2–4]. Different factorization algorithms can be differently parallelized. From the perspective of DSPF application, it has become very hard to choose the right algorithm, hardware and implementation technique. The problem for the industrial real-time DSPF application is exponentially more complex when design and development time, and costs are considered. The aim of this paper is to analyze the utilization of Commercial Of-the-shelf Sparse Matrix Solvers (COTS SMS) for the DMS and more specifically DSPF, which are now more than ever, to maximize the power of parallel processing through multi-core CPU processors or even GPUs [5].

2 Cots Sparse Solvers in DSPF Unlike so many previous works in research and industry which implement their own direct sparse solvers for the DSPF, COTS solvers offer equally performant, but much faster to implement and more feasible solution. A SWOT analysis of using COTS SS, which are elaborated here, is given in Table 1. Two types of sparse solvers in general use in industry are: Direct and Iterative solvers. Both types have advantages and disadvantages, depending on the application. For the Power Systems in general, research up-to-date shows that Iterative method is still not developed enough, having convergence problems with different pre-conditioners. Direct sparse solvers have more predictable convergence, but require large memory to hold the factorized matrix. They are however, more stable, robust and reliable, and for those reasons found greater acceptance in DSPF applications and power systems in general.

Cots Sparse Matrix Utilization Table 1 SWOT analysis of COTS SMS Strengths Developed by dedicated sparse solver entities Very feasible, no special development needed Clear interface to network applications Updateable without additional development Opportunities Make best use of parallel processing, multi-core CPU or GPU, FPGA Ability to chose different SS for different hardware, operating systems and projects on demand Updateable without additional development at small cost

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Weaknesses Special interface needed Wrappers for all used COTS SS necessary Special container class needed, additional memory used Dependency on 3rd party entity Threats Eventual bugs in 3rd party software Product update and support discontinuity Compatibility issues Portability issues

Numerical evaluation of sparse direct solvers for the solution of large sparse symmetric linear systems of equations [5], analyses 10 different COTS solvers and provides the performance profile for different matrix types for each of them. Although the referenced benchmarking work is done only for symmetrical solvers with real numbers, many of the tested ones support also unsymmetrical matrices, and almost all support complex numbers. This is crucial consideration for choosing the right solver, depending on the DSPF implementation. A few direct sparse solvers are developed directly by the processor vendors which can only emphasize the maximum output from their most deep knowledge of the hardware on which the solver runs. According to the Moore’s law the processor performance is doubled every 18–24 months [6]. With currently available 8 and 12-core processors, parallel sparse solvers are inevitable in serious DSPF application. Other than processor vendors, there are serious research entities dedicated to the sparse solver development which are more concentrated on the algorithms for improving the solvers, by constantly finding better solutions for ordering, pivoting and factorization algorithms. This dedicated development of sparse solvers is very time and resource consuming and can be too big task for Power Systems development groups. Given that hardware development is so fast that it constantly requires revision of the sparse solver algorithms and re-implementation and the above stated arguments it can be concluded that COTS SMS can be feasible, performant, up-to-date solution for DSPF. From all of the above, requirements for the COTS SMS in DSPF are defined as follows: 1. 2. 3. 4. 5. 6.

Direct solver type Support float and double complex numbers Must support symmetrical and unsymmetrical matrices Platform independent (Windows/Linux) Acceptable licensing/pricing model Acceptable upgrade/development roadmap

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Fig. 1 COTS SMS interface

cNetObjModel

cDSPFSolver

«interface» iCOTS_SS

cIterativeSS

1 * Iterative SS library

cDirectSS

cMultigridSS

1 * Direct SS library

1 * Multigrid SS library

cFPGASS

1 * FPGA SS library

3 Interface and Wrappers of Cots Sparse Solver The aim is to enable DSPF and DMS to integrate any COTS SMS, and to replace it at lowest possible cost when better one is available, or even to be able to use more than one solver depending on the available hardware platform, operating system. Solution must be so robust to integrate iterative, direct and even GPU and FPGA solvers. Since there are a number of different solvers encapsulated in different libraries, which are created in different programming languages, the Interface must be defined with specific minimum functionalities, each solver must provide. Class wrappers must be used to encapsulate the solvers, which will then in Object Orientated Design OOD inherit from the defined interface. If C++ is the chosen language, as it is widely used in the DMS development, Fig. 1 presents the proposed interface. Template capabilities of C++ are to be used for the input of data types of the solver. The first step is defining the iCOTS_SS interface and its functions, which means that all inheriting wrappers will have to provide those. Solvers main functions are obviously factorization and then solving the system of linear equations with factorized matrix.

4 Sparse Matrix Container There are many methods for storing the matrix data, i.e.: compressed row, compressed column, block compressed row storage, diagonal storage, jagged diagonal storage etc. All of those sparse storage schemes use the continuous storage

Cots Sparse Matrix Utilization

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memory allocation for the nonzero elements of the matrix, or eventually a limited number of zeros when indefinite matrices are used. Overview of the standard formats given in [8, 9] shows that the compressed row and column storage formats are the simplest because they don’t consider the scarcity structure of the matrix, and they don’t store any unnecessary elements. Because of it, they are on the other hand not very efficient, needing an indirect addressing step for every single scalar operation in a matrix–vector product or preconditioned solve. An example of compressed row format for the symmetric matrix with real numbers is given in (1) where only upper triangular elements are stored saving the space of both 0 and symmetric values.

The length of the value and column index arrays is actually the number of non-zero elements in the upper triangular matrix, and the row pointer array gives the location of the first non-zero element within a row. Power networks topology is dynamic in its nature. Switching equipment changing its statuses define the network topology, combining and separating sub-networks. Having different sub-networks and topologies requires an implementation of tracing or graph theory coloring algorithm to collect all galvanically connected network elements, from which a sub-network is composed, at one point in time, by which the network object model is created. Bus bars are essentially graph vertices and branches, i.e. lines and transformers are graph edges. Switches are in general DSPF calculation ignored because their impedances are, when compared to branches, negligible. When one sub-network object model is created, sparse admittance matrix vectors need to be populated. Because of its stacked sparse format, population process requires special sparse matrix container class which will provide fast population and access to matrix elements. Since the benchmark solver is using compressed row format shown in (1), it means for both algorithms that matrix must be populated by sweeping columns for each row, one by one. Because of virtually unknown number of non-zero elements the container must have dynamic size and grow as elements are inserted. This means no continuous memory allocation, but use of single linked lists. This container class is essentially made of one vector-array containing the first column of struct elements defined above. The array itself uses continual memory allocation and therefore the access to each row can be done in a single step. Later when row is found, iteration over the elements in the row by using a pointer to each next element and comparing the column value is needed. It is represented on Fig. 2.

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Fig. 2 Advanced sparse matrix container structure

5 Symmetrical Matrix in DSPF Unbalanced and unsymmetrical DSPF calculations in phase domain require admittance sparse matrix YABC of size 3n, where n is the number of electrical nodes and 3 is a phase multiplicator. Unsymmetrical matrix factorization is in general slower than the symmetrical matrix factorization because of the more complex permutation calculation and larger fill-in calculation. Full unsymmetrical matrix size is n2 and symmetrical (n2 ? n)/2. To achieve the best utilization of the matrix in DSPF, with the proposed modeling two main goals are: 1. Make only one Y admittance matrix factorization 2. Make the Y admittance matrix symmetrical Nodal admittance matrix YABC contains the branch admittances, i.e. lines and transformers. Loads are modeled in an injection vector, because they are dependent on the calculated voltage, which in the end makes this method iterative calculation process. When local control algorithm is implemented with DSPF, tap changer positions and capacitor banks are also considered in an injection vector. To improve the method solvability, matrix numerical conditioning and decrease the number of iterations, advance modeling can be utilized in following: 3. Model constant impedance loads in the matrix 4. Model initial tap positions and capacitor banks in the matrix, and final tap positions simulate as current injections Therefore general equation for the Current Injection method is presented in (3) ½YABC   ½V  ¼ ½IL  þ ½ID   ½IS  ¼ ½I   ½IS 

ð2Þ

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where: IL ID IS

—Load injection vector calculated from the voltage dependent loads. —Injection vector calculated from tap positions of load tap changers and capacitor calculated in local control simulation. —Injection source (Slack) compensation vector.

To avoid first calculation iteration, instead of filling the slack voltage values to (V) and doing one forward–backward calculation, IL vector is initially set as given in (4). Because of mutual coupling between phases and the affect on the angle, initial Slack values at node k must be set with 120 phase shift. IkA ¼ 1\0 ¼ 1 þ j0p:u IkB ¼ 1\  120 ¼ 0:5 þ j  0:8660p:u IkC ¼ 1\120 ¼ 0:5 þ j0:8660p:u

ð3Þ

Is is a compensation vector which ensures that Injection sources—Slack Buses are kept at the referent voltage during the DSPF iterations, keeping the matrix symmetrical at the same time. Isj ¼ YABCjk  Ikn¼0 n k

ð4Þ

Where: —iteration number = 0 —Index of the Slack Bus 

1 0

       1 IS1  0   VS ¼  Y~ABC V1n IS1n I1n

ð5Þ

When all Current injection vectors are added it still has to be ensured that values for Slack Buses are as given in (5), which means all branches and loads on the Slack Buses are ignored. Important to note is that Is vector must be calculated only once and in the first iteration of the Power Flow when only Slack values are populated. The Load vector IL is calculated in each Power Flow iteration and Delta ID in each Local Control Iteration.

6 Result and Analysis This paper presents the results of DSPF Admittance matrix population and factorization with implemented proposed sparse matrix container against the balanced tree container found in C++ Standard Template Library (STL). The test network is created from modified IEEE 34 test feeder [10] by multiplying the feeder 148

18 Table 2 Number of equipment units in the network

D. Ablakovic et al. Type of equipment

Number of equipments units

Bus bars Transformers Lines Loads Capacitors Switching devices

5.071 390 4.770 2.250 180 10.381

(ms)

Fig. 3 Matrix population and factorization times for radial network

350 300 250 200 150 100 50 0

313

196 107 70

Unsymmetric

60

std::map

66

AdvSparse

matrix type Matrix population

Factorization

times and adding them in parallel to the same feeder head bus bar. Additionally connection line segments on Buses 864 and 840 are added in order to provide the meshed network topology. In Meshed configuration two loops are made between each two feeders on the given buses. The network size in total is given in Table 2. Test Application: DSPF—Distribution System Power Flow Benchmark Test Server: 2 9 Intel Xenon(R) E5420 CPU 2.5 GHz, RAM 24 GB Num of processor cores: 2 9 4 = 8 cores Operating System: Windows Server 2003 SP2 Sparse solver: Intel MKL Pardiso [7] Test Tool/Methods: • Win32 API timing functions for profiling used directly in code utilizing number of processor ticks with precision to millisecond (ms). • No code optimization and no SSE2 instructions in compiler optimization. • Average calculation times on 10 runs are presented Results presented show that both matrix population and factorization times can be significantly improved with usage of symmetrical matrix alone, even with usage of Standard Template library collections. Utilizing the Advanced Sparse Matrix container, population time is far better and shows the importance of giving so much attention to the entire process of sparse matrix utilization. Results are presented on Figs. 3 and 4, Table 3.

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Fig. 4 Matrix population and factorization times for meshed network

350

320

300 (ms)

250

211

200 150

111 78

100

64

74

50 0 Unsymmetric

std::map

AdvSparse

matrix type Matrix population Factorization

Table 3 Results of comparison tests for radial and meshed networks Unsymmetrical Symmetric Symmetric std::map AdvSparseMat Radial networks Population Factorization Meshed Population networks Factorization

313 107 320 111

196 70 211 78

60 66 64 74

Finally, from the presented results it can also be seen that results are worse for Meshed network types, which is result of more off-diagonal elements and spread matrix structure. A topic for further research is to determine exactly how the network topology affects the matrix population and factorization times.

References 1. Tinney WF, Walker JW (1967) Direct solutions of sparse network equations by optimally ordered triangular factorization. In: Proceedings of the IEEE, Nov 1967 2. Wu JQ, Bose A (1995) Parallel solution of large sparse matrix equations and parallel power flow. IEEE Trans Power Syst 10(3):1343 (August) 3. Feng Z, Zeng Z, Li P (2010) Parallel on-chip power distribution network analysis on multicore-multi-GPU platforms. IEEE Trans Very Large Scale Integr VLSI Syst, vol PP, Issue: 99 4. Johnson J, Nagvajara P, Nwankpa C (2004) Sparse linear solver for power system analysis using FPGA, Drexel University, HPEC 5. Gould NIM, Hu Y, Scott JA (2005) A numerical evaluation of sparse direct solvers for the solution of large sparse, symmetric linear systems of equations, Technical report, RAL-TR2005-005, CCLRC Rutherford Appleton laboratory 6. Ramanathan RM Intel Multi-core processors: making the move to quad-core and beyond, White Paper. Intel Corporation 7. Intel Math Kernel library reference manual—March 2009 630813-031US 8. Templates for the solution of algebraic eigenvalue problems: a practical guide (2000) In: Bai Z, Demmel J, Dongarra J, Ruhe A, van der Vorst H (eds) SIAM, Philadelphia

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9. Distributed sparse data structures for linear algebra operations (1992) Technical report CS 92–169, Computer science department, University of Tennessee, Knoxville, TN, LAPACK working note 10. IEEE 34 node test feeder, IEEE distribution system analysis subcommittee

A New Graph-Based Algorithm for Persian Text Summarization Hassan Shakeri, Saeedeh Gholamrezazadeh, Mohsen Amini Salehi and Fatemeh Ghadamyari

Abstract Nowadays, with increasing volume of electronic text information, the need for production of summary systems becomes essential. Summary systems capture and summarize the most important concepts of the documents and help the user to go through the main points of the text faster and make the processing of information much easier. An important class of such systems is the ones that produce extractive summaries. This summary is produced by selecting most important parts of the document without doing any modification on the main text. One approach for producing this kind of summary is using the graph theory. In this paper a new algorithm based on the graph theory is introduced to select the most important sentences of the document. In this algorithm the nodes and edges will be assigned with different weights and then the final weight of each one will be defined by combining these values. This final weight indicates the importance of the sentence and the probability of appearing this sentence in the final summary. The results show that considering simultaneous different criteria generate a summary which is more similar to human one.









Keywords Summarization Persian texts Sentence’s graph Extract Cohesion

H. Shakeri (&)  S. Gholamrezazadeh  M. A. Salehi  F. Ghadamyari Islamic Azad University, Mashhad Branch, Mashhad, Iran e-mail: [email protected] S. Gholamrezazadeh e-mail: [email protected] M. A. Salehi e-mail: [email protected] F. Ghadamyari e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_3,  Springer Science+Business Media B.V. 2012

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1 Introduction Nowadays, human faces a large amount of information every day. Significant parts of this information are in text format. Due to this, there is a demand for tools that accelerate the reading and comprehending text documents. Summarization systems enable us to read the most important parts of each document and so increase the speed of reading and comprehending. There are various definitions for summary. Edward Hovy et al. [1] defines the summary as a text that is based on one or more texts; it has the most important information of the main texts and its content is less than half of the main texts. Mani [2], describes the text summarization as a process of finding the main source of information, finding the main important contents and presenting them as a concise text in the predefined template. Some factors such as the language of input text are so challengeable. For example in Persian texts, multi meaning and multi functional words are one of the challenges in summarizing these texts. In [3] the challenges in Persian texts processing is discussed completely. There are three main steps for summarizing texts [4]. These steps are topic identification, interpretation, and summary generation. In topic identification step, the most prominent information in the text is identified. Most of the systems, assign different precedence to different parts of the text (sentence, words, and phrases); then a fuser module mix the scores of each part in order to find the total score for a part. At last, the system presents the N highest score parts in final summary. Several techniques for topic identification have been reported such as methods based on Position, Cue Phrases, word frequency and content counting [1]. Abstract summaries need to go through interpretation step. In This step, related subjects are combined in order to form a general concise content [5] and the additional phrases are omitted. Inferring the topics is difficult; therefore most of the systems generate the extract summary. In summary generation, the system uses text generation method. This step includes a range of various generation methods from very simple word or phrase printing to more sophisticated phrase merging and sentence generation [9]. In other words, the natural language, which is understandable, by user is generated here. The summarization systems are categorized based on the type of generated summary. In this paper we focus on extractive summaries. An extractive summary is generated by selecting sentences from the main text to form a summary without any modification of their original wording. Up to now, many different techniques have proposed to select the most important part of the text such as statistical methods which includes Aggregation Similarity Method [6], Location Method [7], Frequency Method [8], TF-Based Query Method [9], linguistic methods which includes Graph Theory, Lexical Chain, WordNet and Clustering. Graph is an appropriate approach for presenting the relation between sentences in a way that the relation between each two sentences can be shown independent the other ones. Lexical chain uses WordNet in order to identify the relation between words and put them in a chain. Since there is not any WordNet for Persian words, we cannot

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use this technique in our system. Clustering technique makes some clusters from sentences and relates them to each other. Clustering method is unable to examine the relation between sentences in a text whereas graph technique provides this relation for us. Considering these reasons, in this paper we propose a graph-based algorithm for summarizing Persian texts. The structure of this paper is as follows: in Sect. 2, the categorization criteria for summarized text will be discussed. In the third section, we discuss the graphbased approach for summarization and introduce systems which are designed based on it. In the forth section, we introduce our proposed algorithm. In Sect. 5, the experimental results will be analyzed and finally in Sect. 6, the result will be discussed.

2 Related Works Based on the output summary, the text summarization systems are classified. These categorizations are discussed in [10]. So far, many systems which generate a kind of summary that presented in Fig. 1 are designed. Most of these systems are designed for English texts and other language such as Japanese, Spanish and others. But unfortunately very few efforts have been done for Persian texts. The most important systems for Persian text summarization are introduced here. FarsiSum: FarsiSum [5, 11] is a text summarization system for Persian newspaper text/HTML. It uses modules implemented in SweSum [14], a Persian stop list in Unicode format and a small set of heuristic rules. The summarization process includes three phases: Tokenizing, Scoring and Keyword extraction. Words in the document are converted from ASCII to UTF-8 and then compared with the words in stop-list. Automatic Persian Text Summarizer: this system uses a hybrid method to summarize Persian texts automatically. In this system, the below techniques are used to select the sentences which should be presented in final summary: Lexical chains, summarization based on graphs, selecting important sentences based on cue words, number of similar sentences, similarity between sentences and similarity with topic and query. For more details see [12]. Hybrid Farsi text summarization: in [13] a technique based on term co-occurrence and conceptual property of the text is defined. In this method for each two words, the co-occurrence degree is computed. Then lexical chains are created and n top ranked words are selected. After that, a graph is created with words as its nodes. The edges are drawn based on co-occurrence degree between words. The sentence score is computed by summing the gain of all its words and finally n top ranked sentences are selected.

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H. Shakeri et al. Constructing Graph Algorithm Input: sentences from the main text, Base node Output: sentences which make summary 1. For each sentences take part in constructing graph 1.1. Calculate number of edges connected 1.2. Calculate total frequency of words 1.3. Count number of key words 1.4. Count multiple selection criteria simultaneously 1.5. Calculate the rate of deviation from base node //base node is calculated earlier by formula number 1 and is identified for this algorithm as input 2. For each two sentences 2.1. Count shared words 2.2. Count shared key words 2.3. Count common English word 2.4. Count common word with explanation as footer 2.5. Determine if two sentences are located in one paragraph 3. For each sentences calculate formula number 4

Fig. 1 The proposed summarization algorithm based on graph

3 Applying Graph in Extractive Summaries Using the graph for displaying the structure of the text will help us to better understand the connection between different parts. [10]. Graph-based algorithms use a ranking algorithm to rank different sections of a text where each section is considered as a node. Ranking algorithms use various criteria in order to sort the nodes based on priority. The nature of nodes and edges will be defined by the type of text. For example, some sections of the text, words, or sentences can be considered as the nodes. Edges will represent the lexical or semantic connection, or commonalities between the two nodes. Regardless of the type and characteristics of the text that we want to draw the graph for it, a graph-based ranking algorithm includes the following basic steps: 1. Identify units of text-which can include phrase, word or other units- and to consider them as vertices in the graph. 2. Determine the relationships that these units were related to making and using these relationships to drawn edge between the vertices of the graph. The edges can be directed/undirected and weighted/unweighted. 3. Run the graph ranking algorithm repeatedly until all entities (nodes) are sorted according to priority. 4. Sort the vertices according to their rank. As it is stated in the fourth step, after specifying the final score for each node, the nodes are ranking based on their final score. Then, depending on compression rates—Compression Rate defines how the main text should be shortened or determines the length of summary text- in the desired text, sentences with the highest score are selected to attend in final summary. LexRank [15] and TextRank

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[16] are two of the most important algorithms based on the graph. Following, we examined each of these algorithms briefly. LexRank: Erkan and Radov [15] designed LexRank in order to summarizing text in multi-document systems. It is assumed that a sentence which is similar with many other sentences in a cluster, is more central (more important) and closer to the subject. In this algorithm, a fully connected and undirected graph is plotted for the sentences of each cluster. If two sentences share similarities, an edge is drawn between them. The cosine similarity is used to calculate the similarity between two sentences. After the calculation of similarity between sentences and construct a graph, we specify the central sentence using graph for each cluster by the following order. They define a degree of centrality for each sentence which is the number of similar sentences to the desired one. The sentence with the highest degree of centrality is the central sentence. TextRank: TextRank is a graph-based ranking model which is used for all graphs that derived from natural language texts. TextRank is derived from Google page ranking [9] model and is designed for use in single document summarization systems. TextRank is used to extract key words and sentences. A fully connected and undirected graph is used to extract sentences. Each sentence is considered as a vertex (node) in graph. To make an edge between two sentences, a similarity relation is used which is measured as a function of joint concepts. Each edge is also weighted that indicates the importance of relationship. Sentences based on their scores are ranked and the sentences with the highest score are selected [16]. SUMGRAPH [8] and Time stamped Graph [17] are two other summarization systems which are designed based on the graph.

4 Proposed Method As mentioned earlier, in many of current methods, the most important sentences are selected based on final weight of nodes whereas the weight of edges is solely used to determine the weight of nodes. The method proposed in this paper tries to involve all existing relations between sentences in determining the most important sentences. Moreover, the importance of sentences independently is considered. In other words, we consider both the importance of each sentence and the importance of relation between sentences. The reason is that if the content of a sentence is not important, it is worthless for the system, no matter how close is the relation with other sentences. The strength of this algorithm is addressing the importance of sentences independently and simultaneously the relations between them. In this algorithm, a connected and undirected graph is used. We consider undirected graph because it is appropriate well for graphs with weak links [7]. Sentences considered as nodes and relation between them is shown by edges. We consider a weight for nodes and for each edge. Weight of each edge defines the

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degree of importance of the relation between two sentences. The following criteria are used for weighting nodes: • • • •

Number of edges connected to node. Frequency of words in one sentence. Number of keywords in a sentence. Having multiple selection criteria simultaneously (criteria based on them sentences are selected from original text to form a graph). • Rate of deviation from the base node: the base node is the one which is known as the most important and key sentence in the text, and contains the main subject of the text. To determine the base node, we combine weight of all nodes and work out a value for every node. Then we select the highest value and consider the sentence related to it as the base node. For combining weights we apply the following formula: X5 C Wi ð1Þ Tw ¼ i¼1 i where Tw is the overall weight, Ci is coefficient dedicated to ith criteria which indicates the percentage importance of this criterion and Wi is the numeric value of ith criteria which is obtained for a specific sentence. Ci is obtained experimentally and based on a research conducted on the characteristics of Persian texts. After identifying the base node, we calculate the deviation of each sentence from the base node by formula (2). diff ¼

NI D

ð2Þ

where diff is the deviation of each sentence from the base node, N is the number of words in sentences, I is the number of common words with base node and D is the diversity words of the original text. D is calculated by reading text one time and considers each word in its first presence in the text. After calculating the deviation, we reduce the obtained number from the P/D fraction where P is the number of words in base node. Thus, the similarity of each sentence with base node is obtained. S¼

P  diff D

ð3Þ

Indeed, the sentences which have more similarity with the base node are more related to the topic of the main text. Based on the following criteria, two sentences are related to each other and an edge should be considered between them. These criteria are: • Number of words shared between two sentences • Number of keywords shared between two sentences

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• Having common English words (this system is designed for Persian texts) • Having words with common explanation as footer • Existence of two sentences in a paragraph After constructing the graph, ten weights are obtained. Five weights are for nodes—former criteria—and five weights are for edges—latter criteria. Then, all these ten weights for all of the nodes and edges are combined using formula (4) and consequently a final weight for each node is calculated. For each node, the weights assigned to itself and weights of edges which is connected to, are combined. X10 CW ð4Þ Tw ¼ j¼1 j j where Tw is final weight, Ci is coefficient for ith criteria, and Wi is the numerical value for ith criteria which is obtained for a specific node. The formula 4 is the same formula 1 but the boundaries are changed. In formula 1, we combine just five weights which are obtained by weighting nodes criteria in order to identify the base node whereas the formula 4, combines ten weights obtained by weighting edges criteria addition to weighting nodes, to calculate a final weight for each node. We assign higher values to the nodes weighting criteria because if one sentence has no importance itself, is not appropriate to attend in the final summary. Another benefit of the proposed graph to previous algorithms is taking into account the degree of importance for sentences and relationship between them, simultaneously. It enables us to choose the sentences which have main content and are related to the others. This cause the final summary has more cohesion and become more similar with the human one. The following pseudo-code shows the steps of our proposed algorithm.

5 Performance Metrics To evaluate a text summarization system, two widely used metrics are: Precision and Recall [18]. These two metrics are used just for evaluating extractive summaries. Recall is the fraction of sentences chosen by the person that were also correctly identified by the system. A person is asked to select sentences that seem to best convey the meaning of the text to be summarized. Then selected sentences automatically by system are evaluated against the human selection. Recall ¼

system  humanchoicesoverlap sentencechosenbyhuman

And Precision is the fraction of system sentences that were correct.

ð5Þ

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Table 1 Comparison result between proposed algorithm and FarsiSum

Precision Recall F1 ROUGE-1

FarsiSum

Proposed method

0.37 0.52 0.43 0.43

0.52 0.67 0.58 0.62

Precision ¼ ðsystem  human choice overlapÞ=ðsentence chosen by systemÞ ð6Þ F1 is a weighted average of the precision, recall and calculated by following formula [19]: F1 ¼ ð2  ðprecision  recallÞÞ=ððprecision þ recallÞÞ

ð7Þ

ROUGE-N [20] is another criteria which is widely used in evaluating summaries. ROUGE-N is calculated as follows: P P S2fRefrence summariesg gramn 2S Countmatch ðgramn Þ P ð8Þ ROUGE  N ¼ P S2fRefrence summariesg gramn 2S Countðgramn Þ where N stands for the length of the n-gram, gramn , and Countmatch ðgramn Þ is the maximum number of n-grams co-occurring in a candidate summary and a set of reference summaries.

6 Experimental Results In order to evaluate the proposed method, we compare the output of this system with FarsiSum [5] system. Evaluation criteria like Precision, Recall, standard F1and ROUGE-1 are used for comparison. For this purpose, ten scientific papers about computer technology were summarized by FarsiSum system and the algorithm which is presented in this paper. Also, these papers were summarized by a human expert and we consider this human summary as the reference. Compression rate is set to 50%. The results are listed in Table 1. As it is shown in Table 1, the precision and recall and ROUGE-1 are improved. We noticed that considering more criteria and specifically taking English words into account is the reason of difference between our approach and approaches applied in FarsiSum. In Persian texts to avoid mistakes and misunderstanding, specialized words are quoted in the original language. We consider these words as clues that express the prominent parts of a professional text. On the other hand, we consider relationship between two sentences in addition to the importance of sentences. This helps generated summary to be more similar with the human one. In fact, for the sake of cohesion and clarity, human summarizer (expert), chooses sentences which are related to each other.

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7 Conclusion In this article we first review the text summarization systems as well as summary classification criteria. Then, we propose a new method based on graph theory to create an extractive summary for Persian texts. The aim of this method is to consider the importance of sentences independently and at the same time the importance of the relationship between them. Thus, the sentences are selected to attend in the final summary contains more important subjects, and also have more contact with other sentences. As a result, we notice that the sentences in summary text have relationship with each other and become closer to the human generated summary. Evaluation results indicate that the output of proposed method improves precision, recall and ROUGH-1 in comparison with FarsiSum. This algorithm is a part of text summarization system. In future we plan to fine tune the output of this algorithm. For this goal, we can add some additional processing steps as a post processing step to the system. This step can involve processes such as finding the reference of pronouns in the text and replace them, depends on the genre of the text some sentences can be omitted and reduce the redundancy. Additionally, having richer database improves the accuracy of summary.

References 1. Frankel, David S (2003) Model driven architecture: applying MDA to enterprise computing. OMG Press, Wiley, New York 2. Mani I (2001) Automatic summarization John Benjamin’s publishing Co, pp 1–22 3. Shamsfard M (2007) Processing persian texts and its challenges. In: The second workshop on Persian language and computer. pp 172–189. (in Persian) 4. Lin CY, Hovy EH (1997) Identify topic by position. In: Proceedings of 5th conference on applied natural language processing, March 1997 5. Mazdak N (2004) A Persian text summarizer, master thesis, department of linguistics, Stockholm University, Jan 2004 6. Kupiec, Jullian M, Schuetze, Hinrich (2004) System for genre specific summarization of documents, Xerox corporation 7. Rada M (2004) Graph-based ranking algorithms for sentence extraction, applied to text summarization, annual meeting of the ACL 2004, pp 170–173 8. Patil K, Brazdil P (2007) Sumgraph: Text summarization using centrality in the pathfinder network. IADIS Int J Comput Sci Info Sys 2:18–32 9. Wills RS (2006) Google’s pagerank: the math behind the search engine 10. Saeedeh G, Mohsen AS, Bahareh G (2009) A comprehensive survey on text summarization systems’’. CSA 2:462–467 11. Martin H, Nima M (2004) A Persian text summarizer. In: International conference on computational linguistics 12. Zohre K, Mehrnoush S (2007) A system for automatic persian text summarization. In: 12th international CSI computer conference, (in Persian)

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13. Azadeh Z, Behrouz M-B, Mohsen S (2008) A new hybrid farsi text summarization technique based on term co-occurrence and conceptual property of the text, In: 9th ACIS international conference on software engineering, artificial intelligence, networking and parallel/ distributed computing 14. Dalianis H (2000) SweSum—A text summarizer for Swedish, Technical report, TRITA-NAP0015, IPLab-174, NADA, KTH, Oct 2000 15. Erkan G, Radev DR (2004) LexRank: graph-based centrality as salience in text summarization, J Artif Intell Res 22, pp 457–459 16. Rada M, Tarau P (2004) TextRank: bringing order into texts. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP 2004) 17. Lin Z (2006–07) Graph-Based methods for automatic text summarization, Ph.D. thesis, school of computing National University of Singapore 2006–07 18. Nenkova A (2006) summarization evaluation for text and speech: issues and approaches, Stanford University 19. Norshuhani Z, Arian G (2010) A hybrid approach for malay text summarizer, The 3rd international multi-conference on engineering and technological innovation 2010 20. Lin C (2004) Rouge: a package for automatic evaluation of summaries. In: proceedings of the workshop on text summarization branches out, 42nd annual meeting of the association for computational linguistics. 25–26 July, Barcelona, Spain, pp 74–81

The Factors Affect User Behaviour in a Picture-Based User Authentication System: PixelPin Zhang Min, Brendan Ryan and Sarah Atkinson

Abstract Nowadays alphanumeric passwords are still widely used, even though it causes the well-known ‘password problem’. There are a lot of studies on alternative user authentication schemes which try to take place of the textual password. PixelPin is one of the picture-based authentication systems, which are base on Blonder’s patent (Graphical Passwords. United States Patent 55599611996). This paper is the report on our evaluation of the PixelPin by conducting four experiments. In specific, we summarized the issues of PixelPin’s usability and security via web-based user study and focus group. We also tried to understand users’ choice of picture and the way of their memorization of the click-points. A cursor effect was studies in the last experiment. Finally, some issues are proposed to be solved in the future work. Keywords User authentication


Cued recall-based picture passwords
PixelPin

1 Introduction User authentication is a pivotal part of the computer security. After first introduction in the late 1960s [3], alphanumerical passwords are ubiquitous authentication today, using in computer systems, networks, Internet-based systems etc.

Z. Min (&) Horizon DTC, School of Computer Science, University of Nottingham, NG8 1BB, Nottingham, UK e-mail: [email protected] B. Ryan
S. Atkinson Human Factor Research Group, University of Nottingham, Nottingham, UK

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_4,  Springer Science+Business Media B.V. 2012

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As the development of the information technology, more and more systems require users to remember their account names and passwords. In addition, some systems ask users to change passwords regularly. According to the studies conducted by Dhamija et al. [9], human beings are merely capable to remember a limited number of passwords, the proliferation of passwords-system increase the interference [42], therefore people often write down [1], share and use the same or similar passwords for all different system accounts [15]. Moreover, surveys show that frequent passwords are short and meaningful words [26], such as ‘password’, friends’ or family members’ names or names of pets, etc. [6, 32]. The alphanumeric password is thought to be in a dilemma between security and usability, that is, the well-known ‘the password problem’ [6, 19, 32]. Briefly, on one hand, passwords should be easy to remember and authenticated quickly; on the other hand, passwords should be random, should be different on one user’s different accounts and should be changed frequently. Many alternative solutions have been proposed with the aim of finding the best trade-off between security and usability, which can reduce the memory issues on the basis of security rather than at the expense of security. For instance, the token-based authentication (i.e. smart-card, bank card), biometric authentication [17] (i.e. the fingerprint, iris [30], face recognition [20], speech matching [22], etc.). However, these schemes are also accompanies by vulnerabilities and shortcomings, in specific, the expensive hardware and support including deployment and maintenance; the unresolved issues of privacy, impersonation [25], etc. The graphical passwords have been devised based on psychology theory that human can remember pictures better than alphanumeric characters [33] and the ‘picture superiority effect’ [26]. It is thought that the key cause for ‘the passwords problem’ lies in the memorability of the secure passwords, the graphical password will be more memorable, easier to use and therefore will be more secure [39]. In this paper we report on evaluation of a picture-based password system–PixelPin,1 and some findings The rest of the paper is divided as follows. In the next section, we briefly introduce the related research and description of PixelPin. Section 3 reports on our experiment, followed by discussion of results in Sect. 4. In Sect. 5, we summarize our findings and future work.

2 Related Work Graphical passwords were originally described by Blonder [4] in 1996. From 1999 onward, a great amount of graphical password schemes have been proposed. The

1

http://www.pixelpin.co.uk

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early surveys in [23, 35] give the overviews of graphical password systems before 2005; the recent studies in [14] summarize and categorize 12 schemes and review the usability and security issues of those systems and Renaud [29] provides the usability guidelines for designing those systems. Biddle et al. [2] provide a more comprehensive review of published research on the graphical passwords during the first twelve years. In a nutshell, graphical password belongs to the knowledge-based authentication. There are three main categories of graphical passwords: (1) recognition-based (e.g., PassImages [13], Passface [27, 28]), (2) pure-recall (e.g., Draw-A-Secret (DAS) [18], Passdoodles [38]), and (3) cued-recall (e.g., PassPoints [40], Cued Click-Points (CCP) [7]). Some of them have already been commercialized. In Hafiz et al.’s [14] survey study, both usability and security features are recognized and reviewed. According to ISO 9241-11 standard, usability is defined as the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use [16]. The previous studies on the graphical passwords also consider the memorability issue [11], they suggest that the created password must be meaningful, frequently used and fun [5, 36]. The PixelPin As the PassPoints, PixelPin is also based on Blonder-style graphical password [4] of using multiple click-points (no limitation of the number of points) on a single image as the passwords. Owned by OEM partnership Ltd., PixelPin is a web-based authentication system. The basic functionalities are described as follows: Creation—users can use any images (e.g., the thumb nail images provided by PixelPin system, or personal images), then choose any places they prefer to click on, the sequenced points are supposed to be their passwords. Login—after users inputting their account name (registered email address), system displays users’ own images; users enter the same sequence of the points on their images. If correct (both places and order of points), system directly forward to the successfully-login page. Change passwords—there are two ways to change passwords: changing the picture (using another image and choosing several points on it), and changing the points on the same picture (choosing another points). Reset passwords—users click the ‘forgotten your passpoint?’ link, and input their email address. The reset-passwords link is sent to their typed email address, which would guide users to their previous picture to click on the new points as their new passwords. The aim of our evaluation is to find the issues of both usability and security aspects. We also try to understand user’s choice of image and their preference for using different cursors.

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Table 1 Nielsen’s five-point severity rating Rating description 0 1 2 3 4

I don’t agree that this is a usability problem at all. Cosmetic problem only—need not be fixed unless extra time is available on the project. Minor usability problem—fixing this should be given low priority Major usability problem—important to fix, should be given high priority Usability catastrophe–imperative to fix this before product can be released

3 Methodology 3.1 Experiment Design and Procedure Experiment 1—Web-based user study The task lists (including five tasks— creating an account, logging in, changing passwords by changing picture, changing passwords by changing points on the same picture, and resetting passwords) and questionnaires were sent to twelve subjects via Email. The questionnaires were used to collect demographic information, computer skill, Internet browser, task performance, 5-point likert for both difficulty-level of individual tasks and overall satisfaction of PixelPin, the number of their pass-points. One week later, the subjects were asked to log in their own accounts for the recall test and give the feedback about how many attempts they try before logging in successfully. Experiment 2—Focus group. Four human factor experts participated in the focus group session which lasted for two hours. Their interactions with PixelPin were video-recorded individually. Experts were asked to discussion and rate all possible problems we found in the Experiment 1 according to 5-point severity rating (shown by Table 1) from Nielsen’s heuristic evaluation [24]. Experiment 3—Paper-based user understanding. Sixteen participants were involved in this experiment. One sheet of paper (as Fig. 1 shows) with sixteen images printed on was shown to participant individually. They were instructed how to use the picture passwords. Then we ask them to choose one picture they prefer to use as password-picture and to mark the points on it (the number of points were not limited) as their ‘passwords’. We had subjects return after a week for a recall test. Experiment 4—Cursor-choice study. Eight participants took part in our study voluntarily. They were asked to try seven different kinds of cursors (refer to Fig. 6) individually on the laptop when clicking the points on the picture, other than the original ‘Finger’ used by the PixePin. After that they chose only one cursor they would like to use most. To ensure that the reproduction of your illustrations is of a reasonable quality, we advise against the use of shading. The contrast should be as pronounced as possible.

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Fig. 1 Paper-based image for understanding user

If screenshots are necessary, please make sure that you are happy with the print quality before you send the files.

3.2 Participants There are totally 20 subjects (21–45 years old) who participates at least one of our experiments. Eighteen of them are students or stuff (13 subjects with computer science background) in University of Nottingham. None of all participants have used the picture-password before.

3.3 Materials Digital task list sheet, questionnaires were used in Experiment 1; Experiment 2 used video-camera, four problems sheets for severity rating, four laptops for individual interaction with PixelPin; one-page of pictures sheet was required in Experiment 3; a Samsung Q330 laptop was used in Experiment 4.

4 Result and Discussion Experiment 1 Ten questionnaires are received. The performance of each task is shown by Fig. 2. We found only 30% subjects could log in successfully at their first attempt.

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Fig. 2 Task performance

Fig. 3 Subjects’ perception of tasks, following instructions, and overall satisfaction of PixelPin (1 = very easy/good, 5 = very difficult/bad)

On average, people have to try 2.4 attempts before they successfully login. Resetting the password is also suffered the low success rate (60%). The number of participants’ pass-points is 3–4 points mostly, one people even choose only one point. The average number of the points is 3.2 points. According to subject’s 5-point likert on the difficulty-level of tasks and their own satisfaction of PixelPin and following its instruction (as shown by Fig. 3), 50% of subjects find it is difficult to log in, 40% find difficulty in following the PixelPin’s instruction. 10% find it is very difficult to reset the passwords (the reset link does not work). Figure 3 also indicate that most of people’s satisfaction of the PixelPin is on the moderate level (50%), using the picture as the password seems to be more

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Fig. 4 Result of recall test

interesting than the text password, however, the user experience with PixelPin is impaired by the flashing-refresh screen after each clicking, annoying error tolerance, etc. more details will be presented in the next part. Figure 4 displays the memorability of PixelPin passwords once creating and a week later. Only 10% of subjects can log in successfully at the first attempt, 30% can log in after several times; most subjects could not remember their points at all, they have to reset the passwords to enter the system. This might be due to their infrequent use of the PixelPin, and a lack of learning their points after creating. Experiment 2 In the focus group discussion, we focus on both usability and security issues. a. Usability Usability issues often significantly impact the real-world security. We list the PixelPin’s usability issues which are against to Nielsen’s ten usability heuristics [] as follows: #1. Visibility of system status Lack of feedback to keep users informed about what is going on. e.g., • users do not know if their point have been clicked or not; • no message when user inputting wrong email address; • no tips after user clicking wrong sequence of points or incorrect place of points. #3. User control and freedom • ‘‘I don’t like textual passwords, but at least I feel in control with that. But I don’t understand this.’’ • When errors happen, users have to start from the scratch (input the email address again); • Users could not change picture after they choosing one picture during creation phase.

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#4. Consistency and standards • Input the email address firstly and click ‘Sign up’ or ‘Log in’; while this doesn’t work for the resetting password — users have to click the ‘forgotten the Passpoint’ link firstly, then input the email address. #5. Error prevention Users do not know how exact they should click on the points; #8. Aesthetic and minimalist design The ‘Email’ tip is still in the textbox when textbox is activated; The instruction is not clear, e.g., ‘Sign up’ and ‘Log in’ are too closed to be distinguished easily; The picture is not displayed adaptively to the full screen, users have to drag up and down for looking the whole picture; After clicking each point, the picture flashes to the original position, users have to scroll it down again. #9. Help users recognise, diagnose and recover from errors Users do not know when and what errors happened; The only way recovering from errors is re-doing it from the scratch. #10. Help and documentation No help document or video tutorial available. b. Security We find the potential security issues in phases of login, changing password, and resetting passwords. For example, during the resetting passwords session, if user open the resetting link, there is no double-check about ‘who you are’, rather displaying user’s current picture and is allowed to click points as new passwords. Shoulder surfing is classical problem for the graphical passwords. Some other issues, like using PixelPin on the small-screen mobile device, are still open questions. According to the interview of focus group, sharing passwords is not a desired feature for the expected user authentication. There are three kinds of reasons for recall errors: • Password-length error; • Position error: users are confused by the positions of points, or out of the region tolerance. • Order error: users always mix up the clicked points order. Experiment 3 The purpose of this experiment is to understand users’ choice of picture and how can they remember their points, that is, learnability and memorability of different images.

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In terms of the choice of image, earlier studies [36] suggested that image should have semantically meaningful content for high memorability, and the coherent images [3] will be better for visual recognition therefore increase the speed of image perception. From our experiment, we find some initial indications of users’ strategies as follows: • Users prefer to select simple pictures or pictures containing subjects easy to recognize (like people or corner, etc.) • Users click pass-points based on their preference to specific points in the picture, and those preference will be influenced by how much they are naturally attracted to those points. • Different people have their own different preference points or attentions on the same picture, and if applicable, they will use the same pattern to choose points. For example, people who choose eyes and nose on one picture will intend to use the same strategy when using another picture, no matter the gist of picture is human being or an animal; someone prefer to select the corner, peak or crossing points. Nobody in our experiment likes to select the blue sky without cloud as their password. • Users might select points which are most familiar to them, especially when using the private pictures, they might choose the faces of old friends. Or they select pictures related to their existing memory for other thing. • People remember the picture and the sequence points based on their previous knowledge and interpretation of that picture. We also find that the average length of their paper-based picture password is longer than PixelPin’s, with 5.4 points on average, one participant even uses 12 points as passwords. The recall test results (92% correct) is compared with the digital ones (PixelPin’s passwords), shown by Fig. 5. Experiment 4 As shown by Fig. 6, the cross cursor is most popular one than the rest ones. The reason for users do not like to use the original ‘finger’ lies in that people might be puzzled by which part of ‘finger’ is activate area (Is it the point clicked by the fingertip or wrist?). Surprisingly, one subject likes the normal arrow cursor.

5 Conclusion As we mentioned before, the main issues of Blonder-style graphical passwords (or click-based passwords) are shoulder surfing and its use on the mobile device (because of the difficulty of typing on mobile devices). Our experiment also reaffirms the ‘hot-spot effect’ which was firstly proposed in the Dirik et al.’s research upon the PassPoints [10]. The proposed attacks have been proved in the lab study, like ‘Human-seeded’ attack [37] and purely automated attack [31].

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Fig. 5 Users’ preference to use

Fig. 6 Users’ preference to different cursors

These issues are receiving increased attention. Chiasson et al. have devised the Cued Click-Points [7] and Persuasive Cued Click-Points [8] to deal with the hot spots effect. Some studies on the resisting the shoulder surfing [21, 41] consider the recognition-based graphical passwords, but not for the cued recall-based graphical passwords scheme. By our knowledge so far, all these issues are still await to be solve. So, we should put our effort on dealing with those issues in our future work. It might be more work on user interface (UI) design (because UI may unintentionally sway user behavior towards less secure) or adopt new technology to change user’s behavior, like persuasive techniques [12], etc.

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Acknowledgments I would like to thank Brendan Ryan and Sarah Atkinson, who supervised this project, for their supervisions and experiment supporting. I would like to thank Brian Taylor from OEM partnership Ltd for his useful feedback. I would like to thank all anonymous reviewers for their valuable comments and suggestions.

References 1. Adams A, Sasse MA (1999) Users are not the enemy. Commun ACM 42(12):41–46 2. Biddle R, Chiasson S, van Oorschot PC (2011) Graphical passwords: learning from the first twelve years. AMC Computing Surveys 3. Biederman I, Glass AL, Stacy EW (1973) Searching for objects in real world scenes. J Exp Psychol 97: pp 22–27 4. Blonder GE (1996) Graphical passwords. United States patent 5559961 5. Brostoff S, Sasse MA (2000) Are passfacesTM more usable than passwords? A field trial investigation. In: Proceedings of human computer interaction, pp 405–424 6. Brown AS, Bracken E, Zoccoli S, Douglas K (2004) Generating and remembering passwords. Appl Cognitive Psychol 18:641–651 7. Chiasson S, van Oorschot PC, Biddle R (2007) Graphical password authentication using Cued Click Points. Eur Symp On Res In Comput Secu (ESORICS), LNCS 4734:359–374 8. Chiasson S, Forget A, Biddle R, van Oorschot PC (2008) Influencing users towards better passwords: Persuasive cued click-points. In Human computer interaction (HCI), British Computer Society 9. Dhamija R, Perrig A (2000) Déjà vu: a user study using images for authentication. In: Proceedings of the 9th USENIX security symposium 10. Dirik A, Menon N, Birget J (2007) Modeling user choice in the passpoints graphical password scheme. In: 3rd ACM symposium on usable privacy and security (SOUPS) 11. Eljetlawi AM, Bt.Ithnin N (2009) Graphical password: usable graphical password prototype. J Intl Commer Law Technol 4, p 298 12. Fogg BJ (2003) Persuasive technologies: Using computers to change what we think and do. Kaufmann Publishers, San Francisco 13. Furnell SM, Zekri L (2005) Replacing passwords: in search of the secret remedy. Netw Secur 2006(1):4–8 14. Hafiz MD, Abdullah AH, Ithnin N, Mammi HK (2008) Towards identifying usability and security features of graphical password in knowledge based authentication technique. In: Second Asia international conference on modelling & simulation, pp 396–403. IEEE 15. Ives B, Walsh KR, Schneider H (2004) The domino effect of password reuse. Commun ACM 47(4):76–78 16. ISO-International organization for standardization. http://www.iso.org. Accessed Jan 2011 17. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol, Special Issue Image- and Video-Based Biomet 14(1):4–20 18. Jermyn I, Mayer A, Monrose F, Reiter M, Rubin A (1999) The design and analysis of graphical passwords. In: Proceedings of the 8th USENX security symposium, Washington 19. Klein D (1990) A survey of, and improvement to, password security. In:UNIX security workshop II 20. Li SZ, Jain AK (eds) (2004) Handbook of face recognition. Springer, New York 21. Man S, Hong D, Matthews M (2003) A shoulder-surfing resistant graphical password scheme–WIW. In: Proceeding of international conference on security and management, Las Vegas, pp 105–111 22. Markowitz JA (2000) Voice biometrics. Commun ACM 43(9), pp 66–73

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23. Monrose F, Reiter M (2005) Graphical passwords. In: Cranor L, Garfinkel S (eds) Security and Usability: Designing secure systems that people can use, Chap. 9, O’Reilly Media, Sebastopol, pp 157–174 24. Nielsen J (1994) Heuristic evaluation. In: Nielsen J, Mack RL (eds) Usability inspection methods. Wiley, New York 25. Pierce JD, Warren MJ, Mackay DR, Wells JG (2004) Graphical Authentication: justifications and objectives. In: Proceedings of the 2nd information security management conference, Fremantle 26. Paivio A, Rogers TB, Smythe PC (1976) Why are pictures easier to recall than words? Psychonomic Sci 11(4):137–138 27. Passfaces Corporation. The science behind Passfaces. White paper. http:// www.passfaces.com/enterprise/resources/white_papers.htm. Accessed 23rd Jan 2011 28. Real User Corporation, Passfaces TM, http//:www.realuser.com, Accessed on Dec 2010 29. Renaud K (2009) Guidelines for designing graphical authentication mechanism interfaces. Int J Info Comput Secu 3(1):60–85 30. Roizenblatt R, Schor P et al (2004) Iris recognition as a biometric method after cataract surgery. Biomed. Eng. Online 3(2) 31. Salehi-Abari A, Thorpe J, van Oorschot P (2008) On purely automated attacks and clickbased graphical passwords. In: Annual computer security applications conference (ACSAC). 32. Sasse MA, Brostoff S, Weirich D (2001) Transforming the ‘weakest link’ –a humancomputer interaction approach to usable and effective security. BT Technical J 19:122–131 33. Shepard RN (1967) Recognition memory for words, sentences, and pictures. J Verbal Learn Verbal Behav 6:156–163 34. Sobrado L, Birget J (2002) Graphical passwords, the rutgers scholar, an electronic bulletin of undergraduate research, Rutgers University, Camden New Jersey, Vol 4 35. Suo X, Zhu Y, Owen GS (2005) Graphical passwords: a survey In: annual computer security applications conference (ACSAC) 36. Takada T, Koike H (2003) Awase-E: Image-based authentication for mobile phones using user’s favorite images. In: Human-computer interaction with mobile devices and services, vol 2795 Springer-Verlag GmbH, pp 347–351 37. Thorpe J, van Oorschot PC (2007) Human-seeded attacks and exploiting hot-spots in graphical passwords. In: 16th USENIX security symposium 38. Varenhorst C (2004) Passdoodles: A lightweight authentication method, Research Science Institute, Cambridge 39. Wiedenbeck S, Waters J, Birget JC, Brodskiy A, Memon N (2005) Authentication using graphical passwords: effects of tolerance and image choice, In: Proceedings of symposium usable privacy and security, ACM Press, pp 1–12 40. Wiedenbeck S, Waters J, Birget JC, Brodskiy A, Memon N (2005) PassPoints: design and longitudinal evaluation of a graphical password system. Int J Hum Comput Stud 63:102–127 41. Wiedenbeck S, Waters J, Sobrado L, Birget JC (2006) Design and evaluation of a shouldersurfing resistant graphical password scheme, In: Proceedings of advanced visual interfaces (AVI2006), Venice, Italy, 23–26 42. Wixted JT (2004) The psychology and neuroscience of forgetting. Annu Rev Psychol 55:235–269

Analysis of Smartphone-Based Location Information Dohyun Kim, Jewan Bang and Sangjin Lee

Abstract Location information is an important factor for figuring out user’s behavior in digital forensic investigations. Smartphone-based map applications keep record user’s data about location information such as timestamp, GPS information for searched locations. In this paper, we will analyze data generated by famous map applications–Google map, Daum map, Naver map and extract necessary information to assist digital forensic investigations. Keywords Digital forensics application




Location information




Smartphone




Map

1 Introduction Recently, Smartphone users steadily keep on growing and its rate is expected to be increased by almost 30% in 2011 in worldwide mobile phone market. Furthermore, its shipments will soon overtake those of PCs shipped (both desktops and notebooks) in 2012. These rapid growth comes from various smartphone applications such as games or useful applications in daily life.

D. Kim (&)
J. Bang
S. Lee Center for Information Security Technologies, Korea University, Seoul, South Korea e-mail: [email protected] J. Bang e-mail: [email protected] S. Lee e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_5,  Springer Science+Business Media B.V. 2012

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Table 1 Application-related data storage path of genaeral, galaxy S smartphone Default application User application Additional file storage path General Android Phone Galaxy S

/data/data/package name

/data/data/package name /dbdata/databases/package /data/data/package name name

/mnt/sdcard/package name /mnt/sdcard/package name

In numerous smartphone-based map applications are most commonly used among users, and it offers users location search, route search, transportation search, destination viewing, bookmark. Whenever a user access this application, user-searched information (starting point, destination, latitude, longitude and so on) and timestamp are automatically recorded in particular files in application. In a view of digital forensic investigations, this information is key evidences to figure out a user’s behavior and intention as well as to find a critical clue to a case. Thus, it is necessary for us to research this famous map applications connected to digital forensic investigations. In this paper, it will be discussed how to extract inside information recorded in particular files through famous map application—Google map, Daum map and Naver map and how to analyze internal data, related to digital forensic investigations. And we suggested digital forensic tool (MapAn) utilizing smartphonebased map application for the investigation. This paper consists of five parts, not including introduction–related work, the analysis of smartphone-based map applications, tool for analyzing smartphonebased map application, conclusion and future work.

2 Related Work In this chapter, it will be discussed about a technique to extract files which contain history using an application. In case of Android phone, these data are mainly recorded to SQLite Database files while iPhone recorded to SQLite Database files or plist files according to an application.

2.1 The File Path in Android Phone Android phone has a different file path to store applications related to Galaxy S and other Android smart phones. In case of general Android phone, default application—SMS/MMS, address book, e-mail and call history as well as user application are installed in the same file path. On the contrary, Galaxy S installs both default and user application in different file path. However, after installation, a file path for both Android phone and Galaxy S become same on account of file download function. These paths presented in Table 1.

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Table 2 Map application’s function and description Function Description Location Search Route Search Transportation Search View (Street & Air) Bookmark

Finding destination Looking for the way from current location to destination location Finding public transportation to destination Providing view on the street and air Storing searched result

Rooting is necessary for extracting files from a path. General rooting is performed, integrity may get damaged, whereas Temporary rooting does not affect any damage to an internal data. Therefore, by performing Temporary rooting, we use ADB (Android Debugger Bridge), type ‘‘adb pull [The path for extracting a file from Android Phone] [The path for storing an extracted file]’’ instruction, and extract the files asked for [1].

2.2 The File Path in iPhone In case of iPhone, all internal data are encoded; selecting files for extraction is not available. For extracting files without damaging integrity, we use not Jail Breaking but iTunes’s Backup function, the software which is able to sync with iPhone, so as to get all Backup files in iPhone. The path for creating Backup files is ‘‘%APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID]’’. The backup files substitute for the hash value toward the name of the file and are generated in the path. In the process of extracting a file from the path, the real names and path of files need to be figure out among the files named as hash value. With open tool named ‘‘mbdbump.exe’’, not only the files real name, but also file’s path to extract [2].

3 Analysis of Smartphone-Based Map Applications In this chapter, we are going to discuss about the famous smartphone-based map applications and those functions and then analyze files that recorded users history according to an application.

3.1 The Type and Functional Analysis of Map Applications Google map, Daum map, and Naver map are mainly used as the famous map applications. These map applications offer useful functions such as location search, route search, transportation search, view support and bookmark. The description for each function is presented in Tables 2 and 3.

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Table 3 Offered functions by each map application Location Route Transportation Search Search Search Google map Daum map Naver map

View (Street & Air)

Bookmark

O

O

O

X

X

O

O

O

O

O

O

O

O

O

O

Table 4 Target file of Google Map in Android Phone File Name Search_history.db Path General Android Phone /data/data/com.google.android.apps.maps Galaxy S /dbdata/com.google.android.apps.maps File Type SQLite Database

3.2 Analysis of User History about Map Applications To analyze the user history of using map applications, the path to files that recorded user history data and its format should be identified. Here are the steps for analyzing the path and the format of files. First of all, due to the fact that third Party applications record data into files in its own format, files should be analyzed individually according to the applications. Secondly, meaningful data selection and extraction from a lot of data should be conducted to help digital forensic investigations. In map applications, the file name, path and format that recorded in the user data are given in Table 4, 5, 6, 7, 8, and 9. Google Map Android Phone. In Android phone, search ‘‘history.db’’ file record the user history data of Google Map. In viewpoint of digital forensic, meaningful data is data 1 field in suggestions table. When location search is performed, the contents are recorded in order, but the timestamp is not recorded in the field. More recent data is located at the bottom of contents. The meaning of each field is given in Table 10. iPhone. Unlike Android phone, files recorded user history in iPhone of Google Maps are ‘‘b60c382887dfa562166f099f24797e55c12a94e4 (History.plist)’’ and ‘‘a30335a2c 0f0316c9610d868a527b2ade1911542 (com.apple.map.plist)’’. Both files are formed as plist file. In a view of digital forensic, meaningful data from ‘‘b60c382887dfa56216 6f099f24797e55c12a94e4 (History.plist)’’ file are keyword for location search, latitude and longitude. This file does not contain time information, located at the top of the more recent data is the data retrieved. ‘‘a30335a2c0f0316c9610d868a527b2ade 1911542 (com.apple.map.plist)’’ file located in the most recently conducted searches and searches where the location information(latitude, longitude) and time to have the information [4]. The meaning of each element is in Table 11.

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Table 5 Target file of Google Map in iPhone File Name b60c382887dfa562166f099f24797e55c12a94e4(History.plist) Path %APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID] File Type plist File Name a30335a2c0f0316c9610d868a527b2ade1911542(com.apple.map.plist) Path %APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID] File Type plist Table 6 Target file of Daum Map in Android Phone File Name history.db Path /data/data/net.daum.android.map/map/data File Type SQLite database File Name favorite.db Path /data/data/net.daum.android.map/map/data/[Daum Account ID] File Type SQLite database Table 7 Target file of Daum Map in iPhone File Name ae6522d1ef6dd52694d53cc015c04749603ac95a(history.db) Path %APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID] File Type SQLite database File Name 59ea2d577b192bcce21149ce2a8385180d72313e(favorite.db) Path %APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID] File Type SQLite database Table 8 Target file of Daum Map in iPhone File Name mapHistory.db Path /data/data/com.nhn.android.nmap/databases File Type SQLite database Table 9 Target file of Daum Map in iPhone File Name 6be48053cd29804a3a30e78846c30d828c75eb8a(History.db) Path %APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID] File Type SQLite database File Name d5c541e19cdfec4cdbb833719ced812564dd43ba(NMap.db) Path %APPDATA%/Roaming/Apple Computer/MobileSync/Backup/[Device ID] File Type SQLite database

Daum Map Android Phone. History files of Daum Map in Android phone are shown as ‘‘history.db’’ and ‘‘favorite.db’’. In ‘‘history.db’’ file, there are ‘‘route_history’’ table which contains ‘‘startPoint, endPoint, hitcount, and updatetime’’ fields as well as ‘‘word_history’’ table which takes ‘‘key, address, hitcount, updatetime’’ fields. On the other hand, in ‘‘favorite.db’’ file, ‘‘name, and attime’’ fields exists. Both files contain very much important data in digital forensic investigations and the meaning of each field is displayed in Table 12.

48 Table 10 Structure of target file for Google Map in Android Phone File Name seach_history.db Table Name Suggestions (Location Search) Field Name _id data1

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Contents Number Keyword

Table 11 Structure of target file for Google Map in iPhone File Name b60c382887dfa562166f099f24797e55c12a94e4(History.plist) Contents • Result of Location Search • Location of Performed Location Search (Latitude, Longitude) File Name a30335a2c0f0316c9610d868a527b2ade1911542(com.apple.map.plist) Contents • Last Time of Performed Location Search • Last Location of Performed Location Search (Latitude, Longitude) • Contents of Last Performed Location Search

iPhone. In iPhone, history of Daum map is stored as ‘‘ae6522d1ef6dd52694d53cc015 c04749603ac95a (history.db)’’ and ‘‘59ea2d577b192bcce21149ce2a8385180d72313e (favorite. db)’’ and internal structure is as same as ‘‘history.db and favorite.db’’ in Android phone. Naver Map Android Phone. It is ‘‘mapHistory.db’’ file that is stored as history of Naver map in Android phone. Key data as evidence in digital forensics are inside of ‘‘Bookmark’’ table as ‘‘title, time, tel, and addr’’ fields. ‘‘uid, and time’’ fields in ‘‘RecentPub’’ table, ‘‘start, end, and time’’ fields in ‘‘RecentRoute’’ table as well as ‘‘uid, and time’’ fields in ‘‘RecentWord’’ table are meaningful data for digital forensic investigations. (See below Table 13 for each meaning in field.) iPhone. In iPhone, ‘‘6be48053cd29804a3a30e78846c30d828c75eb8a (History.db)’’ and ‘‘d5c5 41e19cdfec4cdbb833719ced812564dd43ba (NMap.db)’’ are history file of Naver map. In ‘‘6be48053cd29804a3 a30e78846c30d828c75eb8a (History.db)’’ file, not only ‘‘uid, and time’’ fields in ‘‘LocHistory’’ table, ‘‘uid, and time’’ fields in ‘‘PubHistory’’ table, but also ‘‘start, destination, and time’’ fields ‘‘RouteHistory’’ table is essential key in digital forensic perspective. And, in ‘‘d5c541e19cdfec4cdbb833719 ced812564dd43ba (NMap.db)’’ file, in viewpoint of digital forensics, we put emphasis on ‘‘title, time, tel, and addr’’ fields in ‘‘Bookmark’’ table, ‘‘uid, and title’’ fields in ‘‘OfflineMapList’’ table and ‘‘uid, start, and destination’’ fields in ‘‘OfflineRouteList’’. The meaning of each field is shown in Table 14.

4 Tool for Analyzing Smartphone-Based Map Application When using map application, a user puts the destination where they are looking for into the search bar. Then, he searches for the route, transportation from the present location to the destination and follows the direction to reach the place. When

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Table 12 Structure of target file for Daum Map in Android Phone File Name seach_history.db Table route_history (Route Field Name Contents Name Search) idx Number startPoint Starting Point for Route Search startX Latitude of Starting Point for Route Search startY Longitude of Starting Point for Route Search endpoint Destination for Route Search endX Latitude of Destination for Route Search endY Longitude of Destination for Route Search hitcount Count of performed Search updatetime Time of performed Search (Unix Time) word_history (Location Field Name Contents Search) idx Number key Keyword for Location Search chosungKey Initial Consonant of Keyword address Address of Location for Location Search posX Latitude of Location for Location Search posY Longitude of Location for Location Search hitcount Count of performing search updatetime Time of performed Search (Unix Time) File Name seach_history.db Table favorite (Bookmark) Field Name Contents Name id Number name Contents of Bookmark type Location: 100, Route: 200 cords Lattitude and Longitude, registered in Bookmark attime Time to Register Bookmark mtime Time of Modified Bookmark route_start_name Starting Point for Route Search route_end_name Destination for Route Search

coming closer to the destination, a user tries to search for the destination again so that they could get more precise information from where they are to where they want to be. In this process, all information is recorded inside of application files whenever he accesses the application. Furthermore, the place where a user was looking for might have been visited. Thus, this recorded information is very much useful in digital forensic investigations.

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Table 13 Structure of target file for Naver Map in Android Phone File Name mapHistory.db Table Bookmark (Bookmark) Field Contents Name Name title Location of Registered Bookmark x Latitude of Location Registered Bookmark y Longitude of Location Registered Bookmark time Time of Registered Bookmark (Unix Time) tel Telephone Number of Location addr Address of Location of Registered Bookmark Bookmark (Transportation Field Contents Search) Name uid Contents for Transportation Search time Time of Transportation Search (Unix Time) File Name favorite.db Table RecentRoute (Route Search) Field Contents Name Name start Starting Point for Route Search startx Latitude of Starting Point for Route Search starty Longitude of Starting Point for Route Search end Destination for Route Search endx Latitude of Destination for Route Search endy Longitude of Destination for Route Search time Time of performed Search (Unix Time) RecentWord (Location Field Contents Search) Name uid Contents of Location Search time Time of Performed Search (Unix Time)

If a criminal intends to commit a crime, normally he sets the place to do and then visits the scene in advance. Instead of going to the place by himself, he is able to check the place through map application. Not only looking around the place by using street view or air view through location search, but also he can take a picture from many angles, capturing every inch of the scene. Those pictures can be sent to e-mail or sent as MMS message. However, the history data from street and air view function are not recorded individually. Accordingly, making a thorough investigation into photo book, e-mail, MMS records is needed in digital forensic investigations. History data gives us the information of the user’s behavior and his route by the hour and is going to be much helpful factor in digital forensic investigations.

File Name Table Name

File Name Table Name

OfflineRouteList (SaveRoute Picture)

OfflineMapList (SaveLocation Picture)

Bookmark (Bookmark)

RouteHistory (Route Search)

PubHistory (Transportation Search)

LocHistory (Location Search)

Table 14 Structure of target file for Naver Map in iPhone 6be48053cd29804a3a30e78846c30d828c75eb8a (History.db) Field Name Contents uid Contents of Location Search time Time of performed Search (Unix Time) Field Name Contents uid Contents of Location Search time Time of Transportation Search (Unix Time) Field Name Contents start Starting Point for Route Search startx Latitude of Starting Point for Route Search starty Longitude of Starting Point for Route Search destination Destination for Route Search endx Latitude of Destination for Route Search endy Longitude of Destination for Route Search time Time of performed Search (Unix Time) d5c541e19cdfec4cdbb833719ced812564dd43ba (NMap.db) Field Name Contents title Location of Registered Bookmark x Latitude of Location Registered Bookmark y Longitude of Location Registered Bookmark time Time of Registered Bookmark (Unix Time) tel Telephone Number of Location addr Address of Location of Registered Bookmark Field Name Contents uid Time of Saved Location Picture (Unix Time) title Location of Saved Picture x Latitude of the place you saved the picture y Longitude of the place you saved the picture radius The radius of where you saved the picture Field Name Contents uid Time of Saved Route Picture (Unix Time) start Starting Point sposx Latitude of Starting Point sposy Longitude of Stating Point destination Destination destposx Latitude of Destination destposy Longitude of Destination xml Driving directorition from Starting Point to Destination

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Fig. 1 Contents of ‘‘MapAn.db’’

Table 15 Structure of ‘‘MapAN.db’’ Field Name Contents time type_id action_id app_id content detail hitcount contact_id deleted_flag

Time (Google Maps does not provide) Type of Application Function of Application Application Name Searched Result (Starting Point for Route Search) Searched Result2 (Destination for Route Search) The Number of Searches Performed (Only provide Daum map) Phone Number for Search Results (Only provide Naver map) Display Deleted Records

Despite of these helpful evidences, in investigation process, too much time is consuming in data opening, analyzing, and selecting for needs. Since all different file formats and paths are generated in Google map, Daum map, and N aver map, data in Android phone do not match with those in iPhone. Thus, we need analysis tool for selecting essential data from extracted file and integrating them into singular format. ‘‘MapAn’’ application is the useful tool for digital forensics by extracting valuable data generated from the files that contain the user data at various angles. Once a user input files that contain user history data, a single database file which called ‘‘MapAn.db’’ comes out and there are information about connect and using hour, the name of application, the searched contents and so forth. By analyzing ‘‘MapAn.db’’ file, we can figure out which map-based application a user use, when he access to the application, what he does with application and where he goes. Coming-out data is appeared in Fig. 1, and Table 15.

5 Conclusion and Future Work Smartphone-based map application such as Google map, Daum map, and Naver map leaves various information in smartphone. Such information like connect hour or searched information is much valuable in a criminal investigation because it

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becomes such a good evidence to find out a criminal. Even though these application is preferred to be analyzed, the numerous applications are out there and have great many approaches to extract data from user’s files depending on various smartphones. In this paper, we suggest ‘‘MapAn’’ that helps us to analyze a file produced from smartphone-based mapping application in a view of digital forensic investigations. Besides ‘‘MapAn’’ application, more effective analysis would be possible if other applications like ‘‘Call history and SMS/MMS’’ can be integrated as well as recovered data changes the shape, combined with a real image map. These application becomes much useful tools for accurate analysis in the future and additional researches should be carried out for future study.

References 1. Lessard J, Kessler GC (2010) Android forensics: simplifying cell phone examinations. Small Scale Disgital Forensics J 4(1):1–12 Sep 2. iPhone Backup Browser, http://code.google.com/p/iphonebackupbrowser/wiki/MbdbMbdx Format 3. Husain MI et al (2011) A simple cost-effective framework for iPhone forensic analysis. Digital Forensics and Cyber Crime. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 53. pp 27–37 4. Google Map, http://maps.google.co.kr/maps 5. DaumDNA: openApi mapAPI coordinate system, http://dna.daum.net/apis/maps/coordinate

Dynamic Visualization for Sensing Information Based on Mobile Sensor Network Su-Hyun Yang, Eun-Ha Song and Young-Sik Jeong

Abstract In a mobile sensor network, when sensor data are sent from a sensor node to the sink node, traffic may be congested at one sensor node, in which case overhead occurs at that node, and this may possibly affect the entire network. Also, this causes frequent data loss as there is excessive traffic or as the topology constantly changes. In this paper, VSDR (Visualization Sensing Data Routing) is designed and implemented, which visually shows on a GML-based map with GPS coordinates the packet paths between sensor nodes as well as the amount of packet traffic. VSDR also includes an algorithm that dynamically reroutes paths in the case of sensor data overhead or a fault occurrence at one or more sensor nodes.










Keywords Wireless sensor network GML Network simulator Sensor node Packet traffic Packet routing Fault tolerance Dynamic re-routing













1 Introduction One of the most important technologies in the ubiquitous society is the wireless sensor network [1], which consists of mobile sensor nodes that are characterized by their computation ability, communication ability, sensing ability, and mobility. S.-H. Yang
E.-H. Song
Y.-S. Jeong (&) Department of Computer Engineering, Wonkwang University, 344-2 Shinyong-Dong, Iksan, Jeonbuk, Korea e-mail: [email protected] S.-H. Yang e-mail: [email protected] E.-H. Song e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_6, Ó Springer Science+Business Media B.V. 2012

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In a sensor network (that has low power demand), micro sensors are used to recognize a variety of situations or changes in the environment and the sensor data are sent to the users. Currently, a lot of research is being done on wireless sensor networks [2]. The main components of a wireless sensor network are the sensor node, which measures or senses something using a sensor and sends the data over the wireless network; the sink node that collects all data sensors measured and sends them to the middleware; and the relay node that works between the sensor node and the sink node in sending sensor data. Sensor data measured by a sensor node are sent to the sink node over the network, and they are subsequently sent to the middleware or to the server through the sink node. In contrast with other types of networks, the primary objective of wireless sensor networks is not to serve as a means of communication but rather as a means of automatic and remote collection of data. Wireless sensor networks have a wide range of applications in a variety of fields including science, medicine, military, and business [2]. Given a sensor network composed of sensor nodes, sink nodes, and relay nodes according to their appropriate placement and function, when data transmission takes place from a sensor node to a sink node, the data traffic may be concentrated at a single sensor node on the way to the sink node. In this case, due to overloading of the sensor node, a problem could be caused for the entire sensor network. In addition, as excessive traffic occurs or the topology constantly changes, there would be frequent data loss. In this paper, the packet paths between sensor nodes and the amounts of the packet traffic are visually shown on a GML-based map with GPS coordinates. The parent node sends a signal upon receiving data from the child node in order to ensure correct delivery of data and to minimize data loss. Also, if while sending data between sensor nodes the amount of traffic exceeds a certain limit, a parent node is automatically chosen among neighboring nodes and the path is changed accordingly; alternatively, the user can manually select a specific node from neighboring nodes to change the path. Even when a fault occurs at one or more sensor nodes, the path can be either automatically changed or the user can directly change the path. Based on this, a network simulator called VSDR (Visualization Sensing Data Routing) is designed and implemented, which takes into account the amount of the sensor data traffic as well as faults at sensor nodes in a wireless sensor network that is made up of sensor nodes.

2 Related Works WSN simulators are a hardware device or a software program designed to predict and analyze network operations and the effects. They are particularly useful in simulating a situation that has a low likelihood of happening in real life, or when the testing setup would be expensive to implement using real components, such as when the network consists of a large number of sensor nodes.

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Table 1 Comparison of characteristics between existing WSN simulators and VSDR Simulator Features and characteristics NS-2

This is an open-source simulator developed for research purposes. It uses the discrete event simulation approach and a wide range of network protocols can be simulated. However, it is difficult to use when there are a large number of nodes and for complicated, large systems. There are also many unnecessary interdependencies between modules. Most Unix-based OS platforms are supported, and a script language called TCL is used to construct the simulation environment GloMoSim This simulator was developed to simulate large networks. PARSEC, a C-based language for parallel simulation was used to develop it. Protocols have been modularized according to the OSI layers. Currently only wireless networks are supported. Although network models or transmission scenarios can be verified, coverage cannot be expressed QualNet As the next version of GloMoSim, this was designed to simulate large wireless networks. It follows the IEEE 802.11 MAC and Physical Layer specifications. Although it supports sensor networks, it has limitations in expressing coverage in that only the RF analysis is supported TOSSIM This software emulates TinyOS to analyze application operations and hardware effects. Measuring the rate of packet loss and CRC detection are the main features. However, scalability is low in that only the Mica series of CrossBow can be used with it, and there are difficulties in adding sensors whose type is not LED NetSim This is a simulation software designed for studying how to set up a network. The simulation takes place in real-time as to allow interaction with the user. Not only can transport protocols, MAC protocols, and routing protocols be simulated, but devices such as switches, routers, and APs can be simulated as well J-Sim This is a Java-based open source wireless sensor network simulator. Execution time, the number of events, and the memory usage according to the size of the network can be measured. An event sent from the target node getting sent to the sink node in the form of packets can be simulated. Coverage cannot be expressed SWNANS SWANS, a wireless network simulator, has been placed on top of Jist, a discrete event simulator engine based on PARSEC. It is open source and unlike NS-2 or GloMoSim, can simulate large networks. However, like other simulators, only protocol verification is possible VSDR This is a Java-based wireless sensor network simulator. Sensor nodes can be placed on a GML-based map taking into account connectivity of sensor nodes and locations of obstacles. Paths are set by calculating the number of hops to the sink node. If during data transmission a fault occurs in the traffic between sensor nodes or at sensor nodes the path gets changed in an efficient way

Some of the simulators available for sensor networks include the following: NS-2 [3], GloMoSim [4], QualNet [5], TOSSIM [6], NetSim [7], J-Sim [8], and SWNANS [9]. These simulators have been developed for a variety of platforms according to their purpose, and were developed using C++ or Java, which supports templates. They include extensive network technologies and use nodes and links as the basic units in constructing a complicated environment or a layered network environment (Table 1).

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Fig. 1 Overall architecture of VSDR

3 Design of VSDR 3.1 Structure of VSDR VSDR is made up of eight main components: User Interface Component, GML Analyzer Component, Map Layer Manager Component, Map Controller Component, Node Manager Component, Target Area Manager Component, Path Manager Component, and Viewer Component. The overall structure of the system is shown in Fig. 1. The User Interface Component provides a user interface for inputting configuration values needed to run VSDR. It is the component using which the map and nodes are set up. The GML Analyzer Component reads a GML document, analyzes it, and then creates map objects and sends them to the Map Layer Manager. The Map Layer Manager Component manages the map objects sent by the GML Analyzer and sets up obstacles. The Map Controller Component is used to control the map—zoom in, zoom out, zoom area, and move—and then to output the results. The Node Manager Component applies the node settings from the User Interface Component to sensor nodes and creates sensor node packets. It also creates sensor nodes that interact with the target area defined in the Target Area Manager, as well as with the obstacles defined in the Map Layer Manager, and operates the sensor nodes. Statistics collected by sensor nodes are also calculated.

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Fig. 2 Packet structure in VSDR

Table 2 Traffic classifications of VSDR with volume of packets Packet volume 2 Mb; 2–4 Mb 4–6 Mb

6–8 Mb

8–10 Mb

Color used to denote

Orange

Red

Blue

Green

Yellow

The Target Area Manager Component sets up and manages areas that need to be observed in the field set up by the GML document. The Path Manager Component checks whether there has been excessive traffic between sensor nodes and whether there has been a fault in a sensor node and, if so, the path is rerouted. The Viewer Component shows on the screen map objects of the Map Layer Manager as well as mobile sensor nodes of the Node Manager.

3.2 Path Visualization According to Change in Sensor Data Traffic VSDR, sensor nodes make up the network while maintaining their connectivity on a map based on GML [10–12]. All of the sensor nodes take into account the number of hops to the sink node in selecting their parent node among neighboring nodes. Each node creates packets every specific period of time. The structure of the packet used in VSDR is shown in Fig. 2. The name of packets that are created at sensor nodes is defined using the sensor node ID and the packet ID. Sensor nodes send created packets to their parent nodes. Sensor nodes with child nodes puts together the packets received from their child nodes and the packets created by themselves and sends it to their parent node. The traffic is determined according to the number of packets sent to the parent node. Supposing the size of a single packet is 8 bytes, the traffic size classifications according to the packet volume are shown in Table 2. If the packet volume exceeds 10 Mb when packets are sent from sensor nodes to their parent node, it is assumed that overhead has occurred and neighboring sensor nodes are probed to find one that is appropriate as a parent node. The one that has lower number of hops than itself as well as low packet volume is chosen as a new parent node and from that point on packets are sent divided between the current parent node and the new parent node. The parent node sends an ACK signal when it has received a packet from a child node in order to confirm that the transmission has been successful. The child node can check that the packet transmission is going well based on whether it has received the ACK signal or not.

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Fig. 3 Packet transmission process of VSDR

Fig. 4 Dynamic re-routing of single mode

The visualization of sensor nodes’ packet paths is classified into two types: (1) Single Mode, in which only a single sensor node is selected and the path from it to the sink node is shown; and (2) Multiple Mode, in which multiple sensor nodes are selected and the multiple paths to the sink node are shown (Fig. 3).

3.2.1 Single Mode In Single Mode, only a single sensor node is selected and the path from it to the sink node is shown. The user can see detailed information about the selected node, and the path of the selected node can be automatically changed or the user can manually select a parent node to change the path. Figure 4 shows the path from Sensor Node A to Sink Node S, as well as the process of rerouting when overhead occurs at a sensor node; specifically, when overhead occurs at Sensor Node D, it selects Sensor Node E among the neighboring nodes as its new parent node, which has lower number of hops to Sink Node S.

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Fig. 5 Dynamic re-routing of multiple mode

From then on, Sensor Node D will send some packets to the original parent node and the rest to the new Parent Node E.

3.2.2 Multiple Mode In Multiple Mode, multiple sensor nodes are selected and the multiple paths to the sink node are visualized. Stated in a different way, the user gets to see not just the path involving a single sensor node but rather all the paths involving multiple sensor nodes to the sink node. Like in the Single Mode, the user can select a specific sensor node to either automatically change the path or manually change it by selecting the parent node for the sensor node. Figure 5 shows the path from Sensor Node A to Sink Node S, as well as the path from Sensor Node F to Sink Node S. In Multiple Mode, paths of more than one sensor node can be shown. When overhead occurs at Sensor Node D, it selects among neighboring nodes Sensor Node E as its new parent node, which has lower number of hops to Sink Node S. From then on, Sensor Node D will send some packets to the original parent node and the rest to the new Parent Node E.

3.3 Path Visualization with Fault In an implemented sensor network, a fault may occur at sensor nodes, in which case the entire network may be damaged or there may be loss of or damage to data from the sensor node to the sink node. The parent node sends an ACK signal upon receiving a packet from a child node, in order to confirm that it has received it successfully. If an ACK signal has not arrived within a specified time, it can be assumed that a fault has occurred at the parent node. If a fault occurs among sensor nodes, the child nodes that has the faulted node as their parent selects a new suitable parent node from neighboring sensor nodes. The one with a lower number of hops than themselves and which has low packet volume is selected as the new parent node and the path is rerouted.

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Fig. 6 Visualization of VSDR with a single fault occurs

Fig. 7 Visualization of VSDR with a multiple fault occurs

There are two types of faults that can occur at a sensor node. In the first type, the fault occurs at a single node (see Fig. 6). In the second type, the fault occurs at multiple sensor nodes (see Fig. 7). Given that there is a path between Sensor Node A to Sensor Node S, if a fault occurs at Sensor Node C, Sensor Node B, which has Sensor Node C as its parent, selects among its neighboring nodes the one with lower number of hops to Sink S as its new parent node, namely Sensor Node E. In Fig. 7, it can be seen that, in addition to Sensor Node B, the path of Sensor Node J is changed as well, which has Sensor Node c as its parent node. In the case of multiple faults as well, the path is rerouted in the same way. In addition, in VSDR, a forced fault can be caused at a sensor node. For the sensor nodes that have the forcibly-faulted sensor node as their parent, the path is rerouted to the sink node by going around it.

4 Implementation of VSDR The first screen that is shown when VSDR is started is shown in Fig. 8. The screen consists of the following. At the top, there is the Toolbar for loading a map and zooming in and out, among other things. On the right, there is the Configuration

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Fig. 8 Setup of VSDR and visualization for packet transmit in VSDR

Panel, for setting up sensor nodes as well as the sensor area shown; the Single Panel, for showing the path and information of a single sensor node; and the Multiple Panel, for showing the paths of multiple sensor nodes. On the bottom, there is the Status Panel, which shows information regarding the status of operation of VSDR. Finally, in the center, there is the Viewer, which shows the status of the map, as well as the status of sensor nodes. The Toolbar is made up of the following buttons: the Map Add button, for adding a map described by a GML document in order to make up the sensor network field; the Zoom In button, for zooming in a loaded map; the Zoom Out button, for zooming out; the Full Extension button, for resizing the map to fit the size of the screen; the Select Zoom Area button, for zooming in a specific area on the map; the Select Target Area button, for selecting a target area on the map; the Add Sensor Node button, which helps with adding an additional sensor node as necessary; and the Sensor Node Position button, for showing the locations of the current sensor nodes. The Configuration Panel is made up of the following: the Range Control, which sets the sensing/communication/supersonic ranges of a sensor node; the Frame delay Slide Bar, which adjusts the movement speed of a sensor; the check boxes, which are used to set whether sensing/communication/supersonic ranges of a sensor node are shown or not; the Degree K Taker, for setting the number of neighboring nodes that sensor nodes need to maintain; and the Number of Sensor Nodes Taker, for setting the number of sensor nodes to be placed within the target area. Finally, on the bottom, there is the Initial Deployment Selector, for selecting the initial location for placement of sensor nodes when VSDR starts up; the buttons for starting and configuring VSDR; and buttons that temporarily stop the operation of VSDR and start it again. The Single Panel is made up of the following: the Node List, which has a list of sensor nodes currently placed on the map; the Node Information, which informs the user of the type and ID of the selected sensor node, as well the parent node ID; the radio buttons for setting whether to automatically reroute the path or to

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manually reroute it when rerouting the path; the Compulsion Fault Select, which causes a forced fault at a sensor node; the Change Node List, for the selecting sensor nodes for which the paths are changed; and the Parent Select List, for selecting among sensor nodes that meet the criteria as a parent node for the selected sensor node. The Multiple Panel is made up the Node list, for repetitively selecting a list of sensor nodes currently placed on the map, and other components that work the same as with the Single Panel: the radio buttons for setting whether to automatically or manually reroute the path when rerouting the path; the Compulsion Fault Select, which causes a forced fault at a sensor node; the Change Node List, for selecting among sensor nodes that meet the criteria as a parent node for the selected sensor node. Finally, the Viewer shows the status of sensor nodes placed on the map, as well as the paths between sensor nodes. In Fig. 8, sensor nodes have been placed on the map described by GML, showing the interconnectedness of the sensor nodes within the communication range. In addition, several sensor nodes have been chosen at random, and their paths to the sink node are shown. When packet transmission begins, the sensor nodes put together packets created by themselves and the packets received from their child nodes and sends them to their parent node. The paths are colored differently according to the amount of packet traffic. In the figure, it can be seen that overhead occurred at a single sensor node. In this case, if the path is rerouted to go in a roundabout way, the traffic between sensor nodes can be reduced.

5 Conclusions In this paper, VSDR was designed and implemented, which visually shows the paths between sensor nodes as well as the amount of packet traffic on a GMLbased map with GPS coordinates. VSDR is a simulator that includes a dynamic path rerouting algorithm when an overhead or fault condition occurs at a sensor node. Data loss is minimized by checking for successful delivery of data by sending a signal upon a parent node’s receiving data from its child nodes. In the case of transmission of data between sensor nodes, if the amount of traffic exceeds a certain limit, the path can be changed by automatically selecting a new parent node among neighboring nodes or, alternatively, the user can manually select among the neighboring nodes the parent node. Even if a fault occurs at a single or multiple sensor nodes, the path can be automatically rerouted or the user can manually change the path. For future work, a technique of path search that uses a variety of routing protocols should be used when overhead occurs in transmission of traffic in a sensor network. Also, the life of sensor nodes should be taken into account in adding a variety of different types of mobile sensor nodes.

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Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0022589, 2011-0003896)

References 1. Zhao F, Guibas LJ (2004) Wireless sensor networks. Elsevier, San Francisco 2. Arkin R, Ali K (1994) Integration of reactive and telerobotic control in multi-agent robotic systems. In: Third international conference on simulation of adaptive behavior, Aug 1994, pp 473–478 3. The Network Simulator—ns-2. http://isi.edu/nsnam/ns/ 4. Bajaj L, Takai M, Ahuja R, Tang K, Bagrodia R, Gerla M (1999) GloMoSim: a scalable network simulation environment. UCLA computer science department technical report 990027 5. Qualnet. http://qualnetworld.com/ 6. Levis P, Lee N, Welsh M, Culler D (2003) TOSSIM: accurate and scalable simulation of entire TinyOS applications. In: Proceeding of first ACM conference on embedded networked sensor system (Sensys 2003), Apr 2003, pp 126–137 7. NetSim. http://tetcos.com/software.html 8. Sobeih A, Chen W-P, Hou JC, Kung L-C, Li N, Lim H, Tyan H-Y, Zhang H (2006) J-Sim: a simulation and emulation environment for wireless sensor networks. IEEE Wirel Commun 13:104–119, Aug 9. Java in Simulation Time/Scalable Wireless Ad hoc Network Simulator. http://jist.ece. cornell.edu/ 10. OpenGIS Consortium, INC., Geography markup language [GML] implementation specification. http://www.opengeospatial.org/ 11. Shekhar S, Vatsavai RR, Sahay N, Burk TE, Lime S (2001) WMS and GML based interoperable web mapping system. GIS: Geogr Inf Syst 106–111, Nov 12. Guo Z, Zhou S, Xu Z, Zhou A (2003) G2ST: a novel method to transform GML to SVG. In: Proceeding of the 11th ACM international symposium on advances in geographic information systems, November, pp 161–168

Model Driven Real-Time Power System Application Development Using Just-In-Time Compilation Izudin Dzafic, Priyanka Mohapatra and Nenad Lecek

Abstract This paper describes a method of developing flexible power system applications using model driven development (MDD) and Just-In-Time (JIT) compilation for usage in real-time industrial solutions. MDD is used to create and process mathematical input models for power applications and to confine application development to creation of such models only. Thus application specific programming ends with mathematical model creation. Once a component model is designed, implemented and tested, it should be possible to reuse it as many times as necessary for a variety of purposes in power system application domain. In order to obtain code reusability a non-linear equation handler and solver are implemented. Both the application-specific converter and the general purpose equation handler and solver are designed as components. A JIT compiler is used to generate native code for given models. Models are compiled at run-time. Whenever the model shall be reused, generated native code is reused. Such approach provides significantly better performance compared to solution based on model interpreter. Compared to traditional, compiled software implementations, such solver implementation provides native code execution performance without programming. This provides a possibility to field experts to develop high performance software solutions for their algorithms, also without advanced knowledge of a specific programming language.

I. Dzafic (&)  P. Mohapatra  N. Lecek Energy Sector, Power Distribution Division, Siemens AG, Humboldtstr 59, 90459 Nuremberg, Germany e-mail: [email protected] P. Mohapatra e-mail: [email protected] N. Lecek e-mail: [email protected]

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Keywords Model driven development analysis Rapid prototyping





Just-in-time compilation



Symbolic

1 Introduction The demand for efficient and re-usable software solutions for power system algorithms has increased significantly in last years. Development of power system applications using the traditional way of programming demands very good software engineering skills and very good domain know-how. A lack of software engineering skills by domain experts should not be an obstacle in development of high performance power system applications. The developer of a software solution for power systems should be more concentrated on the power system algorithms than on software design and implementation. During solution lifetime, some modifications in code are typically requested and implemented. With time, a software solution becomes fragile, often runs into maintenance problems and cannot be re-used because it is very specific to the particular application. This paper analyzes a solution to the above mentioned problems using MDD [1–8]. MDD aims to increase productivity in software development and manage the complexity of software by utilizing modeling to shift from programming in solution-space to modeling in domain-space. As described in papers [4] and [9] the complexities in software development are of two types: Essential and Accidental. The goal of using MDD is to decrease the accidental complexity in order to be able to better deal with essential complexity. In this paper is extensively discussed how to apply MDD to develop industrial and real-time solutions for power system algorithms. The paper proposes usage of MDD just for power system algorithms. A majority of software development is still done with code-centric approach and does not use separately defined models at all. It relies almost entirely on the code written, and expresses the model of the system directly in a programming language such as Java, C++, or C#. It becomes difficult to manage the evolution of these solutions as they scale and complexity increases. Models provide abstractions of a physical system that allow engineers to reason about that system by ignoring extraneous details while focusing on relevant ones. The previous work done in this field [10–15] identifies necessity for creation of re-usable analytical models. Prior to MDD re-usable software was developed by means of defining a domain architecture [16]. A domain model formulated the network using a system of simultaneous nonlinear equations. An object-oriented framework was derived from such a domain model. The domain architecture provided adaptive usage of the framework. MDD is used to create such an analytical model at run-time directly and this model is re-usable for future calculations, unless any topological changes occur in the actual network. For the example of power flow calculation the model created is a set of equations which is later on passed to the code generator. The main aim of using MDD is to keep the application development constrained to model creation step only.

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In earlier works [15] machine modeling was done using declarative style of programming. The emphasis on a declarative style of programming was on the formulation of the problem and not on the solution steps of the problem. This kind of programming permits the mathematical description of a problem at the level of abstraction an expert is used to in this discipline. A completely general environment for the manipulation of mathematical models of all kinds is provided by Solver Q [10–15], including not only numeric but also symbolic computational capabilities. The Solver Q was developed as a generalized tool to solve variety of power system problems, in a way closer to human thinking [10–15]. This paper also focuses on such a solution which combines mathematical representation of power system models and symbolic computations using expression trees and developing a solver to deal with equations related to various problems in power systems. The problem of differentiation of power flow equations in order to compute Jacobian matrices is discussed in papers [17–19]. An automatic differentiation scheme is proposed which can be easily incorporated into a conventional functioncomputational code. The CPU time spent is only a function of the number of functional terms used and not the number of variables which is a major problem in case of symbolic computation. In papers [18] and [19] this method is further extended to use efficient auto-generated 3GL Jacobian, mismatch computation code and high-level symbolic equation and variable coding. The automatic solution to large systems of non-linear network equations by means of Newton– Raphson method using automatic code generation is analyzed. Over the last few years experts are analyzing the suitability of applying MDD to power system solutions. Paper [11] describes its actual application to real-time solutions, for example optimization, solving non-linear algebraic equations and time domain analysis. In previous approaches the numeric methods generated to solve the above mentioned types of equations needed a lot of programming and compilation and execution code generated later also lacked efficiency. Thus such approaches are unsuitable for usage in industrial solutions, which demand good performance and stability. In some other works, the optimization problem in power systems were solved using a combination of black-box tools for optimization and automatic code-generation for first order derivates [20]. The so-called black box tools were combination of a general-purpose optimization package (MINOS) and an automatic code differentiation tool (ADIFOR). Symbolic computational tools are basically designed to obtain explicit symbolic solutions to a variety of linear and nonlinear mathematical problems. In the area of power systems, pioneer work was performed in the late 80s at the University of Wisconsin-Madison in the development, application and use of symbolic computational tools [21] as well as symbolic-assisted numeric computational tools. The development and application of generic symbolic and numeric computational tools and graphical user interfaces for obtaining solutions of nonlinear problems and related equations are widely discussed, especially those associated with the simulation of power system transients. The use of these tools for power system modeling and simulation has yielded some interesting

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and unique results. Symbolic and symbolic-assisted numeric computational tools have now become part of the mainstream programs used for the analysis, modeling and simulation of power systems, particularly in research and education. For example, in paper [21] usage of MATLAB based programs, which are numeric computational tools that can be used to perform a variety of power system studies, has been discussed. These tools are a good example of how symbolic computation can be used in the development of commercial-grade software for power system analysis, as Jacobian and Hessians can be readily and reliably obtained symbolically for a variety of system models to then integrate them into any numeric computational tool. Another usage of such tools is cited in paper [22] where large disturbance behavior of power systems is captured in a systematic way by a model that consists of differential, switched algebraic and state-reset (DSAR) equations. The DSAR model is then differentiated symbolically. Yet another usage [23] shows modeling of a system as hybrid automation. Such models include the ‘mixed logical dynamic system’ (MLD) or a modified version the ‘dynamic mixed integer program’ (DMIP). Then a symbolic tool is used to convert the hybrid automaton model of a power system into one of these forms. Also symbolic numeric steady state power flow model can be developed for fast and comprehensive contingency analysis [24]. This paper introduces the novel approach of using JIT compiler [25, 26] instead of code interpreter [12, 13] to solve a set of numeric methods from the analytical model. The JIT compiler translates source code into native binary code. This compilation process is done only once, and a link is created between the source code and the corresponding compiled binary code. This increases the speed at which a program runs. It speeds up execution of mathematical intensive functions by up to 50 times. The main reason for using a JIT compiler is to reach the performance of static compilation, while maintaining the advantages of source code interpretation. This paper presents a new approach to power system software application development with the following goals: • • • • •

Easier involvement of domain experts in power system application development Code reusability Speeding up programming Easier maintenance of large power system models Independence from programming languages

2 Comparison of Traditional Programming and MDD The difference in the model creation is illustrated with an example. A simple radial feeder of a power system network (Fig. 1) is considered and the input model is created both in a traditional way and by using MDD. Traditionally the initial model for power flow calculation is created by performing topological tracing over the database storing the network configuration

Model Driven Real-Time Power System Application Development Fig. 1 IEEE 4 node test feeder

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2000 ft.

[I12]

[I34]

Load

[I23]

/// Class CModel implements the general model used by PF Calculation class CModel { public: /// Map storing system buses std::map m_mCBus; /// Map storing slack buses std::map m_mCSlackBus; /// Map storing tap changers std::map m_mCTapChan; /// Map storing system transformers std::map m_mCXfmr; /// Map storing system lines std::map m_mCLine; /// Map storing system loads std::map m_mCLoad; /// Map storing system shunts std::map m_mCShunt; /// Map storing system generators std::map m_mCGen; /// Map storing system switches relevant for the current flow calculations std::map m_mCSwitch; };

Fig. 2 Power system model using traditional programming methodology

and collecting the various network equipments in C++ Standard Template Library (STL) containers. The model created for the node test feeder shown in Fig. 1 using C++ and STL containers is represented in Fig. 2. This process is very time consuming and requires extensive knowledge of C++, STL and other programming concepts to be able to write code to create such a model. Additionally it increases memory consumption of the program and is cumbersome when it comes to large power system networks. As can be seen the model thus created is only a collection of the network elements and their static properties. This way of model creation needs more programming to define the set of equations to be solved for power flow calculation. This is the traditional approach. Instead, a mathematical model of the power system network can be directly created from the information available in the input database during topological tracing and stored in an intermediate format. In case no topological changes are reported by SCADA (Supervisory Control and Data Acquisition) this mathematical model remains unchanged and can be re-used for future calculations. In the component based approach, described in this paper the model created is nothing but a set of equations directly created during topological tracing from the input database specific to the purpose of the power system application. It is then stored in an intermediate format (Expression Trees) which will be then used by the

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Fig. 3 Mathematical model representation

code generator in the next step to create auto-generated code. The mathematical model created from the network is shown in Fig. 3. It should be noticed that the equations needed to be solved for power flow calculation are created on the fly and as a part of model creation. The model creation is basically the end of the application specific development. The next steps to solve these equations are described in the following sections. These steps are independent of the physical network and are generic modules to solve any set of equations. Thus MDD is applied to auto generate executable binaries as a part of model creation procedure. For the network presented in Fig. 3, there are eight variables and eight equations to be solved for. The mathematical model consists of the following: • Model constants (admittance of the lines y) • Model parameters (Load values Sload obtained from load curves, measured, or estimated) • State variables (voltage at various nodes V)

3 Features of Power System MDD There are many advantages of MDD; as mentioned in the previous section the process of model creation is purely analytical and presentation of the physical network as a set of mathematical equations. In this section some additional features are listed which makes MDD a good choice for application to real-time

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1 y 12 V1

3 y23

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4 y 34 V4

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I12 = (V1 − V2 )y12

V3

I23

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V2− V3 = 0, where Z 23 = 0 I 34 = (V3 − V4 )y34

⇒ I 34 = (V2 − V4 ) y34 I12 − I 23 = 0 I 23 − I 34 = 0

Fig. 4 Difference in handling of switches

applications. One feature of auto-generating mathematical models for power flow calculations is illustrated in Fig. 4. In the 1st approach in traditional methods of programming the switches in the model are eliminated, by means of node reduction. Since switches have no impedance the two nodes of the switches are reduced to single node. In order to achieve such a node reduction, special handling has to be done in the code and extra lines of code have to be implemented. The same problem is solved in MDD approach by writing mathematical equations applicable to switches in the network. The voltages at both nodes of the switch are equal. This implies no special handling is required and model is created by simply writing the correct set of equations. The model thus generated can be used for further calculations with only a minor change of parameters, unless the topology of the network changes. A topological update implies a change in the number of network elements in a subsystem and thus the mathematical model for this subsystem has to be re-generated. But as such topological changes do not occur very often, re-using the model for calculations is possible and it improves efficiency and performance of the application. Another very important feature of MDD is the possibility to create a new model using composition of models. This implies the output after solving one model can be used as an input for another one. This provides the flexibility of creating smaller models and enables multiple usages of the same model. A combination of two or more models is also possible during run-time. In case a particular topology in the network can be easily solved by simply combining two or more existing models; the models are just combined with each other with minor changes and thus the new model created is only partially re-generated and the previously existing models are re-used. The analytical model created may consist of three kinds of equations to solve for. • Nonlinear optimization • Nonlinear algebraic equations • Time domain analysis

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I. Dzafic et al. Model Creation Input Model (Set of Equations) Code Generator Expression Tree Syntax Validation

Simplification of Equations

Symbolic Computation Lagrange,Jacobian

Initial Guesses

Numeric Methods

RPN Converter

Solver Model Parameters C/C++ Code

OR

Interpreter

OR

JIT Compiler

Calculation Parameters

Output

Fig. 5 Workflow MDD for power systems

Depending on the type of problem to be solved, the code generator goes through different steps to create numeric methods to be passed on to the Reverse Polish Notation (RPN) converter. The code generator is the module which converts the mathematical model described in the section above into a set of tokens in RPN to be solved by the solver in the next step. The following steps are then executed in the code generator in order to generate tokens. The complete workflow is represented in Workflow MDD for Power Systems (Fig. 5). In the first step, the syntax of set of equations generated as a part of model creation is validated. This step makes sure that the equations are meaningful and no errors were made during the process of model creation. It is a very important step in order to avoid problems due to obvious errors, in creation of numeric methods and parsing in later steps. The checks are made for syntax errors like opening and closing of parenthesis, proper end of lines. In following steps, expression trees are used as model holders. The further simplification of the equations is done in this step, to reduce the number of steps required to process the equations in next steps. Depending on the type of the mathematical model created and the type of power systems problem to be solved different kinds of symbolic computations are carried out for generation of numeric methods. The goal of optimization analysis is to find optimal solution for the given criterion. The following paragraphs give a brief review of mathematical formulation needed for automatic code generation for solving the optimization problem in power systems. This mathematical model is going to be used in explaining the proposed approach. The general optimization problem is described as follows:

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Minimize : min f ðx1 ; x2 . . .xn Þ Subject to : Gðx1 ; x2 . . .xn Þ ¼ 0;

ð1Þ

Applying Lagrange Multipliers the Lagrange functions are constructed as shown in Eq. (2). LðX; kÞ ¼ f ðXÞ  kGðXÞ

ð2Þ

where X ¼ ½x1 ; x2 ; . . .xn  k ¼ ½k1 ; k2 ; . . .kk  Applying Karush–Kuhn–Tucker conditions, the equations can be re-written as, rL ¼ 0 i:e; k of X oGm  km ¼ 0; i ¼ 1. . .n oxi oxi m¼1

ð3Þ

and; Gðx1 ; x2 . . .xn Þ ¼ 0 (3) is a set of N nonlinear equations with the same number of unknowns. This is later solved using a Newton–Raphson method. It is important to notice that ‘‘developer’’ writes only equations in the first step (1). A process of automatic code generation transforms mathematical optimization model described with (1) into (2) and (3). An additional step is then required to generate the symbolical Jacobian, needed to solve for Eq. (3). This is, however, part of symbolical Jacobian computation. x1 x2 f1 2 of1 ox

f2

.. . fn

6 of 1 6 2 6 ox1 6 6 6 6 6 . 6 .. 6 6 6 4 ofn ox1

of1 ox2 of2 ox2



x3 of1 ox3

..

 





. ..





. 

   xm 3 of1 oxm of2 oxm

7 7 7 7 7 7 7 .. 7 . 7 7 7 7 5

ð4Þ

ofn oxm

In the case where the symbolic computation is being done only for a set of nonlinear algebraic equations, after model creation the symbolic computation begins directly with generation of Jacobian. The Jacobian is generated for any set of functions f with any set of variables x as represented in Eq. (4).

76 Table 1 Stack contents evaluating expression in RPN Execution step 1 2 3 4 5 Stack Contents 1 1,2 3 3,4 12

I. Dzafic et al.

6 12,5

7 12

8 17,3

9 14

After the creation of the numerical methods, they are passed on to the RPN converter for generation of tokens in RPN in order to be passed on to the solver for final calculation. After the numerical methods are determined and before the first iteration the initial guesses are put into the equations in order to reduce the number of variables and set the boundary conditions. The generation of the numerical methods is an iterative procedure, after each calculation the calculated values are replaced in the Jacobian and the function f in order to determine the next set of equations. This procedure is repeated till the convergence criterion is achieved.

4 RPN Converter A stack machine is a computational model where operands to instructions are always placed in a LIFO data structure, or a stack. The biggest advantage of stack machines is the high density of the code, as operands selection is not encoded in the instructions stream, but they are implicitly gathered from the top of the stack. The taxonomy of these kinds of machines is defined by the number of stacks and the purposes of each of them. The expressions in RPN are evaluated from left to right with the following rules: • Numeric values are pushed on the stack • Operators pop the arguments from the stack and push the result back. The above approach is for binary operators, but the same is extensible to unary functions. Although the features of such architecture were deeply investigated in the late eighties, and several physical platforms were implemented for this model, current mainstream CPUs are register based, with stack support. In RPN calculations occur as soon as an operator is specified. Thus, expressions are not entered wholesale from right to left but calculated one piece at a time, most efficiently from the center outwards. The automatic stack permits the automatic storage of intermediate results for use later: this key feature is what permits RPN interpreters to easily evaluate expressions of arbitrary complexity: they do not have limits on the complexity of expression they can calculate, unlike algebraic interpreters. Table 1 shows the stack contents during evaluation of expression 5-((1 ? 2)4) +3; written in RPN 1; 2; +; 4; -; 5;*; 3; +, [24]. Figure 6 represents the stack status during the execution of the example. The RPN converter is a part of the code generator and it converts the numeric methods from expression trees into RPN to be solved by the solver as the next step.

Model Driven Real-Time Power System Application Development Fig. 6 Stack status during execution of the example

Fig. 7 Automatic code generation

77 push 2

RPN Converter

Fig. 8 Native code generation using JIT compiler

push 1

2

add

1

1

3

Automatic Code Generator

C/C++ Code

RPN Converter

C++ Compiler&Linker

Executable

JIT compiler

Native Code

5 JIT Compiler (Solver) The code generated from the code generator can be passed on to an interpreter, JIT Compiler or converted to C/C++ code to be executed as shown in Fig. 7. The fastest way to solving the equations in case of power system applications is to use the JIT Compiler. C/C++ code is higher level programming language and needs extra compilation and interpretation of the compiled code. This slows down the execution. Interpreted code is translated from a high-level language to a machine code continuously during every execution, whereas statically compiled code is translated into machine code before execution, and only requires this translation once. A common goal of using JIT techniques is to reach the performance of static compilation, while maintaining the advantages of source code interpretation. JIT compilers generally offer far better performance than interpreters. In addition, they can in some cases offer better performance than static compilation, as many optimizations are only feasible at run-time. Automatic code generation is the generation of source code at runtime (Fig. 8). The numeric methods in RPN are converted to C/C++ code at runtime and then compiled to generate an executable. This process though popular is time consuming and slow. The job of the JIT compiler is to convert intermediate code into the machine code or the native code for execution (Fig. 8). With the JIT code the code can be optimized to a particular operating system. The advantage of converting the source code to the intermediate code is that it can run on any operating system provided JIT for that operating system is available. The basic workflow of the solver is shown in Fig. 9. The tokens generated by the RPN converter are passed on to an interface function in the solver as the calculation parameters. Equation (4) represents the Jacobian created during

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Fig. 9 Workflow JIT compiler

∂f1 ∂xn

JIT compiler

f(x)

Table 2 Comparison of performance of power flow calculation Traditional MDD using programming (ms) interpreter (ms) Creation of power system model Power flow calculation Total

Pointer to function f(x)

MDD using JIT compiler (ms)

444

492

511

605 1049

1814 2306

542 1053

Table 3 Comparison of performance of optimal power flow calculation Traditional MDD using MDD using JIT programming (ms) interpreter (ms) compiler (ms) Creation of power system model Optimal Power flow calculation Total

704

784

807

2238

7243

2186

2942

8027

2993

symbolic computation; given the elements Jacobian are not constant but functions as represented in Fig. 9, this function is passed on to the JIT compiler as a set of tokens and after compilation a pointer to the compiled function is returned to the code generator. In contrast to solvers implemented as interpreted code, solvers based on JIT compilation have much smaller memory consumption. This is a big advantage for such solvers to be used in real time applications.

6 Results In order to test the performance of MDD with JIT compiler, tests were carried out on a power systems network with 6108 nodes, 1896 lines, 1212 busbars, 120 transformers and 864 loads. Results are compared with hand made code. The tests were conducted on x86 as well as SPARC processors, on Windows, SUN Solaris, and LINUX platforms. There are two sets of tests conducted for two different power system problems. The performances of power flow calculation using traditional programming methodology and MDD with interpreter and JIT compiler are displayed in Table 2. The second test was carried out for comparing the performances for solving optimization problems. The results obtained are displayed in Table 3.

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As it can be observed from the test results, the creation of power system model is marginally faster using traditional method of programming in all test cases. The solution based on MDD and JIT compiler provides comparable run-time performance as traditionally developed solution and significantly better performance than MDD using interpreter.

7 Conclusion As explained in this paper, MDD has several advantages over traditional ways of programming. It guarantees stability, efficiency and re-usability. MDD, however, cannot completely replace traditional programming. It provides the possibility to the field experts to develop high performance algorithms and analytical models, without wasting much time on traditional ways of programming. The tests results confirm that the performance of the power system applications developed with MDD and JIT compiler is comparable to those developed using traditional methods of programming. As expected, the test results show that MDD solution using JIT compiler has significantly better performance than solution based on MDD with interpreter. These results show a way to relieve power engineers from time consuming and demanding software development for some power applications. At the same time, the power system applications developed with MDD and JIT compiler are simpler and easier to maintain than traditional software implementations. It can be concluded that MDD in future can be used on large scale in industry applications, in order to avail of its advantages. There is also a lot of scope in future to use MDD in power systems real-time applications.

References 1. MDA resources. Object Management Group. [Online]. Available:http://www.omg.org/mda/ index.htm 2. Selic B (2003) The pragmatics of model-driven development. IEEE Software 20(5):19–25 3. The Middleware Company (2003) Model driven development for J2EE utilizing a model driven architecture (MDA) approach. Productivity Analysis, June 2003 4. Selic B (2006) Model-driven development: its essence and opportunities. In: Proceedings of the ninth IEEE international symposium on object and component-oriented real-time distributed computing 5. Gao J, Li D, Zheng S (2006) Model-driven development of real-time software based on OMG standards. In: Proceedings of the first international conference on innovative computing, information and control 6. Ma Z, He X, Kang L (2009) A model driven development platform for service-oriented applications. World conference on services-II 7. Aho P, Merilinna J, Ovaska E (2009) Model-driven open source software development—the open models approach. In: Fourth international conference on software engineering advances

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8. Heijstek W, Chaudron MRV (2009) The impact of model driven development on the software architecture process. In: 36th EUROMICRO conference on software engineering and advanced applications 9. Brooks F (1986) No silver bullet—essence and accident in software engineering. In: Proceedings of the IFIP tenth world computing conference, pp 1069–1076 10. Dzafic I, Tesnjak S, Glavic M (2002) Automatic object oriented code generation to power system on-line optimization and analysis. 21st IASTED international conference on modeling, identification, and control (MIC 2002), Innsbruck, Austria, February 18–21, 2002 11. Dzafic I, Glavic M, Tesnjak S (2004) A component-based power system model-driven architecture. IEEE Trans Power Syst 19(4):2109–2110 12. Glavic M, Dzafic I, Tesnjak S (2005) A general-purpose symbolically assisted numeric computation environment as a support in power engineering education. IEEE Trans Power Syst 19(1):3–12 13. Dzafic I, Alvarado FL, Glavic M, Tesnjak S (2002) A component based approach to power system applications development. In: 14th power systems computation conference, Sevilla, Spain, June 24–28, 2002 14. Alvarado FL, Liu Y (1988) General purpose symbolic simulation tools for electric networks. IEEE Trans Power Syst 3(2):689–697 15. Alvarado FL, Canizares CA, Keyhani A, Coates B (1991) Instructional use of declarative languages for the study of machine transients. IEEE Trans Power Syst 6(1):407–413 16. Bosshart P, Bacher R (1998) A domain architecture for solving simultaneous nonlinear network equations. IEEE Trans Power Syst 13(3):1006–1012 17. Jerosolimski M, Levacher L (1994) A new method for fast calculation of Jacobian Matrices automatic differentiation for power system simulation. IEEE Trans Power Syst 9(2):700–706 18. Bacher R (1996) Computer aided power flow software engineering and code generation. IEEE Trans Power Syst 11(1):490–496 19. Orfanogianni T, Bacher R (1999) Using automatic code differentiation in power flow algorithms. IEEE Trans Power Syst 14(1):138–144 20. Orfanogianni Yy T, Bacher R (2000) Increased OPF Code development efficiency by integration of general purpose optimization and derivative computation tools. IEEE Trans Power Syst 15(3):987–993 21. Canizares CA (2005) Applications of Symbolic computation to power system analysis and teaching. In: IEEE-PES general meeting, San Francisco, CA, June 2005 22. Hiskens IA, Sokolowski PJ (2001) Systematic modeling and symbolically assisted simulation of power systems. IEEE Trans Power Syst 16(2):229–234 23. Kwatny HG, Mensah EF, Niebur D, Teolis C (2006) Symbolic construction of dynamic mixed integer programs for power system management. IEEE PSCE 24. Liao H, Niebur D (2005) Symbolic/numeric steady-state contingency analysis of electric power systems. IEEE 25. The SPARC architecture manual, version 8, 1991, 1992 SPARC International Inc. 26. Intel 64 and 32 Architectures software developer’s manual, volume 2A, Instruction Set Reference, A-M, 1997–2010 Intel Corporation 27. Intel 64 and 32 Architectures software developer’s manual, volume 2B, Instruction Set Reference, N-Z, 1997–2010 Intel Corporation

An Efficient Distribution Sensitive Privacy for Real-Time Applications Arijit Ukil, Jaydip Sen and Sumit Ghosh

Abstract Traditionally privacy preserving database sanitization utilizes some kind of systemic transformation of the original data set and maps the entries into a different set subject to specific utility and privacy requirements. It is obvious that the distribution of sensitive attributes in original database is domain specific and dependent on the application. Another factor that influences the choice of transformation method is the processing and publishing latency constraints, deterministic for handling the required complexity. In this paper, we propose an efficient way of database privacy protection algorithm which provides an optimal operating point for privacy preserving complexity while maintaining the required utility to minimize the information loss. Our algorithm is sensitive-attribute’s distribution aware and the optimization space is limited by the distribution type. Using the conventional k-anonymity and l-diversity techniques, we analyze and show the effectiveness of our proposed protocol, which outperforms the well-known methods in certain domain of database privacy preservation applications.









Keywords k-anonymity l-diversity Privacy-preservation Utility Leptokurtic Information loss

A. Ukil (&)  J. Sen Innovation Lab, Tata Consultancy services BIPL, Sector-5, SaltLake, Kolkata 700091, India e-mail: [email protected] J. Sen e-mail: [email protected] S. Ghosh Sikkim Manipal Institute of Technology Rangpo, East Sikkim, India e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_8, Ó Springer Science+Business Media B.V. 2012

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1 Introduction With the proliferation of World Wide Web and Internet, it is widely accepted that databases and data repositories represent an important asset for many applications. The availability of huge numbers of databases recording a large variety of information about individuals makes it possible to discover information about specific individuals by simply correlating a certain number of available databases. There are also some requirements of releasing sanitized database for research and other purposes. Privacy relates to what data can be safely disclosed without leaking sensitive information about the legitimate owner. As the volumes of stored data increases, privacy concerns are also increasing and this leads to the rapid development of privacy preserving data management research. Number of techniques like k-anonymity has been suggested in recent years in order to perform privacypreserving data mining. Fundamental motivation of privacy preserving data management is to sanitize the database to achieve the needed amount of privacy using some transformation or mapping algorithm. These transformation methods and the mapping of original database and published database are governed by the privacy requirement. The existing schemes for database privacy preservation algorithms like k-anonymity and l-diversity are search over space of possible multi-dimensional solutions, so they are NP-hard in nature [12]. The high dimensionality of these techniques also adds computational overhead [3]. So, these types of solutions are not suitable in real-time systems. In order to resolve the issue of real-life privacy preservation in data management where real-time publication of the data is required; two optimization problems need to be addressed: 1. Trade-off between privacy requirement and utility loss: in this paper, we consider privacy preservation as a hard requirement and utility is to be maximized under that constraint. 2. Trade-off between response/publishing time and privacy protecting techniques’ complexity. In this paper, we attempt to solve these two issues from a completely different perspective, by considering the distribution of the sensitive attributes. Solving the two issues simultaneously as stated above is practically infeasible [7]. It is observed that in many real-life databases the distribution of sensitive attributes follow leptokurtic pattern, where the sample space is more concentrated within certain range. Another point to be noted is that computational complexity of privacy preserving algorithms presented in the literature [7] is high. Approximate solution of k-anonymity is O (klogk) [2], which is improved to O(k) [12] and that of l-diversity is O (l2) [11]. In view of that, we propose a sensitive attribute distribution aware protocol which minimizes the computational complexity while preserving same amount of privacy and utility score. Our algorithm is most optimal in leptokurtic distribution, where as in ideal platykurtic distribution our algorithm does not yield a comparative better result.

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This paper is organized as follows. In Sect. 2, we present related work. In Sect. 3, the problem is presented formally. In Sect. 4, the considered database, which has some real-life implications, is described. Our experiments are based on this kind of database holding some practical properties. We present our algorithm in Sect. 5. In Sect. 6, experimental results and analysis are shown. Finally, we conclude the paper in Sect. 7.

2 Related Work There exists diverse techniques for studying different transformation methods associated with privacy as randomization [7, 14], k-anonymity [4], and l-diversity [11]. Another important factor influencing the privacy preserving method is the utility loss. It is understood that privacy preservation results in utility loss. An absolute privacy protected database has zero utility. Association rule hiding and query auditing are the traditional methods used for utility based privacy preservation [5, 13]. Another issue that is to be addressed is the complexity of the algorithms found in the literature. For some real-life conditions like online streaming of and sharing of users’ private data, vehicular communications, minimizing the processing and publishing time of the original database is of utmost importance. In [13], privacy preservation in data streams is discussed using noise addition, which minimizes the utility of the data. Agarwal and Yu [1] described condensation based data stream privacy by using the concept of incremental updating. Their approach is useful for driving aggregated statistics, not raw data anonymization. So, this scheme is not suitable where perturbation of sensitive data stream is required. However, our objective is not directly related to provide privacy preservation in data streams, but to minimize the latency of privacy preservation process so that it can be fine tuned for real-time databases and data streams.

3 Problem Statement Let T ¼ ft1 ; t2 ; . . .; tn gbe a table with attributes fA1 ; A2 ; . . .; Am g. We assume that T is a subset of some larger population X where each tuple represents an individual from the population. Let K denote the set of all attributes fA1; A2 ; . . .; Am g and  t ½ Ai  denote the value of attribute Ai for tuple t. Now, if C ¼ C1 ; C2 ; . . .; Cp  K;   then we use the notation t½C to denote the tuple t½C1 ; . . .; t½Cp  , which is the projection of t onto the attributes in C while S denote the set of all sensitive attributes and N denote the set of nonsensitive attributes. k-anonymity [7] is a property that captures the protection of released data against possible re-identification of the respondents to whom the released data refer. A table T satisfies k-anonymity if for every tuple t 2 T, there exist k-1 other tuples

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ti1 ; ti2 ; . . .; tii1 2 T such that t½C ¼ ti1 ½C ¼ ti2 ½C ¼    ; tii1 ½C for all C 2 QI. k-anonymity suffers from homogeneity and background knowledge attack. Therefore, the technique of l-diversity was proposed which not only maintains the minimum group size of k, but also focuses on maintaining the diversity of the sensitive attributes. Let a q*-block be a set of tuples such that its non-sensitive values generalize to q*. A q*-block is l-diverse if it contains l ‘‘well represented’’ values for the sensitive attribute S [10]. In order to preserve privacy of a database from attacker, it is absolute necessary that both k-anonymity and l-diversity is to be incorporated. However, it is very impractical in real-life large database where real-time release of data is required, to k-anonymized l-diverse database sanitization. The first problem to be addressed is that how to achieve same kind of privacy measure with lesser complexity. In fact, jointly performing k-anonymity and l-diversity on a database results in computational complexity of O (k* l2) even for approximate computation [11, 12]. The second problem is that this algorithm should not be compromised in terms of sensitive value disclosure probability ðdÞ; which means that release of sanitized database would be such that d\dThreshold . So, the problem is to transform the table T to T*, such that: O ðT ! T*Þ  / (publishing time) dðT ! T*Þ\dThreshold

4 Sensitive Attributes’ Distribution Pattern It is previously discussed that to simultaneously optimize k-anonymity and l-diversity for generic database, large amount of computational complexity is to be handled. In order to circumvent this problem, we need to analyze the distribution of the sensitive attributes of the original dataset. We find that kurtosis is a good indicator of diversity spread. Kurtosis is a measure of the peakedness of a distribution is. As stated earlier, most of the real-life large databases consist of sensitive attributes which are mostly concentrated, i.e. they are leptokurtic in nature. In this paper, we consider only leptokurtic distribution. The sample snapshot from a large medical database is, shown in Table 1. Kurtosis (j) of the original table is shown in Fig. 1 (with unimodal kurtosis j of the above figure is 4

Þ  3: +7.8), where j is defined: j ¼ Eðsl r4

5 Distribution Sensitive Variable Privacy Algorithm The objective is to provide an algorithm that is of low complexity in nature while the privacy and utility measures are not compromised. So, we consider these parameters:

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Table 1 Sample dataset Name Age

Sex

HIV

Salary(K)

John Rita Doug Eva Martin Alice Tim

M F M F M F M

-ve -ve -ve -ve +ve -ve -ve

250 9425 21 291 163 151 208

56 45 19 71 22 43 31

Fig. 1 Sensitive attribute distribution (following leptokurtic distribution)

Normalized Certainty Penalty (NCP): It measures the generalization of attribute Ai in terms of interval size. Consider a table T with the set of quasi-identifying attributes ðA1 ; . . .; An Þ: Suppose a tuple t ¼ ðx1 ; . . .; xn Þ is generalized to 0 tuple t ¼ ð½y1 ; z1 ; . . .; ½yn ; zn Þ; such that yi  xi  zi : Then, we define NCP of tuple t on attribute Ai as: NCPAi ðtÞ ¼

zi  yi j Ai j

jAi j ¼ max t:Ai  min t:Ai t2T

t2T

Adversary’s baseline knowledge (Abase): We formally define Abase as the vector of probabilities representing the distribution of sensitive attribute values in the entire table T: Abase ¼ hp ðT; s1 Þ; . . .; pðT; sn Þi p(U, s) is the probability that a randomly chosen member of U has the sensitive attribute value S. Adversary’s posteriori knowledge (Asan): The adversary can uniquely identify the quasi-identifier equivalence class \ t [ containing the sanitized record of t in T0 . Asan is the distribution of sensitive attribute values within this class \ t [: Asan ðhtiÞ ¼ hpðt; s1 Þ; . . .; pðhti; sn Þi We use k-anonymized l-diverse transformation to privacy preserve the original database. The objective is to maximize the utility score (information gain) while d\dThreshold : It is evident that as the value of k in k-anonymity increases, the lesser

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will be the knowledge gained by the adversary Aknow ¼ ðAsan ; when Abase \AThrshold base Þ looking at the sanitized database. When k [ Kth where Kth is a threshold, the adversarial knowledge gain Aknow becomes zero. Also, when ldiversity is included along with k-anonymity in the sanitization method, there is a substantial reduction of the adversarial knowledge gain, that is, Alknow  Aknow , where Alknow is the adversarial knowledge gain when the sanitization method includes k-anonymity and l-diversity, as we know Aknow ¼ f ðkÞ and Alknow ¼ f ðk; lÞ: But, when k [[ l, Aknow ! Alknow , provided that the threshold value when l-diversity is included in the sanitization method, KlTh , remains same, i.e., KlTh  KTh . This happens due to the fact that for l-diversity, adversary’s accuracy gain decreases with increasing l and for k-anonymity, adversary’s knowledge gain decreases with increasing k. Increasing k value much larger than l of k-anonymized l-diverse sanitization, decreases the privacy disclosure probability ðdÞ which reduces adversary’s information gain (related to l-diversity). We prove that adversary’s information gain is bounded by d. So, providing d is sufficient when k [[ l. Adversary’s information gain (I. Gain) is the amount of information the adversary gains about the original database analyzing the published database. I. Gain (S, Q) is defined as the difference between the entropy of S and the conditional entropy H(S|Q): I: Gain ðS; QÞ ¼ HðSÞ  HðSjQÞ. Lemma If T satisfies d-disclosure privacy, then I:Gain ðS; QÞ\d. We say that an equivalence class \t [ is d-disclosure private with regard to the sensitive attribute S, if for all s e S,    pðhti; sÞ log \d  pðT; sÞ  A table T is d-disclosure private if for every t  EQ , htiis d-disclosure private [6]. Proof   hti  jTs j s and bt;s ¼ pðhti; sÞ ¼ Let, as ¼ pðT; sÞ ¼ jTj jhtij HðSÞ ¼

X

as log as and HðSjQÞ ¼ sS

I: Gain ðS; QÞ ¼ ¼

X sS

as log as 

X tEQ

X tEQ

X

jhtij X bt;s log bt;s tEQ hTi sS

jhtij X bt;s log bt;s sS hTi

bt;s jhtij X bt;s log sS as hTi   pðhti; sÞ log \d  pðT; sÞ 

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Table 2 Symbol and meaning Symbol

Meaning

EQP c cl n m

Set of k-anonymized l-diverse equivalence classes A k-anonymized equivalence class A k-anonymized l-diverse equivalence class Total number of k-anonymized equivalence classes Total number of k-anonymized l-diverse equivalence classes

X

jhtij X b d tEQ T sS t;s X X   d hti  \ s jTj tTQ sS

I: Gain ðS; QÞ\

As,

P P  htis ¼ jTj

tTQ sS

I: Gain ðs; QÞ\d

This shows that if we satisfy d for a certain database, adversary’s information gain can be assumed to be less than the prescribed limit. This proves that satisfying d is very important. This is due to the fact that when the diversity of the sensitive attributes is much more than granularity of inter-group representation, k-anonymity outperforms l-diversity. So, if k  l, adversary’s knowledge gain and accuracy gain is same for k-anonymized and k-anonymized l-divevrse sanitization. As the value of k in k-anonymity increases, there is a greater number of tuples in each equivalence class c 2 EQ , where EQ is set of k-anonymized equivalence class. In case of leptokurtic distribution, the chances of a more diverse set of sensitive attribute values being present in a single equivalence class increases. Therefore, we may predict that the effect of l-diversity decreases in sanitization for a larger value of k. So, in distribution of sensitive attributes for each quasiidentifier has low entropy. This results low value of I:Gain ðS; QÞ. Consequently small value of d is sufficient, for which even when k l, Aknow ¼ Alknow and AACC ¼ AlACC : Based on those arguments, we propose our algorithm, termed distribution aware privacy preservation. This algorithm first detect the j of the sensitive attributes. Then it finds the optimal value of k and l. It settles for k-anonymity only if it finds k [ l. However, different sets of sensitive attributes can have different j values in a single database. In that case, only the sensitive attributes which has j [ 3 are considered. This optimization does not yield better performance gain for tables with sensitive attributes distribution is mesokurtic or platykurtic (Table 2).

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Table 3 Parameter values of the experiment data sets No. Test case 1.

User requests for medical database publication

2.

User requests for salary database publication

Evaluated parameters Required privacy provided Measures k only NCP 27.52 0.238 Aknow Aacc 0.14 q 0.52 Required privacy provided Measures k only NCP 32.76 0.152 Aknow Aacc 0.09 q 0.436

k=5 k with l 32.57 0.202 0.11 0.43 k = 10 k with l 34.55 0.149 0.09 0.436

6 Experimental Results and Analysis We have performed experiments to prove the efficacy of our proposed algorithm. These experimental results and consequent observations are shown in this section. We have considered two test cases, with sensitive attributes in medical domain (patient’s disease/treatment information) and portfolio management in finance (customer’s salary information). We have found following outcomes of the parameters based on which we show the effectiveness of the proposed algorithm. This is shown in Table 3. First, we demonstrate the variation of adversary’s knowledge gain when k-anonymized l-diverse algorithm is used and when only k-anonymized algorithm used. We used leptokurtic distribution of kurtosis j ¼ 7:8 for medical database and j ¼ 5:5 for financial database. It is observed in Fig. 2 that initially Alknow  Aknow upto k = l, when k = l = 5, Alknow ffi Aknow . This confirms our previous analysis. From Fig. 3, it is evident that as the value of k in k-anonymity increases, the lesser is adversary’s accuracy gain ðAacc Þ With Aacc , the attacker/adversary may predict the sensitive attribute value looking at the sanitized database. With increasing value of k, Aacc decreases and when l-diversity is included along with k-anonymity in the sanitization method, there is a substantial reduction of the adversarial accuracy gain, that is, Alacc  Aacc , for k  l; where Alacc is the adversarial accuracy gain when the sanitization method includes k-anonymity and l-diversity. However, when k [ l, Alacc  Aacc (at k = 6 onwards). This again confirms our postulate that k-anonymized l-diverse data set becomes independent of l-value, when k [ l for leptokurtic distribution in terms of Aacc and Aknow . We conduct the same experiments to prove our hypothesis in case of d-variation and it is found that we can draw same conclusion. This is shown in Fig. 4. As the value of k in k-anonymity increases, there is a greater number of tuples in each equivalence class c 2 EQ . So, the probability of sensitive attribute values being present

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Fig. 2 Adversary’s knowledge gain with and without l-diversity

Fig. 3 Adversary’s accuracy gain with and without l-diversity

Fig. 4 d-variation with and without l-diversity

in a single equivalence class increases, thereby decreasing the sensitive attribute disclosure probability. Thus the upper bound of sensitive attribute disclosure d also decreases. This ensures that adversary’s differential entropy (information gain) is bounded by the prescribed threshold, which minimizes the impact of l-diversity on the sensitive attributes. From these observations, it is clear that if the sensitive attribute distribution is leptokurtic, k-anonymity value when crosses optimum value of l of l-diversity, the resultant k-anonymized l-diverse dataset’s privacy and utility parameters can be sufficiently achieved with k-anonymity alone. This, in effect, reduces the computational complexity to a large extent. The k-anonymized l-diverse dataset has approximate complexity of O(k*l2) [11, 12]. As per our algorithm, when optimum

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value of k is larger than optimum value of l, we can achieve same kind of privacy and utility only with k-anonymity, subject to leptokurtic sensitive attribute distribution. This results in substantial decrease of computational overhead: O(k*l2) ? O(k). Though it is to be noted for normal distribution, our hypothesis is not valid. In fact, in platykurtic distribution of sensitive attributes, optimum value of l is very high for significant privacy gain.

7 Conclusion In this paper, we have proposed a privacy preserving algorithm with less publication time which is dependent on the distribution pattern of the sensitive attributes of the database. Leptokurtic distribution of sensitive attributes favours our claim of minimization of computational complexity of the process of privacypreservation of a database. We showed that with leptokurtic distribution of sensitive parameters, k-anonymized l-diverse sanitization is same as only k-anonymized sanitization when k [ l. Through experimental results we have confirmed our hypothesis. Our future work is to extend this algorithm to incorporate t-closeness [9] and to analyze the relationship that t-closeness, k-anonymity and l-diversity holds in presence of different distribution pattern of sensitive attributes.

References 1. Agarwal C, Yu P (2004) A condensation approach to privacy preserving data mining. EDBT 183–199 2. Aggarwal G, Feder T, Kenthapadi K, Motwani R, Panigrahy R, Thomas D, Zhu A (2005) Approximation algorithms for k-Anonymity. J Priv Tech (Nov) 3. Aggarwal CC (2007) On randomization, public information, and the curse of dimensionality. IEEE ICDE conference, pp 136–145 4. Bayardo RJ, Agrawal RJ (2005) Data privacy through optimal k-anonymization. IEEE ICDE conference, pp 217–228 5. Blum A, Dwork C, McSherry F, Nissim K (2005) Practical privacy: the SuLQ framework. ACM PODS conference, pp 128–138 6. Brickell J, Shmatikov V (2008) The cost of privacy: destruction of data-mining utility in anonymized data publishing. ACMKDD 70–78 7. Fung BCM, Wang K, Chen R, Yu PS (2010) Privacy-preserving data publishing: a survey of recent developments. ACM J Comp Surveys (CSUR) 42(4) 8. Li F, Sun J, Papadimitriou S, Mihala G, Stanoi I (2007) Hiding in the crowd: privacy preservation on evolving streams through correlation tracking. IEEE ICDE, pp 686–695 9. Li N, Li T, Venkatasubramanian S (2007) t-Closeness: privacy beyond k-anonymity and l diversity. IEEE ICDE conference, pp 106–115 10. Liu J, Wang K (2010) On optimal anonymization for l-diversity. IEEE ICDE conference, pp 213–224 11. Machanavajjhala A, Kifer A, Gehrke J, Venkitasubramaniam M (2007) L-diversity: privacy beyond k-anonymity. ACM Trans Knowl Disc Data TKDD 1(1)

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12. Meyerson A, Williams R (2004) On the complexity of optimal k-anonymity. ACM PODS conference, pp 223–228 13. Nabar S, Marthi S, Kenthapadi K, Mishra N, Motwani R (2006) Towards robustness in query auditing. VLDB conference, pp 151–162 14. Wang H, Liu R (2011) Privacy-preserving publishing microdata with full functional dependencies. Elsevier J Data Knowl Eng 70(3):249–268

DeSPoT: A Method for the Development and Specification of Policies for Trust Negotiation Tormod Håvaldsrud, Birger Møller-Pedersen, Bjørnar Solhaug and Ketil Stølen

Abstract Information systems are ever more connected to the Internet, which gives wide opportunities for interacting with other actors, systems and resources and for exploiting the open and vast marked. This pushes the limits for security mechanisms which in general are too rigorous to fully adapt to such a dynamic and heterogeneous environment. Trust mechanisms can supplement the security mechanisms in this situation to reduce the risk by means of trusted evidences. We propose DeSPoT, a method for the development and specification of policies for trust negotiation. DeSPoT is created to be easy to use for business level experts, yet demonstrated in an industrial study to be useful for those who develop and maintain the system conducting trust negotiation within acceptable risk. Adherence to a DeSPoT policy should ensure that the target fulfills the organizational level requirements to the trust behavior, and that the target is not exposed to unacceptable risk. The paper gives an example-driven presentation of the method. Keywords Trust negotiation control Trust policy





Trust management
Risk management
Access

T. Håvaldsrud (&)
B. Solhaug
K. Stølen SINTEF ICT, Oslo, Norway e-mail: [email protected] B. Solhaug e-mail: [email protected] K. Stølen e-mail: [email protected] T. Håvaldsrud
B. Møller-Pedersen
K. Stølen Department of Informatics, University of Oslo, Oslo, Norway e-mail: [email protected]

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1 Introduction Systems at the Internet exploit the potential of the open market and the possibility of interacting with a vast number of other systems for the purpose of realizing opportunities. Trust mechanisms are introduced to mitigate the fact that security mechanisms usually are too rigorous to fully adapt to this dynamic and heterogeneous environment. When a system is exposed in an environment it is subject to risk, which we define as the combination of the likelihood of an incident and its consequence for an asset [1]. Security mechanisms are introduced to reduce the risk, but they are not able to eliminate it entirely. Moreover, increased security tends to be at the cost of interoperability. Security mechanisms should be used to achieve the necessary security level, i.e. keep risk below a certain critical level, and leave it to trust mechanisms to treat the residual risk by means of trust and perceived knowledge. In practice we often need to make assumptions regarding uncertain information, and this forces us to take uncertainty into account when making decisions. Even though information is uncertain it provides important indications of the actual situation. The challenge is to make the uncertainty sufficiently visible so that we are aware of its extent and not just of its existence. In trust mechanisms uncertainty is the focal point of judgment, whereas security mechanisms hide uncertainty by the assumption that it is sufficiently small to be ignored in the clear defined situation. To achieve trust, systems may conduct trust negotiation [2] utilizing trust mechanisms. The systematic use of trust mechanisms may be formulated in a trust policy. The developers need specialized methods to support the development and maintenance of the trust policy in the same way as they need specialized methods for the development and maintenance of security policies. A natural way to describe trust behavior is, as for security behavior, by means of rules. Many security systems do not explicitly define the rules, but rather embed them in the implementation of the system as actions triggered by events. For this reason it is natural to aim for a rule-based policy specification language. The contribution of this paper is a method for the Development and Specification of Policies for Trust negotiation (DeSPoT). Correctly enforcing such a policy should ensure that the trust behavior realizes opportunities while keeping risks at an acceptable level. Focusing on the requirements to and the criteria for risk and trust at an organizational level, the method aims to support decision makers in understanding the potential implications of trust mechanisms without going into the low level details of trust negotiation protocols. This is achieved by systematically linking the high level organizational requirements and criteria to the developed trust policy, which in turn is linked to the low level trust behavior. The rest of the paper is organized as follows. We give an introduction to trust negotiation in Sect. 2. In Sect. 3 we provide an overview of our method. In Sect. 4 through Sect. 8, we present the five steps of our method in an example-driven manner. We conclude in Sect. 9 by characterizing our contribution and discussing

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Fig. 1 Example of trust negotiation

related work. The reader is referred to the full technical report [3] on which this paper is based for further details about the DeSPoT method.

2 Introduction to Trust Negotiation To introduce trust negotiation and our terminology we use an everyday example. Assume a policeman wants to check the validity of David’s driver’s license. The scenario is illustrated by the sequence diagram in Fig. 1. The diagram has three lifelines, David, the Policeman and PoliceITSystem. Everything is observed from policeman’s perspective, so we tag him as the target of our analysis and David and the PoliceITSystem as partners. All events at the policeman’s lifeline are tagged to show what kind of event it is with respect to trust negotiation from the policeman’s perspective. In general, the chosen target of analysis is the system or organization for which the method aims to develop and analyze a policy to govern the trust negotiation. The partners are the trustees in potential interactions with the target. When the policeman approaches David’s car he notices that David is intimidated by his pose and checks that the doors of the car are locked. The policeman knocks on the closed window and makes it clear that he wants verbal contact. David indicates that he wants the policeman to identify himself. These two social messages are revealing parts of their respective trust policies; the policeman reveals some of his intentions and David requests some evidence from the policeman. As a reaction to David’s request the policeman shows his ID card and his badge so that David can see for himself that the policeman really is a police officer and that it is his badge. The badge and the ID card are assets to the policeman, which he exposes to authenticate himself. An asset is something of value for the target and therefore needs protection against risk. David rolls down

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Fig. 2 The five step process

the window and hands over his driver’s license on request from the policeman. David’s driver’s license is a prospect for the policeman; it can tell him whether David is allowed to drive or not. Generally, a prospect is something of value for the target that can be provided by a partner. To verify the validity of the driver’s license, the policeman employs the PoliceITSystem and runs a validity check using the license number. For the policeman in this situation, the license number is an asset and the potential response from the PoliceITSystem is a prospect that may validate David’s driver’s license. After the interaction with the PoliceITsystem the policeman knows that David is allowed to drive a car.

3 Overview of the Method The overview of the five steps of the DeSPoT process is given in Fig. 2. The process provides guidance in correctly capturing the target’s trust policy requirements and the trust policy. The next five sections exemplify each step in turn.

4 Step 1: Characterizing the Target The characterization of the target is conducted in close cooperation with the commissioning party, who is usually the target owner. Our example target is a power grid system where we focus on the balancing of the production and the consumption of electrical power. This task is quite complex and for the purpose of this paper we simplify the system so that we are able to focus on the important features of DeSPoT. The balancing of the production and consumption is conducted in real time, but it also needs planning from day to day. It is this day to day planning on which we focus.

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The selected target is the Power Production Organizer (PPO) which is a central software system collecting and spreading key information about power production and consumption. Several systems communicate with the PPO; some are providing necessary information, some are providing business crucial information and others are just providing supplementary information. A power flow sensor measures the flow of power through a line. It informs the PPO how much power is transported through the power line during the last 24 h. A power station provides the PPO with information on produced power during the last 24 h, as well as the expected production capacity. Sometimes the capacity is lower because of maintenance, while in other situations the power stations may have higher capacity than usual, for example due to heavy rain. In addition to this, the power station needs to know how much power it is expected to feed into the grid the next 24 h to adjust its production. When the PPO has collected all the information, the PPO finds the best suited production profile for the next 24 h. It then assigns production quotas for the next 24 h to all the power stations. Now we continue by defining the scales for prospect value, asset value and trust level from the perspective of the target. These scales may be both quantitative and qualitative, depending on the desired granularity of the analysis or what is otherwise suitable for the target in question. The prospect value scale is used to measure the value of objects and information given to the target by partners. The value should reflect the direct value, the sensitivity and how easy it is to fake. For example, a credential that is very easy to fake should be given a relatively low value, whereas an unforgable message about the delivery of a crate with high quality goods can be given a high value. The prospect value scale chosen for the PPO is of five values from 1 to 5. Each of these values must be defined in terms of a precise interval or a qualitative description. Next we define the scale used to measure the value of assets. As already indicated, an asset is something the target already possesses and that should be protected from harmful incidents. An asset may for instance be sensitive information. Breach of confidentiality, integrity or availability, for example, could be exploited to damage the reputation or revenue of the target. In this situation the potential total loss should correspond to the assigned asset value. The asset value scale chosen for the PPO is of four values from 1 to 4, each of which must be precisely defined. The last scale needed to be defined is the trust level scale. We use this scale to measure the target’s trust in a partner’s capability and intention to protect the target’s assets. The trust level scale chosen for PPO is No, Low, Medium and High, each representing an interval of subjective probabilities between 0 and 1.

5 Step 2: Capturing Requirements for the Trust Policy The trust formation policy requirements specify how the target is allowed to use incoming prospects as evidence to form trust. In general, the trust formation policy requirements restrict how the target is allowed to perceive the world with respect

98 Table 1 The trust formation policy requirements

T. Håvaldsrud et al. Prospect value

Maximal trust level

1 2 3 4 5

Low Medium Medium High High

to trust. The trust formation policy requirements for the PPO are defined in Table 1 and regulate how prospects are allowed to influence the trust level. The interpretation of the second row, for example, is that a prospect with prospect value 2 can at most support a trust level Medium. The asset exposure policy requirements specify the trust level the target must have in order to expose assets with a specific value. The trust level is an indirect measure of the likelihood of something going wrong, and the level of this risk can be deduced from the trust level and asset value. The asset exposure policy requirements hence specify the acceptable risk level in this setting. The asset exposure policy requirements for the PPO are defined in Table 2, where each row forms an asset exposure rule requirement. The interpretation of row three, for example, is that when the target has trust level Medium it is allowed to expose assets with value 3 or lower, while the last row allows the target to expose all assets (because 4 is the highest asset value) to partners in which its trust is High.

6 Step 3: Modeling the Trust Policy The trust formation policy specifies how the target should form trust based on prospects and their properties. The first task is to specify the prospects and their values, as illustrated in Table 3. The third row means that a prospect matching the prospect description Consumer authentication has the prospect value 3. A trust formation rule defines what may form evidence and how this evidence should influence the target’s trust level with respect to a partner. The evidence consists of a prospect, a prospect property and an evidence type. The prospect is received from a partner and can be anything that may give insight into this partner’s properties, such as intention or capabilities to take care of the target’s assets. The prospect property may be as simple as the confirmed existence of the prospect itself, but also complex properties such as authenticity and validity of a chained signed electronic certificate. There are two different sorts of evidence, namely supporting and exposing. The supporting evidence may build trust, whereas exposing evidence on the other hand may reduce trust. The exposing prospect rules are overruling the supporting, such that the trust is governed by the worst exposing evidence and otherwise by the best supporting evidence.

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Table 2 Asset exposure policy requirements

Trust level

Maximal asset value

No Low Medium High

1 2 3 4

Table 3 Prospect values

Prospect description

Prospect value

Partner’s access policy Full postal address Consumer authentication Power certificate signature Master certificate signature Certificate validation

1 2 3 4 5 5

Table 4 Trust formation rules Prospect description

Property

Evidence type

Trust level

Power certificate signature Full postal address Consumer authentication Power certificate signature Master certificate signature Certificate validation

Invalid Existing Valid Valid & Correct Correct Trusted

Exposing Supporting Supporting Supporting Supporting Supporting

No Low Medium High High High

The first row in Table 4 should be understood as follows: If a partner provides an invalid power certificate signature, it is perceived as an exposing evidence and results in a trust level no higher than No. Sometimes a prospect property must be verified by another prospect. This is typically the case in chains of certificates. In order to take into account and keep track of such relations, we document these in a designated table as exemplified in Table 5. The first row should be understood as follows. A Certificate validation prospect with the property Trusted & CertValid verifies the Valid property of a Power certificate signature prospect. Moreover, the Trusted property of the former can in turn be verified by the Correct property of Master certificate signature in the third row. The policy reception rules specify how to handle requests from the partner. These are referred to as such, because in trust negotiation the partners expose their policy when requesting assets from the target. The policy reception rules for the PPO are documented in Table 6. The first row should be understood as follows. If the partner requests that the target system provides a Power certificate signature the target may raise the trust level up to Low based on this evidence.

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Table 5 Prospect property verification Prospect description Required property Certificate validation

Trusted & CertValid

Certificate validation

Trusted & CertInvalid

Master certificate signature

Correct

Prospect description

Verified property

Power certificate signature Power certificate signature Certificate validation

Valid Invalid Trusted

Table 6 Policy reception rules Requested asset

Evidence type

Trust level

Power certificate signature Report power flow Get assigned power quota Report power consumption

Supporting Supporting Supporting Supporting

Low Low Low Low

Table 7 Defining asset values Asset description

Asset value

Get total power consumption Target’s access policy Report consumer consumption Report power production Get assigned power quota Report power production capacity Report power flow

1 2 3 3 3 4 4

The assets identified for PPO are listed in Table 7 together with their respective values. The asset Get total power consumption, for example, is assigned the asset value 1. This is a service provided by the target system and exposes an asset. The asset exposure policy specifies how the target may expose assets based on the trust level. The asset exposure rule for PPO is modeled in Table 8. The first column specifies the minimum trust level for exposing the associated asset. Hence, the asset Power certificate signature can be exposed when the trust level is Low or higher. Or, given the trust level Medium, all the assets of the first five rows can be exposed. PPO also needs to expose parts of its own trust policy. These exposures may be sensitive and for that reason we explicitly model how the target is allowed to expose policies through policy exposure rules. These are presented in Table 9. The first row should be understood as follows: The target must have at least trust level No in the particular partner to be allowed to request the partner for the asset Power certificate signature.

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Table 8 Asset exposure rules

Needed trust

Requested asset

Low Low Low Medium Medium High High

Power certificate signature Report consumer consumption Get total power consumption Report power production Get assigned power quota Report power flow Report power production capacity

Table 9 Policy exposure rules

Needed trust

Asset description token

No No No

Power certificate signature Consumer authentication Postal address

7 Step 4: Analyzing the Current Trust Policy with Respect to its Requirements At this point we have both the trust policy and its requirements. In this step we look for possible gaps between them. Every trust formation rule forms trust based on a prospect with a specific value. It can therefore be easily checked against the corresponding trust formation rule requirement which specifies the highest acceptable trust to be formed for a prospect of this value. Consider, for example, the fourth trust formation rule in Table 4. The prospect description is Power certificate signature. According to Table 3 this prospect has value 5. Further on, evidence formed by the rule supports trust level High. To sum up, the rule supports trust level High based on a prospect of value 5. To check whether this trust formation rule adheres to the trust formation requirements we look into Table 1. This table states that evidences based on prospects with prospect value 4 and 5 can support trust level High. This means that the fourth prospect rule in Table 4 meets the trust formation requirements. The second asset exposure rule in Table 8 assigns access to the service Report consumer consumption and requires at least trust level Low. The service Report consumer consumption has the asset value 3 as shown in Table 7. This rule is then exposing an asset with asset value 3 based on a trust level Low. According to the PPO’s asset exposure policy requirement shown in Table 2, it is not allowed to give access to assets of value above 2 when the trust level is at Low. Hence, this is an example of an asset exposure rule that does not adhere to the asset exposure policy requirements. This is the only breach of adherence in the case of our example.

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8 Step 5: Updating the Trust Policy to Reflect its Requirements The trust policy formulated above allows the consumers to report their power consumption just by giving their postal address, which is not hard to fake. If the asset exposure rule had required trust level Medium instead of Low the customer would be required to log on with their Customer authentication which is quite normal for this kind of service. In that case, adherence with respect to the asset exposure policy requirement would be ensured. This change is implemented by inserting Medium instead of Low in the second row of Table 8.

9 Conclusion We have presented DeSPoT, a method for the development and specification of policies for trust negotiation. The trust policy is linked to risk assessment through the requirements for the trust policy. The trust policy must adhere to the trust policy requirements to delimit the trust behavior within acceptable risk. The method supports negative (exposing) as well as positive (supporting) evidences, sensitive assets (credentials), separation between the trust formation and the asset exposure, static adherence check of the policy with respect to the trust policy requirements, and prospects that verify the properties of other prospects which are the general mechanism behind delegation of trust (recommendation). The method is built around a five step process. Our rule-based approach enables the development of a trust policy the enforcement of which ensures trust negotiations within the limits of acceptable risk. The method is independent from specific trust negotiation protocols, and does not assume such protocols to be predefined. Our focus has been to create an easy-to-understand language for trust policies with few details, nevertheless containing the most important trust mechanisms. The language is made to be understandable for people knowing the target (e.g. a company) at the business level, while being useful and understandable for those that develop and maintain the business application. In this way we are able to assemble a trust policy that contains both the risk and asset knowledge from the business level and the technical knowledge about different security technologies in one trust policy. Such a combination may reveal inconsistencies in the perception of the trust domain internally in the company. The approach to system authentication presented in [4] implements trust negotiation as described in [5] to build trust. Approaches like Trust-X [5], [6], [7], TrustBuilder [8], [9] and Protune [10], [11], [12] are examples of policy based access control systems for automated trust negotiation. While these approaches make use of mechanisms for building trust, the involved trust level is only implicit and not extracted as an explicit element of value in itself. Yao et al. presents in

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[13] a value and privacy scoring for credentials and find an optimal exposure with minimal privacy and sufficient value to achieve access. The automated trust negotiation proposed in [14] emphasizes that negative evidences cannot be supported because an agent only controls what it sends and not what it receives and therefore opens for Denial of Service (DoS) attacks. For this reason there is very few that support negative evidence. We believe, however, that negative trust evidence is important in trust management. Revocation of certificates, banning of accounts and blocking of credit cards are examples of activities based on new information resulting in lower trust. Hence, not all digital trust functions are non-decreasing. In our approach every use of evidence is based on the trust in the partner providing it. To conduct a DoS attack through negative evidence, one must exploit misplaced trust and be able to pose as a trusted communication partner. If this posing is possible, then the trust in the evidence is overrated. In a trust policy as well as in a security policy the risk may be underrated as this is a possibility in all risk analysis. The vital thing is to be aware of this fact, and try to avoid it. Acknowledgments The research on which this paper reports has partly been funded by the Research Council of Norway through the DIGIT (180052/S10) project, and partly by the European Commission through the NESSoS network of excellence.

References 1. International Organization for Standardization (2009) ISO 31000 Risk management— principles and guidelines 2. Winslett M (2003) An introduction to trust negotiation. In: iTrust 2003, LNCS vol 2692. Springer pp 275–283 3. Håvaldsrud T, Møller-Pedersen B, Solhaug B, Stølen K (2011) DeSPoT: A method for the development and specification of policies for trust management. Technical report A20174, SINTEF 4. Seigneur J-M, Farrell S, Jensen CD, Gray E, Yong Chen Y (2004) End-to-end trust starts with recognition. In: Secur Pervasive Comput LNCS 2802:130–142 5. Bertino E, Ferrari E, Squicciarini A (2004) Trust negotiations: concepts, systems, and languages. Comput Sci Eng 6:27–34 6. Bertino E, Ferrari E, Squicciarini A (2004) Trust-X: a peer-to-peer framework for trust establishment. IEEE Trans Knowl Data Eng 16(7):827–842 7. Squicciarini A, Bertino E, Ferrari E, Paci F, Thuraisingham B (2007) PP-trust-X: A system for privacy preserving trust negotiations. ACM Trans. Inf Syst Secur 10 8. Lee A, Winslett M, Perano K (2009) TrustBuilder2: A reconfigurable framework for trust negotiation. In: Trust management III, IFIP, Advances in information and communication technology, vol 300. Springer, pp 176–195 9. Winslett M, Yu T, Seamons KE, Hess A, Jacobson J, Jarvis R, Smith B, Yu L (2002) Negotiating trust in the web. Internet Comput IEEE 6(6):30–37 10. Bonatti P, De Coi JL, Olmedilla D, Sauro L (2010) A rule-based trust negotiation system. IEEE Trans Knowl Data Eng 22:1507–1520 11. Bonatti P, Olmedilla D (2005) Driving and monitoring provisional trust negotiation with metapolicies. In: Proceedings of the sixth IEEE international workshop on policies for distributed systems and networks (POLICY’05), IEEE Computer Society, pp 14–23

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12. De Coi JL, Olmedilla D, Zerr S, Bonatti P, Sauro L (2008) A trust management package for policy-driven protection and personalization of web content. In: Proceedings of the 2008 IEEE workshop on policies for distributed systems and networks (POLICY’08), IEEE Computer Society, Washington, DC, pp 228–230 13. Yao D, Frikken KB, Atallah MJ, Tamassia R (2008) Private information: To reveal or not to reveal. ACM Trans Inform Syst Secur TISSEC 12(1):61–627 14. Winsborough WH, Seamons KE, Jones VE (2000) Automated trust negotiation. DARPA Inform Surviv Conf Expo 1:88–102

Extended NIST Prime Family for Efficient Modular Reduction Young In Cho, Nam Su Chang, Chang Han Kim and Seokhie Hong

Abstract The FIPS 186-2 standard recommends five prime fields with a modulus so-called NIST primes for elliptic curve cryptosystems. Primes of the special form such as NIST primes have a property yields modular reduction algorithms that are significantly fast. However the number of NIST primes are not large enough. In this paper, we further extend the idea of NIST primes. Then we find more primes can provide fast modular reduction computation that NIST prime family does not support. Our method provides more efficient modular arithmetic than Montgomery algorithm in prime fields that NIST primes does not support. Keywords Modular arithmetic

 NIST prime  Finite field arithmetic

Y. I. Cho  S. Hong (&) Graduate School of Information Management and Security, Korea University, Seoul, South Korea e-mail: [email protected] Y. I. Cho e-mail: [email protected] N. S. Chang Department of Information Security Systems, Sejong Cyber University, Seoul, South Korea e-mail: [email protected] C. H. Kim Department of Information and Security, Semyung University, Jecheon, South Korea e-mail: [email protected]

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1 Introduction Efficient implementation of modular arithmetic is very important for the high performance of many public-key cryptosystems such as RSA, elliptic curve cryptosystems and pairing based cryptosystems based on prime fields. There are many well-known techniques for efficient implementation of modular arithmetic. For general primes, the most efficient exponentiation algorithm is Montgomery exponentiation algorithm [3]. Montgomery exponentiation algorithm uses a special representation to perform efficient arithmetic, the division and remaindering essentially being performed by bit shifting. If a modulus p has a special form, modular arithmetic can be accelerated significantly. The Mersenne numbers of the form 2m  1 are well-known examples [1]. However, unfortunately, only a few Mersenne primes are useful for cryptography. Solinas introduced the family of generalized Mersenne numbers [4]. They are expressed as p ¼ f ðtÞ, where t is a power of 2. This representation permits fast modular reduction without division operation. NIST prime family is the special case of generalized Mersenne numbers. NIST prime family has the property that can be written as the sum or difference of a small number of powers of 2. Furthermore, the exponent appearing in these expressions are all multiples of 32. This property yields modular reduction algorithms that are especially fast on machines with wordsize 32. The FIPS 186-2 standard recommends five primes so-called NIST primes : p192 ¼ 2192  264  1; p224 ¼ 2224  296 þ 1; p256 ¼ 2256  2224 þ 2192 þ 296  1; p384 ¼ 2384  2128  296 þ 232  1; p521 ¼ 2521  1: The powers appearing in NIST primes are all multiples of 32 except for p521. In this paper, we find more primes can support fast modular reduction by further extending the idea of NIST prime family. Instead of considering modulus with a special form, we try to discover the integer p belongs to Extended NIST Primes (ENP) that provide almost same performance with NIST primes. We show that how to perform modular arithmetic operation with a modulus ENP. Then we present the running time of modular multiplication and exponentiation with a modulus ENP comparing with Montgomery algorithm. ENP support more prime fields that permit very fast modular reduction than NIST prime family does.

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2 Extended NIST Primes In this section, we introduce ENP and discuss its computational efficiency. We denote the number of powers of 2 appearing in p as wt(p). For example, when p ¼ 2a þ 2b þ 1 ð0\b\aÞ; wtðpÞ ¼ 3: Definition 1 (ENP) A prime p is called Extended NIST Prime (ENP) if there exist a generalized Mersenne number q ¼ p  h for some positive odd integer h. In this case, exponents of 2 appearing in q should be all multiples of wordsize except for wt(q) = 2. In other words, we try to find positive integers of the form NIST prime family which have a large prime factor. Then the large prime factor would be ENP. We devote subsection 2.1 to the description of modular arithmetic with a modulus ENP.

2.1 Modular Arithmetic Modular Reduction : NIST primes have a property that modular reduction algorithms are very fast on machines with wordsize 32. Note that we can apply the same modular reduction rule to modular reduction operation with a modulus q of Definition 1 since q is a form of NIST prime family. In order to compute c modulo p for some integer c, we reduce the result of c modulus q. For example, consider ENP p = p285 of bitsize 285 when q = 2288 + 296 - 1 and P 32 32i h = 11. Let c be an integer with 0  c  p2285 . Let c ¼ 17 i¼0 ci 2 be the base-2 32 representation of c, where each ci 2 ½0; 2  1. We reduce c according to the following congruences : 2288 2352 2416 2480 2544

 296 þ 1 ðmod qÞ;  2160 þ 264 ðmod qÞ;  2224 þ 2128 ðmod qÞ;  2192 þ 296  1 ðmod qÞ;  2256 þ 2160  264 ðmod qÞ:

2320 2384 2448 2512

 2128 þ 232 ðmod qÞ;  2192 þ 296 ðmod qÞ;  2256 þ 2160 ðmod qÞ;  2224 þ 2128  232 ðmod qÞ;

Thus we obtain c



c17 2256

þc17 2160 c16 2224

c17 264 þc16 2128

c15 2192 c14 2256

c16 232 þc15 296

c15

þc14 2160 c13 2224

þc13 2128 c12 2192

þc12 296 c11 2160

þc11 264 c10 2128

c8 2256

þc7 2224

þc6 2192

þc5 2160

þc4 2128

þc10 232 c9 296 þc3 296

þc2 264

þc1 232

þc9 þc0

ðmod qÞ:

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Hence, c modulo p can be obtained by adding ten 288-bit integers, c172256+c172160 -c17264, c162224+c162128-c16232, c152192+c15296-c15, -c142256+c142160, c132224+c132128, -c122192+c12296, -c112160+c11264, -c102128+c10232, -c9296+c9 and c82256+c72224+c62192+c52160+c42128+c3296+c2264+c1232+c0 and subtracting p repeatedly until the result is less than p. Algorithm 1 is the modular reduction algorithm with a modulus ENP p285. Algorithm 1 Fast reduction modulo P285 Input: q = 2288 + 296 - 1, an integer c = (c17,…,co) in base 232 with 0  c \p2285 and a modulus ENP p285. Output: c mod P285 1: Define 288-bit integers : S1 = (C8,C7,C6,C5,C4,C3,C2,C1,C0) S2 = (-Cl4, -C13, - C12, - C11, - C10, - C9, 0, 0, 0), S3 = (0,0,0,C14,C13,C12,C11,C10,C9), S4 = (Cl7, C16, C15, C17, C16, C15, - C17 -Cl6 -C15). 2: Return (s1 + s2 + S3 + s4 mod p285). Algorithm 2 Modular multiplication with a modulus ENP Input: two positive integers A = (an-1,  a0)b, B = (bn-1, b0)b and a modulus ENP where b = 232. Output: S = (Sn-1,, S0)b such that S = AB mod p 1: Compute A B 2: Compute U = A B mod q 3: Compute S = U mod p (with rights shift and subtractions) Modular Multiplication : Modular multiplication with a modulus ENP for some integer A and B is performed in three steps. We know that modular reduction in step 2 of Algorithm 2 is very simple like the reduction with a modulus NIST prime family. Comparing modular multiplication with a modulus NIST prime, the extra operation is only step 3. Since we assume that the modular multiplication is implemented in software, we analyze the computational efficiency in terms of word length. Let tm and td respectively denote the time required for a microprocessor to compute word level multiplication and division. According to [2], the running times required to compute long integer multiplication, squaring and division are given as follows: MðnÞ ¼ n2 tm ; SðnÞ ¼ ðn2 þ nÞ=2tm ; Dðn1 ; n2 Þ ¼ ðn1  n2 Þðn2 þ 3Þtm þ ðn1  n2 Þtd ;

Extended NIST Prime Family Table 1 Comparison of modular multiplication running time

109 Modular multiplication

Computational efficiency

Classical Montgomery ENP

ð2n2 þ 3nÞtm þ ntd ð4n2 þ 4nÞtm þ ntd n 2 tm

where M(n) and S(n) are the running times for a long integer multiplication and a long integer squaring respectively, when multiplicands are both n words long. D(n1, n2) is the running time for a long integer division when the dividend is n1 words long and the divisor is n2 words long. For simplicity, we do not consider the time required for additions and subtractions in our analysis. We compare the computational efficiency of the modular multiplication in Table 1. Modular Exponentiation: ENP is especially useful for modular exponentiation since mod p extra operation spends a small part of a whole operation. We use left-to-right binary exponentiation algorithm. In algorithm 3, step 4 and 7 are computed with word additions. Now we restrict the range of q to gain better performance. If q occupies more word blocks than p, then the results of mod q operations in step 4 and 7 may also occupy more word blocks than p does. Algorithm 3 Modular Exponentiation Input: g = (gngn-1.g1g0)b and a positive integer e = (etet-1…e1e0)2. Output: ge mod p. 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11:

A/1 for i = t to 0 do A/AA A / A mod q if ei -1 then A/Ag A / A mod q end if end for A / A mod p Return (A)

So the computational complexity of multiplication in step 3 and 6 may be higher than the complexity of multiplication using the results of mod p operations in iteration. Moreover if h is too big, then modular reduction in step 10 may be expensive. The following remark presents our restriction of the range of q and h for better performance. Remark 1 In order to perform modular arithmetic, we consider q occupying the same number of word blocks with p. And h occupies half of the wordsize.

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Table 2 Comparison of modular exponentiation running time Modular exponentiation Computational efficiency ð3=2n2 þ 1120tÞtm þ ð32tÞtd 3nðn þ 1Þðt þ 1Þtm ð3=2n2 tÞtm

Classical Montgomery ENP

Extra computation – Precomputation Right shift and Subtraction

Table 3 Comparison of the average running time of modular arithmetic operation Modular multiplication (ls) Modular exponentiation (ls) Classical Montgomery ENP

wt(q) = 2

wt(q) = 5

wt(q) = 2

wt(q) = 5

6 11 2

19 33 6

3992 1097 1042

37510 12612 10338

* The bitsizes of p are 700 where wt(q) = 2 and 1649 where wt(q) = 5

If squaring is approximately as costly as an arbitrary multiplication then the expected amount of work is represented in Table 2.

2.2 Experimental Comparisons In this subsection, we compare practical performance of modular arithmetic with a modulus ENP and NIST prime family through experimental result. We find 32*2048 bit primes belong to ENP family with wt(q) = 2*5 and restrict the range of q and h (recall remark 1). We select two ENPs with respectively wt(q) = 2 and wt(q) = 5. Two ENPs are p700 where q = 2702- 1, h = 3 and p1649 where q = 21664 ? 21184 ? 2896-2288 - 1, h = 65531. We generate 700 and 1649 bit random prime numbers for classical and Montgomery algorithm. We compare the running time of modular arithmetic operation with a modulus each bit prime for two random numbers in Table 3. We performed modular arithmetic operation a million times and obtained the average running time for each algorithm. We used OpenSSL crypto library which has been used for implementing of a wide range of cryptographic algorithms. The target architecture was assumed to have a 32-bit word length, the actual timings being implemented on Intel(R) Core(TM) i7 CPU 860 @ 2.80GHz processor. We know that ENP family can provide quite fast modular arithmetic operation in spite of extra operation from Table 3.

3 The Number of ENP Our contribution is the provision of primes satisfying two properties below. • Primes of bitsize that NIST prime family can not support • Primes have the merit of modular reduction as NIST prime family

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Table 4 Comparison of the number of primes Primes\The number of powers of 2 2

3

4

5

Prime fieldsà

NIST prime family ENP family 

45 91

472 911

23119 88799

130 1006

11 84

  : The number of ENP according to corresponding wt(q) à : The number of fields can be applicable

Thus the number of ENP with different bitsize is crucial to our work. As previously stated, we restricted the range of q and h. Table 4 presents the number of 32*2048 bit ENP with wt(q) = 2*5. Comparing NIST prime family, there exist relatively many ENP and they can be used for prime fields with various size.

4 Conclusion In this paper we defined ENP family which is a new family of moduli suitable for cryptography by further extending the idea of NIST prime family. ENP can provide fast modular reduction in prime fields that NIST prime family can not support. We explained how to perform modular arithmetic with a modulus ENP, and we explicitly provided experimental result. We can expect that ENP are very useful for elliptic curve cryptosystems based on prime field.

References 1. Crandall R (1992) Method and apparatus for public key exchange in a cryptographic system. US Patent, 5,159,632 2. Menezes AJ, Oorschot PC, Vanstone SA (1997) Handbook of applied cryptography. CRC Press 3. Montgomery PL (1985) Modular multiplication without trial division. Math Comput 44(170):519–521 4. Solinas J (1999) Generalized mersenne numbers. Technical report CORR 99-39, center for applied cryptographic research, University of Waterloo, Waterloo

High Attack Cost: Hash Based RFID Tag Mutual Authentication Protocol Jung-Sik Cho and James J. (Jong Hyuk) Park

Abstract Radio Frequency Identification (RFID) systems are expected to replace barcode systems in object identification fields in the future, but various security problems are obstructing their diffusion. This paper proposes hash-based RFID tag mutual authentication protocol to solve these security problems of RFID systems. The proposed protocol is designed to demand high cost of acquiring the tag information from attackers. Keywords Hash

 Mutual authentication  RFID

1 Introduction Radio Frequency Identification (RFID) systems are automatic identification systems using radio frequencies, and consist of RFID tags, RFID readers, and a back-end server [1]. Tags store the unique identification information of objects and are attached to the objects to be identified. Reader requests unique identification information from a tag using a radio frequency and the tag returns the stored identification information using the radio frequency sent from the reader. The reader then sends the identification information received from the tag to the back-end server. The back-end server manages in its database (DB) the information of objects with attached tags and

J.-S. Cho Chung-Ang University, Seoul, Korea e-mail: [email protected] J. J. Park (&) Seoul National University of Science and Technology, Seoul, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_11,  Springer Science+Business Media B.V. 2012

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the unique identification information of the objects, and supplies the information of the objects when a reader requests it [1, 2]. Typical security problems that RFID systems must solve are privacy infringement and forgery. These security problems originate from the objects (tags, readers, back-end server) of the RFID system and information leakage during the communication process [3]. Many studies are being conducted to address these security problems of the RFID systems, which are largely divided into studies on the blocking of tag identification itself [3] and studies on the use of tag authentication protocols [3–6]. Blocking tag identification itself presents the potential loss of usefulness and management efficiency and may be abused by attackers. On the other hand, the tag authentication protocols allow the components of the RFID system to authenticate tags by processing and exchanging information in accordance with the defined procedure through cryptographic primitives. As only the valid components can generate and confirm information, the participation of invalid components is restricted and information leakage is prevented. This technique is most actively studied in diverse directions because it enables many variations. This paper reports on a study about a tag authentication technique using hash functions. There have been many difficulties in applying hash functions to lowpriced tags, but as with many other studies on hash-based techniques, this paper also assumes that hash functions can be applied to tags in the near future.

2 RFID System Security and Related Studies The primary reason that RFID systems cause security problems is the limited resources of tags. Tags do not have their own power source, and consist of a small microchip and an antenna. When a reader requests it in accordance with a predefined procedure, the tag returns the stored unique identification information to the reader. The tag generates power using the radio frequency delivered through the reader’s request and performs limited operations before returning the unique identification information stored in the microchip to the reader. In this process, the tag information is leaked by various attacks by attackers [4]. Types of attacks by these attackers include attacks for privacy infringement such as wiretapping, analysis of communication content, and location tracking, and attacks for tag forgery such as replay attack, spoofing attack, and physical attack [6]. Many techniques for security of RFID systems are being proposed, and the security level of these techniques is evaluated by measuring the satisfaction of security requirements in general. The security requirements mentioned here are defined as confidentiality, indistinguishability, forward security, and mutual authentication [3, 4]. Among the RFID security techniques, hash-based techniques offer a high level of security. However, they are regarded as impractical because as the security level becomes higher, the implementation and performance of tags get more unrealistic and the efficiency of back-end server drops. As a typical example, the technique

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proposed by Ohkubo [7] has high a level of security, but the cost of searching tags by the back-end server is very inefficient. On the other hand, the hash lock technique [8] is sufficiently practical in the possibility of tag implementation and the efficiency of back-end server, but it has many weaknesses in security. This technique is particularly vulnerable to indistinguishability. Many modifications of these two techniques have been proposed later. However, they share common problems in their basic forms. When attackers make specific formats of requests or meaningless requests to tags, the tags make the same response or their information is easily leaked [8]. Synchronization is another problem that needs to be solved, but techniques addressing this problem also show security vulnerability [9, 10]. It seems that this problem originates from the usage of random numbers which are used to satisfy the security requirements of RFID systems [6]. Therefore, this paper proposes a technique to solve this problem.

3 Proposed Technique Most RFID tag authentication techniques use random numbers for authentication by readers and protection of tag information. These random numbers, however, are easily exposed because they are transmitted through radio frequencies without any processing. Thus, attackers can obtain them with simple means such as wiretapping and use them to obtain information that they want. To address this problem, this paper proposes a technique to prevent the exposure of tag information by processing the random numbers of readers and tags.

3.1 Notations and Arithmetic Operations The notations used in the proposed technique are defined as shown in Table 1 below. The size of each value is assumed to be 96 bits. To process random numbers, the proposed technique presents the following Eq. 1: Fis ðRÞ ¼ ðR  R mod s þ 1Þð0 : 47ÞjjðR þ s  R mod sÞð48 : 95Þ ¼ RIDi

ð1Þ

The argument in Eq. 1 is a random number. The random numbers are grouped on the basis of the secret value S, and the MSB 48 bit of the minimum value and the LSB 48 bit of the maximum value of each group are combined. The role of the secret value s is to define the range of each group when random numbers, which have been entered as argument, are grouped. The secret value s can be equal for every tag. That is, it is not unique for each tag. Tags have two secret values: St and Sr . The secret values used in Eq. 1 are determined by the following Eqs. 2 and 3 depending on the random numbers that are entered as argument.

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Table 1 Notations Notations R IDk , DATAk S h(), a Fyx ðzÞ, RID

Descriptions This means a random number. Rr is a random number generated by reader, and Rt is the random number generated by tag k ID is the unique identification information (ID) stored in tag k. DATAk is the information of the object attached with the tag k This is the secret value shared by back-end server and tags. Each tag has two secret values: Ss ; St h() is the hash function and a is the hashed value Fyx ðzÞis a function that has z as an argument and performs operation based on x, y is the communication session information. RID is the result value

r

Fis ðRr Þ ¼ RIDri t

Fis ðRt Þ ¼ RIDti

ð2Þ ð3Þ

Secret values do not have ‘0’ and ‘1’. This is to consider the grouping of random numbers.

3.2 Proposed Protocol The proposed technique proposes the protocol as shown in Fig. 1 below. Each step of this protocol is described below. Phase 0: Information sharing between back-end server and tags – The back-end server stores and manages the following tag information in DB: – – – –

DATA: Information of the tagged object. ID : Unique identification information of the tag Sti ; Sri : Secret values currently shared by tag and back-end server Sti1 ; Sri1 : Secret values of the previous session shared by tag and back-end server

Phase 1: Reader’s request – The reader generates a random number (Rr ) in the session and requests tag information using this number. Phase 2: Generation of response message – The tag generates a random number (Rt ) and also generates RIDti using Eq. 3. – The response message is created using the hash function as shown in the following Eq. 4:

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Fig. 1 Proposed protocol

a ¼ hðIDk  Rt  Rr  RIDti Þ

ð4Þ

Phase 3 and 4: Transmission of response – The tag sends the response message (a) generated from Eq. 4 and the random number (Rt ) to the reader. – The reader sends the response message received from tag and the random number generated in the current session to the back-end server. Phase 5: Tag authentication and secret values update – Back-end server searches the tag in the DB based on the information received from reader. – When the tag is found, the back-end server updates the secret values of the tag. The secret values of this session are stored in Sti1 and Sri1 , and the newly updated secret values are stored in Sti and Sri . – If the back-end server fails to find the tag, it searches again using the secret values (Sti1 ,Sri1 ) of the previous session. If no tag is found, it is judged as abnormal authentication message and the session is terminated. – When the tag searching, authentication, and update of secret values are completed, a message is generated with the following Eq. 5:

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DATAk jjhðRIDri  RIDti ÞjjRIDri  Stiþ1 jjRIDti  Sriþ1

ð5Þ

– The components of Eq. 5 are as follows: – Information of the tagged object (DATAk ) to be sent to the reader. – A message to provide authentication of the back-end server to the tag (hðRIDri  RIDti Þ) – A message to provide authentication of the back-end server to the tag (hðRIDri  RIDti Þ) – A message to safely deliver the newly updated secret values to the tag (RIDri  Stiþ1 jjRIDti  Sriþ1 ) Phase 6 and 7: Delivery of message generated by back-end server – The back-end server delivers the message generated through Eq. 5 to the reader. – The reader acquires the information of the tagged object from the message received from the back-end server and sends the remaining message to the tag. Phase 8: Back-end server authentication and secret values update – The tag authenticates the back-end server based on the message received from the reader. – The tag generates RIDri by calculating Eq. 2 with the random number (Rr ) received from the reader. – The tag performs hash using RIDti and RIDri that have been generated. – The tag authenticates the back-end server by confirming that the hashed value is identical to hðRIDri  RIDti Þ that it received from the reader. – When the back-end server authentication is completed, it expels the updated secret values from RIDri  Stiþ1 jjRIDti  Sriþ1 and updates the secret values. – If back-end server authentication fails, the secret values are not updated.

4 Analysis This section analyzes the security level of the proposed technique. Attackers can perform wiretapping to the proposed technique. Information leakage through wiretapping occurs when reader and tag exchange messages through a radio frequency in phases 1, 3, and 7. However, attackers cannot get any tag information just by wiretapping. Even with wiretapping in multiple sessions, attackers cannot get any information of tags and cannot even identify them, because tag information is processed through hash operation, random numbers, and Eq. 1.

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Now we will calculate the cost of attack when the attacker performs an analysis attack of the communication content to acquire tag information from the message obtained through wiretapping. If the attacker obtained a; Rr ; Rt by wiretapping in phase 1 and 3, he will try to estimate the ID and secret values of the tag. At this time, the attacker will perform a brute-force attack and the cost has the complexity of 2192 . If the attacker additionally performed wiretapping in phase 7, the cost of finding the two secret values also has the complexity of 2192 . This is a very high cost compared to the highest cost 296 when the attacker performs a brute-force attack to the techniques of the existing studies. By requiring a high cost of acquiring tag information from attackers, the proposed technique satisfies all the security requirements for RFID systems as described below. • Confidentiality: The proposed technique protects the ID of tags based on hash operation, and the attacker must find the secret values to get it but cannot find them due to high complexity. • Indistinguishability: The proposed technique uses random numbers and grouping of them to make it impossible to predict the message generated by tags in each session. • Forward security: To ensure forward security, the proposed technique is designed in such a way that when the tag information is updated, the back-end server will generate it randomly. • Mutual authentication: The proposed technique provides authentication between back-end server and tags based on secret values, and provides reader authentication based on random numbers generated by reader. The proposed technique solves the user privacy infringement and forgery problems by satisfying all the security requirements as shown above.

5 Conclusion The RFID system is a low-priced non-contact automatic identification technology. Due to the most basic characteristic of using radio frequency with small, chip tags, however, RFID systems has user privacy infringement and forgery issues. To solve these problems, the present paper proposed a hash-based RFID tag mutual authentication protocol. The basic feature of the proposed protocol is grouping of random numbers to address the problem of the exposure of random numbers which are used to protect tag information and provide anonymity, resulting in acquisition of the random numbers by attackers through wiretapping or other means to access tag information. As a result, the proposed technique demands a high cost of brute-force attack from attackers.

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The proposed technique solves the user privacy infringement and forgery problems by satisfying all the security requirements for RFID systems. Future studies will need to find ways to provide higher efficiency of RFID systems. Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology(2011-0024052).

References 1. Finkenzeller K (2002) Fundamentals and applications in contactless smart cards and identification RFID handbook, 2nd edn. Wiley, New York 2. EPC global Inc (2008) EPC Radio-frequency identity protocols class-1 generation-2 UHF RFID protocol for communications at 860 MHz–960 MHz Version 1.2.0. 3. Juels A (2006) RFID security and privacy: a research survey. Sel Areas Commun 24(2):381– 394 4. Yeo S, Kim S (2005) Scalable and flexible privacy protection scheme for RFID systems. In: European workshop on security and privacy in Ad hoc and Sensor Networks—ESAS’05, Lecture Notes in Computer Science, vol 3813. Springer, Heidelberg, pp 153–163 5. Dimitriou T (2005) A lightweight RFID protocol to protect against traceability and cloning attack. In: Proceedings of SECURECOMM’05 6. Cho J-S, Kim S-C, Yeo S-S (2011) RFID system security analysis, response strategies and research directions. In: Ninth IEEE international symposium on parallel and distributed processing with applications workshops, IEEE Computer Society, pp 371–376 7. Ohkubo M, Suzuki K, Kinoshita S (2003) Cryptographic approach to privacy-friendly tag. In: P RFID Privacy Workshop, MIT, Cambridge 8. Weis SA, Sarma SE, Rivest RL, Engels DW (2003) Security and privacy aspects of low-cost radio frequency identification systems. Security in prevasive computing. LNCS 2802:201– 212 9. Liang B (2010) Security and performance analysis for rfid protocols. dissertations and theses collection, paper 52, http://ink.library.smu.edu.sg/etd_coll/52 10. Vahedi E, Ward R, Blake I (2011) Security analysis and complexity comparison of some recent lightweigth RFID protocols. Computational intelligence in security for information systems

Energy-Efficient Multi-Granularity Resource Reservations for Multimedia Services Joohyung Sun and Hyeonjoong Cho

Abstract This work presents the scheduling algorithms to reduce the energy consumption of hard real-time tasks with fixed priorities assigned by a rate monotonic policy and to guarantee that all the I-frames of each video stream meet their deadlines. The proposed scheduling algorithms are called low-power work/ limit demand analysis with multi-granularity (lpWDA-MG/lpLDA-MG) and it is an attempt to reduce energy consumption by incorporating the DVS technique while simultaneously supporting multimedia such as MPEG video stream services. We present results to show several features of the proposed algorithms and show the experimental results by comparing with the existing methods that support MPEG4 streams. The experimental results show that the proposed algorithms tremendously reduce energy consumption by 2–50%.







Keywords Dynamic voltage scaling Low power Multi-granularity Resource reservation Real-time scheduling



1 Introduction Operating System (OS) resource management for real-time and multimedia systems has been an active research area. Resource reservations based on worst-case requirements over-estimates the desired resource requirements of soft real-time

J. Sun (&)  H. Cho Computer and Information, Korea University, Jochiwon-eup, Yeongi-gun, Chungnam 339-700, South Korea e-mail: [email protected] H. Cho e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_12, Ó Springer Science+Business Media B.V. 2012

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multimedia applications, which generally have the following characteristics: (a) highly varying resource consumption rate, (b) large peak-to-average ratio of resource demand, (c) long burst of large requests, (d) tolerance to occasional deadline misses. As a response, Saewong [1] proposed a Multi-Granularity reservation (MG) model. The scheme extends a dimension of the traditional resource reservation such that more detailed Quality of Service (QoS) requirements can be deterministically specified, analyzed and guaranteed. The proposed admission control allocates only the resource budget sufficient to satisfy the given QoS specification, efficiently manages resources across frames to handle fluctuating demands and still maintains the QoS isolation from other reserves and non-real-time tasks. In the meantime, real-time scheduling plays a key role in the low-power design of real-time embedded systems, not only because timing issues are critical, but also because achieving a low-power design is essentially a resource-usage optimization problem. Many scheduling methods have been published (e.g., [2] by Pillai, [3] by Kim, [3], and [4] by Chen). The core idea of these methods is to employ scheduling techniques that can exploit the modern dynamic configuration capabilities of embedded processors, according to the current or expected workload, to achieve energy efficiency. One such capability is dynamic voltage scaling (DVS). To guarantee the timing requirements of real-time tasks even in the worst-case, DVS can utilize only slack times (or idle times) when adjusting voltage levels. The energy efficiency of a real-time DVS algorithm largely depends on how accurately the slack time of each task is estimated. Normally DVS only gets slack from a processor’s idle time or tasks’ early completion. However the MG model can obtain the additional slack times created when the real-time task’s reserve is depleted. Specifically, the amount of depleted task’s workload on each period can be used as the additional slack, which improves the efficiency of the energy savings. In this paper, we propose the real-time DVS scheduling algorithms called low-power work/limit demand analysis with Multi-Granularity reservation model (lpWDA/lpLDA-MG), which are designed to reduce energy consumption by using computed slack while considering the given MG model for MPEG4 streams. The experimental results indicate that the rate monotonic (RM) DVS algorithms combined with the proposed method can reduce energy consumption by 2–50% compared with those of existing algorithms such as multi-granularity reservation (MULTI-RSV). Next, Sect. 2 summarizes related works. Section 2 explains several models for scheduling and presents a motivational example. Section 3 describes the proposed algorithm. Section 4 provides theorems to show the schedulability of the algorithm. Section 5 presents the experimental results. Section 6 concludes the paper.

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2 Models and Objective 2.1 System and Energy Model The multi-granular reserve specification is given by fC; T; Dg; fC x ; ex T g; . . .; fC y ; ey T g; where ex \. . .\ey and 8i; ei 2 Zþ; each tuples specified in [1]. At each granular timer interval (e.g. ex TÞ; a corresponding reserve budget (e.g. Cx ) will be refilled. If the task executes more than the granted amount, then the task’s priority is downgraded to the same level as (or below) that of other nonreal-tasks. This paper focuses on a preemptive hard real-time system in which periodic real-time tasks are scheduled under the RM scheduling policy, where the shorter period task has the higher priority. We suppose that the target processor can continuously scale its driving voltage and clock speed within its operational ranges, ½Vmin ; Vmax  and ½fmin ; fmax ; respectively. A set of n periodic tasks is denoted by T ¼ fs1 ; s2 ; . . .; sn g; where tasks are assumed to be mutually independent. si has a shorter period (i.e., a higher priority) than si if i \ j. Each task si has its own priority prioi , period pi ; worst-case execution time (WCET) wci , and average-case execution time (ACET) aci . The relative deadline di of si is assumed to be equal to its period pi : A task instance is denoted by a single subscript such as sa when no confusion arises. During run-time, we refer to the earliest job of each task that has not completed execution as the current job for that task, and we index that job with cur, for example, Jicur is the current job for task si : We consider Martin’s system-level energy consumption model to derive the energy consumption at a given CPU speed [5]. In this model, when operating at a frequency f, the amount of power that a component consumes is called a component’s dynamic power consumption and denoted by Pd : Pd of CPU is given as S3  f 3 ; where S3 is a constant. In this paper, we only use the traditional energy model. Summing the power consumption of all components, the normalized CPU speed at frequency fi is Si ¼ fm =f : Thus, the energy consumption at a given CPU speed Si is: EðSi Þ ¼ ci  ðC3  S2i þ C2  Si þ C1 þ C0 =Si Þ where ci denotes si ‘s expected execution time at the maximum frequency fm ; C3 ¼ S3  fm3 C2 ¼ S2  fm2 ; C1 ¼ S1  fm ; and C0 ¼ S0 are system-dependent parameters. It is observed that EðSi Þ is a convex function. By Descartes’ Rule of Signs, there is only one value, denoted Slow that minimizes EðSi Þ:

2.2 Objective/Motivational Example There is a special situation in which the real-time scheduling with MG model yields more slack times that can be used to save more energy than the traditional real-time DVS algorithm. In the MG model, when the real-time task’s reserve is

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Table 1 Task sets for motivational example s Reserve

ACET

Act. ETs

1 2

1 0.5

2 1

{2, 4, 4},{8, 20} {1, 5, 5},{4, 20}

depleted, the depleted task’s priority is downgraded to the same level as (or below) that of other non-real-time tasks [1]. Then, the depleted task’s (i.e., the non-real-time task) workload can be added to slack. For the simple explanation of our motivational example, we assume that the slack times only get from the depleted task excluding those from the tasks’ early completion, processor idle time. The next examples show a special situation, which yield additional slack times and two different approaches on how to use the additional slack times are suggested. The example uses the task set in Table 1. Figure 1a illustrates the worst-case RM scheduling without MG model (Reserve) and DVS, neither. Figure 1b illustrates the worst-case RM scheduling on a frequency-scaled processor with MG model (Reserve). In Fig. 1b and c, s1 is depleted at time t = 14, s1 ’s priority is downgraded. Now we determine the amount of slack generated from the depleted task as all of workload: slack2new ¼ wc1 ; and a part of workload: slack2new ¼ wc1  ac1 : In all of workload, s2 ’s slack time includes all workload of the depleted task s1 ; and thus the speed of s2 is scaled down to 0.3333ð¼ wc2 =ðwc2 þ slack2 Þ ¼ 1=ð1 þ 2ÞÞ as in Fig. 1b. Although this method significantly improves the energy efficiency of real-time tasks, it causes negligence of non-real-time tasks, i.e., delayed completions of non-real-time tasks. On the other hand, a part of workload leaves a part of workload of depleted task s1 to itself and the remaining workload of s1 is added to the slack of s2 : Then, the speed of s2 is scaled down to 0.5ð¼ wc2 =ðwc2 þ slack2 Þ ¼ 1=ð1 þ 1ÞÞ as in Fig. 1c. This method advances the completion of non-real-time tasks by sacrificing energy efficiency compared to Fig. 1c. These two aggressive and conservative approaches show the tradeoff between energy efficiency and capability of processing non-real-time tasks.

3 The Algorithm We first propose a simple mechanism for providing voltage scaling while maintaining schedulability of a given real-time task set based on the multi-granularity reserve model. In this mechanism, we select the lowest possible operating frequency that will allow the RM scheduler to meet all deadlines of a given task set. This frequency is set statistically and will not be changed unless the task set is changed. The worst-case response time of a task is the summation of its own computation time and possible preemption from all its higher-priority tasks. This leads to the proof of Theorem 1. The maximum preemption time by si with speed factor in ðt ;t þtÞ time interval ðt0 ; t0 þ tÞ; denoted as Pi 0 0 is given by [1] with modified Ci =Sl :

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Fig. 1 Examples of DVS only considering the MG model

Theorem 1 For a multi-granularity resource reservation system, the worst-case response time for a task si is the smallest solution to the following equation: P ð0;wk Þ ð0;wk Þ where Pj is the preemption time from higher wkþ1 ¼ Ci =Sl þ j\i Pj priority task si in the interval ð0; wk Þ: While Sl is selected in Sl 2 f S1 ; . . .; Snf jS1 \. . .\Snf g where nf is the number of speed factor, the formula in Theorem 1 finds the smallest speed factor, which can be solved recursively starting with w0 ¼ Ci =Sl and terminating when wkþ1 ¼ wk on success or when wkþ1 [ Di on failure. Fig. 2a illustrates the multi-granularity resource reservation non-DVS algorithm (MULTI-RSV). In this figure, s0 and s2 is the real-time tasks and s2 is the non-real-time task. The example of Fig. 2 uses the task set in Table 2. Fig. 2b illustrates these mechanisms, showing sample execution traces under statisticallyscaled RM scheduling. By Theorem 1, the static DVS-MG algorithm is scheduled with the frequency setting as 0.9. Algorithm 1. lpWDA-MG (lpLDA-MG with mm) Compute the available execution time and set the voltage/clock speed for sa 1. Initially, put all tasks into ready Q and, for each task sa ; set uda :¼ dicur :¼ pa ; wrem :¼ lowest granularity execution timeðaÞ; a ðtÞ :¼ wca ; reserve l cur ma l cur m P P da da 2. Initially, compute Ha ðtÞ :¼ a1 ð  wci Þ; NNAa ðtÞ :¼ a1  aci Þ; i¼1 i¼1 ð pi pi 3. When a task sa is replenished, set reservea :¼ lower granularity execution timeðaÞ; set a initial priority to sa ; 4. When a task sa is depleted, set uda :¼ next replenish timeðaÞ; set a priority to sa lower than real-time task; (continued)

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(continued) Algorithm 1. lpWDA-MG (lpLDA-MG with mm) rem 5. When a task sa is activated, l mset lwacur ðtÞm:¼ wca P d p uda Ha ðtÞ :¼ ðwrem  a p a Þ  wci Þ; i ðtÞ þ ð p i

si 2TH a ðtÞ

i

m ðaci þ ð p  pi Þ  aci Þ; i where TaH ðtÞ ¼ f s1 ; . . .; sa1 jprio1 \. . .\prioa1 g 6. When a task sa is completed or preempted, UpdateLoadInfoðTaH ðtÞ; aÞ; 7. When a task sa is scheduled for execution if sa (is real-time task) then slacka ðtÞ :¼ CalcSlackTimeðÞ; mmAa ðtÞ :¼

l

P

si 2TH a ðtÞ

uda

m

l

dcur a pa

else if sa (is non-real-time task) then slacka ðtÞ :¼ 0; wrem ðtÞ

8. set the clock frequency as fclk :¼ slacka ðatÞþwrem ðtÞ  fmax ;   a a wdone mm flimit :¼ max Ai þac ji ¼ 1; . . .; n ; fclk :¼ maxðfclk ; flimit Þ; cur d t i

Algorithm 2. UpdateLoadInfo (C, a) Input: Tasks C and the preempted/completed task index a and the amount of work wdone done for sa in the previous schedule Output: Workloads and reserve are updated to reflect current execution information 10. if (COMPLETION) then 11. if sa (is real-time task) then 12. reservea :¼ reservea  wdone ; uda :¼ uda þ pa ; dacur :¼ dacur þ pa ; end if 13. if sa (is non-real-time task) then dacur :¼ dacur þ pa ; end if l m l cur m d p ð udpia  a pi a Þ  wci ; l m l cur m P d p 15. mm Aa ðtÞ :¼ Aa ðtÞ þ si 2C ð udpia  a pi a Þ  aci ;

14. Ha ðtÞ :¼ Ha ðtÞ þ

16. 17. 18. 19. 20. 21. 22. 23. 24. 25.

P

si 2C

loop from k :¼ a þ 1 until k ¼ n by increasing k Hk ðtÞ :¼ Hk ðtÞ  wrem a ðtÞ; mm Ak ðtÞ :¼ Ak ðtÞ  maxð0; aca  wdone Þ; wrem a ðtÞ :¼ wca ; else if (PREEMPTION) temp :¼ wca  wdone ; loop from k :¼ a þ 1 until k ¼ n by increasing k Hk ðtÞ :¼ Hk ðtÞ  wrem a ðtÞ þ temp; mm Ak ðtÞ :¼ Ak ðtÞ  wrem a ðtÞ þ temp; wrem a ðtÞ :¼ temp;

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Fig. 2 The voltage scheduling examples with MG model in time [60, 66];a MULTI-RSV;b static DVS-MG;c lpWDAMG;d lpLDA-MG

Table 2 Task sets for scheduling example

s

Reserve

ACET

Act. ETs

1 2 3

{1, 3, 3},{4, 30} {1, 4, 4},{4, 40} {1, 6, 6},{5, 60}

0.4 0.4 0.5

0.8 0.8 0.8

3.1 Low-Power Work/Limit Demand Analysis with Multi-Granularity We propose low-power work demand analysis with multi-granularity (lpWDAMG) and low-power limit demand analysis with multi-granularity (lpLDA-MG) as shown in Algorithm 1 with several additional lines marked with mm. Both lpWDA and lpLDA algorithms are based on RM scheduling and the same as MULTI-RSV using the MG model in [1]. That is why lpWDA set the base. In Algorithm 1, the system is first initialized by setting the initial upcoming deadlines (ud), remaining worst-case execution ðwrem Þ; and Ha ðtÞ of each task in lines 1, 2. Ha ðtÞ denotes the estimation of the workload in which higher-priority tasks than sa that must be executed before uda : Whenever a task sa is replenished at time t, tasks’ reserve, priority are updated to be its lower granular execution time and an initial priority assigned by RM rule. When a task sa is depleted at time t, tasks’ uda and priority are updated to be the next replenish time and a lower priority than the other real-time tasks, respectively, in line 4. Whenever a task sa is completed or preempted at time t, wrem a ðtÞ; uda ; and Ha ðtÞ are updated in line 6. In

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line 7, when a task sa is scheduled for execution at time t, CalcSlackTime() computes the available slack for sa according to Hb ðtÞ and Lb ðtÞ; the work required by the lower-priority tasks that were activated before t or will be activated during ½t; da ; where udb is the earliest upcoming deadline with respect to sa : Formally, to describe the slack analysis method using lpWDA-MG, the following notations are defined: TaACT ðtÞ : the subset containing the active tasks at time t is TaACT ðtÞ :¼ f sk jk\a; sk 2 readyQðtÞ; sk is the real-time tasks}, loada ðtÞ : the amount of work required to be processed in interval ½t; da Þ considering only real ðtÞ þ H ðtÞ þ L ð t Þ slack time tasks loada ðtÞ ¼ wrem a a a ðtÞ : the available slack for a sa scheduled at time t. The equation of above terms are decided same on [3, 4]. Consequently, these modifications can get more slack times than that by the existing scheme. Further, this algorithm provides the energy savings. Now let’s show some examples. Figure 2c and d illustrate the real-time DVS algorithm of lpWDA-MG, lpLDAMG respectively. Fig. 2 shows simple and clear examples of the characteristic that gets the slack time from MG. Further, these figures meet all real-time tasks’ deadline. However, lpWDA-MG uses the non-real-time task s1 ’s work in time interval [60, 64] (presented by a diagonal line) to slack for energy savings. Although the task s1 misses its deadline at time t = 64, the real-time task s2 can be scheduled at the low frequency (0.3333) in Fig. 2c. In the same case, lpLDA-MG schedules the real-time task at a frequency that is higher than the frequency of lpWDA-MG, the non-real-time task meets its deadline. The aforementioned algorithms summarize the proposed slack estimation procedure and scheduling considering the MG model. During run time, these algorithms can be executed at every scheduling point such as activation, resumption, completion, and depletion and replenishment of task instances. In estimating the available execution time for the scheduled task, only one instance is examined per periodic task. Therefore, the proposed algorithm does not increase the computational complexity more than the existing online DVS algorithms. The schedule produced by lpWDA-MG and lpLDA-MG guarantees all system deadlines and has a computational complexity of O(n) per scheduling point, where n is the number of tasks in the system.

4 Algorithm Properties/Schedulability Analysis Our schedulability analysis follows an approach similar to that of Saewong [1]. The proposed schedulability test is followed by worst-case response time analysis. In lpWDA [3], they prove the schedulability of lpWDA by using the worst-case response time (WR or WCRT) analysis. We modify the analysis of lpWDA which proves the schedulability of lpWDA-MG. The following theorem proves the schedulability of lpWDA-MG by using the worst-case response time analysis. Note that lpLDA-MG did not affect the schedulability of lpWDA-MG.

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Theorem 2 Given that a set T of tasks is feasible in the RM schedule, the maximum response time of each task in T under lpWDA-MG is less than or equal to its deadline. We almost follow the same Theorem and proof as that in lpWDA [3] except considering the non-real-time tasks. The detailed proof of Theorem 3 is described in lpWDA [3]. Proof Assuming task sa has the highest priority and task sb is a lower priority task with depleted reserve in readyQ. By loada ðtÞ in [3, 4], we get WRa ¼ loada at time t = 0. When sa runs out slacka ; all subsequent jobs of sa have to separately postpone their response times at most slacka unit of times compared to those in their WCET RM schedule. Assuming sc has lower priority than sa and sb is the depleted task, we prove that the length of new WCRT of sc including slacka and the amount of all depleted task’s WCET is less than dc . According to Theorem 2, we have WRWDAMG þ slackaWDAMG  dc c

ð1Þ

The priority of sb is changed temporally to be lower than sc until its next replenish times. Then, WCRT should be newly calculated. The WCRT of sc under lpWDAMG is changed to WRWDAMG ¼ WRnew  wcb c c

ð2Þ

where WRnew is used in the correctness proof of lpWDA [3]. Additionally the slack c times of sa considering the workload of depleted task sb is denoted as slackaWDAMG ðtÞ ¼ slacka ðtÞ þ wcb

ð3Þ

 wcb þ slacka ðtÞ þ wcb  dc By Eq. (1)–(3), WRnew c WRnew þ slacka ðtÞ  dc c

ð4Þ

Later, the same proofs will proceed the lpWDA proof in [3].

5 Experimental Results For simulation-based experiments, we use OMNeT++ [6], which provides a discrete event simulation environment. We use the traditional energy model whose coefficients are configured as S3 ¼ 1:0; S2 ¼ S1 ¼ S0 ¼ 0: In the experiments, the voltage scaling overhead is assumed negligible. The experimental parameters and scenarios of the lpWDA and MG models are enumerated in [1, 3], respectively. In the lpWDA, the reserves of each task can be represented as {{WCET, Period, Period}, {WCET, Period}}, i.e., the hard real-time

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Fig. 3 Normalized energy consumption in a Videophone, b Avionics

tasks can be represented in the MG model. A simple multi-granularity reservefor  MPEG4 video decoder is given by R ¼ ffdtmax ; T; T g; dtavg  gop; gop  T } where gop, T, dtmax and dtavg denote the size of GOP, the frame period, the maximum and average decoding time, respectively. For this case, (m,k)-firm deadline constraint for the stream can be written as mmpeg4 ¼ ðdtavg  gopÞ=dtmax ; gop: As suggested in [7], we assume that the video decoding time of each frame is linearly increasing along with its corresponding frame size. The two following metrics utilized for evaluating the performance of scheduling algorithms include MULTI-RSV, static DVS-MG, lpWDA-MG, and lpLDA-MG: Miss I-frame: the ratio of the number of deadline-missing I-frames to the total number of frames (of all types), Dyn: the ratio of dynamic errors (defined in [8] as the failure of a system to satisfy the timing constraints of at least m frames out of any k consecutive frames).

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Fig. 4 Simulation results for MPEG41

Figure 3 illustrates the experimental results for real-world application task sets in [3]. It shows that lpWDA-MG and lpLDA-MG achieve more energy savings than the MULTI-RSV. The energy efficiency of the proposed algorithms increases as the ratio of BCET to WCET decreases. The proposed algorithms have the same energy efficiency as lpWDA and lpLDA when they schedule real-time tasks as shown in [3]. Additionally, we simultaneously run three streams, Jurassic, News and Lecture with both low workload (RT-U = 0.2, U = 0.45) and high workload (RT-U = 0.35, U = 1.25) as shown in Fig. 4a and b, respectively. RT-U and U denotes the hard real-time task utilization and the total system utilization excluding the video stream utilization. Figure 4 shows the normalized energy 1

In the experiments, Miss I-frame and Dyn do not occur at all methods.

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consumption of each scheduling algorithm when they run multiple video streams. Comparing the energy consumption of MULTI-RSV, it shows that lpWDA-MG and lpLDA-MG reduce energy consumption while Miss I-frame and Dyn do not occur at all. Thus, it confirms that lpWDA-MG and lpLDA-MG reduce energy consumption and at the same time they guarantee the quality of multimedia services.

6 Conclusions There is a special situation in which real-time scheduling with the MG model yields more slack times that can be used to save more energy than that by the traditional real-time DVS algorithm. We propose the real-time DVS scheduling algorithms called lpWDA-MG/lpLDA-MG to reduce energy consumption using computed slack by analysis considering the given MG model. Then, we prove the schedulability of lpWDA-MG using the worst-case response time analysis even if slack is increased by the MG model. The experimental results show that the proposed algorithms reduce the energy consumption by 2–50%. Acknowledgments This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0011534).

References 1. Saowanee S, Ragunathan R (1999) Multi-granularity resource reservations. In: Proceedings of the 26th IEEE international real-time systems symposium (RTSS7 ‘05). IEEE Computer Society, Washington, DC, USA, pp 143–153 2. Padmanabhan P, Kang Shin G (2001) Real-time dynamic voltage scaling for low-power embedded operating systems. SIGOPS Oper Syst Rev 35(5):89–102 3. Woonseok K, Jihong K, Sang LM (2003) Dynamic voltage scaling algorithm for fixedpriority real-time systems using work-demand analysis. In: Proceedings of the 2003 international symposium on low power electronics and design (ISLPED ‘03). ACM, New York, USA, pp 396–401 4. Da-Ren C (2010) Slack computation for DVS algorithms in fixed-priority real-time systems using fluid slack analysis. J Syst Archit, in press, corrected proof, Available online 15 October 2010, ISSN 1383–7621 5. Martin TL, Siewiorek DP (2001) Nonideal battery and main memory effects on CPU speedsetting for low power. IEEE Trans Very Large Scale Integration (VLSI) Syst 9(1):29–34 6. VARGA A (2005) OMNeT ++ Discrete event simulation system. http://www.omnetpp.org/ Accessed 22 June 2005 7. Bavier A, Montz B, Peterson L (1998) Predicting MPEG execution times. n SIGMETRICS/ PERFORMANCE’98, international conference on measurement and modeling of computer systems 8. Hamdaoui M, Ramanathan P (1995) A dynamic priority assignment technique for streams with (m, k)-firm deadlines. IEEE Trans Comput 44(12):1156–1174

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9. Bren M, Xiaobo SH, Gang Q (2007) Transition-overhead-aware voltage scheduling for fixedpriority real-time systems. ACM Trans Des Autom Electron Syst 7:249–264 10. Bernat G, Burns A, Liamosi A (2001) Weakly hard real-time systems. IEEE Trans Comput 50(4):308–321

Single Camera Vehicle Detection Using Edges and Bag-of-Features Syed Jahanzeb Hussain Pirzada, Ehsan Ul Haq and Hyunchul Shin

Abstract Vehicle detection is becoming a necessary part of Automatic Cruise Control (ACC) and Advanced Driver Assistance Systems (ADAS). Our main focus in this paper is on improving the performance of single camera based vehicle detection systems. Edges are one of the main characteristics of an object, which carries most of the information about an object in an image. In this paper, it was observed that horizontal edges are strong feature for vehicle detection. Therefore, we generated initial candidate using Horizontal Edge Filtering (HEF) on canny edge map. These initial candidates are further verified using Bag-of-Features (BoF) with K nearest neighbor algorithm. A threshold is used on differences of histograms of training and test images for matching the vehicles. In this paper, the combination of edges (initial candidate) and bag-of-features (final verification) has improved detection rate significantly as compared to other well known methods. Our method has 96% detection rate on roads inside a city and 98% detection on highways.




Keywords Bag-of-features Canny edge detection K nearest neighbour Vehicle detection system





Horizontal edge filtering


S. J. H. Pirzada (&)
E. U. Haq
H. Shin Department of Electronics and Computer Engineering, Hanyang University, Ansan, South Korea e-mail: [email protected] E. U. Haq e-mail: [email protected] H. Shin e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_13, Ó Springer Science+Business Media B.V. 2012

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1 Introduction Nowadays travelling on vehicles has become a necessity instead of luxury. However at the same time, number of deaths due to the vehicular accidents is also increasing each year. Worldwide vehicles accidents effects every year between 20 to 50 million people [1]. Therefore, developing an advanced driver assistance system (ADAS) is becoming more and more important. A lot of research is going on developing a vision based vehicle detection system [2, 3]. The main goal of vision based vehicle detection systems is to accurately and efficiently identify dangerous conditions [4]. Usually accidents can be avoided by detecting lanes [5], pedestrian [6] and nearby vehicles [7]. In this paper, we have presented a new vehicle detection system for detection of nearby vehicles. Edge is one of the main characteristics of an object, which carries most of the information of an object in an image. That’s why edge information is very important in image processing, particularly, in the area of object detection and recognition algorithms [8]. There are many edge detection methods used for vehicle detection like Canny [9], Sobel [10] and SUSAN [11] edge detectors. Many papers has also compared [12, 13] different edge based methods for different applications. In this paper, based on comparisons [12, 13], we have found canny edge detector as best technique for finding vehicle edges. It was observed that horizontal edges are strong feature for vehicle detection [15]. So we have applied Horizontal Edge Filtering (HEF) to filter the long horizontal connected edges to detect only vehicle from all the edges in the image. BoF is becoming as an important object recognition and classification technique. BoF was derived from ‘bag-of-words’ (BoW) modeling for text retrieval like Google search. BoW technique is a text analysis algorithm discriminating documents by occurrence of particular words that each document contains [14]. In this paper, initial candidates generated using edges are further verified using Bagof-Features (BoF) with K Nearest Neighbour (KNN). BoF is used for verification because this technique verifies the vehicle and non vehicle candidates effectively. In past decade, there has been a lot of research on real-time detection of vehicles. In [15] authors have reviewed most of the techniques. In [15–17] authors emphasize that vertical and horizontal edges are very powerful heuristic for initial candidate detection. Matthews et al. [16] has used edge detection to find vertical edges for detection of left and right position of vehicle. But vertical edges are not very strong heuristic for on road vehicles as compared to horizontal edges. Moreover, some obstacle on road may also have same vertical and horizontal edge profile as of a car therefore we need an additional verification step. Some authors [18, 19] also used shadow of car and combination of horizontal and vertical edges for vehicle detection. Handmann et al. [17] proposed a template based on the observation that the rear/frontal view of a vehicle has a ‘‘U’’ shape (i.e., one horizontal edge, two vertical edges, and two corners connecting the horizontal and vertical edges). Hence, edges prove to be effective candidate to be used for initial candidate detection. Therefore we selected the HEF for initial candidate selection.

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Fig. 1 Edge map generated by canny edge detection for rear view of vehicles

Although using only edges do not provide good detection rate so we combine edge based initial candidate detection with verification using BoF algorithm. The remaining paper is organized as follow. Section 2 introduces to Canny Edge Detection. Section 3 explains the BoF algorithm. Section 4 explains methodology of proposed technique. Section 5 provides experimental results. Section 6 concludes this paper.

2 Canny Edge Detection Canny edge detector performs smoothing with a linear combination of exponential functions and then detects edges by higher order derivative operators. Canny tried to optimize a set of criterion to maximize the performance of already available edge detection methods. First requirement was low error rate, i.e. high response to edges while minimum response to surfaces or planes. Second criterion was that edges should be well localized on the boundaries. Third criterion was to have only a single response to a single edge. First Canny edge detector smoothes the image to remove noise. Next it calculates gradient of the image. Then next step is to perform the non maximum suppression. In this step all those pixels which are not at the maximum are suppressed. The edge map is further improved by hysteresis, which tracks along the non suppressed pixels. Canny edge detector next uses two thresholds. If a pixel’s value is above higher threshold then it is selected as an edge. However, if a pixel’s magnitude is below the second threshold then it is set to zero. In case if magnitude is between the two thresholds then it is set to zero unless it is connected to an edge pixel. In this paper, canny edge detector is used on images with rear view of vehicles as shown in Fig. 1.

3 Bag-of-Feature Algorithm BoF modelling is carried out by constructing a vocabulary of visual words from a set of Scale Invariant Feature Transform (SIFT) descriptors. That is, it should go through several steps; keypoint detection, extraction of SIFT descriptor, and vector

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Fig. 2 Shows the flow chart of main methodology

quantization. Keypoint-based regions are located at salient points such as corners and blobs. Thus meaningful keypoints are selected. Next step is the extraction of SIFT descriptor. SIFT descriptors are extracted from a set of detected keypoints. Then vector quantization is carried out to generate a visual vocabulary using a set of SIFT descriptors extracted from various images. Clustering algorithms such as k-means and k-median are used for vector quantization. After k clusters are generated by clustering, the centre of each cluster becomes a visual word. Therefore, the database of SIFT descriptors should be sufficiently large and various to generate visual words. A feature vector of each image is represented by the occurrence of the visual words; it can take typical histogram of visual words. Histogram expresses the frequency of the visual word in any given image.

4 Methodology In this paper, main methodology involves two main steps, initial candidate generation using edge based method and verification using BoF algorithm as shown in Fig. 2.

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Fig. 3 a Shows region of interest (RoI) generation, b shows canny edge map of RoI image, c Shows results of horizontal edge filtering on an image

4.1 Initial Candidate Generation Initial candidate generation is very important for vehicle detection as whole detection accuracy is very much dependent on it. In this paper, for initial candidate generation, we used HEF of canny edge map. For reducing the computation time, we have selected region where there are cars and roads in the input images. For region of interest (RoI) selection we cropped image from top and bottom only. Canny edge detector is applied on RoI of input image to generate edge map as shown in Fig. 3b. In images with rear view of vehicles it was noticed that long connected horizontal edges are usually belongs to vehicles. Depending on this observation HEF is applied on canny edge response for detection of vehicles. In HEF, each row of whole edge map is searched for at least ten consecutive 1’s. Whenever a number of consecutive ones are found, they are preserved as a long connected horizontal edge. Following this criteria whole image is scanned and only those long connected edges are assigned value 1, while small unconnected edges is assigned value 0. As a result small unconnected edges are neglected and only long connected edges are used. Vehicles rear view generally contains long horizontal edges due to the shape of bumper and back windscreen and roof of vehicle etc. These long edges are located close to each other and also vertically aligned. This information can be used to obtain initial candidate. We have observed that if there are three long edges close to each other and vertically aligned then it can be considered as an initial vehicle candidate and labelling rectangle is generated around this region as shown in Fig. 3c.

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4.2 Verification Using Bag-of-Feature Verification of initial candidate is an important step in our approach, as it reduces number of false matches and improves accuracy of the detection. In this paper, we used BoF algorithm for verification. BoF is one of the most used and successful techniques for object detection. In BoF algorithm, there are three main steps; visual vocabulary generation, training class generation and testing. In the Visual Vocabulary Generation, keypoints and descriptors are generated by using Harris corner detector and Scale Invariant Feature Transform (SIFT) Descriptors [20] respectively. Each descriptor represents a single patch. After patches are formed from all images k-mean clustering [21] is used to cluster similar patches together. After k (k = 500) clusters are generated by clustering, the centre of each cluster becomes a visual word. Visual vocabulary for vehicle and non vehicle class are generated separately. We use 500 images of vehicles from Caltech database and 500 images from random Google links for non vehicle database for vocabulary generation. In the second step training for class generation is performed to generate training histogram of vehicle class and non vehicle class. Same algorithm is followed in training images as of vocabulary generation except the last part of vocabulary generation is replaced by generation of training histograms. In the training cycle 500 images from Caltech vehicle dataset [22] is used for training of vehicle class and many random images (about 500 images) from different Google links is used for non vehicle class. In the third step testing is performed on the labelled images extracted from edge response during initial candidate detection. For test images, histograms are generated using same training class generation algorithm. Then K nearest neighbour algorithm is used for matching the training and test images histograms. If the difference in neighbour distance between histograms of training and test images is less than a threshold then it’s acceptable as a vehicle. Otherwise, the image is considered as a non-vehicle class. A threshold value of 1.50 is used for comparing neighbour distances of histogram; this value is selected by hit and trial method. Then the non vehicle class histogram is matched with histogram of test image and if difference is less than the threshold, then it’s confirmed as a non vehicle. Verification of vehicles double checks the results from edge response and hence improves detection by removing false initial detection.

5 Experimental Results Experiment is performed using MATLAB version R2008b on a PC with Intel Core2 Duo processor with 2 GB random access memory (RAM). In our experiment we extracted images from real life video. The video is captured from camera which is mounted inside a vehicle at a fix place to shoot the road.

Single Camera Vehicle Detection Table 1 Results of vehicle detection S.No 1 2 3 4 5 6

Total vehicle candidates Initial candidates detection Non-vehicles detection Verified candidates Missed vehicles Detection rate

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Image sequence Inside city

Highways

240 234 3 231 9 96%

150 169 22 147 3 98%

(a)

(b)

(d)

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Fig. 4 a shows detection of vehicles in highway, b shows detection of vehicles inside city roads, c shows Wrong detection using inside city roads images, d shows Wrong detection using highway image, e shows undetected vehicles in circles inside city roads. f shows a missed vehicle in a circle which is far from the camera

Table 1 show the results of vehicle detection by our proposed method. The experiment is performed on two different cases, roads inside the city and on highways. Total vehicle candidates represent the amount of total vehicles present

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in all the frames of video. Initial Candidates detection represents total candidates detected. Non-vehicle detection represents the total non vehicle detection, which is removed by verification process. Verified candidates represent the total vehicles detected after verification by BoF method. Missed Candidates represents number of vehicles that are not detected. The missed vehicles are due to overlapping vehicles and large distance of vehicles from target vehicle. Detection rate represent the rate of detection of vehicles. Fig. 4a shows the detection of vehicles in highways and Fig. 4b shows detection of vehicles in city roads. Fig. 4c shows non vehicle and wrong vehicle detection in highways and Fig. 4d shows it in city roads after initial candidate detection. Fig. 4e shows missed overlapping vehicles. Fig. 4f shows a missed vehicle which is far from the camera. There were many overlapping vehicles on roads inside city as compared with on highways. So the detection rate is less on roads inside city.

6 Conclusion In this paper, edge based initial candidate detection is used with bag-of-feature based verification. The combination of two methods has helped to detect all vehicles using edge based method and remove false vehicle detection by verification through BoF algorithm. So, combination of two well known techniques is used for improving the detection rate of vehicles. Our results show around 98% detection of vehicles on highways where there is low traffic and 96% detection on roads inside city where there is high traffic. In this work we have emphasized on day time sunny and cloudy weather conditions. In future, this work will be enhanced for different weather conditions. Acknowledgments This work was supported by the Ministry of Knowledge Economy (MKE) through IDEC Platform center (IPC) at Hanyang University. Moreover, Jahanzeb and Ehsan were supported by ‘Higher Education Commission (HEC) from the Govt. Of Pakistan’ under the scholarship program titled: MS level Training in Korean Universities/Industry.

References 1. World Health Org. World Report on Road Traffic Injury Prevention, [Online]. http:// www.who.int/violence_injury_prevention/publications/road_traffic/world_report/factsheets/ en/index.html 2. Sun Z, Bebis G, Miller R (2005) On-road vehicle detection using evolutionary gabor filter optimization. IEEE Trans Intell Transp Syst 6:125–137 3. Sun Z, Bebis G, Miller R (2006) On-road vehicle detection: a review. IEEE Trans Pattern Anal Mach Intell 28:694–711 4. Trivedi MM, Gandhi T, McCall J (2007) Looking-in and looking-out of a vehicle: Computervision-based enhanced vehicle safety. IEEE Trans Intell Transp Syst 8(1):108–120

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5. McCall J, Trivedi MM (2006) Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation. IEEE Trans Intell Transp Syst 7(1):20–37 6. Gandhi T, Trivedi MM (2008) Computer vision and machine learning for enhancing pedestrian safety in Computational Intelligence in Automotive Applications. Springer, Berlin, pp 59–77 7. Gandhi T, Trivedi MM (2006) Vehicle surround capture: Survey of techniques and a novel Omni video based approach for dynamic panoramic surround maps. IEEE Trans Intell Transp Syst 7(3):293–308 8. Hongna Z, Qi W (2003) Image measuring technique and its applications. J Electr Meas Instrum 9. Canny JF (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–714 10. Russ JC (2002) The image processing handbook. CRC Press, Boca Raton 11. Smith SM, Brady JM (1997) SUSAN- A new approach to low level image processing. Int J Comput Vis 23(1):45–78 12. Musoromy Z, Ramalingam S, Bekooy N (2010) Edge detection comparison for license plate detection. IEEE Trans Control Autom Robot Vis (ICARCV), pp 1133–1138 13. Fan C, Ren Y (2010) Study on the edge detection algorithms of road image. In: Third international symposium on information processing, pp 217–220 14. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: Proceedings international WWW conference computer networks and ISDN systems, pp 107–117 15. Sun Z, Bebis G, Miller R (2004) On-road vehicle detection using optical sensors a review. In: IEEE international conference on intelligent transportation systems, Washington, DC, 3-6 October 2004, pp 585–590 16. Matthews N, An P, Charnley D, Harris C (1996) Vehicle detection and recognition in greyscale imagery. Control Eng Pract 4:473–479 17. Handmann U, Kalinke T, Tzomakas C, Werner M, Seelen W (2000) An image processing system for driver assistance. Image Vis Comput 18(5):367–376 18. Tzomakas C, Seelen W (1998) Vehicle detection in traffic scenes using shadows. Technical Report 98-06. Institutfur Neuroinfor-matik, Ruht-Universitat, Bochum, Germany 19. Srinivasa N (2002) A vision-based vehicle detection and tracking method for forward collision warning In: Proceedings of the IEEE intelligent vehicle symposium, pp 626–631 20. Lowe DG (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vision 60:91–110 21. MacKay DJC (2003) Information theory, inference, and learning algorithms 22. Caltech Cars Dataset. http://www.robots.ox.ac.uk/*vgg/data3.html

A New BiGaussian Edge Filter Ehsan Ul Haq, Syed Jahanzeb Hussain Pirzada and Hyunchu Shin

Abstract Edge detection has been the foremost step in image processing and computer vision, because an edge representation drastically reduces the amount of data to be processed. Although classical methods of edge detection like Sobel, Canny, etc. are simple to use but has a dilemma between noise removal and edge localization. If noise is to be removed by using a low pass filter then edges are blurred. However, if edges have to be preserved then noise severly corrupts the edge map. In this paper, we have proposed a new method of edge detection, BiGaussian edge Filter, which simultaneously removes noise from real life images, while generating well localized edges. We have compared our method using images form Berkely’s segmentation data set. Experimental results show the robustness of our method to noise in real life images.







Keywords Edge detection Gaussian filter Noise removal Bilateral filter Canny edge detector Sobel edge detector BiGaussian edge filter







E. U. Haq (&)  S. J. H. Pirzada  H. Shin Department of Electronics and Computer Engineering, Hanyang University, Ansan, South Korea e-mail: [email protected] S. J. H. Pirzada e-mail: [email protected] H. Shin e-mail: [email protected]

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1 Introduction Recently, understanding contents of images has become a significant objective of image processing and computer vision researchers. An image contains a lot of information to be classified and interpreted by a machine. However, depending upon the application at hand much of this information could be redundant. Therefore, edge detection has been a primary component of a wide range of application such as image enhancement, compression, recognition, retrieval, morphing, etc. A major reason for this being the foremost step of object recognition is also that edge representation drastically reduces the amount of data to be processed. The edges correspond to major geometrical and photometrical changes in image objects and hence provide important visual information [1–3]. Edge detection is a process of identifying and locating these sharp discontinuities in an image. In the ideal case, the result of applying an edge detector to an image should return a set of curves that correspond to discontinuities in surface orientation. Edge detection has been extensively researched for decades in computer vision, but it has still not been matured to an optimal level [1–6]. Although the classical methods of edge detection like Sobel, Prewitt, Robert’s Cross, etc. [7, 8] are very simple to use and detect edges seamlessly in case of synthesized images. However, these methods are highly sensitive to noise in real life images, therefore, failing to achieve any satisfactory results [1], [2], [8]. Moreover, the problem with differential edge detectors is that one has to search for either local extremas (first derivative) or zero crossings (second derivative). In this paper, we have presented a new simple and non iterative method of edge detection, BiGaussian edge detector, that aims to overcome these above mentioned cons of earlier methods. The underlying idea of BiGaussian edge detector is to reduce noise in spatial domain of an image while simultaneously finding edges in intensity range of the image. As a consequence, this filter acts as a standard domain low-pass filter, which averages away small and weakly correlated differences between pixels caused by noise. Moreover, it also simultaneously acts a standard range high-pass filter to highlight differences among neighbouring pixels. Rest of the paper is arranged as follows. Section 2 briefly overviews the theory of edge detection. Then Sect. 3 explains some drawbacks in these earlier techniques. Next, in Sect. 4 some related work is elaborated. Further we have explained our new method of edge detection in Sect. 5. After that Sect. 6 discusses the comparison results and finally Sect. 7 concludes the paper.

2 Theory of Edge Detection First and second order derivatives of an image are the most popular methods of edge detection [2, 3, 8]. An edge can be positive or negative maxima of the absolute value of first order derivative. Alternatively, it is also the zero crossings of

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the second order derivative of the image. Gradient vector magnitude’s maximum value would be at the location of edge, whereas, the angle of gradient vector would be orthogonal to the edge. Edge detection based on second order derivatives are usually performed using the Laplacian, which is actually an approximation of the second order derivative of the image f, along the gradient direction. A simple approximation of the first order derivatives fx and fy is done by calculating the differences along the respective axes as follows fs ðs; tÞ ¼ f ðs; tÞ  f ðs þ 1; tÞ; ft ðs; tÞ ¼ f ðs; tÞ  f ðs; t þ 1Þ

ð1Þ

Where fs(s,t) and ft(s,t) are the approximations of gx and gy around the pixel (s,t). These can also be represented by masks, as follows:     f ðs; tÞ 1 ; ft ðs; tÞ ¼ ½f ðs; tÞf ðs; t þ 1Þ ð2Þ fs ðs; tÞ ¼ ½1  1 f ðs þ 1; tÞ 1   1 Hs ¼ ½1  1; Ht ¼ ð3Þ 1 To avoid the problem of asymmetry usually an odd number of pixels are used, as shown by equation below: 2 3 f ðs  1; tÞ ð4Þ fs ðs; tÞ ¼ ½ þ1 0  1 4 f ðs; tÞ 5 f ðs þ 1; tÞ "

þ1 ft ðs; tÞ ¼ ½f ðs; t  1Þf ðs; tÞf ðs; t þ 1Þ 0 1 " # þ1 Hs ¼ ½ þ1 0  1 ; Ht ¼ 0 1

# ð5Þ

ð6Þ

Several other similar approximations to the first order derivative along different orientations and with various scales have been proposed. To use these operators, a product is performed with the image using following equation, fx ðs; tÞ ¼

X X i

j

f ðs þ i; t þ jÞ : ðHx Þij

ð7Þ

Sobel: 2 1 0 14 Hs ¼ 2 0 4 1 0

3 2 þ1 þ1 1 þ2 5; Ht ¼ 4 0 4 þ1 1

þ2 0 2

3 þ1 0 5 1

ð8Þ

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Prewitt: 2

1 0 1 Hs ¼ 4 1 0 3 1 0

3 2 þ1 þ1 1 þ1 5; Ht ¼ 4 0 3 þ1 1

þ1 0 1

  þ1 þ1 ; H135 ¼ 0 0

0 1

3 þ1 0 5 1

ð9Þ

Roberts:  H45 ¼

0 1

 ð10Þ

These kernels [7, 8] are designed to generated vertical and horizontal edges, with one kernel for each perpendicular direction. All of these are only suitable for well-contrasted noiseless images. Marr and Hildreth [2] argued that an edge detecting operator should be a scalable differential operator, which can compute first or second derivative at different scales. Being scalable would allow large operators to detect blurry edges while small operators to detect fine edges. They achieved these goals using Laplacian of Gaussian (LoG) operator. It was also observed by Marr and Hildreth that LoG can be approximated by Difference of Gaussians (DoG). Although Canny edge detector [1] is better than all the detectors presented so far and is also being used most widely. However, it is also more complex and computationally expensive. Canny presented edge detection as an optimization problem with constraints. His optimization objectives were high signal to noise ratio, well localization of edge points and single edge response. He formulated a mathematical expression for these objectives and then tried to find an optimal solution for that expression. However, due to the fact that it is very difficult to find a closed-form solution satisfying all criterions, he showed that using first derivative of a Gaussian approximation successfully achieves 80% optimality.

3 Drawbacks of Earlier Techniques The classical methods of edge detection are highly sensitive to noise in real life images, therefore, fails to achieve any satisfactory results [5, 6, 9]. To differentiate actual edge gradients from noise gradients, concept of thresholding was introduced. To reduce the effect of this noise in real images, Canny and others first applied a low pass Gaussian filter on the images before calculating the gradient. Although this technique improved edge detection significantly, however it introduced other problems. First, if strength of low pass filter is low then noise is not removed, whereas if strength is increased then actual location of the edges is lost. To find the actual location of edges these algorithms further use some use edge localization techniques, making these sophisticated algorithms computationally very expensive (Fig. 1).

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Fig. 1 Canny Edge maps with different Gaussian strengths. a. Input image. b. More noise with narrow Gaussian low pass filter. c. Original location of edges lost with wider Gaussian low pass filter

Moreover, the problem with differential edge detectors is that one has to search for either local extremas (first derivative) or zero crossings (second derivative). However, techniques for finding either of them are not optimized, hence resulting in a lot false extremas and zero crossings (in case of canny edge detector).

4 Related Work In 1998, Tomasi and Manduchi [10] all introduced a smoothing filter that can also preserve edges by using a combination of domain-range filters. The bilateral filter adaptively adjusts its kernel weights depending upon the geometrical closeness (domain term) and photometric similarity (range term) of the neighboring pixels. If surrounding pixels of the filtered pixel are from the same object having similar intensity, then they are given larger weight. However at the edges where intensity difference is higher, surrounding pixels are weighted lower so that they have less effect on the filtered pixel. A standard bilateral filter is expressed as follows: Jp ¼

  1 X Iq f ðjjp  qjjÞg jjIp  Iq jj q2X Kp

ð11Þ

where p is the pixel being filtered with intensity Ip and q is the surrounding pixel with intensity Iq. Jp is the resulting pixel in the filtered image, Kp is normalization factor and X is the set of surrounding pixels in the window. In the above expression, function f(||p - q||) is called domain term, while g(||Ip – Iq||) is called range term. Generally, both f(||p - q||) and g(||Ip – Iq||) functions are taken to be Gaussian functions. In this paper, we have used the similar concept of bilateral filter to overcome the above mentioned drawbacks of earlier techniques and have introduced a new method of edge detection namely, BiGaussian Edge Filter.

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5 BiGaussian Edge Filter BiGaussian Edge Filter detects edges, while preserving their original location and reducing noise simultaneously. It achieves this by means of a nonlineaer combination of nearby image values. Unlike other methods, it is noniterative and simple. Furthermore, our strategy detects edges based on photometric similarity and geometric closeness. The underlying idea of BiGaussian edge detector is to reduce noise in domain of an image while simultaneously finding edges in range of the image. Two spatially close pixels can be compared perceptually. Spatial closeness means that they are located in same neighbourhood (close in domain). On the other hand, perceptual similarity means they are similar in intensity (close in range) that is a smooth surface, whereas, perceptual difference indicates an edge. In our method, we have exploited this information to device a new edge detector. To reduce noise, a Gaussian function of the Euclidean distance between its arguments can be applied to image in spatial domain, as follows:  2 gðp  qÞ ¼ e

12

jjpqjj dd

ð12Þ

whereas, simultaneously, to detect edges an Inverse Gaussian function of the Euclidean distance between its arguments can be applied in the intensity range as follows,  2   1 jjIp Iq jj ð13Þ h Ip  Iq ¼ 1  e2 dr When combined the final equation would becomes as follows, Jp ¼

  1 X I gðp  qÞh Ip  Iq q2X q Kp

ð14Þ

Where, p is the pixel under consideration and q are the surrounding pixels in neighbourhood. Ip and Iq is the intensity of pth and qth pixels. This method of noise removal with one Gaussian on domain and edge detection with another Inverse Gaussian on range will be denoted as BiGaussian edge filtering. It updates the value at p with an average of close and similar pixels. In a small neighbourhood, one Gaussian is applied on a same small neighbourhood in spatial domain to reduce noise. In same region, in case of smooth neighbourhood, pixel values are similar; therefore, Inverse Gaussian working on similarity assigns approximately zero weight to such pixels. On the other hand, consider now the case of sharp boundary in the small neighbourhood between a dark and bright region. When BiGaussian edge filter is centred on a pixel on bright side of the boundary, the similarity Gaussian function will assign higher weights to pixels on dark side and approximately zero weights to pixels on bright side. Conversely, when filter is centred on a dark pixel then bright pixels are weighted more instead.

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Fig. 2 Results of applying different edge detectors on synthesized image without noise. a. Input image without noise. b. Canny edge detector. c. BiGaussian edge filter

As a consequence, this filter acts as a standard domain low-pass filter, which averages away small and weakly correlated differences between pixels caused by noise. Moreover, it also simultaneously acts a standard range high-pass filter to highlight differences among neighbouring pixels. The width of Gaussian function in the domain is selected based on desired amount of low pass filtering. Similarly, the spread of Gaussian function on the range is set to achieve the desired amount of thickness of edges, or, the desired amount of combination of pixel values in the neighbourhood.

6 Results Evaluation of edge detecting methods is a very challenging task [5], [6], [11]. For our purpose, we have evaluated our method using both computer generated and real life images. Since Canny edge detector gives most promising results [1], [9], therefore in this paper we have shown explicit comparison with Canny edge detector only. To verify our method, first we tested it on a simple computer generated image. In case of no noise, both Canny and BiGaussian Edge detectors generated the precise edge map, as shown in Fig. 2 below. Next, we corrupted the image with Gaussian noise. In this case, when canny edge detector was applied with narrower low pass Gaussian filter then it failed to remove noise, as shown in Fig. 3b. On the other hand, increasing the strength of low pass Gaussian filter destroyed the edge’s location, as shown in Fig. 3c, where corners of the box are lost and are rounded off. However, Fig. 3d shows the result of our BiGaussian Edge filter, which contains noiseless and well localized edge map. To verify our scheme on real life data, we have used images from the Berkeley segmentation data set [11]. The table below shows the result of applying Canny and BiGaussian edge detector to images from Berkeley segmentation data set. Once again even with real life image to obtain a noiseless edge map Canny edge detector has to smooth image, which in turn destroyed the fine details. As shown in Fig. 4b, tops of both minarets and tomb are rounded off. Moreover, edges at boundaries of minarets have also moved outward. In case of BiGaussian edge

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(b)

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Fig. 3 Results of applying different edge detectors on synthesized image with noise. a. Input image with noise. b. Canny edge map with with light noise removal. c. Canny edge map with high noise removal. d. BiGaussian edge filter

(b)

(a)

(c) Fig. 4 Comparison with real life images. a. Input image. b. Canny edge detector. c. BiGaussian edge detector

filter, these artifacts have been removed. Note that the result of canny edge detector is after Non maximal suppression and Hysteresis thresholding. However, we have not applied these techniques to our method to show the explicit comparison. To obtain well connected edges, an edge linking technique can be applied.

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7 Conclusions Earlier techniques of edges detection have contradiction between noise removal and edges’ localization. In order to obtain desirable results, one has to fine tune numerous parameters for a specific applications. If strong noise removal filter is used then original location of edges is lost. Otherwise, if original location has to be preserved then noise corrupts the edge map. In this paper, we have presented a BiGaussian Edge filter that can overcome this dilemma while maintaining the detection performance. Experimental results, both on synthesized images and on famous data sets have shown that our method outperforms Canny edge detector. BiGaussian edge detector successfully generates noiseless edge map, with edges at their exact position. Moreover, this method is computationally less expensive as it is non iterative and doesn’t search for extremas or for zero crossings. Acknowledgments This work was supported by Ministry of Knowledge Economy (MKE) through IDEC Platform center (IPC) at Hanyang University. Moreover, Ehsan and Jahanzeb were supported by ‘Higher Education Commission (HEC)’ from the Government of Pakistan under the scholarship program titled: MS level Training in Korean Universities/Industry.

References 1. Canny J (1986) A computational approach to edge detection, transactions on pattern analysis and machine intelligence 679–698 2. Marr D, Hildreth E (1980) Theory of edge detection London. Proc Royal Soc Ser B 207(1167):187–217 Feb 29 3. Basu M (2002) Gaussian-based edge-detection methods—a survey. IEEE Trans Syst Man Cybern 4. Torre V, Poggio T (1986) On edge detection. IEEE Trans Pattern Anal mach intell arch 8(2). March 5. Bowyer KW, Kranenburg C, Dougherty S (1999) Edge detector evaluation using empirical ROC curves, computer vision pattern recognition (CVPR ’99). Fort Collins, Colorado. June 6. Heath M, Sarkar S, Sanocki T, Bowyer KW (1997) A robust visual method for assessing the relative performance of edge detection algorithms. IEEE Trans Pattern Anal Mach Intell 19(12):1338–1359 7. Matthews J (2002) An introduction to edge detection: the sobel edge detector. http:// www.generation5.org/content/2002/im01.asp 8. Gonzalez RC, Woods RE (2010) Digital image processing, 3rd edn. Prentice Hall, Ohio 9. Juneja M, Sandhu PS (2009) Performance evaluation of edge detection techniques for images in spatial domain. Int J Comput Theor Eng 1(5). Dec 10. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of the sixth international conference on computer vision, pp 839–846 11. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the eighth IEEE international conference on computer vision 12. Tagare HD, deFigueiredo RJP (1990) On the localization performance measure and optimal edge detection. IEEE Trans Pattern Anal Mach Intell 12(12):1186–1190 Dec

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13. Huertas A, Medioni G (1986) Detection of intensity changes with sub-pixel accuracy using Laplacian-Gaussian masks. IEEE Trans Pattern Anal Mach Intell PAMI- 8(5):651–664 14. Argyle E (1971) Techniques for edge detection. Proc IEEE 59:285–286 15. Shin M, Goldgof D, Bowyer K, Nikiforou S (2001) Comparison of edge detection algorithms using a structure from motion task, IEEE Trans syst man cybernetics—part B: cybern 31(4). Aug

Self-Healing in Smart Distribution Networks Using Data Exchange Model and Reliable Multicast Protocol I. Dzafic, N. Lecek and T. Donlagic

Abstract Manual interventions are most common way of handling today’s power networks. Replacing manual interventions with a sophisticated algorithm which would be able to recognize the problem and solve it in an optimized time, is in the very near future, and will allow the network to be more flexible, and prevent network operators from making a mistake. The approach described in this paper shows that, by enabling data exchange between different substations, and by exploiting substations unused resources, it is possible to obtain a self-healing distribution network in which risks of manual interventions are reduced to minimum.



Keywords Self-healing Data exchange Distribution networks Control centers



 Substations  Network

topology



1 Introduction Distribution network has experienced a bloom in recent years thanks to the Smart Grid idea. Smart networks are the future core of the distribution power system, and therefore many investors and researchers are involved in its establishment. One I. Dzafic (&)  N. Lecek  T. Donlagic Siemens AG, Humboldtstrasse 59, 90459 Nuernberg, Germany e-mail: [email protected] N. Lecek e-mail: [email protected] T. Donlagic e-mail: [email protected]

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important goal of Smart Grid is to introduce a network capable of healing itself without operator’s involvement, i.e. ‘‘self-healing network’’. The concept of selfhealing brings the network reliability and thus, high quality service to consumer. Some of the self-healing network approaches [1, 2] have already been tested and applied in real systems. Others are described through published papers [3, 4]. Reference [5] gives historical background and explains necessity of self-healing network. Reference [1] offers a very good review and examples of self-healing technologies present today. However, these approaches are currently developed only for specific smaller independent electric systems. Most existing approaches [6] describe the concept of agents based self-healing network in which intelligent agents, distributed over network, communicate and share information mutually reconfiguring the local system according to its needs. The approach is still theoretical and is under research at present. Solution described in this paper introduces model of self-healing in distribution networks that could be used as a general solution in future smart power systems. Substations local control centers (LCC) perform local network application calculations, configuration analysis and control and distribute results to neighbor LCCs. Data exchange model based on Pragmatic General Multicast Protocol (PGM) is applied as a communication channel between substations in case of a network configuration change. Detailed algorithm of the solution is described. The algorithm is being implemented at the moment, and first results are expected in the near future.

2 Distribution Network Topology and Control Distribution network has a major role in the power system. It supplies consumer directly and therefore its healthiness is of great importance. To keep the network running properly, one has to be aware of its configuration. Since distribution network is prominent to configuration changes, it is hard for the operator to keep track of all occurred events and handle the network correctly. As a consequence, very often, attempted manual interventions lead to a mistake, and the price in those cases is paid much more than losing expensive equipment or a profitable customer, it is paid with human lives. Allowing network reconfiguration operations to be executed automatically, can release network operators from most of their work. In such system they may need to take control only in very exceptional cases.

2.1 Present Distribution Network Control Today’s distribution networks are controlled by an operator who monitors and controls the network from the Control Center (CC). CC collects all data about the

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current network state from the substations’ local control centers. SCADA (supervisory control and data acquisition), on the other hand, collects all field data, and serves LCCs. Based on the disposable data, operator, with the results of the distribution network applications (DNA) contributed by CC, makes an analysis, and when necessary, manually sends requests for configuration change in order to correspond to the new network state. This kind of network control obliges full responsibility to the operator in order to answer network problems. Sometimes, this leads to a time consuming tasks, and sometimes an error. Every circuit breaker operation is a risk of its kind, and should be thoroughly analyzed before deciding upon it.

2.2 Local Control Center as a DNA Solver Each network configuration change is followed by data exchange, first between field devices and LCCs, then between LCCs and CC. Having the network data, CC analyzes the complete network configuration. In this way, great amount of data is being processed and results in time consuming operations. Substation’s LCC has only one purpose—to enable data exchange between SCADA and CC. If LCC’s resources would be used for local DNA calculations as well, LCC would not have to exchange network configuration with CC, and thus CC would not have to handle the analysis of the complete network. Having LCCs to analyze only its network configuration would allow application of parallel computing on a large distribution power networks. However, to make this solution implemented in practice, another way of communication, between substations’ LCCs, has to be established. When a network configuration change occurs, affected substations have information about the change per se, but only for the area each is primarily supplying. In order to be able to understand new network configuration, affected substation should be notified about changes in neighboring substations as well. This requires careful design, implementation and deployment of communication software. Each LCC needs dedicated communication software to send data and requests to other LCCs and to receive data and requests from other LCCs.

3 Communication Patterns and Protocols In Smart Grid, LCCs are not monitored and controlled by central, global Control Center. From software architecture point of view, Smart Grid is decentralized, distributed system in which every node (LCC) communicates with neighboring LCCs. The distance between neighbor LCCs results in low network bandwidth and higher network latencies [2]. In case LCCs are inter-connected and communicate using Wide Area Networks (WANs) typical network bandwidth is in range 0.010–600 Mbps and network latency is 100–500 ms.

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The communication software should take into account the nature of such system. As there is no central server, client–server communication system is not an option. LCCs are communicating in similar way as peer-to-peer applications [7]. The key issue is how to figure out that other LCCs are available in neighborhood. Thus, appropriate communication protocol, that supports publish-subscribe communication pattern, is needed. OSI model [8] defines seven protocol layers. Applications use Application Layer (Layer 7) protocol. As name suggests, such protocols are used by application processes and are application specific. The protocols are layered. Higher layer uses only services of layer below. For example, in case TCP is chosen as Layer 4 (transport) protocol, it has impact on application level protocol as well. TCP is connection-oriented, reliable, ordered and bidirectional, unicast, transport protocol. User Datagram Protocol (UDP) [9] is an example of connectionless and unreliable transport protocol. UDP supports broadcast and multicast data delivery. For communication in Smart Grid solutions, transport protocol which combines following properties of TCP and UDP is needed: connectionless, reliable data delivery, ordered data delivery, send multicast, broadcast and unicast, unicast response. The issue considered in this paper is how to reliably and with high-performance deliver data from n data sources (LCCs) to m data consumers (LCCs) in case one application (one data source) sends data to n listeners (data consumers) dispersed within WAN. In cases more than one listener is involved, unicast solutions, based on reliable protocol such as TCP/IP protocol, are not practical. TCP/IP is connection-oriented, bidirectional, reliable protocol and transferring data from source to destination requires connection management. Furthermore, TCP/IP v4 does not support multicast and in case TCP/IP is used to deliver data to n listeners, the same data should be send over network n-times. This increases network traffic and leads to network problems such as network congestion and jitter. The net result of such naïve approach is degraded quality of service. Listeners process data with different performances, and in case of TCP/IP based data delivery, data provider has to handle these delays. This is possible to handle in case number of listeners is small and data amount transferred in data notifications are small. However, the ideal solution for data delivery to n listeners would be to use reliable multicast transport protocol that is connectionless, i.e. does not require connection management. User datagram protocol (UDP) is an example of connectionless protocol that support efficient multicast of data packets. With single data transmission, packet reaches members of multicast group. Considering only user data to be transferred, the data transfer of m bytes to n receivers over TCP/IP results in network transfer of (n 9 m) bytes, compared to m bytes over UDP. However, since UDP is unreliable protocol, packets could be lost, delivered out of order, or duplicated. The problem with direct usage of UDP in applications is that power applications normally require ordered and reliable data delivery. In case UDP is used as transport protocol and that applications must use reliable data delivery, these

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4 Data Exchange Model for Smart Operations Algorithm, as in Fig. 2, makes an overview of the data exchange model for distribution networks. Detailed explanation of algorithm is described through following steps: (1) In planning mode, initial default network configuration plans are distributed over all substations’ LCCs either by CC either by distribution center. Each substation receives only configuration data of the network part it is

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supplying. Normal-open points are used as a distinction between different suppliers.1 Assuming every network device’s initial status measured, SCADA sends the data to each substation’s LCC, and thus, initial network configuration is confirmed. In case of initial network configuration change, substation’s LCC is notified again through SCADA. New network configuration analysis is started. Switches statuses are being examined, and if their changed statuses caused network to be distributed differently then in its default state, substations’ LCCs have to be updated accordingly. Due to the fact that substations have only access to default network configuration, communication between different substations has to be established in order to start the information sharing process. After acquiring necessary data, new network state is recognized and set in each LCC. Each additional change in network state is followed by new data exchange between substations.

Example of distribution network, as in Fig. 3a, can be used for practical demonstration of the proposed data exchange approach. Four substations are supplying four independent configurations (C1, C2, C3, and C4 on Fig. 3a). Margin between configurations is presented with the dashed line. Substations work in parallel and serve as a backup one to another. Reconfiguring the default network such that CB2 changes its state, followed by state changes of DIS1 and DIS2, substations start analysis of the new configuration. LCC2 analysis shows that C2 now includes network elements that have previously belonged to C3. The data about these elements are not known to the LCC2, and therefore it has to request data exchange with LCC3. After the data exchange process, LCC2 can apply new network data in establishing stable configuration. If, for some reason, DIS4 needs to be closed, LCC1 has to first determine the status of DIS5 by communicating with LCC2. Only if DIS5’s status is set to open, LCC1 can close DIS4. Similar, when closing DIS3, the LCC that whishes to close it (LCC3 or LCC4) has to first exchange information about statuses of the disconnector DIS6 or CB4. 1

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5 Self-Healing in Smart Networks Data exchange model previously described requires network state pre-analysis by the operator. Operator is the one that, after network configuration change, based on the DNA applications results, can conclude about the possible new network state and apply switching procedure which leads to it. The switching procedure, which leads to data exchange algorithm execution, is a missing part that could make selfhealing networks possible. The point of self-healing process is to optimize the energy supply process in case of a network configuration change. Network adaptation after the configuration change is a redundancy dependent action. If the network has good linkage to other network configurations it is very likely that the network can withstand many different ways of supplying. Other attribute that makes network capable of selfhealing is power reserve. If there are enough power reserves in the system, network can support additional loads when required. Determination of power system reserves is an important process in preserving a stable network condition. System stays in a stable condition when power generation equals power consumption. Having an overproduction or underproduction, can make a system unstable and lead to a total system breakdown. Basically, system reserves are calculated as a difference between actual power consumption and maximum power generation in system:

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routes to open is recognized by the main LCC. In order to establish new routes, some network elements, which are not under control of the main LCC, but by the LCCs of other substations, connected to main LCC, have to be manipulated. Therefore, switching procedures are sent to these substations. Data is sent to all LCCs in the network (PGM), but only those LCCs that are related to the message, accept the data. Upon acceptance each substation’s LCC analyzes the request, and recognizes if there are new network elements beyond its configuration. Requested LCC exchanges data with main LCC (using, for example, TCP/IP protocol) in order to have an insight in the complete network which its substation would supply in case of the execution of the requested switching procedure. Afterwards, state estimation is made and reserves are calculated. Requested LCC sends reserves information to main LCC. Main LCC identifies does any of the requested LCCs have a direct NO point to main LCC and have enough reserves to cover all unsupplied loads. If found, main LCC will confirm the switching request to the identified LCC, and the loads will be supplied successfully. If none of substations could fully support those loads, main LCC will still request the switching procedure to be made. In this case, the requested LCC will have to create its own switching procedure which would detach some of its regularly supplied loads, send a request to other substations for the newly created switching procedure and apply the switching procedure that was requested before. When applied, the substation that detached some of the loads now becomes main substation which is requesting others for their reserves. This is a sort of chain reaction which finally draws all network reserves in order to supply as many unsupplied loads as possible. Of course, this process has to be stopped if the solution converges too slowly or diverges. For example, if the satisfactory solution cannot be found after involving three additional substations, solution finding process is stopped. Considering the example from Fig. 3a, in case that CB2 trips, self-healing algorithm would apply following steps: 1. CB2 trips 2. LCC3 (main)—start analysis. 3. LCC3—set DIS2 and DIS6 to open. 4. LCC3—send requests: to LCC2—close DIS1; to LCC4—close DIS3. 5. LCC2 and LCC4—analyze switching request 6. LCC2 and LCC4—send request to LCC3 for missing network data 7. LCC3—exchange required data 8. LCC2 and LCC4—calculate reserves and respond to LCC3 9. LCC2 response: not enough reserves to support new elements 10. LCC4 response: enough reserves to support new elements

11. LCC3—repeat switching request 12. LCC4—close DIS3 13. LCC2—open DIS8 and close DIS5 14. LCC2—send request to LCC1—close DIS4 15. LCC1—analyze switching request 16. LCC1—send request to LCC2 for missing network data 17. LCC2—exchange required data 18. LCC1—calculate reserves and respond to LCC2 19. LCC1 response: enough reserves to support new elements 20. LCC2—confirm switching request 21. LCC1—close DIS4 22. LCC2—close DIS1

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After algorithm execution, network elements are supplied with reconfigured network as in Fig. 3b. Loads between DIS2 and CB2 are left unsupplied. Network is kept radial.

6 Conclusion Keeping the consumer satisfied with the constant power supplies is a major success for every network operator. With all the problems that distribution network is facing today, but yet with the opportunities that Smart Grid is offering, this task became a great challenge. Algorithm which would provide ability of the network to heal itself without the operator support is a progress which is still waiting to be implemented in large distributive networks. Background of one such algorithm was presented in this paper. It uses available distribution resources to locally control the system. Each substation makes DNA execution for the network it is supplying. In case of the configuration change it communicates and exchanges data with other substations in the network in order to reconfigure network so that it satisfies new network condition. Exploiting most of the distribution network resources would make network applications more efficient in their execution. Local control principle avoids unnecessary data exchanges, leaving room for other data which might need to be exchanged in future. Improvements gained with this approach would significantly change today’s solutions and the upcoming requests in distribution networks.

References 1. Li T, Xu B (2010) The self-healing technologies of smart distribution grid. In: 2010 China international conference on electricity distribution (CICED), 13–16 Sept 2010, pp 1–6 2. Rasmussen KS (2009) A real case of self healing distribution network. In: 20th international conference and exhibition on electricity distribution—part 1, 2009. CIRED 2009, 8–11 June 2009, pp 1–4 3. Samarakoon K, Ekanayake J, Wu J (2010) Smart metering and self-healing of distribution networks. 2010 IEEE international conference on sustainable energy technologies (ICSET), 6–9 Dec 2010, pp 1–5 4. Xiang G, Xin A (2011) The application of self-healing technology in smart grid. In: Power and energy engineering conference (APPEEC), 2011 Asia–Pacific, 25–28 March 2011, pp 1–4 5. Amin M (2001) Toward self-healing energy infrastructure systems. IEEE Comput Appl Power 14(1):20–28 6. Zhou HJ, Cao JZ, Guo CX, Qin J (2010) The architecture of intelligent distribution network based on MAS-SOA. In: Power and energy society general meeting, 2010 IEEE, 25–29 July 2010, pp 1–6 7. Colouris G, Dollimore J, Kindberg T (2005) Distributed systems concepts and design, 4th edn. Pearson Education Limited, New Delhi, pp 70–73. ISBN 0-321-26354-5

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8. Zimmermann H (1980) OSI reference model—the ISO model of architecture for open systems interconnection. IEEE Trans Commun 28(4):425–432 Apr 9. Postel J (1980) ‘‘User datagram protocol’’, STD 6, RFC 768, USC/Information Sciences Institute, August 1980. Internet engineering task force (IETF): RFC768 user datagram protocol 10. Birman KP, Chen J, Hopkinson EM, Thomas RJ, Thorp JS, Van Renesse R, Vogels W (2005) dcOvercoming communications challenges in software for monitoring and controlling power systems. Proc IEEE 93(5):1028–1041, May 11. OMG available specification: data distribution service for real-time systems, version 1.2, formal/2007-01-01 12. Internet engineering task force (IETF): RFC 3208: PGM reliable transport protocol specification

Applying Event-Based Framework to Support Power System Integration Nenad Lecek and Izudin Dzafic

Abstract This paper describes a real life integration scenario of two power control systems. For that purpose we designed event-based framework and use it to create soft-real time gateway application service that facilitate integration of two complex, event-driven, distributed systems. The event-based framework uses publish-subscribe event delivery and supports asynchronous messaging, including asynchronous transactions. Extensible event-driven applications can be based on that framework. Event listeners and event type subscriptions could be added or removed at runtime. The framework simplifies application design and implementation because it solves concurrency related issues and access to shared resources by design—application developer does not need to deal with multithreading at all and still can implement flexible, multithreaded, thread-safe, event-driven applications.




Inter-process communication Keywords Asynchronous messaging Multithreading Publish-subscribe Message oriented middleware










1 Introduction The need to integrate distributed, event-driven, in-house developed power control system with external systems lead to considerations to connect systems by a Broker architecture [1]. The system had many Application Programming Interfaces (APIs) N. Lecek (&)
I. Dzafic Power Distribution Division, Siemens AG, Energy Sector, Humboldtstr 59, 90459 Nuremberg, Germany e-mail: [email protected] I. Dzafic e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_16,  Springer Science+Business Media B.V. 2012

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that provide data and control access to internal data providers. These interfaces were designed with specific application or (sub)system functionality in mind. Analysis showed that these APIs are too low-level for system integration purposes. Existing client APIs provide relatively narrow functionality and therefore lacked right level of abstraction required for integration usage. Interfaces on higher level of abstraction were needed. The problem is to orchestrate data received from these data providers, their interaction and provide consistent system views for such complex, event-driven and distributed system. The system needs to provide Application Programming Interfaces (APIs) that support system integration. These APIs external clients could use to communicate with the system. The external system could be considered as an external client. The Broker architecture has two main parts: Server-Side and Client-Side Broker. The Server-Side Broker is added to internal, event-driven, distributed system and is responsible to provide abstraction layer for external clients and to hide complexity of internal system. The Client-Side Broker library communicates with Server-Side Broker and provides consistent, minimal and event-driven interface to external clients. External systems use ClientSide Broker library to communicate with the Server-Side Broker. Many event-based frameworks already exist [2, 3] and are used in CORBA Event Service, JMS, WS-Eventing, WS-Notification and others. Make or buy decision depends on many factors. The key issue to address is how internal data providers and event-based framework will collaborate and support major functionality of ServerSide Broker: consistent data presentation to external clients. These issues are application and system specific and cannot be addressed with Component of the Shelf (COTS) packages. This leads to issues: for each data provider client is required to add bridge component that convert objects and methods calls from event framework to internal data provider client APIs. In order to cope with existing system complexity approach to concurrency is crucial. Events can and should be handled in parallel if their processing does not require access to the same shared resource. Concurrency and synchronization shall be addressed by design [4]. The challenge is to choose design that ensures solutions free of deadlocks, livelocks, race conditions, priority inversions and relieves developers from necessity to explicitly deal with multithreading. Active objects, described in [5, 6] are robust and relatively simple alternative to explicit thread programming. Event-based framework should ensure loose coupling between event listeners and efficiently dispatch events to registered event listeners. Thus, event-based framework implements Observer design pattern [7]. Furthermore, asynchronous communication between client and server-side Broker is needed to achieve service responsiveness [8]. This paper describes a simple and flexible event-based framework that serves as backbone for Server-Side Broker service and simplifies system integration. The paper is organized as follows: Sect. 2 (Motivation) describes problem to be solved and goals solution should achieve, Sect. 3 (Event-Based Framework) describes the key elements and design of created framework, Sect. 4 (Integration with External Systems) shows how event-based framework and Client-Side Broker were applied to address integration of two complex distributed systems and Sect. 5 (Conclusion) presents concluding remarks.

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2 Motivation The desired solution for integration of internal distributed system with external systems had to be simple to use, user-friendly, reliable and with high performance. A gateway application service is needed. A concrete integration problem requires solving the following issues: (1) Internal distributed system is event-driven. A gateway application should also be an event driven service. The service should handle events as they arrived and should be able to react properly to events even if they come out of order, are late, duplicated or are lost. Components within a system work in parallel and share some resources, leading to possible race conditions. (2) Gateway service responsiveness. Internal distributed system is soft-real time system and gateway performance is very important. Latency should be minimized. (3) Service maintainability. Gateway application service must handle a number of special, complex message sequence scenarios with many similarities. New use cases and defect fixes can be expected during product maintenance phase. The challenge is to provide proper and flexible initial design that allows maintenance without side-effects. (4) Service extensibility. A maintenance or service extension should not require a complete system shutdown. Testability is important. Service modifications shall not require repeating complete system regression testing. Thus, important is to use testable design, e.g. use stable interfaces, interface versioning and cleanly isolate external dependencies. In such case automated unit tests are helpful, if kept up to date, and relatively small effort is required to execute updated regression tests. (5) Service usability. In gateway applications, the challenge is to ensure correct and consistent mapping between internal system context, internal data views and provide public, simple to use, symmetrical interface and data views that suffice clients needs. The internal distributed system is complicated. Data providers are distributed to different computers. To provide replies for relatively simple requests, communication with several data providers is typically required. E.g. simple data request to provide current values for couple of realtime measurement values require data from several data providers: security service check user authorization, name service provides textual descriptions for values and client accessing Shared Memory Mapped File (SMMF) from Supervisory Control and Data Acquisition (SCADA) provides real-time values. It is expected that internal system complexity will continue to increase in following years. Beside that, dispersed development teams are working on various subsystems for longer than decade. As a result of technological changes over these years, a number of implementation technologies and tools are in use. This,

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accidental difficulty had to be addressed too. Figure 1 shows simplified component diagram before integration with external system. Even simplified system view shows that direct integration is not practical solution. A client side component that directly accesses internal data providers simply needs to create far too many connections, handle many interfaces and maintain internal data caches and differences in implementation technologies. Such monolithic component would be too costly and too difficult to develop and maintain. A similar problem as integration with external system emerges in development of internal, Graphical User Interface (GUI) applications. Internal UI applications could directly contact data providers dispersed within complex distributed system. In such case, development and maintenance of GUI applications become unnecessary difficult and error-prone. Internal UI applications also suffer from system complexity. UI applications need to provide consistent data views and are in some cases forced to implement complex application logic to achieve that. From client’s point of view, integration solution has similar needs as internal UI applications. UI applications and external systems are considered as similar type of entities, as external clients. However, some differences exist, security for example. Authentication and authorization is different for UI application within the system compared to client application from external system. Except security considerations, similar set of interfaces are used for both purposes. These concerns justify the need to move the complexity of data retrieval and data forwarding into a separate layer. The missing layer is a Broker [1] and it finds place between internal data providers (internal services) and external clients, as depicted in Fig. 2.

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A Presentation Data Service is a Server-Side Broker. It is event-driven and serves as Message Gateway, described in [9]. A Messaging Bridge [9] is needed to provide consistent data views and responsiveness between integrated systems. Presentation Data Service serves as messaging gateway application for external clients and UI applications. With gateway application service in place, internal complexity of distributed system is hidden from external clients. The presentation data service could be seen as single point of failure. It required flexible design that can cope with such problem. Sought after properties of such gateway application service are: • • • • •

Robustness Responsiveness Maintainability Extensibility Systematic approach to concurrency These properties influence service design. Applied design must be

• modular • extensible • relieve application developer/maintainer from need to deal with concurrency issues

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3 Event-Based Framework A number of services and expected changes in configuration and requirements make plugin architecture and middleware based on Messaging [9] an attractive choice for internal architecture of Presentation Data Service, as Fig. 3 shows. Considered option was to use event-based framework based on XML messages, SOAP, web services. This was abandoned for the following reasons: (a) parsing XML messages significantly degrade service performance; (b) required is low level layer interface between clients of internal data providers and message handlers called from callback of third party solution; (c) in system is introduced additional dependency on third party solution. Middleware technologies, such as CORBA Event Service are also abandoned to avoid adding complexity. To avoid overhead of complex middleware technologies and dependencies on external products, we designed and implemented relatively light weighted event-based framework in C++, with support for asynchronous messaging, flexibility of loosely coupled data providers, event-typed notifications and high performance event delivery. Publish-subscribe event notification combined with active object design pattern [4] form the basis for event-based framework applied in Presentation Data Service. Each plugin is specialized to handle communication with dedicated service and contain client APIs to access that service. Plugins could be added or removed. This way, application data layers (i.e. application logic and data cache) are decoupled from data providers. Concurrency is necessity in case responsiveness is important. Concurrency is often achieved using multithreading. The key concern related to multithreading is software maintainability. Explicit design of threading significantly decrease system maintainability and must be avoided for that reason. The problem with explicit design of threads is that does not scale well beyond trivial cases with few shared resources. Data providers and data caches are shared resources. It becomes especially difficult in case data providers or data caches belong to different layers. This may lead to considerations to use recursive mutexes. In case of many data providers, complex interactions cannot be avoided. Concurrency and synchronization shall be addressed by design. Events can and should be handled in parallel if their processing does not require access to the same shared resource. System organization and approach to multithreading is crucial.

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Publish-subscribe event notification combined with active object design pattern [1] form the basis for event-based framework applied in Presentation Data Service. External clients and UI applications are integrated using plugins. This application service is message oriented middleware. Application service is event driven, and efficient handling of event messages through event-based framework is desired. Plugins are loosely coupled. They communicate and exchange data only through event messages. Figure 4 shows plugin class diagram. Plugins use active object design pattern [5] to receive and consume event messages. Active object has thread safe queue and use worker thread (consumer thread) to process messages taken from that queue. A private event processor is assigned to every consumer. Event processor handles event messages specific to plugin functionality. In worker thread, queued event message are taken out sequentially, in FIFO order and given to event processor. Event processor does event message processing, usually as synchronous call. Event processor lives in consumer’s private thread, and has access only to own data pool and does not share or have access to data accessible from other threads. Each plugin implements interface IEventListener and registers for event messages types it wants to receive. Chosen subscription granularity is event message type. An implementation class, denoted here as EventListenerPluginX contains active object. Incoming event message is put into internal event message queue of active object. Active object’s private worker thread takes message from the queue and passes it to event processor. The message is interpreted there and requested operation is executed. On completion of message processing, event processor may want to post a message to inform other interested plugins. Therefore, event processor needs access to event mediator, which provides public, thread safe, re-entrant method to publish event message to subscribers. It is necessary to allow creating and publishing event message from private thread of active object. Thus, all plugins have access to event mediator. Plugin publishes event message to subscribers by calling EventMediator.Dispatch(eventMsg). All public methods of event mediator are thread-safe and reentrant.

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Fig. 5 Event-based framework—overview

The main reason to use event-based framework is to ensure clean, easy to use and highly responsive multithreaded solution and at the same time to solve multithreading issues by design. Important feature of designed event-based framework is that multithreading is hidden from application developer and this is achieved almost without performance penalties. Efficient event delivery assumes that event objects are not copied when passed from event producer to event consumers. This application service is implemented in C++ and copying is avoided by disciplined use of thread-safe smart pointers [10]. Efficient event-based framework has been designed as backbone for presented gateway service. Figure 5 shows overview of event-based framework used in Presentation Data Service. Internal data providers are accessed with proprietary APIs. Figure 5 depicts involved data providers: Data Distribution Service (DDS), Name Service, Supervisory Control and Data Acquisition (SCADA), Interprocess Communication (IPC) and Data Layer for internal application logic and data cache. EventMediator provides publish-subscribe interface for event delivery from one publisher to n subscribers. Event mediator implements Mediator and Observer design patterns 7. Event-based framework consistently uses asynchronous messaging, without blocking. Framework does not even offer functionality to block and wait for request completion. Synchronous, blocking requests are not used at all. This leads to issue how to map external request and response. Figure 6 shows Event Mediator used in Presentation Data Service. Event mediator supports asynchronous request/reply monitoring. In method Dispatch(), event mediator examines event message type and does bookkeeping for asynchronous request/response messages. Figure 7 shows sequence diagram for asynchronous request/response handling within Presentation Data Service application. If message is asynchronous request, a Correlation Identifier [9] is extracted from event message. This correlationId (id1) is added to OpenAsyncRequestList and event is published to subscribers. The purpose of CorrelationId is to hold unique mapping between asynchronous request and response. In case message is asynchronous response and CorrelationId is found in OpenAsyncRequestList the

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IEventMediator +Dispatch(eventMsg) +Subscribe(evtType, evtListener) +Unsubscribe(evtType, evtListener) +RemoveListener(evtListener) +RemoveAllListeners() +AddTimer(timerDescription) +RemoveTimer(timerDescription)

1 .. 1 CEventMediator -AddAsyncRequest(CorrelationId) -RemoveAsyncRequest(CorrelationId); -RemoveAllAsyncRequests(); -bool AsyncRequestExists(CorrelationId);

1 .. 1

Open AsyncRequestList TimerList (contains a list of active timers)

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Fig. 6 Event-based framework—event mediator

EventProcessor

eventMediator

openAsyncRe questList

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createInternalEvent(evtReq1)

createInternalCorrelationId(id1) evtReq1.assignCorrelationId (id1, START_TRANS) Dispatch(evtReq1) isAsyncRequest() Add(id1) Dispatch(evtReq1) ReadScadaSMMF

createInternalEvent(evtResp1) evtResp1.assignCorrelationId (id1, END_TRANS) Dispatch(evtResp1) Remove(id1) Dispatch(evtResp1)

Fig. 7 Sequence diagram for asynchronous request/response

message is delivered to subscribers and CorrelationId (id1) is removed from OpenAsyncRequestList. In case message is asynchronous response and CorrelationId cannot be found in OpenAsyncRequestList the message is not forwarded to subscribers and is logged for later analysis. Such response message is either late or duplicate. Event mediator uses private timer thread to periodically check OpenAsyncRequestList and remove expired requests from the list. In case expired asynchronous request has been found, timer thread creates timeout response message and publish it to subscribers. This way requestor is also notified about request timeout.

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IpcPlugin

1 .. 1

IpcClient

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IpcMsgHandler

1 .. 1

1 .. 1

correlationIdSet + saveIpcCorrelationId(id, ipcId) + removeIpcCorrelationId(id)

eventMediator

0 .. n correlationIdMapper -CorrelationId -ipcCorrelationId

Fig. 8 IpcPlugin class diagram

External clients send requests to Presentation Data Service as asynchronous request/response IPC messages. For convenience of client’s application implementation, external clients use client-side Broker library (PDS ClientLib). This library can use blocking calls, i.e. block own, caller’s thread and wait for response, using Half-Sync/Half-Async [11] design pattern. Inter-process communication (IPC) between client and service is based on interprocess message queue. IpcPlugin is responsible to handle IPC communication and accept all asynchronous requests issued by external clients. The problem arise how to map client’s asynchronous request/response to internal handling of asynchronous request/response within Presentation Data Service. Figure 8 depicts class diagram for IpcPlugin and required bookkeeping classes. IpcClient handle low level IPC communication. IpcMsgHandler deserialize and process incoming IPC messages in private thread (IPC callback thread). Within IpcMsgHandler are created internal event messages and published to subscribers using event-based framework (Fig. 9). IpcPlugin receives and de-serialize IPC messages within IpcMsgHandler, in IPC callback thread (t1). External correlationId (IpcCorrelationId) is extracted from incoming IPC message. For tracing asynchronous transaction within eventbased framework, internal CorrelationId object is created. Internal CorrelationId is unique within Presentation Data Service. Data members of IpcPlugin are not accessible from IPC callback thread. This forces us to create event evtCorrelationId with mapping of external and internal correlation Id and publish it to IpcPlugin. Event evtCorrelationId contains internal CorrelationId, IpcCorrelationId, transactionState, transactionStartTime, and transactionDeadline. After evtCorrelationId is published, internal CorrelationId is assigned to internal event message and event is published to event mediator. IpcPlugin is subscribed for asynchronous reply event message. In worker thread of IpcPlugin is received event message asyncResponse and executed search through set of mapped correlation IDs. If

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DecodeMsgBuffer createInternalEvent(evtReq1) createCorrelationId(id1) Dispatch(evtCorrelationId1) Dispatch(evtCorrelationId1) evtReq1.assignCorrelationId (id, START_TRANS)

saveIpcCorrelationId

Dispatch(evtReq1) isAsyncRequest() Add(correlationId) Dispatch(evtReq1) ReadScadaSMMF

Dispatch(evtScadaValues) Dispatch(evtScadaValues1) Remove(correlationId) findIpcCorrelationId Serialize(evtScadaValues1) SendResponse(ipCorrelationId, evtScadaValues1) removeIpcCorrelationId

Fig. 9 Mapping client asynchronous transaction to internal asynchronous request/response

correlationId is found within correlationIdSet, IpcCorrelationId is known and client queue that receive response is identified. In case transaction state is received as completed, correlationID info is removed from correlationIdSet. Otherwise, in case correlationID cannot be found within correlationIdSet is logged for later analysis and message is not delivered to client. Periodical timer event triggers checking whether asynchronous request expired. If correlationId is expired, it is removed from correlationIdSet and timeout event message is published. The requestor is a registered listener for such timeout event message and instead of response it receives timeout notification message with correlationId from request message.

4 Key Design and Implementation Issues The event message granularity is one of the most important aspects of asynchronous messaging. Fine grained event message types require increased number of events to be handled and lead to complicated interaction between them. As a consequence application logic could be difficult to follow. In some cases this lead to decision to model application logic with state models although behavior is essentially stateless. Another extreme could be too coarse event types. Such event messages typically contain complex data structures that are difficult to use, and complex and time consuming to serialize and de-serialize. Experience proved that

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is better to start with small number of events and keep them symmetrical. E.g. in case exist event message type EVT_OPEN, then EVT_CLOSE should be also provided. The exact level of required event granularity and data members associated with event message is domain specific decision. In absence of exact method to guide this decision, good taste and experience is needed. Symmetry, no duplicated event types, easy understandable event purpose from event name indicates appropriate level of event type granularity. Important impact on efficient event delivery has copy policy for event messages. The framework should not copy event messages while dispatching event messages from event producers to event consumers. Copying complete event messages would degrade performance and require event sizing policy. Our framework solves these issues with disciplined usage of thread-safe, reference counted, immutable smart pointer objects to event messages. Event producers create and dispatch smart pointer object to framework and framework forwards such object to event consumers. Smart pointer objects are typically much smaller than event message objects and introduce very small overhead during event delivery. Smart pointer objects ensure efficient event delivery and proper resource management of event messages. Event mediator has additional task, i.e. it handles timers. Timers in this eventbased framework are asynchronous and non-blocking. The timer thread periodically checks for expired timeouts and publish timeout events for expired timers. In case timer expired and is one-shot timer, it is removed from TimerList. In case of periodical timer, timeout event is delivered and timer is rearmed. Here is taken special care that timers are not drifting. These periodical timeout events are used to trigger processing at regular intervals, such as polling of some data provider, client heartbeat checks and periodical calculations.

5 Integration with External Systems Integration with external systems is supported through IpcPlugin. IPC communication is used for performance reasons. External clients communicate with Presentation Data Service using Inter Process Communication (IPC), i.e. using inter-process message queues. External clients establish point-to-point communication with Presentation Data Service. Presentation Data Service uses IPC plugin to read incoming messages from own, dedicated IPC queue, denote it PDS queue. IpcPlugin sends responses and event notification messages to external clients into their queues. External clients are expected to support following communication tasks: • Establish connection to IPC queue (PDS queue). • Periodically send heartbeat message to PDS queue so that service knows that client is still alive. • Handle reconnect requests from client side.

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Presentation Data Service

IpcPlugin

Other Plugins ...

External Client2

IpcDaemon (holds IPC queues)

1 .. 1

PDS queue

PDS ClientLib

1 .. n

Client Queues

Fig. 10 Communication between PDS and external clients

• Serialize and efficiently send messages to PDS queue. • Create own queue to receive event response and notification messages. • Parse received event response and notification messages and forward them to callback objects for further processing. • Provide methods to send message using SendNotification(), SendSynchronousRequest() or SendAsynchronousRequest(). • Cleanly close connection. Every external client (and UI client) should handle these tasks. In order to avoid redundant implementations, a client-side Broker library (PDS ClientLib) was designed and implemented. This library implements communication to server-side Broker (PDS) and provides C++ interface to send and receive data using application level event messages. Figure 10 depicts communication between presentation data service and external clients. External clients use PDS ClientLib library to communicate with Presentation Data Service. PDS ClientLib handle details of IPC communication and offers convenient C++ interface to external clients. To send request to PDS, external client need to create message object and pass it to the library. The library does object serialization and place serialized message buffer into PDS queue. In response direction similar activity take place. IpcPlugin serialize response message and place it to client’s queue. PDS ClientLib provides notification callback. This callback is called in case IpcPlugin sends notification (fire and forget) messages to client. IPC communication uses proprietary protocol to avoid performance loss in serialization and deserialization of open protocols based on XML messages. In case other protocols should be supported, event-based frameworks allows to extend Presentation Data Service with plugins providing these protocols (e.g. open XML protocols, such as SOAP, WS-* and others).

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6 Conclusion Integration of one complex distributed system with another system is difficult. Perhaps the most important factor is to provide usable and simple interface to external clients. The internal system complexity is hidden behind gateway application service designed for integration purposes. The main tasks of such service are to provide consistent data presentation and simplify communication with internal system at the same time. In this paper it is described how event-based framework was used to create flexible, robust soft-realtime presentation data service. The central building blocks of presented eventbased framework are publish-subscribe event message delivery combined with active objects. Publish-subscribe event delivery is a key element to achieve high cohesion and loose coupling for data providers. A robustness of application service is achieved by moving data providers into active objects. Active objects have very minimal interface and such narrow interface contribute to service testability. Data providers are cleanly separated from the rest of the service. Such separation addresses service robustness and also testability. Valuable property of event-based framework is possibility to use dummy data providers (stubs). Furthermore, clean threading model was applied. Data providers are visible only within worker threads of active objects and they do not have access to shared resources. Application logic is considered as internal data provider and placed into one plugin. The benefit of such design is that service developers do not need to deal with multithreading and can still create, multithreaded, highly responsive service. Asynchronous messaging is chosen to assure service responsiveness. At the same time, convenience of client side programming and integration is provided by simple interface of client library. Presented event-based framework is in production use and provides required functionality and flexibility.

References 1. Buschmann F, Henney K, Schmidt DC (2007) Pattern oriented software architecture: a pattern language for distributed computing, vol 4. Wiley, Chichester. ISBN-13: 978-0-47005902-9 (hbk) 2. Huang Y, Gannon D (2006) A comparative study of web services-based event notification specifications. In: ICPP workshops, IEEE Computer Society, pp 7–14 3. Henning M (2006) The rise and fall of CORBA. ACM Queue 30 June 2006, pp 29–34 4. Samek M (2002) Practical statecharts in C/C++ quantum programming for embedded systems. CMP Books. ISBN 1-57820-110-1 5. Lavender RG, Schmidt DC, Active object—an object behavioral pattern for concurrent programming. In: Proceedings of pattern languages of programs. http://www.cs.wustl.edu/ *schmidt/PDF/Act-Obj.pdf 6. Petitpierre C, Eliëns A (2002) Active objects provide robust event-driven applications, SERP’02 7. Gamma E, Helm R, Johnson R, Vlissides J (1995) Design patterns: elements of reusable object-oriented software. Addison-Wesley, Reading

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8. Zdun U, Voelter M, Kircher M, Design and implementation of an asynchronous invocation framework for web services, conference: ICWS-Europe’03 9. Hohpe G, Woolf B (2008) Enterprise integration patterns, designing, building and deploying messaging solutions. Addison-Wesley, Boston, ISBN: 0-321-20068-3, January 2008, 11th Printing, pp 163–169, 472 10. Boost web site. Smart pointers. http://www.boost.org/doc/libs/1_47_0/libs/smart_ptr/ shared_ptr.htm 11. Schmidt DC, Stal M, Rohnert H, Buschmann F (2000) Pattern-oriented software architecture: patterns for concurrent and networked objects, vol. 2. Wiley, Chichester. ISBN 0-471-60695-2

Utilizing Converged Networks for Green Network Operations Xiaodong Yang

Abstract The Converged network is the trend of network development. It can not only provide advanced new features to the end users, but also provide a novel way of green computing to the network operators. This paper presented the idea of utilizing converged networks for green network operations. The converged network based solution is presented and the benefits of the proposal are discussed. Cobb–Douglas model is for the first time used for analyses and the results show that the converged networks consume less network operation resources compared with non-converged networks and converged networks also perform better when the network operation scale is increased. Some open research issues and the future work are further discussed in the conclusion part of this paper. Keywords Converged network

 Green computing  Network operation

1 Introduction Green computing has already become a hot research topic recently. The word ‘‘green’’ here usually means ‘‘low energy, low carbon’’. Most of the research works being done are focusing on data center power efficiency [1], network operation algorithms [2], etc. Few research works are done for designing telecom network operations. Besides, green computing refers not only to consumed operation power reduction, but also to the economic use of operation construction materials and other network resources.

X. Yang (&) School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_17,  Springer Science+Business Media B.V. 2012

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Fig. 1 Converged networks of DVB-H and UMTS

Therefore, this paper will, for the first time, present and analyze a novel approach for green network operations-utilizing the converged networks. During the analysis, Cobb–Douglas model is for the first time used for green network operations. Converged networks refer to the networks where different kinds of networks are converged and the services or infrastructures are somehow shared and optimally co-utilized. In another word, converged networks make fully use of a common network infrastructure to serve the operation functionalities of two or more different networks. For example, the converged broadcast and telecom networks are making fully use of a common IP core network and the related service infrastructures, thus saving operation cost and reducing power/cooling energy consumptions. In this sense, even the network construction materials can be used more economically and the relevant costs are thus saved. On the other hand, the converged networks have for a long time been studied and proposed to provide users with high speed services and more advanced interactive features efficiently and economically [3]. As an example, a typical converged network between DVB-H and UMTS is shown in Fig. 1 [3], where DVB-H is the European standardized mobile broadcast standard and UMTS is one of the Third generation mobile telecommunications standards. In Fig. 1, the converged network coverage is an overlap cellular structure where the DVB-H cells and the UMTS cells are overlapping each other. The service head-end part is the same for both DVB-H and UMTS networks. And the two networks also share a common IP core network. The terminal used in the converged networks is also a kind of converged terminal thus saving the cost on user devices by avoiding using two different devices. This paper will focus on the green network operation features provided by the converged networks.

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The converged networks could be composed of many different kinds of networks. For easy description and without loss of generality it is assumed in this paper that the converged networks are converged DVB-H/UMTS networks. This paper tries to exploit the green features of the converged networks. The solution of utilizing converged networks for green network operations are proposed and analyzed. The green features here mean that the network operation resources such as power energy, network construction materials, man power, etc. are consumed much less thus producing ‘‘green’’ effect. This paper is organized as follows: Section 2 describes the research backgrounds for green computing solutions. Section 3 presents the proposed novel solution of utilizing converged networks for green network operations. Section 4 focuses on the analyses of the network operation resource consumptions and the results are obtained by using Cobb-Douglas model and relevant comparisons are made. Section 5 concludes the paper and also describes some open issues and the future work.

2 Research Background For some time, people have been only interested in devoting the efforts to increase the processing power of equipment and inventing algorithms to realize it. The resources consumed by the equipments such as power energy and construction materials have for a long time been assumed being always available and affordable. However, with the idea of low carbon economy and especially with the establishment and development of data centers, people begin to change their minds. And for this reason, green computing has become a very hot research topic. Nevertheless, most of the previous research works about green computing were only focusing on the energy consumption reduction in data centers. This is, of course, partly due to the considerable power consumptions of the data centers and partly due to the still growing power demand of the larger data centers [4]. As it is already said that the solutions for green computing were first developed to address the power consumption issues in data centers, we have to make it clear that the technology behind the enormous power consumption is mainly cloud computing. Cloud computing is the collection of scalable, virtualized resources, which is capable of hosting applications and providing required services to the users and can charge on a per-user basis [5]. Cloud computing makes use of hundreds and thousands of computers to run parallelly thus consumes considerable amount of power energy for processing and cooling [6]. And this kind of energy consumption cost due to power usage for processing and cooling usually accounts for a great percent of the whole cloud computing operation cost. Thinking from another side of the data center network, paper [7] presented the thin clients and their features of being environmentally responsible than desktop computers most obviously regarding to power consumptions. Moreover, paper [7] mentioned that thin clients have far less parts than a desktop computer and therefore require also far less resources to manufacture and contain far fewer

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hazardous materials to the surrounding environment. In this respect, paper [7] introduced other resources consumption issues besides the popular power consumption issue. Again regarding to data centers, paper [2] divides the costs of data centers into different categories such as servers, infrastructures, powers and networks. It proposed approaches to reduce these different categories of costs. It also argued that networking and systems innovation could be the key to reduce data center costs. Paper [8] pointed out that the energy expense per unit of computation has location dependent features, in spite of the fact that energy expenses are becoming an increasingly important fraction of data center operating costs. Therefore, as complementary to the conventional energy reducing approaches, paper [8] proposed to use energy cost-aware routing algorithms to reduce the power consumption cost of data centers by exploiting the variations due to fluctuating electricity prices. As presented above, most of the previous research works are addressing the issues of resource consumptions regarding to data centers. In this paper, it is argued that the issue regarding to data centers is not the only research issue of green computing, though the application to data centers is indeed one of the important application areas. By arguing this, this paper tries to point out that the converged networks also match very well to the idea of green computing by saving different network operation resources such as network infrastructure cost and network operation cost compared to the non-converged networks. The converged network itself is not a new idea. Converged networks provide a lot of advanced features to the existing services. Actually this is exactly what converged networks are designed to be at the beginning [9]. And for this reason, most of the research works regarding to converged networks are focusing on the services provided by converged networks and the improved converged network infrastructures. For example, paper [3] addressed the intersystem soft handover issue in the converged DVB-H and UMTS networks. However, no research works are done to exploit the converged networks for green computing up to the writing of this paper. Exploring the green features of converged network operations is a novel issue. This issue will surely expand the domains of green computing research and provide another incentive for adopting the converged networks. The details are presented in the following sections.

3 The Proposed Solution Exploring the features of green computing in converged network operations is a novel issue. Regarding to the converged DVB-H and UMTS networks, the proposed solution can be illustrated in Fig. 2. Figure 2 takes the converged broadcast (DVB-H) and telecom (UMTS) networks as an example. However, the proposed solution can be easily extended to other converged networks, in spite of the fact that most of the following aspects are specific to the converged broadcast

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Fig. 2 Illustration of green features in converged networks

and telecom networks. The different green computing features of the converged networks are shown in Fig. 2.

4 Analyses and Results In this section, the comparison between the network operation resource consumptions for converged networks and non-converged networks will be compared. First, the relationship between network operation resources consumptions as output and related different variables as inputs will be estimated. The network operation resources here mean processing power and cooling energy, construction materials, man power, etc. and can be divided into fixed and variable resources consumptions. Fixed consumptions are not sensitive to the change of the volume of input network resources, but variable consumptions do. It is also reasonable to believe that the variable resources consumptions are affected by typically the following different variables: distance of the transmission, type of network, operator strategy. • Distance of the Transmission: No matter the network transmission is wire based or wireless based, the longer the transmission distance is, and the more transmission power will be consumed. Longer distance also means more network construction materials being consumed and more man power being used for repair and maintenance.

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• Type of Network: Normally wired networks consume more resources than wireless networks. Moreover, different wired or wireless networks consume different resources. For the same service, different network modes such as unicast and broadcast consume also differently. Even for the same kind of network, different services on the network will consume different amounts of resources. For example, video services consume more resources than voice or text based services. • Operator Strategy: Each network operator usually has its own network operation strategies based on the company policy, which may lead to differences in network operation resources consumptions. For example, the different network technologies and standards being used, clean or non-clean power being used, even different network equipments being used could mean different resources consumptions. In order to make the analyses quantitatively, the above three input variables as parameters will be expressed as follows: • D: Distance of the transmission, DC refers to the value for converged networks and DN refers to the value for non-converged networks. • T: Type of network, furthermore, TC refers to the value for converged networks and TN refers to the value for non-converged networks. • O: Operator strategy, furthermore, OC refers to the value for converged networks and ON refers to the value for non-converged networks. For easy mathematical calculations and without loosing generality, the value of D, T, and O can be defined and to be set in the range from 0 to 1 depending on the resources consumptions of the network operations caused by the variation of the different corresponding variables. The less the network operation resources are consumed, the smaller the value D, T and O will be. Based on the descriptions in Sect. 3, for converged networks, statistically the following equations exist: DC hDN ; TC hTN ; OC hON : Suppose R is the total network operation resources consumptions, R can be calculated by using the following equation: R ¼ b0 þ b1 D þ b2 T þ b3 O:

ð1Þ

Where b0 is the fixed network operation resources consumptions; D is parameter for the network transmission distances; T is the parameter for the type of networks; O is the parameter for operator strategies. The coefficient bi (i = 1, 2, 3) is the elasticity of network operation resources consumptions with respect to different independent variables D, T and O. It shows the percentage of change in total resources consumptions resulting from a 1% increase. Let RC refer to the network operation resources consumptions of converged networks and let RN refer to the network operation resources consumptions of nonconverged networks, then we have the following equations:

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RC ¼ b0 þ b1 DC þ b2 TC þ b3 OC :

ð2Þ

RN ¼ b0 þ b1 DN þ b2 TN þ b3 ON :

ð3Þ

By using DC hDN ; TC hTN ; OC hON : (2), and (3) it is easy to get the following: RC hRN :

ð4Þ

From (4) it is easy to see that the converged network operation resources consumptions are less than those of the non-converged networks. In this way, converged networks produce ‘‘greener’’ effects. Applying simple mathematical transformations to (1), the following equation is obtained: e R ¼ e b0 e D


b1

eT


b2

eO


b3

:

ð5Þ

:

ð6Þ

Again, (5) can be transformed to (6) as follows: e R ¼ e b0 e D


b1

eT


ðb2 þOTb3 Þ

Equation (6) is obviously a best fit to the Cobb–Douglas model function. Cobb–Douglas function [10] takes the form as follows: PðL; K Þ ¼ bLa K b :

ð7Þ

Where P is total production, L is labor input, K is capital input, b is total factor productivity, a and b are the output elasticities of labor and capital. And if a þ bh1:

ð8Þ

then the return to scale is decreasing. In another word, with increasing of the input, the output is decreasing. Otherwise, the return to scale is constant or increasing. Cobb–Douglas functions are production functions and are widely used to represent the relationship between an output and inputs, like vehicle operation cost analysis [11]. It is reasonable to assume that the Cobb–Douglas model can be used for the network operation resource consumption calculations. Specifically the Cobb–Douglas models for converged networks and nonconverged networks with input variables D and T can be expressed as follows: RC

e

¼e

b0

e


DC b1

e

TC

 
b2 þOTCC b3

:

ð9Þ

 
b2 þOTNN b3

:

ð10Þ

.

eRN ¼ eb0 eDN


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e TN

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Regarding to the network type parameter T, for easy comparisons, reasonably suppose TC = TN, then according to (3), b1 þ ðb2 þ

OC ON b Þhb þ ðb2 þ b Þ: TC 3 1 TN 3

ð11Þ

As R is proportional to eR, According to the general Cobb–Douglas function model (7), (8) and network resource consumptions model (9), (10), (11), the return to scale feature of RC is comparatively decreasing than that of RN. This means, with the proportional increase of input variables such as D, the consumed resources for converged network operations RC are increased with less proportion than that of non-converged networks RN. In another word, with the increasing of the network operation scale, the converged networks perform better than nonconverged networks regarding to network operation resource consumptions. This result shows that the converged networks not only consume less network operation resources than non-converged networks, but also have ‘‘greener’’ effects when the network operation scales are increased. It is to be noted that (9) uses eD and eT as the input variables. If eD and eO or eT and eO are used as input variables, similar results can obviously be obtained. The analyses above are based on the theoretical calculations. Real network operation data are needed to further verify the feasibility of the network operation resource consumption models being used. In spite of a lot of further work regarding to the analyses and test are planned, the results obtained from the analyses here in this section have already showed that the converged network operations are ‘‘greener’’ than non-converged network operations.

5 Conclusions Telecom operators consistently rank among the top three electricity consumers nationally in most EU countries [12]. To keep ahead of technology development and user service requirements, telecom operators are again building and expanding costly new networks, such as 3G, even 4G networks. On the other hand, the converged broadcast and telecom networks utilize most of the existing network infrastructures and are capable of providing similar user services. However, the features of green computing of the converged networks are rarely discussed. For this reason, this paper raised the issue of green network operations and proposed a way of utilizing the converged networks to address such issue. The analyses based on Cobb-Douglas model are first used for the proposed solution. The results showed that the network operation resources consumptions in converged networks are less than those of non-converged networks. And with the increasing of the network operation scale, the converged networks perform better than the non-converged networks. Further evaluation of the Cobb-Douglas model for the network operation resource consumptions using real data from operators are needed.

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Lab tests and field trials are needed and hopefully the operators could plan and integrate the solution into their commercial operations in the near future. Acknowledgments This work was supported by Zhejiang Qianjiang Scholars Program D of China (Grant No. QJD1002004) and Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1111103).

References 1. Liang L, Hao W, Xue L, Xing J, Wen Bo H, Qing Bo W, Ying C (2009) Green Cloud: A new architecture for green data center. In: Proceedings of the 6th international conference industry session on autonomic computing and communications industry session, pp 29–38 2. Greenberg A, Hamilton J, Maltz DA, Patel P (2008) The cost of a cloud: research problems in data center networks. ACM Sigcomm Comput Commun Rev. 39(1):68–73 3. Xiaodong Y, Owens TJ (2008) Intersystem soft handover for converged DVB-H and UMTS networks. IEEE Trans Veh Technol 57(3):1887–1898 4. Kurp P (2008) Green computing. Commun ACM 51(10):11–13 5. Manish P, Jong SP (2009) Cloud computing: future solution for E-governance, In: Proceedings of the 3rd international conference on theory and practice of electronic governance, pp 409–410 6. Weiss A (2007) Computing in the clouds. netWorker 11(4):16–25 7. Ritschard MR (2009) Thin clients: make them work for you. In: Proceeding of the ACM SIGUCCS fall conference on user services conference, pp 41–45 8. Asfandyar Q, Rick W, Hari B, John G, Bruce M (2009) Cutting the electric bill for internetscale systems. In: Proceedings of the ACM SIGCOMM 2009 conference on data communication, pp 123–134 9. Xiaodong Y (2008) Handover in DVB-H—investigations and analysis. Springer, Heidelberg, ISBN: 978-3-540-78629-0 10. Tan BH (2008) Cobb–Douglas production function. Teaching Materials, Carnegie Mellon University 11. Maryam H (2004) Operating costs for commercial vehicle operators in Minnesota. Thesis, University of Minnesota 12. Diala A, Melissa B, Nancy B, Davide Va (2009) Telecom operators in a carbon constrained world, Arthur D. Little report (2009)

A Ring and Clustering Routing Scheme Based on Data Field in Sensor Network Wenwen Dai, Kaihua Xu, James J. (Jong Hyuk) Park, Naixue Xiong and Yuhua Liu

Abstract This paper proposes a routing algorithm named RCBDF (Ring and Clustering Based on Data Field).The algorithm uses the concept of physical field to form clusters on the ring network model. The routing algorithm mainly considers the remaining energy of nodes and the coverage of the cluster to balance energy consumption in network. Simulation analysis shows that: the routing policy will balance the energy consumption of the network at a large extent and greatly extend the network lifetime.







Keywords Wireless Sensor Networks Clustering Data Field Communications

W. Dai (&)  Y. Liu Department of Computer Science, Huazhong Normal University, Wuhan, China e-mail: [email protected] Y. Liu e-mail: [email protected] K. Xu College of Physical Science and Technology, Huazhong Normal University, Wuhan, China e-mail: [email protected] J. J. Park Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea N. Xiong Department of Computer Science, Georgia State University, Atlanta, USA e-mail: [email protected]

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1 Introduction The wireless sensor network is a self-organizing network system, which is formatted by multiple hops radio communications among the tiny sensor nodes. Those sensor nodes are deployed in the monitoring area to perceive collection and dispose the information from the monitoring objects in the network, and send the information to observer [1]. Because of the sensor nodes have restrictions due to energy and communication ability, the research on routing technology on the network layer is pivotal in the architecture of wireless sensor networks. In very large scale monitoring sensor networks, clustering technique has been proposed to address routing scalability and energy conservation issues. LEACH is a milestone of hierarchical routing. However, it will shorten the network lifetime, which because of it does not take into account the current residual energy of the candidate node when select cluster head. Meanwhile, LEACH doesn’t consider the manner of non-cluster head to join the cluster and the communications among clusters. All that is likely to cause uneven cluster and easily result in death of cluster head far from base station due to consumption of excess energy. So, this article proposes a ring and clustering routing algorithm based on the data field to balance the energy consumption and prolong the network lifetime.

2 Related Work There are many literatures about the optimization target of WSN energy efficiency. It is proved [2] that the non-uniform energy consumption of nodes is inevitable under the conditions of the non-uniform node placement and data reporting continuous in multi-hop WSN. Previous work, such as [3] and [4] pointed out that it could improve energy efficiency in network by hierarchical node deployment and data fusion. [5]’s studies focus on energy efficient clustering algorithm in distributed heterogeneous WSN. The elected cluster head node is based on the ratio of the residual energy and the average energy of the nodes in network. It makes the nodes have high initial energy and residual energy and have more chance to become a cluster head. It can balance the network energy consumption and prolong network lifetime. However, this approach has a flaw: all the remaining energy of nodes will reduce when the network running for some time, and it leads to the number of elected cluster head node becomes less and less for each round. Eventually leading to unbalanced network energy consumption and reducing the network lifetime greatly. Therefore, this paper analyzes this problem and uses data field theory to form cluster on the ring network model. It takes into account not only the global residual energy but also the coverage of clusters and distance among nodes. Meantime, it uses Dijkastra algorithm to establish the shortest path tree at data communication phases to eliminate the link cost by single-hop communications.

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Fig. 1 The ring base network model

3 Network Model and Problem Description 3.1 Network Model Nodes deploy in a circular area of radius R randomly. This paper divides the monitoring area into S rings evenly to facilitate the establishment of multi-hop path. The center of the ring is the sink node and the width of each ring is h (units of meter, the same below), shown in Fig. 1. This article assumes that the sensor network has the following properties: (1) the sensors will work independently and be stationary once deployed; (2) the width of each ring is same; (3) each node has a unique identifier ID; (4) transmission strategy is so perfect that there is no conflict and retransmission; (5) all of the nodes have simple data fusion capabilities and there always have data to sent.

3.2 Energy Model The Literature [6]’s energy consumption model is used for the analysis and simulations throughout the rest of this paper. Thus, to transmit k-bit message a distance d, the radio expends:  kEelec þ kefs d2 d\d0 ð1Þ ETx ðk; dÞ ¼ ETelec ðkÞ þ ETxamp ðk; dÞ ¼ kEelec þ keamp d 4 d  d0 and to receive this message, the radio expends: ERx ðkÞ ¼ ERxelec ðkÞ ¼ kEelec

ð2Þ

The electronics energy, Eelec , depends on factors such as the digital coding, modulation, filtering, and spreading of the signal, eamp and efs are amplifier

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magnification, k is the number of bytes by sensor node sent or received, d is the distance between the transmitter and receiver. d0 is the threshold of the distance: if the distance is less than the threshold, the free space model (d2 power loss) is used; otherwise, the multi-path model (d4 power loss) is used.

4 Analysis of RCBDF 4.1 The Value Selection of Threshold LEACH protocol does not take into account the node’s residual energy and makes the selected cluster head may not be the optimal cluster head. An approach to the solution of this problem is presented by literature [7], it adjusts the value selection of threshold as formula (3) shows: TðnÞ ¼

1

p ðr

p En  mod 1=pÞ En

residual ðrÞ average ðrÞ

ð3Þ

Where En residual ðrÞ denotes the current residual energy of nodes, En average ðrÞ is the initialized energy of the nodes. This ensures that the higher residual energy has the higher probability to be cluster head and it will improve the network lifetime. So, this paper uses the revision of the formula in the simulation, the revision is: Tk ðnÞ ¼

pk Ekn  1  pk ðr mod 1=pk Þ Ekn

residual ðrÞ average ðrÞ

ð4Þ

Where, Tk ðnÞ is the select threshold in ring k; pk is the percentage of cluster head in ring k; Ekn residual ðrÞ denotes the current residual energy of nodes in ring k; Ekn average ðrÞ is the initialized energy of the nodes in ring k.

4.2 The Choice of the Radius of Clustering In order to ensure that the clusters can cover the region of the ring Maximum and make a more balanced distribution of the cluster head on ring network model, this paper derives the proper radius of cluster as follows: The area of ring k is: Sk ¼ pðkhÞ2  ½pðk  1Þh2 ¼ pð2k  1Þh2

ð5Þ

And the area of a single cluster in ring k is: SchðkÞ ¼ ph2chðkÞ

ð6Þ

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In theory, all the clusters’ area in ring k is equal to the area of ring k, that is: Sk ¼ mk SchðkÞ

ð7Þ

So, from the equations of (5–7), we can obtain the radius of cluster in theory, which is: rffiffiffiffiffiffiffiffiffiffiffiffiffi 2k  1 h ð8Þ hchðkÞ ¼ mk However, it can not guarantee the cluster’s radius will distributed with the theory value in real environment. Hence, this paper revises the radius as follows: h0chðkÞ ¼ xhchðkÞ ðx [ 1Þ

ð9Þ

The value of the distance modified by coefficient x, which is usually a value of greater than 1. This paper uses it as 2 according to the path attenuation factor.

4.3 Cluster Formation Based on Data Field Physics problem is a physical quantity related to distribution in a certain regions of space called ‘‘field’’. Usually, the interaction effect among neighboring particles and the concept of the ‘‘field’’ method are introduced to a few domain spaces, which is used to describe the distribution law of various physical quantity or mathematical function [8]. Nodes will influence each other in wireless sensor networks. The candidate cluster head with much remaining energy and more nearly from the base station have a greater influence while other candidate cluster head is smaller. Therefore, we take the advantage of this feature for cluster formation in wireless sensor networks, consider the area of each ring as a potential field and use the theory of data field to make cluster. Potential field is a scalar field. The criterion of potential requires its function must be smooth, continuous, limited and monotonous. So, in order to better show the characteristics of clusters, this paper uses normal distribution function that has good mathematical properties to define the potential in field data [9]. Definition 1 There are n objects fx1 ; x2 ; . . .; xn g in space X, so when xi stands of the position of the candidate cluster head, the potential at node x generated by the candidate cluster head xi in the data field is as follows: jjxxi jj 2 Ui ðxÞ ¼ mi e o ð10Þ Where, jjx  xi jj is the distance between node and the candidate cluster head. This paper uses the value of RSSI between node and candidate cluster head node as the

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distance. mi is the quality of the candidate cluster head, here, we uses the ratio of the residual energy of candidate cluster head and the residual energy of all nodes in the ring. Influence factor o is used to control the degree of interaction between objects. The value of o will affect the intensity of cluster: the smaller the value the more compact cluster; the other hand, the sparse cluster [10]. Here the value is 5.

4.4 Inter-Cluster Communication Mechanism The communications among clusters has a great impact on the balancing of energy consumption after the establishment of the cluster. In LEACH, cluster heads communication with each other by single-hop, which causes the cluster head node consumes large amounts of energy by Long-distance transmission of data and dies early. Hence, the algorithm of Dijkastra is used in this paper to establish the minimum cost routing tree as the inter-cluster communication routing tree.

4.5 Algorithm Description Summing up the above analysis, the algorithm implementation steps are as follows: Step1. Network Initialization N nodes are deployed randomly in the monitoring area. The sink node broadcasts the information including the ring width h, the total number of nodes and the area radius R. Nodes in the network calculate its ring number according to the information sent from base station; Step2. Every node calculates the threshold Tk ðnÞ by formula (7) and generate a random number x (0\x\1). Then, the node determines whether they can be a cluster head by compare x with Tk ðnÞ; if the value of x is more than Tk ðnÞ; the node is to be candidate cluster head, on the other hand, to be non-cluster head; Step3. Candidate cluster head node sends out a message in its broadcast radius obtained by formula (10). The message includes its own ID and location information, if there are multiple candidates within the broadcast radius of the cluster head node, then select the candidate cluster head node who has large remaining energy as cluster head and the other must abandon the competition; Step4. The non-cluster head obtains its potential generated by each cluster head node with the formula (10) and selects the large potential as its cluster head node; Step5. Establish the minimum cost routing tree for communications by Dijkastra; Step6. If all the nodes are dead, the network is over; otherwise repeat the process 3–7.

A Ring and Clustering Routing Scheme Table 1 Experimental parameter values Parameters Value Monitoring area Base station coordinates Number of nodes Node initial energy Packet size Rings

(-240, -240) * (240, 240) (0, 0) 1000 2J 2000 bit 3

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Parameters

Value

x h emp Eelec EDA efs

2 5 0:0013 pJ/bit/m4 50  109 J=bit 5 nJ/bit/signal 10 pJ/bit/m2

5 Simulation Analysis 5.1 Simulation Environment and Parameters For our experiment, we divide the monitoring area into three rings evenly. The ring width h = 80, Cluster head node broadcasts a radius of 2h: Other parameters are shown in Table 1.

5.2 Performance Analysis This paper compares RCBDF with the classic clustering routing algorithm LEACH from energy efficiency and network lifetime. 1. Network lifetime The network lifetime is defined by the number of nodes in each round of survival in this paper. We compare the RCBDF protocol with the original LEACH protocol after 100 tests, as is shown in the Fig. 2, for LEACH, the first node died at 540 rounds and all nodes died at 1400 rounds, but, in RCBDF, nodes start to die at 1080 rounds and extend the network lifetime to 2500 rounds, it prolongs the network lifetime greatly by the algorithm of RCBDF compared with LEACH. This explains that the single-hop communications in LEACH will consume large energy in the large area with high-density nodes. However, RCBDF divides the area into many small ring areas, and uses the heterogeneous cluster formation in each ring. This strategy makes the RCBDF algorithm effective extend the network lifetime much more than LEACH protocol. 2. Energy efficiency In Fig. 3, it shows that the energy efficiency by LEACH and RCBDF routing algorithm. We can clearly see that the energy consumption of RCBDF significantly lower than LEACH. Thus, RCBDF can balance the node energy consumption distribution more effectively, reduce the network’s energy

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Fig. 2 Network lifetime

Fig. 3 Energy consumption per round

consumption greatly, and extend the network life cycle in effect; it is suitable for Advanced Systems which is better in intrusion detection and firewall monitoring.

6 Conclusion In this paper, we focus on reducing energy consumption of wireless sensor network, so we propose the RCBDF. The algorithm uses the concept of data field for cluster formation on ring network. Cluster by data field will not only ensure the coverage of the monitoring area but also adjust the density of cluster according to actual need. In selection of cluster head, the remaining energy of nodes and the distance between the transmitter and receiver are considered. And, we build the

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multi-hop communication routing tree by Dijkastra at stable communication phase. So, this algorithm can effectively reduce the link cost and greatly balance the node energy consumption. The most important, the algorithm has good scalability to adapt to large-scale network.

References 1. Liu Y, Luo Z, Xu K, Chen L (2010) A reliable clustering algorithm based on LEACH protocolin wireless mobile sensor networks. IEEE Computer Society, Singapore, 10–12 Sept 2010 2. Wu X, Chen G, Das SK (2008) Avoiding energy holes in wireless sensor networks with nonuniform node distribution. J IEEE Trans Parallel Dist Syst 19(5):710–720 3. Li J, Mohapatra P (2005) An analytical model for the energy hole problem in many-to-one sensor networks. In: Proceedings of 62nd IEEE vehicular technology conference. IEEE Communications Society, NewYork, pp 2721–2725 4. Li J, Mohapatra P (2007) Analytical modeling and mitigation techniques for the energy hole problems in sensor networks. Perv Mobile Comput 3(3):233–254 5. QING Li, Qing-Xin ZHU, Ming-Wen WANG (2006) A distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. J Softw 17(3):481–489 6. Heinzelman WR, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks.IEEE Trans Wirel Commun 1:660–670 7. Handy MJ, Haase M, Timmermann D (2002) Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In : Proceedings of the Fourth IEEE conference on mobile and wireless communications networks, Stockholm, erschienen, S 368–372, ISBN 07803-7606-4. World Scientific, Singapore, Sept 2002 8. Xu K, Meng Y, Liu Y, Xiong N, Yang LT, Zheng M (2010) An effective method to improve the resistance to frangibility in scale-free networks. The 13th IEEE international conference on computational science and engineering (CSE-2010). IEEE Computer Society, Hongkong, China, 11–13 Dec 2010 9. Zhang K-B, Orgun MA, Zhang P, Zhang P (2006) Hypothesis oriented cluster analysis in data mining by visualization. ACM, Venezia, Italy, May 254–257 May 2006 10. Dai X, Gan W, Li D (2004) Study of image data mining based on data field. J Comput Eng Appl

A Case Study on Cooperative Real-time Quality Monitoring Service Utilizing Sensor Networks Hangbae Chang, Jonggu Kang, Yanghoon Kim, Jaehwan Lim and Youngsub Na

Abstract Introduced an ERP system for increasing their profitability and a point of production system, shop floor controller system etc. for managing sites, however, data has still been managed manually in production sites, and the difficulty lies in the real-time process control due to limitations of the introduced information system. Therefore, this paper studied examples of cooperative real-time quality monitoring services utilizing sensor networks that could manage the real-time production process by integrating various technologies of ubiquitous sensor networks together with RFIDs considering characteristics for each workplace as well as for each industry. Keywords RFID


Sensor
Network
ERP
Management

H. Chang (&)
J. Kang
Y. Kim
J. Lim
Y. Na Department of Business Administration, Daejin University, Hogukro 1007, Pocheon-Si, Gyeonggi-Do, Korea e-mail: [email protected] J. Kang e-mail: [email protected] Y. Kim e-mail: [email protected] J. Lim e-mail: [email protected] Y. Na e-mail: [email protected]

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1 Introduction Manufacturing companies have an insufficient internal capacity following to adoption of a mass production strategy mainly with end products, lack experience of changing into advanced business based on knowledge such as core component technologies and knowhow etc., the production is moved to China and Southeast Asia of a low-wage system according to a level of labor-cost dependent manufacturing services, small and medium businesses have a vulnerable technological level, and businesses are mainly promoted by large companies. General manufacturing companies have introduced an enterprise resource planning (ERP) system to the production sites, and bar codes etc. are attached to use at a level of component packaging units by combining electronic devices with the existing installed manufacturing facilities. It is required studies on a method to manage production processes in real time by applying various ubiquitous sensor network technologies together with RFIDs considering characteristics for each workplace as well as industrial characteristics. Therefore, this paper would like to study on examples of a cooperative real-time quality service utilizing sensor networks by not restricting only to the existing static electronic tag (for example, RFID) [1] technologies and by applying the entire new IT technologies such as ubiquitous sensor networks etc. suitable to each site and situation.

2 Related Studies 2.1 Concepts of Sensor Networks Sensor networks are composed of smart sensor node hardware, sensor node platform software, network infrastructure, service server system for application services and so on. The smart sensor node hardware consists of general hardware sensors, which obtain (sense) surrounding environmental information, sink nodes, which are the link nodes for interworking with external networks, especially internet, and gateways [1]. This hardware has a characteristic that should be designed as a robust structure with low-power because it is difficult to maintain and repair and it has to be worked for from a few days to a few years once it is deployed [2]. It may become different significantly according to its application field and should have flexibility and modularity to use effectively even for any structure, consequently, it has a limit that could attach various sensors and easily replace by hardware fitted for the purpose [3]. The sensor platform software efficiently enables to operate sensor nodes and networks normally, and to implement sensor network based application services. It could be divided into a middleware for a sensor node itself (node middleware) and a middleware for providing and managing services of the sensor platform (network middleware.) The network infrastructure is composed of numerous sensors, ad-hoc and multi-hop communication between nodes [4]. It has a characteristic different from the existing wireless local area network (LAN) or cellular networks [5]. In addition, it is

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Fig. 1 Advantages of sensor network

constructed with tens to hundreds of sensor nodes, and it has also a function to cooperate each other by installing numerous sensor nodes within a narrow area.

2.2 Features, Advantages and Disadvantages of Sensor Networks The major features are that each node has a simple structure of resources restricted to a low-power and low-price system operated with batteries that is not a network for a general purpose that is data-centric in the communication, which supports wireless communications between heterogeneous nodes and so on [6]. In addition, it has features that carries out intelligent functions by giving a computing power, extends the installation domain even to various areas where could not be installed, numerous sensor nodes could be processed in the concentrated area, and sensor nodes are cooperated each other to perform the function. In addition, phenomena in the real world could be observed more accurately and reliably by merging information collected from the individual node, the compact system could reduce costs compared to the conventional sensor systems, and it could collect data observed in detail from various perspectives because a single task is carried out by dividing it into several nodes (Fig. 1).

3 Cooperative Real-time Quality Monitoring Service Utilizing Sensor Networks 3.1 Demand Survey on Applications of Sensor Networks The purpose of this study is not to ask the intention of introducing high-level sensor networks for manufacturing companies having no IT capability, but to conduct an exploratory study on applicability of sensor network technologies in

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terms of resolving insufficient unmet needs on the current company’s information service for manufacturing companies having a reasonable level of IT capability, so that the number of companies for surveying is limited to 37. For service models to be designed, a sensor network service is first drawn based on the result considering all the 3 sectors such as the demand company’s intention of introducing new technologies such as sensor networks etc., possibility of introducing on the production process, possibility of cooperating with partners. As a result of aggregating scores for each item, a cooperative real-time quality monitoring service model utilizing sensor networks is first designed for automobile and process industry.

3.2 Cooperative Real-time Quality Monitoring Service in the Automobile and Process Industry Domestic manufacturing companies focus on the quality control guaranteeing a high quality for responding more effectively and strategically to the reduced demand (market) in the fierce competition environment following an economic crisis. Because quality competitiveness for finished products is determined by a quality control level of subcontractors for components as well as a quality control level of the production company for finished products, the production company for finished products has developed a horizontal quality control activity by conducting guidance on quality and evaluation regularly for improving quality of subcontractor’s components. Because there are thousands to tens of thousands of components due to the characteristics of automobile and process industry, the existing regular visit or passive management method could not but have various limits in terms of management levels, effects and costs etc.

3.3 Process Analysis on the Cooperative Real-time Quality Monitoring Service in the Automobile and Process Industry Examining features of the component’s quality control process for the cooperative real-time quality monitoring service in the automobile and process industry, each subcontractor in the supply chain has a feature carrying out a similar process to inspect quality (Fig. 2). First, when raw and subsidiary materials etc. are stocked, a warehousing inspection is carried out, an inspection (quality inspection for semifinished products) is conducted between processes during the production (processing, assembly) process, and then a product inspection is performed for the end products. In conducting the warehousing, inter-process and product inspections like this, a result of the quality inspection is mostly made manually to enter and store it, and then its own shipment result report is written to share the quality information when delivering the end products. Therefore, because it is difficult to

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Fig. 2 Current process of car and device industry

expect the exact quality information and the cooperative infrastructure is also insufficient for remaining the quality competitiveness, there are many problems on effective guidance of improving the quality and traceability of the quality history. Like this, to resolve problems when monitoring a quality cooperatively in real time in the automobile and process industry, a service process should be required to design by applying sensor network technologies.

3.4 Process Design of the Future Service in the Automobile and Process Industry In order to promote a cooperative quality management policy effectively in the automobile and process industry, it is designed to construct environments for the preventive quality, and to obtain and analyze the quality information in real time. For improving a current limitation due to connecting the quality instrument with the measurement hub, which collects the measured data, via a wired network, it is designed by applying the Zigbee technology as Fig. 3 illustrates. It is designed to introduce the interworking technology between the quality instruments, which use the Zigbee technology, and the measurement hub, the low-level protocol converting technology for delivering the quality measurement information directly to the data server, the middleware platform technology for integrated management of heterogeneous data collecting devices and connections of applications, the Zigbee localization technology for managing the instruments and data’s history

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Fig. 3 Improves process design of can and device industry

information efficiently, and the real-time quality information monitoring and analyzing system at each point.

4 Verification of the Cooperative Real-time Quality Monitoring Service in the Automobile and Process Industry 4.1 Evaluation of the Attractiveness Because there are very few preceding studies that designed the business service evaluation model utilizing the sensor network technology, it is evaluated the model derived by using the ubiquitous service evaluation model. For the service model suggested in this paper, it is conducted the expert evaluation of 3 persons in the academy and 3 persons in the industry according to detailed evaluation items such as technical considerations, feasibility and acceptability etc. The evaluation’s result is as Table 1, and it is measured by 5-point measurement unit. The technical considerations for the designed service obtains relatively lower score than other evaluation domains, and such a result shows that the development level for core technologies is still insufficient for its commercialization. In addition, the feasibility for the designed service obtains higher scores except some services in general, and such a result is based on the expectation that the potential

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Table 1 Result of the attractiveness evaluation on the service process of sensor networks Evaluation item Detailed item Evaluation result Technical considerations

Feasibility

Cost validity for core technology Development level of core technology Current dissemination level of core technology Reliability of core technology Level of service values Availability of core resources Concreteness of the target market Size of the target market Definiteness of principal investment agents Profitability of the service

Score

Total

3.4 4.1 3.8 3.3 3.9 4.1 3.7 3.9 3.7 3.9

14.6

23.2

market for sensor networks would continue to grow in the future. Finally, the acceptability for the designed service is inspected that there is no aversion about applying the service to the production sites actually, and such a result is considered because the sensor network technology is not the emerged form of new technologies but is developed for improving a limitation of the existing technology (RFID.) Like this, as a result of the attractiveness evaluation for the designed service, it could obtain the result more than ‘average’ in terms of technical considerations, feasibility and acceptability.

4.2 Expected Effects of the Cooperative Real-time Quality Monitoring Service in the Automobile and Process Industry By applying sensor networks to the cooperative real-time quality monitoring service in the automobile and process industry, it could be obtained the qualitative expected effects as follows. The huge amount of data could be collected and backed up for quality, and the process and quality could be visually managed in real time. In addition, it could build the prior quality prevention and quality guarantee system, and the horizontal quality management system could be built by sharing and utilizing the quality information. In addition, the lead time for improving the quality such as an action for unsuitable components could be reduced quantitatively, and the input time could be decreased by entering the quality information in real time. And it could have an effect that the quality control cost is reduced by sharing the real-time quality information.

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5 Conclusion Even though the manufacturing companies have introduced an ERP system in the production sites, and they use bar codes attached in the component packaging unit level by combining electronic devices with the existing installed production facilities, information is not flowed efficiently between the enterprise management system and the production system. Not carrying out procurement, distribution and sales by collecting information on the production process in real time brings about another inefficiency. Therefore, in order to collect information on the production process in real time, this paper designs the method to do the cooperative real-time quality monitoring by applying various ubiquitous sensor network technologies together with RFIDs considering characteristics for each workplace as well as industrial characteristics, and the evaluation is conducted for experts. The propose service model could provide an integrated quality management system to general companies manufacturing products by operating several workplaces (factories) as well as the industry constructing a supply chain like the heavy industry field such as automobile, shipbuilding and process industry etc. In addition, if this service model is applied also to the industries handling large semiproducts such as the shipbuilding and process industry etc. rather than compact components like automobile parts, it is expected that there would be a better effect.

References 1. Bhattacharyya D, Kim T, Pal S (2010) A comparative study of wireless sensor networks and their routing protocols. Sensors 10(12):10506–10523 (MDPI AG, Kandererstrasse 25, CH4057 Basel, Switzerland) 2. Lee J, Kapitanova K, Son SH (2010) The price of security in wireless sensor networks. Comput Netw 54:17:2967–2978 (Elsevier Science BV, Amsterdam) 3. Raymond D, Marchany R, Brownfield M, Midkiff S (2006) Effects of denial of sleep attacks on wireless sensor network MAC protocols. In: Information assurance workshop, 2006 IEEE, pp 297–304 4. Liu Y, Wang C, Qiao X, Zhang Y, Yu C (2009) An improved design of ZigBee wireless sensor network. In: International conference on computer science and information technology 2009 (ICCSIT 2009) 5. Lei Y, Zhang Y, Zhao Y (2009) The research of coverage problems in wireless sensor network: wireless networks and information systems. In: International conference on WNIS ‘09 6. Xia J, Law CL, Zhou Y, Koh KS (2010) 3–5 GHz UWB impulse radio transmitter and receiver MMIC optimized for long range precision wireless sensor networks. IEEE Trans Microwave Theo Tech 58(12):4040–4051

High Availability Archive for Time-Based Data Tobias Haag, Thomas Meier and Izudin Dzafic

Abstract An approach for archiving time-based data is presented. The basic idea is to split data intelligently into separate time-dependent areas having different logical and physical properties. It is especially useful and sustainable when dealing with just a few but very big data tables, when all-time availability becomes a principal issue or when it is necessary to have a fast restore in case of a disaster recovery—even if the database reaches sizes over one TB. Keywords Archive systems storage Backup and restore Area





Power systems
Huge database
Oracle
Data
Short Term Area
Mid Term Area
Long Term

1 Introduction Generally historical data collected in industry processes is becoming more and more important for direct operational work, optimization decision support systems [1], disturbance studies and even quantitative risk analysis [2]. Over the past years the number of measurements increased significantly and so did the total amount of data to be stored. T. Haag (&)
T. Meier
I. Dzafic Siemens AG, Humboldt str. 59, 90459 Nuremberg, Germany e-mail: [email protected] T. Meier e-mail: [email protected] I. Dzafic e-mail: [email protected]

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Short Term Area Raid 1 / 10

Move data

Mid Term Area

Import: reload / restore Mid Term

Long Term Area

Raid 5 Export file

Export

Fig. 1 Logical and physical data storage

Archiving terabytes of time-dependent data leads to some technical problems. Usually in such systems a huge amount of dynamic data is stored with a high throughput up to several thousands of records each second. Terabytes of numerical values, status data and system messages—all of them addressed by timestamp— might be stored in just a few big data tables. Static data like configurations and master data however is stored in a lot of tables, but referring to its total data volume it is more or less negligible for the overall size of the database. This is also true for Power Systems, where the described solution is already in use. On the one hand recently archived data is of greatest interest. For example a Power System’s operator uses trending based on very recently archived values in order to compare data and to control the power network. If necessary, this data must be edited manually. Data availability is vital and very time critical. On the other hand data must also be kept online for up to a few years in order to support reporting and other significant investigations. Appropriate queries are usually less time critical as the queries of the operators, but must be available for monthly or yearly reports. However manual changes are quite unlikely for nonrecent data. Finally old data must—in any way—still be available for several years for legal reasons. 10 years and more are quite usual. It is commonly accepted that this old data can be exported out to files and stored somewhere, for example on tapes. If necessary, it must be reloaded to the archive system first. Another requirement is that the database must be available again in after short time in case of a failure—even if the database is restored through a backup. One possibility to get a recovery faster is to reduce the size of the backups. Since still all data must be backed up, the data which shall be available soon have to be backed up in a different way as older data. This means in case of Power Systems, the data of for example the last three months has to be available soon and the system can store new values into the database. The restore of older data may take more time, since it is not critical for operational use cases. Because the old data has to be exported to files for offline archiving, these files provide the possibility to be used as backup (Fig. 1). For the outlined solution the database is split into three parts (logical locations): 1. Short Term Area: contains the most recent data. 2. Mid Term Area: contains older data. It is still online, but data is considered to be read-only. The Mid Term Area should be stored on separate disks.

High Availability Archive for Time-Based Data STORAGE_UNIT

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TABLE_ID TABLE_GROUP_ID TABLENAME TABLESPACE LOCATION TIME_STAMP_FROM TIME_STAMP_TO PARTITION_RANGE

ARCHIVE_TABLE_GROUP PK

TABLE_GROUP_ID

TABLE_GROUP_NAME TRANSFER_RANGE_HOURS

EXPORT_DIRECTORY IMPORT_DIRECTORY

PARTITION_RANGE_UNIT

Fig. 2 Entity-relationship diagram: archive tables

3. Long Term Area (LTA): consists of data having been exported to external files. It has to be reloaded, if a user wants to access it. Because the files are already created when moving data from Short Term Area to read-only Mid Term Area, they incidentally also constitute a backup of the Mid Term Area. This split implies some severe logical consequences. As the data is no longer stored in one single relational table, the actual (and varying in time) location must be known for access. This task can be solved by an API, which defines an interface and returns the data from the correct tables. It can also be used to initiate a reload of Long Term data, in case the requested time range is stored completely or partly in the Long Term. Since the data of Mid Term Area was already exported to Long Term Area files, it can no longer be allowed to modify the data in the Mid Term Area—otherwise these changes would get lost in case of a recovery. Because the API is also used for writing/editing data, it must also take care of this important restriction. The Mid Term Area can be stored on a RAID 5 (‘‘RAID’’ = Redundant Array of Independent Disks) because there is mainly read access, except the movement of data from Short to Mid Term and from Mid to Long Term. The Short Term Area should be stored on a RAID 10 or RAID 1 for best read and write performance.

2 Algorithm of Data Transfer The described solution is implemented based on Oracle Enterprise Edition [3] with the Partitioning Option [4]. Although Oracle already provides all necessary basic functionality, the actual solution must still be implemented. Currently there is not any feature like ‘‘Short, Mid or Long Term solution’’ directly offered by Oracle. As the API and also the internal management of the data must know where the data is located, some bookkeeping tables are used storing the necessary information in relational tables (Fig. 2). The information is split into following parts: 1. Data archives can be spread over Short, Mid and Long Term. The table group joins these areas logically together. Therefore every data archive is assigned to one table group consisting of Short, Mid and Long Term parts.

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Fig. 3 Entity-relationship diagram: long term archiving

2. The underlying data table. In case of the Long Term Area this can also be a ‘‘logical’’ table, which does not exist physically in the relational database. It is nothing but a bookkeeping entry. Thus a table group consists of the following tables stored on different physical locations: (a) (b) (c) (d) (e)

One physical Short Term Area table One physical Mid Term Area table One ‘‘logical’’ Long Term Area table (just a bookkeeping entry) 0 to n only temporarily existing intermediate transfer tables 0 to n real reload tables, if data was reloaded from LTA to the online system

3. Each table consists of 0 to n storage units, which reflects the Oracle partitions for the Short Term and Mid Term tables. The time range of the data contained by a particular storage unit gets recorded. This time range is left-justified (end time is excluded), because Oracle’s range partitioning is used and the partitions are created with the ‘‘less than’’ statement. The storage unit’s current status is changing during the move from Short Term to Long Term. This must be recorded very detailed in order to enable the process to resume in any case without manual actions. 4. The files of Long Term Area (LTA) are grouped in LTA units. One LTA unit consists of one or more files and one or more storage units. Usually one LTA unit consists of one file only, but it is possible to limit the size of the LTA files. Also the parallel export option from Oracle could be used, if a LTA unit consists of more than one file (Fig. 3). The whole mechanism is working in the background. All background jobs are implemented in PL/SQL and started by the Oracle Scheduler [5, 6]. The following steps have to be executed regularly:

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1. The partitions for the Short Term tables must be created and the information must be stored in the storage units table. As in Power Systems new values get the current timestamp, it is not necessary to create too many partitions in advance. But nevertheless at least partitions for some days should be available. So there is time enough to react to potential technical problems—for example no space left on disk—without disturbing the running applications. 2. The partitions must be analyzed for better performance. The statistics on the tables/partitions are used by the Oracle optimizer to get the best read performance. Analyzed but empty partitions, which are filled up later, decrease the performance until the partitions are analyzed again. A separate background job is used for statistics gathering which guarantees that only filled partitions are analyzed, as it is known, when the data is stored. 3. The data must be moved from the Short Term Area to the Mid Term Area as soon as the Short Term retention period is reached. This is the core functionality and it must be done in the following steps: (a) Merge partitions to bigger partitions. This step is optional but it helps to reduce the number of partitions in Mid Term. It can be used to increase the retention period of the Mid Term because there is a restriction in Oracle concerning the maximum number of partitions in one table (about 32000 partitions). But the experience shows, that the access to partitioned tables slows down already, if the table has more than 10000 partitions. (b) Create a table with the exact physical layout as the partition. This table will be used later on for the export to files and also for the move to Mid Term. There has to exist exact one table for each partition, which has to be moved. For easier maintenance it gets the same name as the partition. (c) Exchange the data between the partition and the table. Using the command ‘‘ALTER TABLE … EXCHANGE PARTITION …’’ it is only an internal bookkeeping change from Oracle. The extents of the partition are assigned to the table and vice versa. This also generates nearly no entry in the redo logs and therefore also not in the archived redo logs. This helps to reduce the required space for archived redo logs during transfer from Short Term to Mid Term. (d) The Long Term archive files have to be created now. This is just an export of the transfer tables using the Oracle Data Pump export [7]. Data Pump has a PL/SQL interface [8], this makes it easy to use in the background job scheduler with the Oracle Scheduler. The export has to update the LTA unit information. Creating the LTA files has to be finished before moving the data to the Mid Term, otherwise there would be no backup of this data. (e) The next step is to move the transfer tables to the Mid Term. It is a physical move using the Oracle ‘‘ALTER TABLE … MOVE …’’ command. This statement also produces nearly no entries in the redo logs. It may decrease the fragmentation of the data segment because originally inserted data might not have been inserted concurring to the index.

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(f) When the tables are moved, the partitions must be created on the Mid Term table. (g) Afterwards the Oracle ‘‘EXCHANGE’’ command is used to exchange the data from table to the partition in Mid Term. If there are indexes on the tables, they have to be rebuilt then, otherwise they will be not usable. In case of index-organized tables the rebuild is not necessary. (h) Drop the empty transfer tables. (i) Analyze the transferred partitions in Mid Term, as the optimizer statistics get lost during transfer and would be also no longer valid as the move command results in an optimized storage of data by Oracle. 4. When the retention period in Mid Term is reached, the partitions have to be dropped. As the Long Term archive files are already created, just the bookkeeping must be updated, that the data is now in Long Term.

3 Backup and Restore There are also some additional commands necessary for backup and restore to make this approach working [9, 10]. When the tablespaces for the Mid Term are created, they have to be excluded from the backup. RMAN’s command ‘‘CONFIGURE EXCLUDE FOR TABLESPACE …‘‘excludes a tablespace for all backups made after execution of this command. When a restore is necessary, the Mid Term tablespaces must be dropped first, otherwise RMAN will try to restore them. This can be done by SQL command ‘‘ALTER DATABASE DATAFILE ‘…’ OFFLINE FOR DROP’’ before the restore command. The tablespaces must be also excluded at recovery: ‘‘RECOVER DATABASE SKIP TABLESPACE …’’. After having successfully restored the Short Term, the Mid Term tablespaces must be dropped and recreated. Finally the configuration to exclude the tablespace from backup must be executed again, because this configuration was lost with the drop of the tablespace. Some PL/SQL procedure recreates the Mid Term tables and updates the internal bookkeeping tables. Since logically for the API the overall time range covered by Mid Term must be contiguous, the restore has to be started with the newest data and has to be continued in direction from newest to oldest—an important restriction of Mid Term restoration. With this approach the normal move from Short Term to Mid Term can be done already, even if the Mid Term is not yet completely restored. If the restore takes long, it is possible, that some data has already reached the retention period and thus has no longer to be restored. Because range partitions are used and created with the ‘‘less than’’ statement, the partitions have to be split during restore of the Mid Term. This is supported by Oracle with the ‘‘ALTER TABLE … SPLIT PARTITION …’’ command. Because a split of partitions causes an internal renumbering of the partitions by Oracle, this approach will slow

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down the more data is restored in Mid Term and the split command has to be executed. Therefore all necessary partitions are already created when recreating the Mid Term. In case the restore of Mid Term takes some time and the retention period of an empty partition is reached, the partition will be just dropped by the normal background process and the status of the storage unit will be updated.

4 Restore/Reload of Mid Term Area Reload and restore are mostly automated. The only exception is that the necessary files are not loaded automatically from a backup medium like tape—some user interaction is still needed. The necessary files will be listed by restore/reload. The load can be also automated, but as there are different solutions available for the archiving a generic method is not available. The reload of Long Term data and restore of Mid Term Area are nearly the same, there are just a few more steps necessary in case of a restore. The following steps are executed during restore/reload: 1. Start of the reload or restore. The time range and the type of data must be specified. In case of a reload this information is already passed by the API. Internally a reload/restore job will be created. The jobs are stored in tables, so it is possible to check the status in different sessions. A background job, which is implemented in a way, that it can be restarted in case of any issues, takes over the work. 2. First the time range of the reload/restore will be adapted to fit to the partition size because only partitions as a whole can be imported. 3. Now the files to import will be identified using the LTA bookkeeping tables. As long as the necessary files are on disk, the reload/restore imports them step by step. If there are one or more files missing, the background job lists them in a way, that the user can see them through the UI. As long as the reload/restore job is not cancelled it will retry to import the files after a configured time. 4. In case of a reload, the job is finished after the import. The background job inserts for each imported table (which was a partition before) an entry in ARCHIVE_TABLE, so the API can find the reloaded data. 5. In case of a restore, the reloaded table will be exchanged with the corresponding partition in Mid Term. The reloaded table will be dropped afterwards. The status of the storage unit will be updated, that the data is available in Mid Term. At each start of a reload job, the user can specify how long the reloaded data should be kept in the online system. It is possible to modify this date as long as the data is online. It may happen that the same storage unit is part of more than on reload job. In this case, the data will be only unloaded, if all reload jobs are expired. The unload itself is just a drop of the table and an update of the

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Table 1 Database sizes of two productively working reference systems Data inflow MB/min Online retention period in months Size in GB 8 3.5

Total

Short Term

Mid Term

Short Term

Mid Term

24 4

3 2

21 2

980 300

6,800 300

bookkeeping table. The expiration of reload jobs is checked by the same background job, which also executes the reload. Furthermore a separate tablespace is used in order to limit the maximum reloaded data. This tablespace is not auto-extendable, so the maximum used space for reload is already allocated at installation. If the required space was underestimated, the tablespace size can be still modified using Oracle commands.

5 Result The Short/Mid Term solution provides the flexibility to fulfill different customer needs by also assuring a stable overall workload and an acceptable performance— even if Short and Mid Term data accesses are mixed. The following table shows two exemplary configurations, which are already in productive use for more than 2 years now. The configurations differ due to different use cases. The first system needs data over two years for some reports, which is not required for the second system (Table 1).

6 Conclusion and Risks This paper presented an approach for reducing the restore time of a large database. The goal was to provide an algorithm which • makes it possible to restore the most recent data fast; • does not accept data loss and ensures high data consistency of Short and Mid Term Area (by moving complete partitions and providing a detailed bookkeeping and an intelligent API for data retrieval); • can be automated; • just uses Oracle features only. The current version of Siemens’ Historical Information System (HIS) contains all these features already implemented and proven in practice. A very exact bookkeeping is essential for backup, restore and data retrieval by API. Any failure or even any inaccuracy (e.g. concerning left- or right-justified time ranges) can

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cause significant data loss—logically (data retrieval by API) or even physically (backup/restore). By accessing the intermediate transfer tables if necessary, the API is basically designed to provide data access even while the Short to Mid Term data transfer is in progress. However, it cannot be guaranteed that data can be read without any gap in all potential circumstances without the API having logically locking the transfer. But HIS avoids any lock by intention, because it must not be possible at all to block the important (background) data transfer by simply reading data. The coded algorithm is critical concerning transactional locking issues, potential error handling and the possibility to restart broken jobs again. Since Oracle’s SQL data definition language commands like ‘‘ALTER …’’ already terminate currently open transactions, on the one hand no regular rollback is possible anymore and on the other hand any intermediate changes in bookkeeping tables become persistent. In case of failure all changes must be undone explicitly. This is why any real implementation must be very carefully checked and intensively tested.

References 1. Niu CL, Yu XN, Li JQ, Sun W (2005) The application of operation optimization decision support systems based on data mining in power plant. In: Proceedings of 2005 international conference on machine learning and cybernetics 2. Rastogi UJ, Srinivas VK, Guptan RG (2010) Databases and their analysis for applications in quantitative risk analysis of NPP. In: 2nd international conference on reliability, safety and hazard (ICRESH), 2010 3. Oracle Database, http://www.oracle.com/technetwork/database/enterprise-edition/overview/ index.html 4. Oracle Partitioning, http://www.oracle.com/technetwork/database/options/partitioning/index. html 5. Oracle Scheduler Concepts, http://download.oracle.com/docs/cd/E11882_01/server.112/e17120/ schedover.htm#i1106753 6. Oracle Scheduler: Scheduling of Jobs and Administration, http://download.oracle.com/docs/ cd/E11882_01/server.112/e17120/scheduse.htm#i1033533. http://download.oracle.com/docs/ cd/E11882_01/server.112/e17120/schedadmin.htm#i1007589 7. Oracle Data Pump, http://download.oracle.com/docs/cd/E11882_01/server.112/e16536/part_ dp.htm#i436481 8. Oracle Data Pump PL/SQL Interface, http://download.oracle.com/docs/cd/E11882_01/appdev. 112/e16760/d_datpmp.htm#i1007277 9. Oracle Backup and Recovery User’s Guide, http://www.oracle.com/pls/db112/to_pdf? pathname=backup.112/e10642.pdf 10. Oracle Backup and Recovery Reference, http://www.oracle.com/pls/db112/to_pdf?pathname= backup.112/e10643.pdf

A Multivariate Spatial Outlier Detection Method Based on Semantic Similarity Fatemeh Azam and Ahmad Baraani-Dastjerdi

Abstract Spatial outlier detection is usually done in local neighbourhoods. We use an interpretable similarity measure to calculate the similarity between the regions of influence of spatial objects using spatial and non spatial attributes and relations both in the semantic description and in the similarity measure. A systematic method for similarity threshold selection is presented that can be used to categorize objects by their behavioral pattern in semantically similar neighborhoods. This paper’s main contribution is developing a multivariate two level outlier detection method. Real world data is used to evaluate our method. Keywords Spatial outlier detection

 Semantic similarity

1 Introduction A spatial outlier is a spatially referenced object which its spatial or non spatial attributes are significantly different from other spatial objects in its neighbourhood. In this paper we’re interested in finding patterns of behaviour in spatial data sets and outliers are data objects that do not follow any regular pattern. As it is expected that objects located nearby tend to show similar behaviour, therefore outlier detection is usually done in local neighbourhoods. An Objects neighbours are usually determined by spatial distances [1–4]. Since spatial objects are F. Azam (&)  A. Baraani-Dastjerdi Department of Computer Engineering, University of Isfahan, Isfahan, Iran e-mail: [email protected] A. Baraani-Dastjerdi e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_21, Ó Springer Science+Business Media B.V. 2012

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influenced by their surrounding, therefore for any consistent analysis, spatial and non spatial attributes and relationships as well as the object’s region of influence need to be taken into consideration. A spatial objects’ region of influence contains underlying spatial or non spatial attributes and relations which can affect the objects’ behavior [5]. This paper proposes a method in which effective parameters and attributes on an object’s behavior are extracted from different data sources and are used to model the objects’ behavior in its region of influence. An interpretable similarity measure and a systematic similarity threshold selection algorithm will be used to categorize objects by their behavioral pattern. Therefore outlier detection will be conducted both globally and locally in semantically similar neighborhoods. The paper is organized as follows. In Sect. 2 we will present some related work in the area of spatial outlier detection. In Sect. 3 this paper’s proposed method is presented. Section 4 gives experimental results on real world data taken from www.mapcruzin.com and evaluates the method. Section 5 concludes the paper.

2 Related Work Spatial outlier detection methods fall in two main categories, methods based on multi dimensional spaces and graph based methods. The first models spatial data objects as points in a multi dimensional space and uses concepts such as distance [1, 2], density [3] and wavelet transformation [6, 7] for outlier detection. In these methods neighborhoods are defined according to the Euclidean distance. In the graph based methods neighborhoods are defined by the existence of edges between spatial objects or the adjacency spatial relationship [4, 8]. Other methods form a network of grids on the whole space and use KNN to find the neighboring objects [9]. In most of the mentioned methods neighborhoods are defined according to distance, spatial relations or clustering methods. These methods do not consider the spatial objects’ region of influence which can be used to model semantic relations between spatial object attributes. However [5, 10] define a method to form micro neighborhoods around each spatial object and use the Jacard coefficient to calculate the number of matches between non spatial features of any two adjacent micro neighborhoods and combine them to form macro neighborhoods if the Jacard coefficient meets a certain threshold. This method uses exact matches to define similarity between data objects and does not take into account the underlying semantic relations or dependencies between attributes. Other methods suggest the use of anthologies to define semantic similarity and figure causal relations between data objects [11]. But the use of heterogeneous anthologies for spatial and non spatial data as well as different conceptual levels of comparison and similarity calculation makes these methods fairly complicated in design and implementation. In another way outlier detection can be seen as univariate or multivariate. Multivariate outlier detection is able to detect irregularity along multiple or single attributes of the data object. The most common of these methods is based on the

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Mahalanobis distance introduced by P. C. Mahalanobis in 1936. The Mahalanobis distance is basically for continuous data, although methods have been presented which extend it to be used in categorical data as well [12]. Other methods in multivariate outlier detection implement a similarity function between data objects and use KNN method for outlier detection [13]. In semantic outlier detection we need an expressive distance function that not only calculates the distance between multivariate data objects, but is also able to express and analyze the difference/ similarity between attribute values in the form of associate rules. Semantic similarity requires considering spatial and non spatial features and relations both in the semantic description and similarity measure. The mentioned requirements of this literature make each of the pre-mentioned methods inappropriate in some senses.

3 Proposed Method With the use of a semantic similarity measure for spatial data we intend to find similarly behaving data objects in their region of influence, and change the concept of neighborhood from spatial distance to semantic neighborhoods, containing similarly behaving data objects. In fact each semantic neighborhood is comprised of smaller neighborhoods, called the region of influence of the data objects. Since the notion of similarity is strongly affected by the density and distribution of other objects relevant to a data object [14], we argue that similarity threshold selection should be performed per object, according to the density of similarity measurement values, and a novel systematic approach for threshold selection is presented. Semantic neighborhoods are created by combining similar regions of influence. Outlier detection will be done both locally in each semantic neighborhood and globally across the whole space using separate novel scoring functions.

3.1 Region of Influence Creation The spatial objects under study for outlierness will be represented by a point in space. In order to define the spatial boundary of the region of influence for a data object we will use Voronoi cells [15]. The Voronoi diagram makes it possible to determine the position of the nearest cell generator to a point of query, thus we will use it to define the spatial boundary of the region of influence for the data objects. Semantic relations and associate rules between the data objects are extracted from the information layers describing the objects and their region of influence; therefore in order to create reasonable rules it is important that the selected attributes are correlated and interdependent. In spatial data mining a common practice is to materialize spatial relationships into traditional input columns [16–19]. Semantic similarity calculation

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3.2 Semantic Similarity Calculation There are two main assumptions about semantic similarity, commonalitydifference and spatial distance. In this paper the similarity measure will be based on commonality-difference between attribute values. In the proposed method the similarity between each two values of an attribute is evaluated based on the semantic relations between other attributes under the condition that these values hold. Therefore similarity between each attribute value pair is first calculated based on the conditional probability distributions of other attributes under the condition that those values hold. Then similarity between two data objects is calculated as the sum of similarity between their attribute values. Some of the possible measures that can be used for measuring similarity between probability distributions are Kullback–Leibler [20], Lin [21] and Rached [22]. In this paper the Kullback–Leibler will be used. In order to apply this measure to the mentioned problem in this paper, we will use [23]’s notation. First the difference between each pair of possible attribute values vi ; v0i of attribute of attribute A are calculated based on the conditional probability distribution of other attributes. This measure is defined as follows:    0 ! X       P v jv P vj jv j i uA vi ; v0i ¼ vj P vj jvi lg  0  þ P vj jv0i lg  i  ð1Þ P vj jvi P vj jvi The second step of the similarity measurement process is to calculate the difference between two data objects X and Y by summing the difference between the values of corresponding attributes Ai , and is calculated as follows: X u ðX; Y Þ ¼ u ðx ; y Þ ð2Þ i¼1 Ai i i

3.3 Similarity Threshold Calculation After similarity measurement a threshold value that signifies the values measured as similar or non similar is needed. As the distance between an object and its neighbors decreases its density increases, and as a result the perception of similarity between objects decreases [14, 24, 25]. This notion implies that for dense closely distributed values of u the distance threshold should vary from loosely distributed values of u. In fact we argue that for each object its threshold value depends of the range of u ðX; Yi Þ values. The proposed method for threshold selection in this paper is based on significant changes in similarity measures u ðX; Yi Þ in a sorted list of similarity measures between data object X and other data objects Yi . Our threshold selection algorithm implements the SD test by considering a normal distribution on u ðX; Yi Þ. The test accepts values less than l þ cr as values with low dispersion and the first value that is more than l þ cr fails the test because it increases the dispersion. In order to prevent large or unreasonable values for the threshold, the average percent of objects possible of being similar to a reference data object should be calculated and

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Fig. 1 Threshold selection algorithm

Fig. 2 Semantic neighborhood creation

applied as a higher filter on the algorithm (we call it sx ). The threshold selection algorithm is presented in Fig. 1.

3.4 Semantic Neighborhood Creation After threshold selection, similarity lists are built for each data object X, by comparing u ðX; Yi Þ for each data object Yi with the threshold value. The similarity lists can then be merged according to the transitive property to form partitioned semantic neighborhoods with similar properties. The algorithm for creating semantic neighborhoods is shown in Fig. 2. Outlier detection After grouping similarly behaving objects in semantic neighborhoods, outlier detection can be conducted in two levels called global and local. Global outlier score (GOS): Global outlier detection detects the ‘‘outlierness’’ of a data object based on its positioning relevant to its semantic neighborhood and the rest of the data objects. A global outlier score is calculated for each data object and we define it as the distance between a data object X and the object that caused its threshold value Ythrsh ; compared against the number of objects in X’s semantic neighborhood. GOS is mathematically defined as:

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u ðX; Ythrsh Þ size of ðsem neighborhoodkx Þ

ð3Þ

Where u ðX; Ythrsh Þ, is the distance between data object X and the object that caused the threshold value for X, and sem neighborhoodkx is X’s semantic neighborhood. The GOS compares the distance that partitions X from the rest of the dataset against the number of objects neighboring X in its semantic neighborhood. The higher u ðX; Ythrsh Þ and the lower the size of ðsem neighborhoodkx Þ, the more outlier score an object achieves. This is because we expect a global outlier X to be significantly different from the rest of the data set, while belonging to a small sized semantic neighborhood. Ythrsh for X is a the first significantly different value in the sorted list of u ðX; Yi Þ and conceptually it partitions X from the rest of the objects, therefore this distance qualifies as an appropriate distance that is able to compare how significantly different X is from the rest of the data objects. The size of X’s semantic neighborhood is also another parameter that can score its ‘‘global outlierness’’. Comparing these two parameters by means of a fraction enables us to combine relevant affective parameters on global outliers in a single scoring function. According to this function objects belonging to single member or very small sized groups that have a relevantly high u ðX; Ythrsh Þ are expected to be more ‘‘globaly outliered’’. Local outlier score (LOS): Local outlier detection detects the ‘‘outlierness’’ of a data object in its local set. For each object in a local set an outlier score in defined as the minimum distance between data object X and the rest of the objects in its semantic neighborhood compared against the maximum distance between other objects in X’s semantic neighborhood and is mathematically defined as follows:   MIN u X; semneighborhood kxX   Max u Xisemnegihborhood k ; Xjsemneighborhood k xX



ð4Þ

xX

Where MIN uðX; sem neighborhoodkxX Þ is X’s distance   to the rest of the semantic neighborhood, and the parameter Max u Xisem neighborhoodk ; xX

Xjsem

neighborhoodk xX

ÞÞ is the maximum distance between other data objects in the set,

or mathematically put, the diameter of sem neighborhoodkxX . The local outlier of a set is a data object that is different enough to be considered an outlier and similar enough to be a ‘‘local outlier’’. LOS indicates that a data object is ‘‘locally outliered’’ if it is far enough from the rest of the set, while the rest of the set is close enough to each other. A high value of MIN uðX; sem neighborhoodkxX Þ means that  a local data object is far enough from  the rest of the set and a low value of Max u Xisem

neighborhoodk xX

; Xjsem

neghborhoodk

means that the other data objects

xX

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in the set are close enough to each other. The combination of these two parameters in one fraction enables us to score the local outlier by the parameters that are affective on it.

4 Experimental Results and Evaluation In this section we will investigate the results of applying the proposed method on real world data. SQL Server 2008 was used for managing the spatial data. Matlab’s ‘‘voronoin’’ function was used for creating Voronoi cells and vertices. A code was written for turning the resulting cells and vertices into shape files. In order to evaluate the proposed method, experiments were conducted on real world data taken from http://www.mapcruzin.com. The data sets dams00x020 (United States dams), and county20thcentury (demographic information by county in the USA) were used. In the experiments the objects of study for outlierness are ‘‘the dams of California’’ and the behavior being investigated is ‘‘the hazard of the dams malfunctioning’’. The documentation provided with the dams00x020 dataset describes the attribute ‘‘Hazard’’. Based on this information the potential hazard of a dam is influenced by the regions’ population or significant infrastructure and agricultural resources in the region nearby the dam. Therefore the information layer added was the county20thcentury dataset showing demographic information by county in the USA. Data regarding the state of California was taken from these datasets. Attribute selection was based on correlation calculation between the ‘‘Hazard’’ attribute and other attributes. Continues attributes were transformed to categorical data using Weka’s MDL module. The correlation test was conducted using Chi-square test for dependency with 95% significance level. The added spatial relationship to the context was the adjacency relationship between dams, which was added as a new attribute based on the locus of points between a defined adjacency length and adjacency width. Using the algorithms presented in the proposed method threshold selection and semantic neighborhood creation were preformed. The parameters used in the algorithms were: sx ¼ 2%; c ¼ 1; a ¼ 99%; e ¼ 0:5

4.1 Evaluation Method Comparison between our method and the KNN method is done for global and local outlier detection. Our method for global outlier detection is compared with the KNN method and Mahalanobis distance by comparing how early they are able to find actual outliers. In the KNN method the outlier score is the distance to the kth nearest neighbor. To evaluate the methods, we count the number of true outliers in the top p portion of the sorted scores, where p ¼ dn; and n is the number of actual outliers. Let p0 be the number of actual outliers in the top p predicted outliers.

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Fig. 3 Global outlier detection, comparing proposed method with Mahalanobis and KNN

Table 1 Global outlier detection accuracy compared with the KNN method Method Accuracy for d ¼ 1 Accuracy for d = 2 Proposed method K=3 K=6 K = 10 Mahalanobis

7/10 5/10 5/10 5/10 6/10

10/10 6/10 6/10 6/10 8/10

Fig. 4 LOS in one of the semantic neighborhoods of proposed method

0

The accuracy of the method is measured as pp . 10 actual outliers added to the dataset were used for evaluation. Figure 3 shows method accuracy for d ¼ 1 (last 10) and d ¼ 2 (last 20). As can be seen in Fig. 3 the five global outlier scores series tend to show a higher slope at the tails of the series which are supposed to be outliers. But in Table 1 it can be seen that the proposed method shows better accuracy in finding the actual outlier in the sorted outlier score list. Another interesting observation was that all actual outliers were placed in single value sets, which further confirms the validity of single sets being global outliers.The score we proposed for local outliers was used to score the local outliers. Since local outlier detection fairly depends on local set creation we present our local outlier detection results in one of the different semantic neighborhoods created based on our semantic neighborhood creation method in Fig. 4.

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5 Conclusion The main contributions of our method are using semantic similarity for spatial outlier detection and defining two conceptual levels of outlier detection each with its own score functions. Comparing our results with KNN and Mahalanobis scores, showed that our method is semantically more reliable and interpretable.

References 1. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the ACM SIGMOD 2. Bay SD, Schwabacher M (2003) Mining distance-based outliers in near linear time with randomization and a simple pruning. In: Proceedings of the ACM SIGKDD 3. Hu T, Sung Y (2003) Detecting pattern-based outliers. Science Direct- Elsevier, Amsterdam 4. Shekhar S, Lu C, Zhang P (2001) Detecting graph-based spatial outlier: algorithms and applications (a summary of results). Technical report Computer Science and Engineering Department, UMN, pp 01–014 5. Adam NR, Janeja VP, Atluri V (2004) Neighborhood based detection of anomalies in high dimensional spatio-temporal Sensor Datasets. ACM symposium on applied computing 6. Barua S, Alhajj R (2007) A parallel multi-scale region outlier mining algorithm for meteorological data. In: Proceedings of the ACM GIS 7. Lu CT, Kou Y, Zhao J, Chen L (2006) Detecting and tracking regional outliers in meteorological data. Science Direct, Elsevier, Amsterdam 8. Jin W, Jiang Y, Qian W, Tung AKH (2006) Mining outliers in spatial networks. In: Proceedings of international conference on database systems for advanced applications 9. Kou Y, Lu CT, Chen D (2006) Spatial weighted outlier detection. In: Proceedings of the SDM 10. Janeja VP, Atluri V, Adam NR (2004) Detecting anomalous geospatial trajectories through spatial characterization and spatio-semantic associations. ACM symposium on applied computing 11. Janeja VP (2007) Anomaly detection in heterogeneous datasets. Rutgers, The State University of New Jersey, Newark 12. McCane B, Alber M (2007) Distance functions for categorical and mixed variables. Patt Recog Lett 29:985–993 13. Chandola V, Boriah SH, Kumar V (2008) Understanding categorical similarity measures for outlier detection. CS technical report 08-008, Computer Science Department, University of Minnesota 14. Yoo C, MacInnis DJ (2004) Same or different? how distance and variation affect similarity judgments. Psychol Mark 21:209–277. doi:10.1002/mar.20002 15. Okabe A, Boots B, Sugihara K, Chiu S (2000) Spatial tessellations: concepts and applications of Voronoi diagrams. Wiley, West Sussex, pp 291–410 16. Quinlan J (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Fransisco 17. Barnett V, Lewis T (1994) Outliers in statistical data, 3rd edn. Wiley, West Sussex 18. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of very large databases 19. Jain A, Dubes R (1988) Algorithms for clustering data. Prentice Hall, Upper Saddle River 20. Kullback S, Leibler R (1951) On information and sufficiency. Ann Math Stat 22:786 21. Lin J (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inf Theo 37(1):145–151

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22. Rached Z, Alajaji F, Campbell L (2001) Rényis divergence and entropy rates for finite alphabet markov sources. IEEE Trans Inf Theo 47(4):1553–1561 23. Le SQ, Ho TB (2005) An association-based dissimilarity measure for categorical data. Elsevier Patt Recog Lett 26:2549–2557 24. Krumhansl CL (1978) Concerning the applicability of geometric models to similarity data: the interrelationship between similarity and spatial density. Psychol Rev 85:445–63 25. Appleman IB, Mayzner P (1982) Application of geometric models to letter recognition: Distance and density. J Exp Psychol Gen 111:60–100

Cohesion Between Size of Commit and Type of Commit Maryam Marzban, Zahra Khoshmanesh and Ashkan Sami

Abstract Predicting what type of work was performed by developers in OSS has attracted several researchers. Previous studies have shown that fixing bugs are usually done by modifying very few files. In this study, we try to predict the trends and characteristics of how developers commit source code to version control systems (SVN, CVS, etc.). We want to investigate if number of files that are committed together is an indication of type of activity. In other words, is number of files in a commit related to the type of activity? Interestingly, we found that a strong correlation exits between type of activity and number of files involved in a commit. To make sure our study is not biased for a specific project, we have analyzed ten projects. Parsing messages in commits and processing the results, we found in coherent with previous studies bug fixing involves few files in a commit and commits that affect several files are usually related to adding functionality. Keywords Version control system characteristic Cohesion between size of commits and activity


Type of

activity in OSS




M. Marzban (&)
Z. Khoshmanesh
A. Sami CSE and IT Department, Shiraz University, Shiraz, Iran e-mail: [email protected] Z. Khoshmanesh e-mail: [email protected] A. Sami e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_22, Ó Springer Science+Business Media B.V. 2012

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1 Introduction Over the years, open source software has become an important cornerstone of the software industry, and the version histories of these systems are typically recorded in version control system such as SVN or CVS. This history is a valuable source of data for understanding the evolution process [1]. In the paper presented here we aim to better understand committer’s activity. We want to investigate if there is any relation between committer’s activity and number of files that are committed together. We feel that a better understanding of this process will allow us to predict a type of activities for the future maintenance tasks. Commits recorded by version control systems keep track of changes to the software system. These commits happen any time a committer add, delete or change something in a source code. We use a tool named CVSAnalY to analyze and extract data such as, whom and when the event happens—what was the revision number and the comments attached to the change, from version control systems and import them to the MySQL database. Here, we focus on ten open source projects changes. We collect all important data, such as a number of files for each project’s commits and then categorize them into five intervals from extra-small to extra-large. One of the other important data is describing message. When a change is committed developers add a message describing the change. By analyzing these messages we can find most common vocabularies over a commits. We calculate the distribution of vocabularies over the commits and present the most often used vocabularies over each size category of commits. The paper is organized as follows. Section 2 describes a related work. The details of our data are given in Sect. 3. The results of the Categorization performed and a distribution of vocabulary are presented in Sects. 3 and 4 discusses some final conclusions.

2 Related Work Commit changes presented here is stored in a subversion repository, and this repository saves rich information about the evolution of a project. It is obvious that researchers have an interest to work on different point of change. Oliver Arafat and Dirk Riehle studied a commit size distribution of open source software. They review the total distribution and distinguish three categories of code contributions using a size-based heuristic: single focused commits, aggregate team contributions, and repository refactoring. They also suggest that distinguishing these commit categories by size will benefit future analyses [3]. Purushothaman and Perry discuss the impact of small changes on software projects [4]. They observe that during maintenance, nearly 10% of all changes

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made to the project under investigation were one-line changes. Hindle et al. [5] studied release artifacts that can be accessed from the software repositories. They try to find release patterns that happen around a release. They found that MySQL documentation and testing commits occur more before a release than after. While the build commits increased, source code changes decreased around release time.

3 Tools In this chapter we extract the data necessary for the study from the subversion control systems of ten open source software. For this data collection we use a tool named CVSAnalY. Then we categorize the data; and finally we describe a relation between these categories and commit activities.

3.1 CVSAnalY Tool Source code repositories store metadata such as user-IDs, timestamps, and commit comments in addition to the source code differences across versions. This metadata explains the why, who, and when a source code change. Modern source-control systems, such as Subversion, preserve the grouping of several changes in multiple files to a single change-set as performed by a committer. Version-number assignment and metadata are associated at the change-set level and recorded as a log entry. CVSAnalY is a tool created by Libre Software Engineering Group at University Rey Juan Carlos that extracts statistical information out of CVS (and recently Subversion) repository logs and transforms it in database SQL formats. It has a web interface—called CVSAnalYweb—where the results can be retrieved and analyzed in an easy way. We use this tool for analyzing our ten open source software, and extract commit information from subversion repository. Figure 1 show a sample of subversion information that is extracted by CVSAnalY. As we can see in Fig. 1, Source code repositories store metadata such as committer, timestamp and a text message describing the change entered by the developer. In addition to table we indicate in Fig. 1, we have some other tables such as ‘action’ table that maintains information about commit id and all files involved in a commit or paths involved in a change-set (Fig. 2). As shown in the figure, for example, the changes in the files ‘trunk/src/bug-buddy.c’ and ‘trunk/src/gdbbuddy.c’ are committed together and the commit 80 is assigned to the entire change-set.

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Fig. 1 CVSAnalY analyze subversion repository and import result data into a SQL file

Fig. 2 Commits and related files generated by CVSAnalY

3.2 MonoConc Pro Tool This tool is a fast text searching program that is used in the analysis of English or other texts. It generates a concordances or listings of all the occurrences of any words in a given text with a frequency of each word (alphabetical order and frequency order). As shown in Fig. 3, a combination of all words, translation, update, bug, remove, fix, configure, add and etc., creates a given text. The frequency of

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Fig. 3 Count of words calculated for a given text

occurrences of each word is calculated and indicates in ‘Count’ column. For example, the frequency of ‘translation’ is 301 or 4.9% of total words.

4 Data Collection In this section, first we obtain data from ten open source projects over large number of commits performed during at least 5 years and at most 8 years and then we categorize data and extract commit messages vocabulary. Finally we indicate a correlation between each categories and vocabularies.

4.1 Commit Categories We determine a number of files involved in a commit as commit size or we can say the total number of files that are added, deleted, and/or modified in a commit indicate a commit size. Calculating a commit size is not expensive. As we mention in previous subscription (CVSAnalY tool), whole commits and related files exist in ‘action’ table. Therefore, we can calculate a number of files changed for each commit. Our approach would indicate that the commit with a larger number of files is larger. For our study we select 10 open source projects (the name of projects and their evolutions are shown in Table 1): bug-buddy epiphany gconf-editor gedit gnome-desktop gnome-terminal

graphical bug reporting utility web browser based on the Webkit rendering engine application for the GNOME computer desktop environment official text editor GNOME desktop environment terminal emulator for the GNOME desktop environment

236 Table 1 Ten open source projects and the years of their evolution history

M. Marzban et al. Name

Duration

Bug-buddy Epiphany Gconf-editor Gedit Gnome-desktop Gnome-terminal Metacity Nautilus-cd-burner Sound-juicer Yelp

1999–2008 2002–2008 2001–2008 1998–2008 1998–2008 2001–2008 2001–2008 2002–2008 2003–2008 1998–2008

Fig. 4 Number of commits distributed over number of files for all projects (32,043 commits)

metacity nautilus-cd-burner sound-juicer yelp

window manager package provides a method to write files to CD/DVD burner CD ripper help viewer

Here, we first calculate a number of files involved in all commits, and then use a distribution to classifying them into different categories. Committers group files in one commit wither it has been an added, modified or deleted. Our unit of change, the commit, has different number of files that are committed together. For our ten projects this range is from 1 to 323 files. Most of the times committer change two files and modifying more than 30 files is rare. Figure 4 shows the number of commits distributed over number of files. According to these numbers, the distribution of our commit size is limited to four regions. In the case of our data, the number of files per commit can each

Cohesion Between Size of Commit and Type of Commit Table 2 Top 20 frequent term over all projects

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Term

Count

Data File Update Bug Remove Fix Doc Configure Add New Include Help Main Use Action Make Move Change Code Build

3,771 2,471 2,309 2,072 1,834 1,631 1,498 1,387 1,377 1,268 1,070 996 939 923 851 760 676 604 561 523

be distributed over these four regions. We classify the size of each commit based on these regions as small, medium, large, and exception. In exception region we have only two files in a most commits are in this region. As we can see in Fig. 4 the distribution skew to the right. Most of the commits are in the exception or small categories and the other regions are not considerable and do happen with a non-trivial frequency. The largest commits are often those that touch every file.

4.2 Commit Activity Commit size was described in a previous subscription, now we want to know what type of activity or changes usually happen for each category. For this purpose we extract the most frequent words used in commits’ messages over a ten projects. At first we consider the top most 30 frequent words, and then we find the top most frequent words-sets for each category of all projects. For each project, we collect all commits’ messages and save them in a text file. Then by using MonoConc tool we extract most frequent terms from a text file. At the end we combine all ten projects and take the top most 30 frequent terms. Below table shows the list of the top 20 words of ten projects Table 2. After building most frequent terms, we try to identify term combinations like ‘file-fix’, ‘bug-fix’ and etc. over all projects. Table 3 has a list of term combinations that are ordered by their frequency. As we can see in table, for small and

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Table 3 Frequent terms and number of commits using terms (distribution) for all projects over each categories Exception Small Medium Large Term

Count

Term

Count

Term

Count

Term

Count

Fix-bug Make-file Fix-file Add-file Add-bug Use-bug Update file Move-bug Fix-data Remove-bug Make-bug New-bug Update-bug Fix-code New-file

708 261 234 216 178 176 142 128 126 120 118 108 106 104 98

Make-file Fix-bug Update-file Fix-file Move-file Add-bug Use-bug Move-bug Make-bug Change-file Remove-bug New-file Configure-file New-bug Move-code

541 454 292 276 275 223 215 214 191 176 169 155 146 140 128

Make-file Add-file Use-file New-file Move-file Remove-file Configure-file Add-bug Update-file New-bug Add-data Make-data Add-code Fix-bug Use-code

481 465 428 318 250 217 200 19O 186 183 159 141 129 121 117

Add-file Make-file New-file Move-file Use-file Remove-file Update-file Configure-file Add-data Make-data Add-code Change-file Fix-bug New-code New-data

191 181 154 133 125 110 106 100 93 66 60 59 57 56 56

exception categories most activities are belong to ‘fixing or updating files or bug’, but the type of activities of medium and large regions are ‘make, add, new or move files’. The list of frequent term combination indicates that small and large categories have different trend, in small categories most activities are related to bugs (fixing bugs or file) but in large commits adding new files or data is more common. It seems that types of changes correspond to size categories have a correlation with commit messages. However, there is at least some evidence of this phenomenon.

5 Conclusion This paper work on the version histories of ten open source projects to uncover a correlation between type and size of commit source code to subversion system (SVN, CVS). The goal is to characterize what a typical commits with respect to the number of files committed together. Actually commit messages are used along with the characteristic. The result data indicate that a large amount of commits has small or exception size (1–4 files are involved in a commit). Furthermore, we analyze vocabulary associated with the commit messages. The vocabulary data shows interesting sets of terms used across different categories of commits. The activity such as adding files or data is belong to large categories but small categories have fixing bug or file activities. It seems that commit data can provide an insight into the purposes of the changes.

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References 1. Kagdi H, Collard ML, Maletic JI (2007) A survey and taxonomy of approaches for mining software repositories in the context of software evolution. J Softw Maint Evolut Res Pr 19(2):77–131 (March/April) 2. Santiago D, Liliana T (2008) Database specification deliverable ID: D3.1, Version: 2.0, Oct 10 3. Arafat O, Riehle D (2009) The commit size distribution of open source software. HICSS. pp 1–8 4. Purushothaman R, Perry DE (2005) Toward understanding the rhetoric of small source code changes. IEEE Trans Software Eng 31(6):511–526 5. Hindle A, Godfrey MW, Holt RC (2007) Release pattern discovery via partitioning: methodology and case study. ACM Special Interest Group on Software Engineering, Washington, pp 19–27 6. Alali A, Kagdi H, Maletic J (2008) What’s a typical commit? A characterization of open source software repositories. ICPC. IEEE, Amsterdam, pp 182–191 7. Scacchi W (2007) Free/open source software development: recent research results and emerging opportunities. ESEC/SIGSOFT FSE (Companion), pp 459–468 8. Hindle A, Germán DM, Holt RC (2008) What do large commits tell us: a taxonomical study of large commits. MSR. ACM Press, New York, pp 99–108 9. Romain Robbes (2007) Mining a change-based software repository. MSR. p 15 10. Ohloh,Inc.Ohloh API. See http://www.ohloh.net/api

Software Defect Prediction Using Transitive Dependencies on Software Dependency Graph Javad Kamyabi, Farzaneh Maleki and Ashkan Sami

Abstract In software production process, quality assurance resources are limited by time and cost. In order to achieve high quality, managers need to detect the defect prone parts of code and allocate the resources to them. So far, researchers have used some methods such as complexity metrics, design metrics and network measures for software defect prediction. Although these methods are somewhat efficient, still there isn’t a global method for it. In this paper we have presented two new definitions which are dependency tree and circular dependency. Dependency tree covers all of the direct and indirect dependencies circular dependency evaluates all of the cyclic chains of dependency for each component. What has been differed our work from the previous related works is that we have presented that using transitive dependencies is efficient in defect prediction.













Keywords Defect Prediction Dependency graph Dependency tree Circular dependency

J. Kamyabi (&)
F. Maleki Department of E-Learning, Shiraz University, Shiraz, Iran e-mail: [email protected] F. Maleki e-mail: [email protected] A. Sami Department of Engineering, Shiraz University, Shiraz, Iran e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_23, Ó Springer Science+Business Media B.V. 2012

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1 Introduction One of the most challenging presented issues in software quality assurance is software defect debug. According to the announcement of National Institute of Standards and Technology in 2002, software defects caused a 60 billion damage to USA economy [1]. Quality assurance is limited by time and cost; therefore managers try to allocate the resources to those parts of program code which are most likely to contain defects in comparison to the other parts. Many efforts have been done to help software project managers in order to test softwares and it has been always tried to determine the defect prone code parts by presenting various metrics of defect prediction. These metrics have been somewhat efficient. A group of these metrics determine software defects by measuring the code characteristics such as McCabe complexity metric [2–4]. These metrics are acceptable by many researchers and they can be easily extracted from the code. Another group of the metrics helps software teams in defect prediction by using the dependency between code components such as modules, classes and files. Zimmerman researched on network metrics in 2008 and showed metrics based on dependency have twice recall as much as the recalls of complexity metrics [5]. In this paper we have tried to present two hypothesizes for defect prediction based on the dependencies between the code components. The level of dependencies that we worked on is file level. The two metrics in this paper we have working on uses both of direct and transitive dependencies between the components for identifying those parts of the program which contain defects. The obtained metric on different versions of Eclipse and Apache Tomcat will be studied. The outline of the paper is Studying the previous literatures (part 2), Analyzing the main motivation of this paper (part 3), Data collecting (part 4), Analyzing the obtained result according to the metrics (part 5), Conclusion (part6).

2 Related Works The previous researches in defect prediction by data mining can be categorized in three groups: complexity metrics, historical data and dependencies. Binkley and Scotch presented coupling dependency metrics. Their researches indicated that these metrics are successful in improving software designing quality [6]. Alberg and Olson used complexity metric for predicting those modules that create run time failures. Their predictor models could identify 20% of code Paper Preparation modules that contained 47% of the total defects [7]. Nagappan discovered some metrics for predicting post released defects by researching on 5 Microsoft projects and presented a report about how post released defect can be created based on the history [8]. Bal presented that churned codes and the dependency to churned codes could cause post released defects [9].

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Hodpel could predict whether if a module contains defect or not by using the combination of complexity metric and historical data [10]. Ostrand studied on 2 huge software systems based on historical data up to 17 releases for predicting the files with the highest defect density. The defect predictor model identified 20% of the total files of the 2 software systems as the files that are containing defect and could identified 71 and 92% of the total defects [11]. Gravis demonstrated that metrics based on historical data perform better than complexity metrics by using a very huge system with long life time [12]. As it is obvious from the result of the researches based on the historical data, metrics that are based on historical data perform better than complexity metrics. But there are some problems in using the metrics based on historical data. The long term record of program defects may not exist. Henry and Kafura presented an issue as information follow in 1998, they presented a new metric named complexity metric using fan-in and fan-out and demonstrated that program designed by high rate of fan-in and fan-out have an inappropriate design [13]. Clarke and Pogdulsky in 1990 presented a formal model. The connection between 2 parts of the code can be discovered from the program source code based on this model [14]. Schroter worked on an issued named actual import dependencies in 2006. He also considered the kind of dependency in addition to counting fan-ins and fan-outs [15]. Microsoft team researches in 2007 demonstrated that using code churns and detecting the dependencies to churned codes can predict the post release defects successfully [16]. In 2008 Zimmermann and Nagappan had an experiment on defect detection using dependency, and they showed that the recall in their experiment is twice as much as the recall in Complexity metrics [5]. In 2009 Türhan demonstrated that defect prediction by dependency graph is much more successful than defect prediction by complexity metrics in big programs but in short program there is not much difference [17].

3 Motivation We can show the dependencies by construing the entire software as a low level graph. In this paper we will consider each fan-out as a dependency. According to the software engineering principals a high average of dependency for each component and also circular dependencies between pieces of code are known as weakness in software design. So in the simplest case we can use fan-out’s and directed circular dependencies to specify the defect proneness of pieces of code. You can see the simplest type of circular dependency in Fig. 1. As you see if Node A is depended to node B and node B is also depended to the node A, we have a simple circular dependency. So the first thing we are going to analyze is the relation between simple circular dependencies and defect proneness of nodes. As you see we may have two intermediate nodes that make a transitive circular dependency. And we can have even more intermediate nodes that make more

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Fig. 1 Direct circular dependency

complex circular dependencies. The main questions we want to answer are whether we can use transitive dependencies and also transitive circular dependencies to predict defective nodes or not. Beneath you can see the hypothesizes we have worked on in this paper: H1 We can use simple direct dependencies in dependency graphs for software defect prediction and specify the quality of software H2 Transitive dependencies are related to defect proneness of software components like directed dependencies H3 We can increase the precision of our experiment by transitive dependencies due to the involvement of more nodes in a circular dependency‘s cycle. H4 We can use simple circular dependencies to focus on high probable defect prone nodes for limited debug time H5 We can use transitive circular dependencies to focus on high probable defect prone nodes for limited debug time H6 We can increase the precision of our experiment by transitive circular dependencies due to the involvement of more nodes in a circular dependency‘s cycle.

4 Data Collection In this experiment we have used two versions of the Eclipse project and one version of Apache Tomcat as our datasets. At the first step we have extracted the dependency graph of these programs by the Class Dependency Analyzer tool (CDA). The function of this tool is to analyze Java class files in order to learn more about the dependencies between those classes [18]. Then we have written a tool to parse the xml output of CDA (Class Dependency Analyser) to make the matrix of our dependency graphs. And finally we made a matrix of dependencies that presents the neighborhood of nodes in the dependency graph.

4.1 Dependency At first we will describe the dependency. A dependency is a fan-out from one component to another component. And in software dependency graph it‘s been presented as a directed tie to another node.

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Fig. 2 Dependency between two components

As you see in Fig. 2, there may be a multiplicity in dependencies. In this case we neglect it, and consider it as just a single dependency.

4.2 Dependency Tree Each node in software dependency graph, normally have some fan-outs. So if we start from any node, we have some dependencies to other nodes and those nodes also might have several dependencies to other different nodes so we can draw a tree from this chain of dependencies for each node. We call it, ‘‘Dependency Tree’’. A dependency tree shows all direct and indirect dependencies for each node so we can count the nodes of our dependency tree to evaluate the number of total dependencies of the root of the tree. And then we can evaluate the effect of total transitive dependencies on defect proneness of each node.

4.3 Circular Dependency If we have a circuit in the chain of dependencies, that starts from one node and ends to itself, we have a circular dependency. As you see in Fig. 3, there is a directed tie from node A to D and a directed tie from node D to A. so there is a simple circular dependency for both of nodes A and D. And there is a chain of dependencies from A to D, D to C and then C to A. so there is a circular dependency by the two intermediates D and C for node A. More complex circular dependencies can be occurred by more participant nodes in the cycle of dependencies. In this paper we have worked on circular dependencies with maximum 4 intermediate nodes.

4.4 Analyze We have used the number of transitive dependencies in the two defined concepts in this paper as a selection measure to select defective nodes. We can select different groups of nodes by increasing or decreasing the number of dependencies. The charts below show the behavior of selected nodes (True) and selected defective

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Fig. 3 An example of circular dependencies between software components

Fig. 4 Behavior of T (Selected nodes) and TP (Selected defective nodes) in circular dependencies and dependency trees in eclipse 2.0

nodes (True Positive) toward each other. We have used ‘‘CD’’ instead of ‘‘Circular Dependency’’ and ‘‘DT’’ instead of ‘‘Dependency Tree’’ in the charts. For example ‘‘CDO1’’ means a circular dependency by 1 intermediate and ‘‘DTL2’’ means Dependency Tree by the depth of 2 (Fig. 4). As you see in all of the charts above, by going forward on the X axis, the number of selected nodes is dropping sharply. Thus, the density of defective nodes increases. So that, the density of defective nodes, is higher than 90% in some places. The obtained behavior in Eclipse 3.0 and Tomcat 6.0.32 is the same as the obtained behavior in the above charts. We have used the function Precision that measures the percentage of detected defective binaries to the total detected binaries. The fewer false positives (incorrectly predicted as defect-prone), the closer the precision to 100. [5]. At last we have used the Chi-square test to find out if our hypothesis separates defective and non-defective nodes significantly or not. We can show the capacity of detected defective components among all selected components based on a measure is called Precision. For example the precision 70% means that seventy percent of nodes you are selected from source code are

Software Defect Prediction Using Transitive Dependencies Table 1 Results of experiment

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Source code

Analyze

Precision (%)

Chi-square

Eclipse 2

CDO1 CDO2 CDO3 CDO4 DTL1 DTL2 DTL3 CDO1 CDO2 CDO3 CDO4 DTL1 DTL2 DTL3 CDO1 CDO2 CDO3 CDO4 DTL1 DTL2 DTL3

48 70 84 77 52 36 57 49 70 94 90 49 52 57 46 50 60 80 66 71 62

Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant

Eclipse 3

Tomcat 6.0.32

defective. So in this paper we are trying to find a way to increase the Precision, and in the following, the best precision results obtained from this experiment are shown: According to the results of Chi-square test, all the measures based on the two main hypothesizes in this paper, have been separated defective and non-defective nodes significantly. As you see in Table 1 we could target a group of components by a high rate of defective nodes but still we do not have any idea about a specified number for circular dependencies to evaluate, to have the best result in addition in our experiment, by increasing the Precision, Recall is been decreased and increasing the Recall could be our next goal in next experiments. It’s a limited study. We will continue our experiment to higher depths of transitive dependencies in next studies. Also, we have worked on 3 open source projects that are written by java but we are confident that dependency has predictive power for other projects—we will repeat our experiments for more datasets.

5 Conclusion In this experiment 2 types of transitive dependencies have been used in defect prediction as 2 metrics for the first time and we worked on 3 java open source and showed that using transitive dependencies for defect prediction could be effective.

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Our goal was to increase the chance of defect detection in limited time; we have focused on improving the Precision results in our paper. The results of this empirical study are coming below: Transitive dependencies are effective on quality of software design like direct dependencies. We can use transitive dependencies in dependency tree and/or other forms of transitive dependencies in software defect prediction. Transitive circular dependencies are effective in defect proneness of components, because we have more intermediates participating in circular dependency and issues like code churn or being defective, can increase the probability of defect proneness of participating components. By using transitive dependencies, we can really target a group of software components by high probability of defect proneness. High precision in software defect prediction, can be useful in limited time and cost for software debug process.

References 1. Tassey G (2002) The economic impacts of inadequate infrastructure for software testing. National Institute of Standards and Technology, Gaithersburg 2. Basili VR, Briand LC, Melo VL (1996) A validation of object orient design metrics as quality indicators. IEEE Transactions on Software Engineering 22:75–761 3. Nagappan N, Ball T, Zeller A (2006) Mining metrics to predict component failure. In: International conference on software engineering. China, pp 452–461 4. Subramanyam R, Krishnan MS (2003) Empirical analysis of CK metrics for object-oriented design complexity:implications for software defects. IEEE Trans Software Eng 29:29–310 5. Nagappan N, Zimmermann T (2008) Predicting defects using network analysis on dependency graph. In: The 30th international conference on software engineering (ICSE ‘08). Leipzig, Germany, pp 531–540 6. Binkley AB, Schach SR (1998) Validation of the coupling dependency metric as a predictor of failures and maintenance measures. In: International conference on software engineering. Kyoto, pp 452–455 7. Alberg H, Ohlsson N (1996) Predicting fault-prone software modules in telephone switches. IEEE Trans Software Eng 22:886–894 8. Ball T, Nagappan N, Zeller A (2006) Mining metrics to predict component failures. In: International conference on software engineering. China, pp 452–461 9. Ball T, Nagappan N (2005) Use of relative code churn measures to predict system defect density. In: International conference on software engineering. St. Louis, MO, pp 284–292 10. Allen EB, Aud SJ, Hudepohl JP, Khoshgoftaar TM, Mayrand J (1996) Emerald: software metrics and models on the desktop. IEEE Softw 13:56–60 11. Bell RM, Ostrand T, Weyuker E (2005) Predicting the location and number of faults in large software systems. IEEE Trans Software Eng 31:340–355 12. Graves TL, Karr AF, Marron JS, Siy H (2000) Predicting fault incidence using software change history. IEEE Trans Software Eng, pp 653–661 13. Kafura DG, Henry SM (1998) Software structure metrics based on information flow. IEEE Trans Software Eng 7:510–518 14. Clarke LA, Podgurski A (1990) A formal model of program dependences and its implications for software testing, debugging, and maintenance. IEEE Trans Software Eng 16:965–979

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15. Schröter A, Zeller A, Zimmermann T (2006( Predicting component failures at design time. In: International symposium on empirical software engineering. Rio de Janeiro, Brazil 16. Ball T, Nagappan N (2007) Using software dependencies and churn metrics to predict field failures: An empirical case study. In: International symposium on empirical engineering and measurement. Madrid, Spain, pp 364–373 17. Bener A, Tosun A, Turhan B (2009) Validation of network measures as indicators of defective modules in software systems. In: 5th International conference on predictor models in software engineering, Vancouver, Canada 18. Duchrow M (2010) May 16 programmer’s friend, class dependency analyzer (CDA) V1.13.1. Retrieved July 11, 2011, from http://www.dependency-analyzer.org/

Test Case Generation for Classes in Objects-Oriented Programming Using Grammatical Evolution Jirawat Chaiareerat, Peraphon Sophatsathit and Chidchanok Lursinsap

Abstract This paper proposes a dynamic test case generation approach for Object-Oriented Programming classes, using evolutionary search to find test cases that would satisfy a branch coverage criteria. Grammatical Evolution (GE) is used to search for a solution in accordance to user-specified grammar, thus making the algorithm more flexible than the traditional genetic programming. Rather than generating test cases directly, source code for an Intermediate Test Script (ITS) is generated from the grammar. It is then evaluated and translated into source code by ITS interpreter. Such a provision makes it easy to produce test cases that have object and literal reference, whereby improve the performance of GE. We’ve tested the proposed method with several java classes from open source projects and yielded high code coverage results.





Keywords Test case generation Code coverage Object-oriented programming Grammatical evolution Intermediate test script





J. Chaiareerat (&)  P. Sophatsathit  C. Lursinsap Department of Mathematics and Computer Science, Faculty of Science, Advanced Virtual and Intelligent Computing (AVIC) Center, Chulalongkorn University, Bangkok, 10330 Thailand e-mail: [email protected] P. Sophatsathit e-mail: [email protected] C. Lursinsap e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_24, Ó Springer Science+Business Media B.V. 2012

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1 Introduction Test case generation is inherently a vital part of software testing. Because testing all possible test data is infeasible, test case generation therefore uses different criteria to select and generate only a subset of test data that guarantees a high quality of the test set. These criteria are called code coverage. Previous works in test case generation can be classified into two groups, namely, static and dynamic approaches. The static test case generation relies on static analysis of the program source code. They usually apply the same concept as procedural test case generation by using symbolic execution and constrained solving. On the other hand, the dynamic approach models the test case generation as a search problem to find an optimal solution, which gives the highest code coverage. This approach executes the test object iteratively by tuning the test case until a satisfied result is reached. Various optimization techniques are used in dynamic test case generation including Genetic Algorithm [1–3], Simulated Annealing [2, 3], Memetic Algorithm [2, 3] and Strongly-Typed Genetic Programming [4, 5]. This paper proposes a dynamic approach test case generation for an object using GE to find test cases that would satisfy branch coverage criteria. Each test case is translated into Intermediate Test Script (ITS) format based on user-specified grammar. The idea is to find an executable program or program fragment that will yield a good fitness value for the given objective function.

2 Test Case Generation Framework The Test Case Generation Framework consists of 4 modules: Code Instrumentation, Grammar Generator, Search, and Evaluation Module. Referring to Fig. 1, Java source code is instrumented by Code Instrumentation module. This process will insert certain code to the original source code which will be used for collecting branch coverage information during the evaluation of each test case. The instrumented source code is then compiled into a Java class file by java compiler. This compiled Java class will later be fed as an input to the Evaluation Module. The Grammar Generator Module, as stated by its name, will automatically generate grammar for test cases. At this step, users can review the generated grammar and modify it according to their specific grammar. The Search Module then derives these test cases through the defined grammar based on genetic algorithm. This process transforms the test cases into ITS which later are evaluated by the Evaluation Module. The Evaluation Module processes the compiled Java class, together with Intermediate Test Script using its interpreter, to produce branch coverage information. The resulting branch coverage information is then fed back to the Search Module for selecting the best test case in each generation.

Test Case Generation for Classes

Grammar Generator Module

Java Sourcecode

Grammar

Code Instrument Module

Result Testcase

Search Module

Instrumented Sourcecode

Intermediate Test Script

Java Compiler JavaClass file

Fig. 1 Test case generation framework

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Coverage Information Evaluation Module ITS Interpreter

3 Code Instrumentation Code Instrumentation is the process of inserting certain code to the original source code, wherever there’s a condition check and method declaration. The objective of code insertion is to let the newly inserted code collect information on path and coverage each time they are executed. Apart from collecting the mentioned information, they are also used for calculating branch distance. Branch distance information is used to evaluate how much each branch is closed to being taken. We used Java Parser for parsing and inserting new code to the original java source code automatically. In a situation that two test cases have the same degree of coverage, branch distance is used to determine which one is a better and suitable for potential test case. The value of branch distance represents how much a branch is close to being taken. In this paper, branch distance is calculated, using the formula shown in Table 1. The lower branch distance means the branch is closer to be covered. From Table 1, dist(x) is the branch distance of condition x and k is the smallest possible value of the branch distance (we used 0.0001 in this paper). The branch distance in the above table is then normalized by the following equation distðAÞnormalize ¼ 1  ð1 þ 0:1Þ0distðAÞ :

ð1Þ

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Table 1 Branch distance calculation Operation

Branch distance

dist(A == B) Dist(A! = B) Dist(A [= B) Dist(A [ B) Dist(A \= B) Dist(A \ B) Dist(A and B) Dist(A or B)

Abs(A - B) k B-A B-A?k A-B A-B?k Max[dist(A), dist(B)] min[dist(A), dist(B)]

4 Intermediate Test Script and Grammar In this proposed method, each test case is represented in ITS Format. Grammar for the ITS of a class under test is automatically generated from the source code by the Grammar Generator. The proposed framework generates test cases in Intermediate Test Script (ITS) format instead of a java source code. In ITS, each parameter of the method call can be referred to the previous created object or literal value. The advantage of using the same parameter helps improve the search process. Moreover, ITS also reduces the time for compiling the source code, since it can be run directly by ITS interpreter. Grammar of ITS for the class under test is generated from a java source code. Tester can modified the grammar by inserting some heuristics, which can help reduce the search space, making it easier for the Search Module to discover the optimum solution. Using grammar to represent test cases have an advantage in flexibility since various types of parameter such as array, string and object reference can be created by grammar. In this paper, we used grammar in BackusNaur Form (BNF) as an input of GE.

5 Search and Evaluation Grammatical Evolution performs the process of searching for a test case that has the best code coverage for the class under test. In this paper, we used GEVA [6] for our experiment. Traditionally, Genetic Algorithm (GA) is used to find the optimal solutions to a search problem. Genetic algorithm is classified as global search heuristics. Genetic Programming (GP) is for finding computer programs that satisfy user-defined criteria. Grammatical Evolution (GE) [7, 8], on the other hand, is an evolutionary computation technique that can be considered as a grammar-based GP. A program is represented by an ordered-list of integer. GE uses genotype-phenotype mapping process to map an ordered-list of integer to a computer program. With the help of

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Table 2 Experimenta result (GE) Class name Total Achievable branches branches

Branch coverage (mean)

Branch coverage (%)

Stack StringTokenizer Vector LinkedList BinTree BinomialHeap BrentSolver SecantSolver Complex

10.00 39.00 123.00 122.00 37.00 74.67 28.00 18.86 51.80

100 97.50 96.09 93.84 100.00 85.82 96.55 99.26 95.92

10 40 128 130 37 87 29 19 54

10 39 123 122 37 77 28 19 52

grammar, GE also provides a very flexible way to control an algorithm. The user can define a grammar that biases to produce very specific form of program, or can incorporate domain knowledge of the problem into the underlying grammar. GE, GA, and GP use crossover and mutation operations to modify each individual and reproduce new populations. In this paper, a standard single point crossover; and nodal mutation [9] is used since it gives the best result among the three in our experiment. Fitness function will be calculated based on coverage information collected during the execution of each test case in the population. The following equation is used in the calculation: fitnessðtÞ ¼ ðb  covÞ þ 1  ð1:1Þ0bd

ð2Þ

where b is the total number of branches, cov is the total branch coverage of test case t, and bd is the total branch distance.

6 Experimental Results We used selected java class for our experiment including J2SDK version 1.4.2_12, Java Path Finder [10] version 1.3r1258 and Apache Commons Math version 1.1. The experimental results are shown in Table 2 which compares the proposed algorithm with Acuri [3] and Wappler [5] in Tables 3 and 4 respectively. In the proposed algorithm, GE is executed for 200 generations with the number of population equals to 50. The probability of crossover and mutation of GE is configured to 0.9 and 0.1, respectively. Average performance execution of the algorithm is based on 50 runs. Branch Coverage also includes the number of method calls and try/catch statements. The branch coverage percentage is the ratio of mean branch coverage over total branch. The total branches were not stated in Acuri [3], while the achievable branches and branch coverage were not stated in Wappler [5].

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Table 3 Experimenta result (Acuri’s memetic method [3]) Class name Total Achievable Branch coverage branches branches (mean)

Branch coverage (%)

Stack StringTokenizer Vector LinkedList BinTree BinomialHeap BrentSolver SecantSolver Complex

– – – – – – – – –

– – – – – – – – –

10 – 100 84 37 79 – – –

10.00 – 100.00 84.00 37.00 77.66 – – –

Table 4 Experimenta result (Wappler’s EvoUnit [5]) Class name Total Achievable Branch coverage branches branches (mean)

Branch coverage (%)

Stack StringTokenizer Vector LinkedList BinTree BinomialHeap BrentSolver SecantSolver Complex

100 93.70 – 98.30 – – 96.30 100.00 90.20

8 29 – 68 – – 27 17 51

– – – – – – – – –

– – – – – – – – –

The result shows that our algorithm is capable of generating almost all types of test cases by virtue of grammar which was evident by the high coverage outcome.

7 Conclusion We propose a grammar-based test case generation for a class in Object-Oriented by using Grammatical Evolution and Intermediate Test Script. This method also supports various features of Object-Oriented Programming through the grammar. The results confirm that the proposed method can generate high coverage test cases. Moreover, being grammar-based, this technique has great flexibility and advantages in that it is possible to incorporate heuristics and characteristic of the class under test into the grammar to reduce the search space of the problem.

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References 1. Tonella P (2004) Evolutionary testing of classes. In: Proceedings of the 2004 ACM SIGSOFT international symposium on software testing and analysis (ISSTA’04), ACM, New York, pp 119–128 2. Acuri A, Yao X (2007) A memetic algorithm for test data generation of object-oriented software. In: IEEE congress on evolutionary computation (CEC 2007), pp 2048–2055, IEEE 3. Acuri A, Yao X (2008) Search based software testing of object-oriented containers: information sciences, vol 178, issue 15, pp 3075–3095, Elsevier Science, New York 4. Wappler S, Wegener J (2006) Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In: Proceedings of the 2006 conference on genetic and evolutionary computation (GECCO’06), ACM, New York, pp 1925–1932 5. Wappler S (2008) Automatic generation of object-oriented unit tests using genetic programming. PhD thesis, Technical University of Berlin 6. O’Neill M, Hemberg E, Gilligan C, Bartley E, McDermott J, Brabazon A (2008) GEVA: grammatical evolution in java. SIGEVOlution 3(2):17–22 ACM New York 7. O’Neill M, Ryan C (2001) Grammatical evolution. IEEE Trans Evol Comput 5(4):349–358 8. O’Neill M, Ryan C, Keijzer M, Cattolico M (2003) Crossover in grammatical evolution. Genetic programming and evolvable machines, vol 4, issue 1, pp 67–03, Academic Publishers, Kluwer 9. Byrne J, O’Neill M, McDermott J, Brabazon A (2009) Structural and nodal mutation in grammatical evolution. In: Proceedings of the 11th annual conference on genetic and evolutionary computation (GECCO), ACM New York, pp 1881–1882 10. Visser W, Pasareanu CS, Khurshid S (2004) Test input generation with java pathFinder. In: Proceedings of 2004 ACM SIGSOFT international symposium on software testing and analysis (ISSTA’04), ACM New York, pp 97–107 11. Buy U, Orso A, Pezze M (2000) Automated testing of classes. In: Proceedings of 2000 ACM SIGSOFT international symposium on software testing and analysis (ISSTA 2000), ACM New York, pp 39–48 12. Sen K, Agha G (2006) CUTE and jCUTE: concolic unit testing and explicit path modelchecking tools. In: 18th international conference on computer aided verification (CAV’06), LNCS vol 4144, pp 419–423, Springer, Berlin 13. Sen K, Marinov D, Agha G, (2005) CUTE: a concolic unit testing engine for C. In: Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering, ACM New York, pp 263–272 14. Xie T, Marinov D, Shulte W, Notkin D (2005) Symstra: a framework for generating objectoriented unit tests using symbolic execution. In: Proceedings of the 11th international conference on tools and algorithms for the construction and analysis of systems (TACAS 05), LNCS vol 3440, pp 365–381, Springer, Berlin

An Efficient Access Control Scheme for Online Gaming Server Hye-Young Kim

Abstract Massively multiplayer online games (MMOG) are projected to one of the rapidly growing entertainment services that will gain large popularity in near future. When there is a request from the client, we almost apply dynamic method using Accept( ) of looping method; thus, there could be process of connecting synchronously lot of client in most of Online gaming server engine. However, this kind of method causes online gaming server which need to support and process the clients, longer loading and bottlenecking. Therefore we propose the object polling scheme to minimize the memory fragmentation and the load of the initialization to the client using an AcceptEx( ) and static allocation method for an efficient gaming server of the Online in this paper. We design and implement the gaming server applying to our proposed scheme. Also, we show efficiency of our proposed scheme by performance analysis in this paper. Keywords Gaming server


Object polling
Session

1 Introduction Online game is game of form that many clients connect to server through communication network and play their games in real time [1] Therefore, it must be able to access many players and should be able to guarantee the speed,

This work was supported by 2011 Hongik University Research Fund. H.-Y. Kim (&) School of Games, Hongik University, Seoul, South Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_25, Ó Springer Science+Business Media B.V. 2012

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scalability, reliability and security to play the game smoothly in online gaming [2]. When there is a request from the client, there could be process of connecting synchronously lot of client in most of online gaming server engine. However, this kind of method causes online gaming server which need to support and process the clients, longer loading and bottlenecking [3, 4]. Therefore we propose the object polling scheme to minimize the memory fragmentation and the load of the initialization to the client using an AcceptEx ( ) and static allocation method for an efficient gaming server of the Online in this paper. We design and implement the gaming server applying to our proposed scheme. Also, we show efficiency of our proposed scheme by performance analysis in this paper. The rest of paper is organized as follow. We explain in detail the background and the proposed scheme for an efficient online gaming server in next chapter. In Sect. 3, we describe our proposed architecture and provided the detailed functions and procedures. We show an effectiveness of proposed scheme through the performance analysis of our scheme and previous online gaming server in Sect. 4. Finally, we conclude the paper in Sect. 5 by presenting conclusion and future research.

2 Proposed Scheme 2.1 Proposed Background The most of online gaming servers necessary the sockets which take charge of communication with clients and they are necessary the buffers for copy of user data when they send/receive the data on asynchronous input/output. However, If the gaming server creates or release the socket and buffer whenever the access request and exit request from the clients access, they could not ignore the load which for a create the socket and an allocate the heap memory. Because there are lots of creation and delete of socket for connection and exit with client object that connect or disconnect to gaming server are happened frequently, they cause of memory fragmentation by the several dynamic memory allocation. It cause of lots of load and bottleneck, and it is important reason of obstruction of performance of online gaming server [5]. Therefore, we use and apply statically allocation scheme to reuse next time assigning socket or memory beforehand statically as need that is expected to online gaming server. So, our proposed scheme integrates resources of memory and so on that session and client who communicates with each client need and use object polling method to design to one object. Reusing socket and buffer same time connection of large quantity and addition delete frequent login server’s load as curtailment efficiency of online gaming server performance raise can.

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2.2 Object Polling Method The online gaming server reuse socket using AcceptEx ( ) TransmitFile ( ) function that are WinSock extension functions for object polling in Windows Operating System. An object polling method that apply socket reusability function divides and manages by map to use when do unicast, multicast, broadcast according to necessity in server being activated after become list and connection for unused list when wait for an opportunity before is activated after connected making beforehand like thread polling that use in IOCP (Input Output Control Protocol). We design the Session Mange class to manage session in data structure. Gaming server inserts and connects listen socket to IOCP list after create session dynamically as quantity which is necessary. After access request by asynchronous version of Accept ( ), gaming server is notified completion, and do asynchronous access request through AcceptEx ( ) that achieve work about connection. At successful connection completion through socket that is linked to IOCP Input Output Control Protocol) in worker thread, gaming server receives address of the client through GetAcceptExSockaddrs ( ) and add connected session to using map after search in unused list of the Session Manager and deletes in unused list. Also, in case of termination is confirmed through worker thread, delete resource about the client because request socket reusability using TransmitFile ( ) so that may can reuse socket that is used to AcceptEx ( ) and search in using map of Session Manager, and function call- AcceptEx ( )—after insert again to unused list and access request asynchronous again. We diagrammatize our proposed scheme in Fig. 1. Object polling methods which manage the object statically to apply in our proposed scheme minimize the happened load of the gaming server whenever clients connect the session.

3 Implementation of Gaming Server Applying Our Proposed Scheme 3.1 Implementation Environment We implement gaming server in Windows XP Operating System using DirectX 9.0c SDK 2008 Octobers applying object polling method for efficient online gaming server in this paper.

3.2 Gaming Server Architecture Session class process asynchronous access request and request through function call of AcceptEx ( ) using Client socket and Listen socket that exist to session

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Fig. 1 Proposed scheme

using function call of PrepareAccept ( ). Our proposed scheme use AcceptOn (OVERLAPPED* Ov) to receive address of the client through function call of GetAcceptExSockaddrs ( ) after function call of AcceptEx ( ). Also, CloseSocket ( ) make reuse the socket using function call of the ttransmitFile ( ) after use function call of the shutdown ( ) to terminate and reuse the transmission of the session class. In our paper, we suggest that Session Manager class insert a number of object that have been arranged into a OffSession (List) through dynamic creation. And we implement CreateNewSession ( ) to function call, PrepareAccept ( ), to process asynchronous connection request. Also, we designed ActiveSession (Session* p_Session) to add to OnMap (Map) after we delete Session class that receive by transfer factor in Session class in OffSession (List) 1. Create session dynamically by function call of CreateNewSession( ) 2. Asynchronous access request by function call of AcceptEx( ) through function call of PrePareAccept( ) in created session.

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3. 4. 5. 7. 8. 9. 10. 13.

14. 15.

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Inset session which have been created in Session Manager into InActiveList. Attach ListenSocket to IOCP Port. Create Thread, call to Worker Thread, and stand by function call of GQCS( ) Receive session address through GetAcceptExSockaddrs( ) when happen to accept event. Attach IOCP post to returned socket by function call of AcceptEx( ). Delete session in InActiveList which connected by function call of AcceptEx( ), and insert ActiceMap. Process Recv./Send. Session that when detected GQCS ( ) of Worker Thread END Event, and terminate send-receive work through function call of shutdown ( ) and make reactivated through function call TransmitFile( ). Delete session in ActiveMap of Session Manager, insert again in InActiveList. again asynchronous input/output request to Terminated Session through function call of PrePareAccept ( ).

Reuse the object through repeat 1 to 15 For using CreateIOCompletionPort ( ) in Init ( ) in IOCP Handler class, create IOCP port, and used CreateIoCompletionPort ( ) for connection of socket that is passed on IOCP port by socket value in IocpListenSockJoin (SOCKET Sock). Also, after connection completion, implementedd IocpJoin (Session* p_Session) method using CreateIoCompletionPort ( ) serving to receive connected Session by transfer parameter and connect on IOCP port. Acceptor class process function call of Listen ( ) and Bind ( ) by port number that is passed in ListenAndBind (UINT n_port) method and manage Listen socket [6]. We explained by simple pseudo code as follows about important part of online gaming server engine that apply and implement object polling which our proposed scheme. Listen socket connect on IOCP port CreateIoCompletionPort ((HANDLE)m_ListenSock,m_HandleIocp,(ULONG_PTR)0,0) Add to unused list after object create and asynchronous access request function call of AcceptEx( ) for ( int I = 0; I \ number of object polling; i ++) { Session* p_Session = new Session( ) Session- [ AcceptEx( ) //asynchronous access request by function of AcceptEx( ) InActiveList.push_back(p_Session) //add session in unused list }

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Fig. 2 Running screen shot of gaming server

When Worker Thread that detect IOCP completion queue makes sure access, delete the session in unused list and add to using map a) Delete session on unused list for (std::list \ Session* [ ::iterator iter = nActiveList.begin( ); iter ! = InActiveList.end( );) { if(*iter == p_Session) { ActiveMap.insert(socket value, Session pointer); Iter = InActiveList.erase(iter); } else iter ++; } b) Add to using map std::map \ UINT,Session* [ :: iterator iter = OnMap.find (p_Session- [ GetSocketValue( )); if(iter ! = OnMap.end ( )) { InActiveList.push_back (p_Session); iter = ActiveMap.erase (iter); } When terminate the access request of session in Worker Thread that detect IOCP completion queue, the gaming server engine delete the session in using map resource of client. And it adds again to unused list after socket reusability request

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Fig. 3 Number of successful access session

Fig. 4 Performance analysis

using function of TransmitFile ( ). Also, again function call AcceptEx( ). The function call of Shutdown ( ) that explain above is as following: shutdown (m_SessionSock, SD_BOTH);

4 Performance Analysis To analysis the performance of online gaming server engine which is applied our proposed scheme in Section Chap. 2 and 3, online gaming server engine were compared with existing game engine about performance speed as increasing rate of the number of session. We use Microsoft Window XP Professional Version 2002 and Service Pack 2 for operating system in our implementation. Also our computer consist of Intel (R) Core (TM) 2 Duo CPU T9300 @ 2.50GHz3.0 GB RAM. Applied methods for the test are easy method of movement, conflict of box and client that is chatting possible. As socket event is detected by EventSelect( ) function on client-side is programmed DirectX9.0C, the packet is received and analyzed. The float value of the coordinates x, y, z, the float value of angle, ViewDis, ID and char type are corresponding with each other. Figure 2 shows the running programs of gaming server.

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In the Accept method, the simultaneously accessible maximum numbers of session are 5 allowed the Listen function. So a lot of thread is required to increase the maximum number [7, 8]. However in case of function call AcceptEx( ) of IOCP, the maximum number is the number of pre-generated object with a small amount of thread. In order to perform efficiently large and simultaneous requests, 256 sockets generated in advance put on the unused list temporarily and then if connection requests are allowed, the sockets are inserted additional on using list from unused list. Figure 3 is shown the difference between method of dynamic memory allocation and static memory allocation as amount of sending and receiving packet and the difference between the Accept-looping method using an synchronous connection request of client and AcceptEx( ) method using an asynchronous access request. As the existing gaming server dynamically allocates socket and buffer whenever client access requests, the load of the server to create the socket and the load to allocate heap memory are weighted. The more sessions are increased, the width of the load increasing were significantly. But the load of gaming server with our proposed method is less increased, because socket and memory in Client connection as initial stage of gaming server is statically allocated. The result of mentioned analysis is illustrated in Fig. 4.

5 Conclusion In this paper, we proposed the asynchronous access request method and object polling method for efficient online gaming server. So we could minimize the memory fragmentation of online gaming server engine and the load that is occurred to initialize of session connection of the client object. Also we designed a game engine applied by proposed scheme and implement it. We could prove the efficiency of our proposed scheme through the performance analysis. However we cannot estimate the threshold which is optimized to the static allocation of client, we applied to the 256 sockets and 4 k byte memory to static allocation. Therefore, in the future research, we will design analysis model for the accurate prediction about the session connected the online gaming server and study more efficiency and reliable engine for online gaming server by offering the threshold value to the static allocation of client side. Acknowledgments This work was supported by 2011 Hongik University Research Fund.

References 1. Kim SW (2007) Window network programing, Hanbit Media, Seoul 2. Lee S-W, Kim H-Y (2008) An efficient message management scheme in game engine. J Korea Game Society 8(2)

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3. Duck M, Read R. Data communication and computer network. Prentice Hall, Englewood Cliffs 4. Kurose JF, Ross KW. Computer networking. Addison Welsey, Reading 5. Han D-H (2008) Online game server programming benchmarking. Information Publishing Group, pp 340–377 6. Han D-H (2007) Online game server programming. Information Publishing Group, pp 214–577 7. Nam-Hyoung, Moon H-J, Lee S-H (2007) Design and implementation based on load uniformity between game server. J Korea Inform Commun Soc Vol. 32(3) 8. Chen J, Knutsson B, Wu B, Delap M, Amza C (2007) Locality aware dynamic load management from massively mutiplayer games, PPoPP

Performance Evaluation of a New Scheduling Algorithm in Data Grid Environment N. Mansouri, Gh. Dastghaibyfard and A. Horri

Abstract Data Grid is a geographically distributed environment that deals with large-scale data-intensive problems. The main problems in data grid are job scheduling and data management. Generally, job scheduling in Grid has been studied from the perspective of computational Grid. In Data Grid, effective scheduling policy should consider both computational and data storage resources. In this paper a new job scheduling algorithm, called Combine Scheduling Strategy (CSS) is proposed that considers number of jobs waiting in the queue, location of required data and the computing capacity of sites. Scheduling cannot be effective unless to combine it with replication. Therefore, we have discussed various strategies in scheduling and replica optimization. Simulation results demonstrate that CSS gives better performance compared to the other algorithms. Keywords Data grids

 Data replication  Job scheduling  Simulation

1 Introduction A large number of scientific applications such as high energy physics, climate transition, and earth observation generate huge amounts of data per year. Today, management and efficient use of large distributed resources are important issues in N. Mansouri (&)  Gh. Dastghaibyfard  A. Horri Department of Computer Science and Engineering, College of Engineering, Shiraz University, Shiraz, Iran e-mail: [email protected] Gh. Dastghaibyfard e-mail: [email protected] A. Horri e-mail: [email protected]

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scientific research and commercial application. Grid is a solution for this problem. Grid can be divided as two parts, Computational Grid and Data Grid. Computational Grids are used for computationally intensive applications that require small amounts of data. But, Data Grids deals with the applications that require studying and analyzing massive data sets. The size of data that are requested in Data Grid is from terabytes to petabytes. Effective scheduling of jobs is necessary in such a system to use available resources such as computational, storage and network efficiently. If a job is assigned to a site where all the required data are available, then it would have no data transmission delay or reduced turnaround time. Of course, a scheduler should also consider parameters such CPU workload, features of computational capability, network load, resource utilization and response time. Data replication is another key optimization technique to manage large data by storing data in a wisely manner. The replication strategies decide when to perform replication, which files to replicate and where to store replicas. Storing replica close to the users or grid computation nodes improves response time, fault tolerance and decreases bandwidth consumption. Therefore, appropriate replica management framework is critical in complex systems such as Data Grids that access large amounts of data. Some researchers have evaluated the impact of data replication on the job scheduling performance in the Data Grid [1–3]. In this paper a novel job scheduling algorithm that considers the number of jobs waiting in queue, the location of required data and the computing capacity of sites is proposed. It is called Combine Scheduling Strategy (CSS). The aim of CSS is to reduce the job execution time by reducing job data access time. It is simulated in OptorSim and compared with various combinations of scheduling and replication strategies. The test results show that CSS successfully reduces the mean job execution time. The rest of this paper is as follows: Sect. 2 describes the related works. In Sect. 3, scheduling problem is explained. CSS algorithm is proposed in Sect. 4. Simulation results based on the OptorSim are presented in Sect. 5. Final section contains the main conclusions of this work and future works.

2 Related Work Zhang et al. [4] have investigated the impact of parallel download on job scheduling performance in Data Grid. In their proposed method, data file is divided into fragments and placed on various replica servers. The replica fragments are transferred concurrently from several replica servers when a replica is requested. They have evaluated the performance of the parallel downloading system with the traditional non-parallel downloading systems using different grid scheduling strategies. The results of simulation indicated that it can improve the network bandwidth consumption and speed up the data access. When network bandwidth is relatively low and computing power is relatively high, parallel downloading has been effective.

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Mohamed et al. [5] proposed the Close-to-Files (CF) scheduling algorithm that schedules a job on sites with enough processing capacity close to a site where data are present. CF uses an exhaustive algorithm to check all combinations of computing sites and data sites to select a site with the minimum computation and transmission delay. CF has better performance in comparison with Worst-Fit (WF) job placement strategy, which allocates jobs on the sites with the largest number of idle processors. In [1] the authors proposed a framework that supports job scheduling and data replication in Data Grid. Several replication strategies and their performance are evaluated with some scheduling heuristics. The data replication algorithms increase the scheduling performance from the perspective of job computing time. In particular, the combination of shortest turnaround time scheduling heuristic (STT) and centralized dynamic replication indicates prominent performance. In [6] the authors developed the Hierarchical Cluster Scheduling algorithm (HCS) and the Hierarchical Replication Strategy (HRS) to enhance the data access efficiencies in a grid. HCS considers hierarchical scheduling and uses cluster information to decrease search time for a proper computing node. HRS replication algorithm uses the concept of ‘‘network locality’’ as Bandwidth Hierarchy based Replication (BHR) strategy [7]. HRS has two advantages in comparison with BHR: First, BHR checks all sites to find the best replica, while in HRS required replica within the local cluster has high priority. Second, BHR uses popularity of replicas at cluster level but HRS uses at site level. HCS scheduling along with HRS replica strategy improve data access time and the amount of inter-cluster communications in comparison to others scheduling algorithms and replication strategies. Foster and Ranganathan [8, 9], have proposed six distinct replica strategies for a multi-tier data: No Replica, Best Client, Cascading Replication, Plain Caching, Caching plus Cascading Replica and Fast Spread. They also introduced three types of localities, namely: Temporal locality: The files accessed recently are much possible to be requested again shortly. Geographical locality: The files accessed recently by a client are probably to be requested by adjacent clients, too. Spatial locality: The related files to recently accessed file are likely to be requested in the near future. They evaluated these strategies with different data patterns: access pattern with no locality, data access with a small degree of temporal locality and finally data access with a small degree of temporal and geographical locality. The results of simulations indicate that different access pattern needs different replica strategies. Cascading and Fast Spread performed the best in the simulations. They have presented in another work [10] the problem of scheduling job and data movement operations in a distributed ‘‘Data Grid’’ environment to identify both general principles and specific strategy that can be used to improve system utilization and/or response times. They have also proposed framework with four different job scheduling algorithms, as follows: (1) JobRandom: select a site randomly, (2) JobLeastLoaded: select a site where has the least number of jobs waiting to run, (3) JobDataPresent: select a site where has requested data, and (4) JobLocally: run jobs locally. These job scheduling

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strategies are combined with three various replication algorithms: (1) DataDoNothing: there is no replication and data may be fetched from a remote site for a particular job, (2) DataRandom: when popularity of the file exceeds a threshold, a replica is created at a random site, (3) DataLeastLoad: when the threshold for a file exceeds, a replica is placed at the least loaded site. They can enhance performance by scheduling jobs where data is located and using a replication policy that periodically creates new replicas of popular datasets at each site. The results also show that while it is important to consider the impact of replication on the scheduling strategy, it is not always necessary to couple data movement and computation scheduling. In [11] the problem of co-scheduling job dispatching and data replication in large distributed systems in an integrated manner is presented. They used a massively-parallel computation model that contains a collection of heterogeneous independent jobs with no inter job communication. The proposed model has three variables within a job scheduling system: the job order in the scheduler queue, the assignment of jobs to the nodes, and the assignment of data replicas to data stores. Finding the optimal tuple requires an exhaustive search and it is costly because the solution space is very big. The results show that deploying a genetic search algorithm has the potential to achieve better performance than traditional allocation methods. In [12] the authors proposed a two-phase dynamic replication algorithm coupled with two-stage job scheduling to provide an integrated environment for efficient access to data and job scheduling. In the first decides what to replica and the second phase, selects best network link from local node to the nodes containing other replicas of same file. The jobs are scheduled to sites where have maximum availability of required data files and replica creation is based on the access load. The simulation results show that two-level job scheduling and two-phase dynamic replication strategy have better performance in terms of both job execution time and number of replicas created.

3 The Scheduling Problem Data Grid environment is comprised of storage resources (SE’s) which store the data and compute resources (CE’s) that execute the jobs. It is possible that the each resource have both storage and computation capabilities. The step-by-step procedure for scheduling jobs is shown in Fig. 1. The Resource Broker collects information about the available computational resources and the datasets through a resource information service and the Data Catalog respectively. Then, it uses a scheduling policy to calculate the cost of running a job on a group of candidate sites i.e. the scheduler determines where to dispatch a job based on the cost of the computational resource and data transfer time. The applications or jobs typically need large datasets for their execution; so the main factor in execution time is the total access cost of the required data. The

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Fig. 1 The scheduling procedure in Data Grid

job is dispatched to the site with the minimum estimated cost by the scheduler. Job processing results are transferred to the Resource Broker or another storage resource. This process is repeated until all the jobs are scheduled.

4 The Proposed Scheduling Algorithm The proposed Combined Scheduling Strategy (CSS), first checks if the requested files are available in the local storage, then job will be executed otherwise; files should be transferred and replicated. CSS considers the number of jobs waiting in the queue, size of requested data files and size of computing elements (CE) of the site. Let Ji ¼ fF1 ; F2 ; . . .; Fm g be the m required files for job i. Let FSIZEi,j be size of requested data files that are not available in site j for Job i. X FSIZEi; j ¼ size of all unavailable files Fk for Ji ð1Þ Loadj ¼ Qj = Sj

ð2Þ

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where Qj and Sj are the number of jobs waiting in queue and size of CE’s of site j, respectively. These factors have an important role in job scheduling. So sum of them along with proper weights (w1, w2) for each factor yields the combined cost of executing Job i in site j as follows: CombinedCosti; j ¼ w1  FSIZEi; j þ w2  Loadj

ð3Þ

This function can be tailored, because it is defined as a weighted combination of two former metrics. Next combined cost for each Job is computed and the job will be assigned to the site with minimum combined cost i.e. CombinedCostmin ¼ MINnj¼1 CombinedCosti; j ; where n is the number of sites:

5 Experiments In this section first simulation tool and configuration are explained and then simulation results are given.

5.1 Simulation Tool CSS is implemented and evaluated in OptorSim, a data grid simulator developed by the EU Data Grid project, as shown in Fig. 2 [13, 14]. The OptorSim has several parts: • Computing Element (CE) represents computational resource in Data grid. • Storage Element (SE) represents data resource in Data grid. • Resource Broker (RB) gets the user’s job and allocates each job to proper site based on the selected scheduling policy. • Replica Manager (RM) at each site controls data transferring and provides a mechanism for accessing the Replica Catalog. • Replica Optimizer (RO) within RM implements the replication algorithm shown in Fig. 2b. When a file is requested by a job, RO would locate the best replica in terms of the file’s Logical File Name (LFN) and determine whether it should be replication, or create a temporary cache of the file locally.

5.2 Configuration For the experiments, we used the same testbed configuration in our previous work [15]. The Grid topology has 3 regions and on average three LAN’s in every region (Fig. 3). Table 1 specifies the simulation parameters used in our study. CERN

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Fig. 2 a OptorSim architecture. b An expanded illustration of grid site

(where the data are originally produced) has a high capacity of 300 GB, and the storage capacity of all other sites is 30 GB. There are 6 job types, and each job type on average requires 16 files (each is 1 GB) for execution.

5.3 Simulation Results and Discussion In this experiment a combination of various replication and scheduling algorithms are evaluated. Four scheduling strategies have been considered, as follows: (a) Random scheduler selects a computing node to execute specific job randomly. (b) Shortest Queue scheduler calculates all of the queue length of computing nodes and selects one that has the least number of jobs waiting in the queue. (c) Access Cost scheduler assigns the job to computing element where the file has the lowest access cost (cost to get all files needed for executing job). (d) Queue Access Cost scheduler selects computing element where has the smallest sum of the access cost for the job and the access costs of all jobs waiting in the queue. More details about these schedulers are given in [16]. Also five replication algorithms have been used for evaluation, namely: (a) Least Frequently Used (LFU) always replicates files to local storage system. If available space in local storage is not enough, the replica which has been accessed the least number of times in the past is deleted. (b) Least Recently Used (LRU) always replicates files to local storage system. If the local storage space is full, replica that has the oldest last access time is deleted to make space for new replicas.

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Fig. 3 Grid topology in the simulation

Table 1 Simulation parameters

Topology parameters

Values

Number of regions Storage space at each site (GB) Inter LAN bandwidth (Mpbs) Intra LAN bandwidth (Mpbs) Intra region bandwidth (Mpbs)

3 30 1,000 100 10

Grid job parameter

Value

Number of jobs Number of jobs types Number of file access per jobs Size of single file (GB) Total size of files (GB) Job delay (ms)

2,000 6 16 1 97 2,500

(c) Bandwidth Hierarchy based Replication (BHR) [7] decreases the data access time by maximizing network-level locality and avoiding network congestions. Sites are divides into several regions. Network bandwidth between the regions is lower than the bandwidth within the regions. So, if the required file is placed in the same region, its fetching time will be less. Also, if the new replica is already duplicated in other site in the same region, the optimizer in BHR will terminate. (d) Modified BHR [17] is an extension of BHR strategy. The difference between Modified BHR and BHR is Modified BHR tries to replicate files in a site where has accessed the file for the most time based on the assumption that it may also be required in future.

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Fig. 4 Mean job execution time for various job scheduling and replication algorithms

(e) 3-Level Hierarchical Algorithm (3LHA) [15] is replication algorithm for a 3-level hierarchy structure. It considered a hierarchical network structure that has three levels. First level are Regions that are connected through internet i.e. have low bandwidth. Second level comprises local area network’s (LAN) within each region that have moderately higher bandwidth comparing to the first level. Finally the third level are the nodes (sites) within each the LAN’s, that are connected to each other with a high bandwidth. 3LHA among the candidate replicas selects the one that has the highest bandwidth to the requested file. Similarly, use the same technique for file deletion. Figure 4 shows the mean job execution time for 2000 jobs using the above mentioned scheduling and replication strategies. 3LHA has better performance than BHR since it considers the differences between intra-LAN and inter-LAN communication. Also, if the available storage for replication is not enough, 3LHA will only replicate those files that are not available in the local LAN. So it will not delete those file that have a high cost of transfer i.e. bandwidth is an important factor for deleting and this leads to a better performance comparing with LRU and LFU methods. Modified BHR improves the mean job execution time by storing the replica in the most frequently accessed site. Figure 4 shows that a suitable choice of data replication algorithm can enhance Grid performance. In Random scheduling the mean job execution time obviously increases because it doesn’t consider effective factors. It probably selects CE that has large number of jobs in the queue and it is possible to be idle in other CEs or must spend a lot of transmission time for getting required files. In Shortest Job Queue Scheduling each CE receive approximately the same number of jobs. If CE’s have low network bandwidth, then file transfer time will be high and overall job execution time will increase. Access Cost Scheduling selects a CE based on its access cost. CE’s with lower access cost may receive large number of jobs to execute.

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Fig. 5 Mean job time based on varying number of jobs

Fig. 6 Mean job execution time based on varying size of files

So, overall performance is decreased. The Queue Access Cost considers not only shortest job queue but also access cost. Therefore, the Queue Access Cost decrease total job execution time. Finally, CSS is the best job scheduling algorithms for the large number of jobs because it schedules jobs close to the data whilst ensuring sites with high network connectivity are not overloaded and sites with poor connectivity are not left idle. Figure 5 displays the mean job time based on changing number of jobs that use sequential access pattern. As number of jobs increases the performance of CSS increases in comparison with other methods. It is similar to a real grid environment where a lot of jobs should be executed. Figure 6 shows the effect of file size on the execution time for 5 algorithms. CSS has less job execution time in comparison with other strategies especially when size of the files is increased. It is necessary to note that in data-intensive applications, the locations of data required by the job affects the performance

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significantly. This performance will be more visible in the real data grid environments where dataset sizes reach to many terabytes.

6 Conclusion and Future Work Replication and scheduling problem has been investigated both separately and jointly by researchers. Effective scheduling of jobs in a dynamic Data Grid environment is complex since it deals with huge amount of data. In this paper, a novel Combined Scheduling Strategy (CSS) is proposed that considers number of jobs waiting in queue, the location of required data for the job and the computing capacity of sites. To evaluate the efficiency of the job scheduling policy, OptorSim is used to simulate a real data. Results are shown for various scheduling and replica optimization policies. It is evident that the choice of scheduling algorithm has a significant impact on system performance. CSS takes less job execution time than other strategies especially when number of jobs or size of the files or both increases. For future work, we plan to evaluate CSS with more scheduling and replication algorithms as well as investigating more complicated scheduling algorithms and replica placement strategies to improve the overall system performance.

References 1. Tang M, Lee BS, Tang X, Yeo C (2006) The impact of data replication on job scheduling performance in the data grid. Fut Gener Comput Syst 22:254–268 2. Rangmathan K, Foster I (2003) Simulation studies of computation and data scheduling algorithms for data grid. J Grid Comput 1:53–62 3. Takefusa A, Tatebe O, Matsuoka S, Morita Y (2003) Performance analysis of scheduling and replication algorithms on grid datafarm architecture for high-energy physics applications. In: Proceedings of the 12th IEEE international symposium on high performance distributed computing (HPDC’03) 4. Zhang J, Lee B, Tang X, Yeo C (2008) Impact of parallel download on job scheduling in data grid environment. Seventh international conference on grid and cooperative computing 5. Mohamed HH, Epema DHJ (2004) An evaluation of the close-to-files processor and data co-allocation policy in multiclusters. International conference on cluster computing, IEEE Society Press, San Diego, pp 287–298 6. Chang R, Chang J, Lin S (2007) Job scheduling and data replication on data grids. Fut Gener Comput Syst 23:846–860 7. Park S, Kim J, Ko Y, Yoon W (2004) Dynamic data replication strategy based on internet hierarchy BHR. Lecture notes in computer science publisher, vol 3033. Springer, Heidelberg, pp 838–846 8. Foster I, Ranganathan K (2001) Design and evaluation of dynamic replication strategies for high performance data grids. In: Proceedings of international conference on computing in high energy and nuclear physics, Beijing

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9. Foster I, Ranganathan K (2002) Identifying dynamic replication strategies for high performance data grids. In: Proceedings of 3rd IEEE/ACM international workshop on grid computing. Lecture Notes on Computer Science, vol 2242, Denver, pp 75–86 10. Foster I, Ranganathan K (2002) Decoupling computation and data scheduling in distributed data-intensive applications. In: Proceedings of the 11th IEEE international symposium on high performance distributed computing, HPDC-11, IEEE. CS Press, Edinburgh, pp 352–358 11. Vazhkudai S (2003) Enabling the co-allocation of grid data transfers. In: Proceedings of the fourth international workshop on grid computing 12. Suri PK, Singh M (2009) JS2DR2: An effective two-level job scheduling algorithm and twophase dynamic replication strategy for data grid. International conference on advances in computing, control, and telecommunication technologies 13. OptorSim—a replica optimiser simulation 14. http://grid-data-management.web.cern.ch/grid-datamanagement/optimisation/optor/ 15. Horri A, Sepahvand R, Dastghaibyfard Gh (2008) A hierarchical scheduling and replication strategy. Int J Comput Sci Netw Secur 8 16. Cameron DG, Carvajal-Schiaffino R, Paul Millar A, Nicholson C, Stockinger K, Zini F (2003) Evaluating scheduling and replica optimisation strategies in OptorSim. 4th international workshop on grid computing (Grid2003), Phoenix, Arizona, IEEE Computer Society Press 17. Sashi K, Selvadoss Thanamani A (2011) Dynamic replication in a data grid using a modified BHR region based algorithm. Fut Gener Comput Syst (Elsevier) 27:202–210

A Novel Dynamic Replication Algorithm in Hierarchical Data Grid N. Mansouri and Gh. Dastghaibyfard

Abstract Data grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to limitation of available resources in grid as well as production the large number of workloads, efficient use of the grid resources becomes important challenge. Replication provides decreased bandwidth consumption, improved fault tolerance, and enhanced scalability. Although replication is a key technique, but the problem of selecting proper locations for placing replicas i.e. best replica placement in Data Grid has not been widely studied, yet. In this paper a modified three-layer hierarchical replication algorithm is proposed for dynamic creation of replicas, replica selection and especially replica placement which selects best location. The simulation and experiment results show the proposed replication strategy outperforms over current strategies about 14%. contain at least 70 and at most 150 words. It should be set in nine-point font size and should be inset 1.0 cm from the right and left margins. There should be two blank (10-point) lines before and after the abstract. This document is in the required format. Keywords Data Grid


Replication
Simulation

N. Mansouri (&)
Gh. Dastghaibyfard Department of Computer Science and Engineering, College of Engineering, Shiraz University, Shiraz, Iran e-mail: [email protected] Gh. Dastghaibyfard e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_27,  Springer Science+Business Media B.V. 2012

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Fig. 1 Grid layer architecture

1 Introduction Data intensive applications such as high-energy physics, bioinformatics, climate modeling, earthquake engineering. which process and produce huge amounts of data, are the building blocks in Data Grid. The big challenge is how to manage these amounts of data that require high capacity mass storage systems; high bandwidth networks and finally how to disseminate efficiently such data among users that are distributed over a wide geographic area. Data Grids aggregate a collection of distributed resources placed in different parts of the world to enable users to share data and resources [1–3]. Figure 1 shows the components of a Data Grid in a layered architecture [4, 5]. The grid fabric layer involves distributed computational resources, storage resources, and instruments that are connected by high bandwidth. The connectivity layer contains protocols used to query resources in the grid fabric layer and to control transferring data between them. Data Grid service layer consists of several services such as replication, data discovery and resource brokering. The application layer contains the user applications that work within a virtual organization environment. The size of data that are requested in Data Grid is from terabytes to petabytes. Meanwhile, even though the memory and storage size of new computers are ever increasing, they are still not keeping up with the request of storing large number of data. The major challenge is a decision problem i.e. how many replicas should be

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created and where replicas should be stored. Hence methods needed to create replicas that increase availability without using unnecessary storage and bandwidth. There are three key issues in all the data replication algorithms as follows: • Replica selection: process of selecting replica among other copies that are spread across the grid. • Replica placement: process of selecting a grid site to place the replica. • Replica management: the process of creating or deleting replicas in Data Grid. Generally, replication algorithms are either static or dynamic. In static approaches the created replica will exist in the same place till user deletes it manually or its duration is expired. On the other hand, dynamic strategies create and delete replicas according to the changes in grid environments, i.e. users’ file access pattern. Almost all data replication studied in Data Grid environments have focused on creating and deleting replicas methods [6–11], but best replica placement has not drawn much attention of researchers. Best replica placement is an important issue and it is considered in this paper, since it plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time. In this work a novel data replication strategy, called Modified three-Layer Hierarchical Replication (M3LHR) algorithm is proposed. M3LHR extends proposed algorithm in [12] and stores each replica in an appropriate site i.e. best site in the requested region that has the highest number of access for that particular replica. The paper is organized as follows. Section 2 gives an overview of some related work on data replication strategies. In Sect. 3 a M3LHR algorithm is proposed. Section 4 shows the simulations results. Finally, conclusions and future research works are presented in Sect. 5.

2 Related Work Recently, modeling Data Grid environments and simulating different data replication strategies as well as basic file replication protocols [13] has drawn researchers attention. In [14] the authors have presented two dynamic replica strategies, Simple Bottom Up and Aggregate Bottom Up, for the multi-tiered grid. The main point of these strategies is to place the replicas as close as possible to the clients. Files are replicated from down levels to up levels according to their popularity. Both strategies decrease the mean response time of data access in comparison with the static replication algorithm in a multi-tiered grid. Fast Spread is a replication algorithms that works well for random request patterns [15]. It places replica at each node along its path to the best client. If one of these nodes does not have enough free storage for the new replica, a group of replicas may be deleted without considering such deleted files may be more

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important than the new replica. To overcome this problem Bsoul et al. [16] extended Fast Spread strategy by proposing a dynamic threshold that determines the replica should be stored at each node or not. The results showed that the proposed strategy achieved better performance compared with Fast Spread. Zhao et al. [17] proposed a dynamic optimal replication strategy (DORS) to overcome the problem of limited storage capacity in each node. They presented a replica replacement strategy based on the access history, file’s size and the network condition. They also empirically presented a threshold to determine whether one file should be replicated in other nodes or not. They achieved better performance in comparison with LFU and LRU strategies. In [18], Khanli et al. presented a dynamic replication strategy for a multi-tier data grid, i.e. predictive hierarchical fast spread (PHFS) which is an extended version of fast spread. PHFS predicts future requests according to spatial locality, and pre-replicates them in various layers of the multi-tier data grid to improve storage resources usage. They showed PHFS strategy is appropriate for applications in which the clients use a context for duration of time, similar to the scientific applications that users work on a project. Park et al. [19] presented a Bandwidth Hierarchy based Replication (BHR) which decreases the data access time by maximizing network-level locality and avoiding network congestions. They divided the sites into several regions. Network bandwidth between the regions is lower than the bandwidth within the regions. So, if the required file is placed in the same region, its fetching time will be less. BHR strategy has a deficiency, replicated files are placed in all the requested sites not the appropriate sites. In [20] the authors introduced a concept called fragmented replicas. When replication takes place, instead of copying the whole replica, it just stores partial contents that is needed locally. It can significantly save the storage space for not storing unnecessary data. They also presented a block mapping procedure to set the distribution of blocks in every available server for next replica retrieval. A client can retrieve a fragmented replica quickly by employing the block mapping procedure. The results of simulation indicated that the co-allocation schemes enhance download efficiency in a fragmented replication system. Another replication method, called Branch replication schema (BRS) have been presented in [21]. BRS provides more scalability than serial solutions through the parallel replication of a file in several sites. In BRS each replica is comprised of a disparate set of subreplicas organized using a hierarchical tree topology where subreplicas of a replica do not overlap. To improve the scalability and performances for both read and write operation, they used parallel approach [22]. The simulation results showed that BRS improves data access performance for files with different sizes and provides an efficient way for updating data replicas. Foster and Ranganathan [15], have proposed six distinct replica strategies No Replica, Best Client, Cascading Replication, Plain Caching, Caching plus Cascading Replica and Fast Spread) for multi-tier data. They also introduced three types of localities, namely:

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• Temporal locality: The files accessed recently are much possible to be requested again shortly. • Geographical locality: The files accessed recently by a client are probably to be requested by adjacent clients, too. • Spatial locality: The related files to recently accessed file are likely to be requested in the near future. These strategies evaluated with different data patterns: first, access pattern with no locality. Second, data access with a small degree of temporal locality and finally data access with a small degree of temporal and geographical locality. The results of simulations indicate that different access pattern needs different replica strategies. Cascading and Fast Spread performed the best in the simulations. Also, the authors combined different scheduling and replication strategies. In a data grid reducing job’s waiting time in queue and execution time depends on where to assign job for execution and where to get the required data files. Therefore, scheduling jobs at proper sites and getting replicas from proper sites are important factors from user’s point of view. We leave the job scheduling problem [23], which studies how to dispatch jobs into Grid sites for execution, and it’s coupling with data replication, as our future research. Some researchers have studied the relationship between data replication and job scheduling [24–26].

3 The Proposed Method In this section, first network structure is described, and then a novel M3LHR algorithm is proposed.

3.1 Network Structure The grid topology of simulated platform is given in Fig. 2. Which has three levels similar to what is given in [12] i.e. regions’, LAN’s within each region and nodes within each LAN.

3.2 Modified Three-Layer Hierarchical Replication Algorithm When a job is allocated to local scheduler, before job execution the replica manager should transfer all the required files that are not available. So, the data replication enhances the job scheduling performance by decreasing job turnaround time. M3LHR has three parts:

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Fig. 2 Grid topology in the simulation

Replica Selection: For all requested files of job i, Replica Manager first tries to select the unavailable requested file if possible within the local LAN next within the local region. Otherwise it generates a list of replicas that are available in other regions. Then from this list it selects the replica that has the lowest transfer time to requested site. Replica Placement: According to the temporal and geographical locality the replica is placed in the best site (BSE). To select the BSE, M3LHR creates a sorted list (by number of replica access) of all SE’s that requested that particular replica in the region. Now the replica will be placed in the first SE of the above sorted list, if more than one SE is candidate for BSE, one can be selected randomly. Therefore, replica is not placed in all the requested sites. Hence, storage cost as well as mean job execution time can be decreased. Assume list 1 shows the sorted list created for replica R, now M3LHR selects site S7 from LAN3 which is shown in Fig. 3. Replica Management: If enough storage space exists in the BSE, the selected file is replicated. Otherwise if the file is available in the local LAN, then the file is accessed remotely. Now, if enough space for replication does not exist and requested file is not available in the same LAN, one or more files should be deleted using the following rules: • Generate a least recently used (LRU) sorted list of replicas that are both available in the site as well as the local LAN. Now start deleting files from the above list till space is available for replica. • If space is still insufficient, then repeat previous step for each LAN in current region randomly. • If space is still insufficient, generate a LRU sorted list of the remaining files in the site and start deleting files from the above list till space is available for replica. Fig. 4 describes M3LHR strategy.

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Fig. 3 Replica selection and replica placement strategies

4 Experiments 4.1 Simulation Tool M3LHR is evaluated using OptorSim [27], a simulator for Data Grids, which is s developed by the European Data Grid (EDG) project. OptorSim has several important components such as computing element (CE), storage element (SE), resource broker (RB), replica manager (RM), and replica optimizer (RO). Computing elements and storage elements are used to execute grid jobs and store the files respectively. Additional details about OptorSim are available in the literatures [28, 29].

4.2 Configuration With OptorSim, it is possible to simulate any grid topology and replication strategy. So OptorSim code has been modified to implement the hierarchical structure, since it uses a flat network structure. It is assumed the network has three

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Fig. 4 M3LHR algorithm

regions and on average two LAN’s in every region. The storage capacity of the master site is 250 GB, and the storage capacity of all other sites is 30 GB. Bandwidth in each level is given in Table 1. The number of storage elements and the number of computing elements are set 11 and 10, respectively. Table 2 specifies the simulation parameters and their values used in our study. There are six job types, and each job type on average requires 16 files (each is 2 GB) for execution. To simplify the requirements, we assumed that the data is read-only.

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Table 1 Bandwidth configuration Parameter

Value (Mpbs)

Inter LAN bandwidth (level3) Intra LAN bandwidth (level 2) Intra Region bandwidth (level 1)

1000 100 10

Table 2 General job configuration Parameter

Value

Number of jobs Number of jobs types Number of file access per jobs Size of single file (GB) Total size of files (GB) Job delay (ms) Maximum Queue Size

500 6 16 2 200 2500 200

4.3 Simulation Results and Discussion The proposed M3LHR algorithm is compared with four replication algorithms namely, No Replication, Least Frequently Used (LFU), Least Recently Used (LRU), and BHR. In No Replication strategy files are accessed remotely. When storage is full, LRU deletes least recently accessed files and LFU deletes least frequency accessed files. The BHR algorithm stores the replicas in a site that has a high bandwidth and replicates those files that are likely to be requested soon within the region. Figure 5 shows the mean job execution time for the sequential access pattern. Obviously, the No Replication strategy has the worst performance as all the files requested by jobs have to be transferred from CERN. In this simulation LRU and LFU have almost the same execution time. BHR improves data access time by avoiding network congestions. M3LHR mean job execution time is about 14% faster than BHR since it considers the differences between intra-LAN and interLAN communication. If the available storage for replication is not enough M3LHR will not delete those file that have a high transferring time. It also improves the mean job execution time by storing the replica in the most frequently accessed site of the requested region. Valuable information can be gained by monitoring storage resources usage. Since resource cost is proportional to the resource used, so minimizing storage usage is a must. Figure 6 shows the storage usage which is the percentage of available spaces that are used. No Replication strategy has best storage since it gets files from CERN. LFU and LRU are always replicate when a request is made, hence they use a lot of space. BHR strategy performs better than the previous three strategies since it keeps at most one copy of file in the region. The proposed M3LHR strategy has minimum storage usage among the current algorithms because it place replicas in the appropriate site so reduces unnecessary replication.

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Fig. 5 Mean job execution time for various replication algorithms

Fig. 6 Storage resources usage for various replication algorithms

Fig. 7 Mean job execution time based on varying size of files

Figure 7 shows the mean job time for varying file sizes for the four algorithms. Since the storage size is fixed, increasing in file size will decrease the average number of copies of each file, so placing replicas in the suitable sites significantly increases performance. Here M3LHR outperforms the other methods as file size

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increases since it prevents from creating unnecessary replicas. It is expected that even in the real data grid environments the difference will be even more considerable as dataset sizes reach to many terabytes.

5 Conclusion and Future Work Data replication is a frequently used technique that can enhance the data availability and fault tolerance. Since a grid environment is dynamic, network latency and user behavior may change. To address these issues and storage capacity limitation, a well-designed dynamic replication strategy is necessary in data grid. In this paper a dynamic data replication strategy, called M3LHR for a three level hierarchical structure is proposed. M3LHR deletes those file that are exist in local LAN (i. e. only files with minimum time for transferring) when free space is not enough for the new replica. It also stores the replicas in the best site where the file has been accessed for the most time instead of storing files in many sites. Experimental results of M3LHR with OptorSim simulator show that it has less job execution time in comparison with other strategies especially when the grid sites have comparatively small storage size. Also, that performance increases as file size increases, since the proposed method decreases unnecessary replication by placing replica in the best site. To the best of our knowledge, in the real Data Grid environments these advantages will be even more significant as dataset sizes reach to many terabytes. In future work, M3LHR can be combined with a proper scheduling to improve performance. Data transferring between different grid sites is time consuming and consequently scheduling jobs to the appropriate sites is necessary. Searching for advanced replica replacement methods certainly enhances replication strategies. Acknowledgments The authors would like to thank Iran Telecommunication Research Center (www.itrc.ac.ir) for their financial support.

References 1. Figueira S, Trieu T (2008) Data replication and the storage capacity of data grids. Springer, Berlin, Heidelberg, pp 567–575 2. Lamehamedi H, Szymanski B, Shentu Z, Deelman E (2002) Data replication strategies in grid environments.In: Proceedings of the 5th international conference on algorithms and architectures for parallel processing, pp 378–383 3. Cameron D, Millar A, Nicholson C, Carvajal-Schiaffino R, Stockinger K, Zini F (2004) Analysis of scheduling and replica optimisation strategies for data grids using OptorSim. J Grid Comput 2:57–69 4. Foster I, Kesselman C, Tuecke S (2001) The anatomy of the Grid: enabling scalable virtual organizations. Int J High Perform Comput Appl 15:200–222

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5. Baker M, Buyya R, Laforenza D (2002) Grids and grid technologies for wide-area distributed computing. Softw Pract Exper 32:1437–1466 6. Chervenak A, Schuler R, Kesselman C, Koranda S, Moe B (2005) Wide area data replication for scientific collaborations. In: Proceedings of the 6th international workshop on grid computing, pp 1–8 7. David WB (2003) Evaluation of an economy-based file replication strategy for a data grid. International workshop on agent based cluster and grid computing, pp 120–126 8. Deris M, Abawaji J, Suzuri H (2004) An efficient replicated data access approach for largescale distributed systems.In: IEEE international symposium on cluster computing and the grid 9. Ranganathan K, Iamnitchi A, Foster I (2002) Improving data availability through dynamic model-driven replication in large peer-to-peer communities. In 2nd IEEE/ACM international symposium on cluster computing and the grid, pp 376–381 10. Chang R, Chang J, Lin S (2007) Job scheduling and data replication on data grids. Futur Gener Comput Syst, Elsevier, 23: 846–860 11. Abdi S, Pedram H, Mohamadi S (2010) The impact of data replication on job scheduling performance in hierarchical data grid. Int J Appl Graph Theory in Wireless Ad hoc Netw Sensor Netw (GRAPH-HOC) 2 12. Horri A, Sepahvand R, Dastghaibyfard Gh (2008) A hierarchical scheduling and replication strategy. Int J Comput Sci Netw Secur, vol 8 13. Stockinger H, Samar A, Allcock B, Foster I, Holtman K, Tierney B (2002) File and object replication in data grids. J Clust Comput 5:305–314 14. Tang M, Lee BS, Yao CK, Tang XY (2005) Dynamic replication algorithm for the multi tier data grid. Futur Gener Comput Syst, Elsevier 21: 775–790 15. Foster I, Ranganathan K (2002) Identifying dynamic replication strategies for high performance data grids.In: Proceedings of 3rd IEEE/ACM international workshop on grid computing, Lecture notes on computer science, vol 2242, pp 75–86 16. Bsoul M, Al-Khasawneh A, Kilani Y, Obeidat I (2010) A threshold-based dynamic data replication strategy. J Supercomput 17. Zhao W, Xu X, Wang Z, Zhang Y, He S (2010) A dynamic optimal replication strategy in data grid environment.In: International conference on internet technology and applications 18. Khanli LM, Isazadeh A, Shishavan TN (2010) PHFS: a dynamic replication method, to decrease access latency in the multi-tier data grid. Futur Gener Comput Syst, Elsevier, vol 27, pp 233–244 19. Park S, Kim J, Ko Y, Yoon W (2004) Dynamic data replication strategy based on internet hierarchy BHR. Lecture notes in computer science, Springer, Heidelberg, vol 3033, pp 838–846 20. Chang R, Chen P (2007) Complete and fragmented replica selection and retrieval in Data Grids. Futur Gener Comput Syst, Elsevier, vol 23, pp 536–546 21. Pérez JM, Carballeira F, Carretero J, Calderón A, Fernández J (2009) Branch replication scheme: a new model for data replication in large scale data grids. Futur Gener Comput Syst, Elsevier, vol 26, pp 12–20 22. Carballeira F, Carretero J, Calderon A, Garcia JD, Sanchez LM (2007) A global and parallel file systems for grids. Futur Gener Comput Syst 23:116–122 23. Venugopal S, Buyya R (2008) An scp-based heuristic approach for scheduling distributed data-intensive applications on global grids. J Parallel Distributed Comput 68:471–487 24. Chakrabarti A, Sengupta S (2008) Scalable and distributed mechanisms for integrated scheduling and replication in data grids. Proceedings of 10th International Conference on Distributed Computing and Networking (ICDCN) 25. Chervenak A, Deelman E, Livny M, Su M, Schuler R, Bharathi S, Mehta G, Vahi K (2007) Data placement for scientific applications in distributed environments. Proceedings of IEEE/ ACM international conference on grid computing 26. Dang NN, Lim SB (2007) Combination of replication and scheduling in data grids. Int J Comput Sci Netw Secur 7:304–308

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27. OptorSim, http://edg-wp2.web.cern.ch/ 28. Cameron D, Carvajal-Schiaffino R, Millar P, Nicholson C, Stockinger K, Zini F (2003) Evaluating scheduling and replica optimization strategies in OptorSim. Forth international workshop on grid computing, Phoenix, USA 29. Bell W, Cameron D, Carvajal-Schiaffino R, Millar P, Nicholson C, Stockinger K, Zini F (2004) OptorSim v1.0 installation and user guide

Intelligent Application Migration within a Self-Provisioned Hybrid Cloud Environment Wei-Jen Wang, Yo-Ming Lo, Shao-Jui Chen and Yue-Shan Chang

Abstract A typical hybrid cloud connects a private data center to a portion of a public cloud. It can potentially scale up the computing pool of a private cloud on demand. This work presents a novel adaptive computing framework that enables intelligent automatic application migration between a private data center and a public cloud. In the case that the workload of the private data center is too high, the proposed framework can create more computing resources in the public cloud automatically, and then move applications to the public cloud in order to reduce the workload of the private data center. The proposed framework employs the multi-agent technique to manage all resources, monitor system behavior, and negotiate all actions in the hybrid cloud. We have built a prototype of a hybrid cloud environment to demonstrate that the proposed framework is able to perform intelligent application migration and self-provisioning. Keywords Application migration based intelligence


Cloud computing
Hybrid cloud
Agent-

1 Introduction The emerging cloud computing technology [1–3] aims to deliver flexible, ondemand computing resources as services to different users. According to the NIST’s definition of cloud [4], a cloud is a share pool of configurable computing W.-J. Wang (&)
Y.-M. Lo
S.-J. Chen Department of Computer Science and Information Engineering, National Central University, 300 Jhongda Rd., Jhongli, Taoyuan, Taiwan Y.-S. Chang Department of Computer Science and Information Engineering, National Taipei University, 151 University Rd., Sanhsia, New Taipei, Taiwan

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Fig. 1 The idea of the proposed framework

resources that can be rapidly provisioned and released. Recently, the deployment model for hybrid clouds [5–8] is becoming more popular in both the academia and the industry. A typical hybrid cloud [9] connects a private datacenter to a portion of a public cloud through a virtual private network (VPN). It adopts the concept of Virtual Private Cloud (VPC) [8–10], which is a user-defined group of virtual resources on a public cloud. In 2010, Amazon announced its virtual private cloud [8] service that can provision a private, isolated cloud on Amazon Web Services (AWS) Cloud, where the users can launch computing resources in a user-defined virtual network. In Taiwan, the cloud computing center of Industrial Technology and Research Institute (ITRI) [11] also launched a hybrid cloud project to support the VPC service in their public cloud. One benefit of using VPC is that, the VPC model can scale up the computing pool of a private cloud on demand. When the workload of a private cloud is high, the hybrid cloud environment can dynamically create several virtual machine instances in the public cloud to form a VPC and then move some workload to the VPC. As a result, the quality of service in a hybrid cloud can be ensured by resource provisioning and service migration. To enable intelligent, dynamic application migration in a hybrid cloud, one may adopt the agent technology [12– 15] for system monitoring, self-provisioning, computation coordination, and automatic load balancing. Ian Foster et al. [12] have pointed out that agent technology can enhance the ability of problem solving of grid. We believe it can enhance the cloud computing environment as well. For example, a multi-agent intelligent system can be installed on a hybrid cloud. Some agent is responsible for system monitoring, while some decides how and when to perform system reconfiguration in a hybrid cloud. This research aims to provide an intelligent, agent-based framework that supports dynamic application migration in a hybrid cloud, in particular dynamic application migration between the public cloud and the private cloud. Figure 1 shows the concept of proposed framework where there are two major components. The first component is a smart multi-agent system that detects the status of the hybrid cloud and manages the resources in the hybrid cloud. We used the Java Agent Development framework (JADE) [16] to implement this component. The

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second component is a hybrid cloud environment where a set of mobile applications are running. The proposed framework is able to adjust the workload by moving applications automatically. When existing computing resources are not enough to serve all applications, the proposed framework will notify the public cloud to create more computing resources automatically. We have built a prototype of the proposed framework. In the future, we plan to implement the proposed framework on a larger hybrid cloud environment that connects a small private data center in National Central University and a virtual private cloud in the ITRI public cloud [11].

2 Related Work Agent-based systems [12–18] have been widely studied in the past decade. The fundamental computing element in an agent-based system is the agent, which acts as an independent, identical, and autonomous process with the ability to communicate with other agents [16]. Different agents may perform different tasks, and they can work together to accomplish a complex task in a distributed environment. Some agent-based systems such as JADE [14, 16] and SALSA [19] support dynamic agent migration. The mobility feature gives more flexibility and more efficiency to programmers and system administrators [13]. Many software engineers and researchers focused their research on either the study of agent-based systems [12–14, 16–19] or the study of hybrid cloud models [5, 6, 8–10]. However, the topic of automatic service and application migration in a cloud is relatively scarce in the literature. Zhang et al. [7] proposed a workload factoring service to make the best use of public cloud services along with data centers. They used a fast algorithm to detect frequent data items, and enabled factoring incoming requests on volume and data content upon an environment where data popularity may change in different time slots. Amoretti et al. [15] presented a framework that supports code mobility, and used a pull strategy for service migration. Kaisler and Money [20] proposed a model to examine feasibility of service migration in a cloud computing environment by considering security, integration, and service implementation. Hao et al. [21] considered the problem of service selection and migration in clouds. They developed a framework to facilitate service migration, and designed a cost model and a genetic algorithm to determine the tradeoffs on service selection and migration.

3 The Proposed Framework This section will present the proposed framework, consisting of a smart multiagent system called the federated broker, and a hybrid cloud environment that hosts a set of mobile applications. In our hybrid cloud computing model, we

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assume that the resources in the private data center must be fully utilized before any resources in the public cloud are used. This assumption comes from the constraint that the users must pay for using the public cloud resources and people do not want to waste money for no reason. We also assume that applications are always submitted to the private data center. The applications then wait in a queue for execution. An application is executed in the private data center directly if the private data center is capable of running the application. Otherwise, it may be transferred to the public cloud for execution.

3.1 Federated Broker The federated broker, mostly implemented in JADE [16], is a smart multi-agent system for system monitoring and reconfiguration decision. The federated broker consists of three major components—the system monitoring agent, the service migration agent, and the reconfiguration decision agent. Figure 2 shows the three major components of the federated broker and the interactions among these components and the hybrid cloud environment. Each cloud of the hybrid cloud environment has a cloud administration agent in it to interact with the federated broker. It gathers the information of physical machines and applications, and sends the data to the system monitoring agent of the federated broker. The system monitoring agent then processes the primitive data and saves the digested data in a profiling database. The reconfiguration decision agent periodically retrieves data from the system monitoring agent and the profiling database, and then uses the information to decide whether the system requires reconfiguration. If the reconfiguration decision agent decides to perform reconfiguration, it will notify the service migration agent to move some applications from the private data center to the public cloud. Virtual machines in the public cloud will be created if the computing pool in the public cloud is not enough. The three major components of the federated broker are described below: • System Monitoring Agent (SyMA): the agent receives data from cloud administration agents, such as the identity of an application, the status of an application, and the workload information of the hardware. Then it filters the data and puts historical data in a profiling database. It does not make any decision for system reconfiguration. Some fresh data such as the current state of the private data center are sent to the reconfiguration decision agent directly. • Reconfiguration Decision Agent (RDA): the agent is supposed to use the current state of the private center and the historic data in the profiling database to predict the future workload of the private data center. The prediction result and a pre-defined reconfiguration policy are used together to decide how to adjust the workload ratio of the private data center and the public cloud. When the workload of the private data center is high, the RDA will create more virtual machine instances in the public cloud and move applications to the public cloud.

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Fig. 2 The federated broker and its interactions with the hybrid cloud

It also periodically evaluates the state of the public cloud, and may terminate some virtual machine instances in the public cloud if they are not used. • Service Migration Agent (SeMA): while the agent receives a reconfiguration decision message, it will send commands to the private data center and the public cloud to ask for application migration. If the public cloud does not have enough virtual machines to host incoming applications, the agent will check the reconfiguration policy and then create more virtual machines on the public cloud if necessary and legal. The agent does not decide which application should move to the public cloud. Instead, it tells the manager of the private data center to move a certain percentage of workload to the public cloud.

3.2 Hybrid Cloud Environment The hybrid cloud environment consists of two set of computing resources. One is the private data center, and the other is the public cloud that provisions resources on demand. There are three major components in the hybrid cloud environment. Figure 3 shows how the three components in the hybrid cloud environment interact with users and the federated broker. The details of them are described below: • Local Monitoring Agents (LMA): each machine of the private data center has a local monitoring agent in it. The LMA periodically transmits the observed machine status to the private cloud administration agent, which will then organize the data and send the data to the system monitoring agent of the

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Fig. 3 The federated broker and its interactions with the hybrid cloud

federated broker. Note that the public cloud does not have any LMA in it. Similar functionality should be provided through the API of the public cloud. • Cloud Administration Agent (CAA): both the public cloud and the private data center of the hybrid cloud environment have their own cloud administration agent. Each CAA is a central manager of its computing pool. It is responsible to gather system information and to send the information to the federated broker. It is also responsible to receive commands from the federated broker and to execute the commands for the federated broker. • Application Management Agent (AMA): the AMA is designed to manage applications in the private data center or the public cloud. To execute an application, the user has to submit the application to the AMA in the private data center. The application then waits in a queue. As soon as the application is selected for execution, it will be registered in the private CAA and then be executed in the private data center. When the AMA receives a message for application reconfiguration from the federated broker, it will ask the selected application to save its state. The state along with the program of the application will be sent to the public CCA by the private CCA. Then, the migrated application is resumed by the public CCA.

4 Migration Policies and Preliminary Results The current implementation of the proposed framework uses average CPU load and the number of applications as the criteria to judge whether a reconfiguration is required. To execute an application in the proposed framework, a pre-define

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number of virtual machines for the application must be provided. The information will be used to judge whether the resources are enough to serve a newly incoming application. The framework will create enough virtual machines in the public cloud to host the application. Applications that are submitted to the AMA of the private data center are put in a waiting queue first. A waiting application is executed immediately if there are enough resources in the private data center. Otherwise, the private CAA notifies the federated broker for the situation, and then the federated broker contacts the public CAA to create more resources for the waiting applications. When the resources are ready, the public CAA will notify the federated broker, which will then ask the private CAA to migrate some applications to the public cloud. Finally, some waiting applications are moved to the public CAA for execution to reduce the workload in the private data center. We have built a prototype consisting of one server (2 quad-core CPUs and 8 GB RAM) with four VMs as the private data center and another server (each with 2 quad-core CPUs and 16 GB RAM) as the public cloud. The maximum number of VMs that can be created in the public cloud is set to be 5. We designed an experiment to evaluate the prototype. In the experiment, we submitted 5, 10, 15, and 20 applications to the prototype. Each application is a distributed application that demands 2*3 machines to calculate a maximum likelihood estimation problem with execution time of 30*50 s. In addition, we used three different policies to see how the proposed framework works in the experiment. The first policy, no migration, only executes applications on the private data center. This policy represents the traditional computing model that only uses a private data center for computation. The second policy, migration based on the number of applications, only moves applications to the public cloud while the private data center is overloading. The number of applications to run on the public cloud is set to 50% of the number of total applications. The third policy, migration based on the execution time of applications, moves applications to the public cloud while the private data center is overloading as well. The proposed framework tries to re-arrange 50% of the total remaining expected execution time of applications in the public cloud. Figure 4 demonstrates the result of the experiment. It shows that the proposed framework with intelligent application migration can effectively reduce the total execution time of the applications. This can be attributed to that the proposed framework automatically creates as many virtual machines in the public cloud as the number of machines in the private data center, and then moves applications to the public cloud. The result also shows that using execution time for decision-making is more effective. Note that the proposed framework using the third policy creates the same amount of virtual machines in the public cloud as that using the second policy. The third policy is more efficient because the applications in the private data center and the applications in the public cloud almost finish at the same time.

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Fig. 4 The result of executing different number of applications using different policies

5 Conclusions and Future Work In this paper, we presented a novel computing framework for hybrid clouds based on multi-agent technology. The framework is able to create virtual machine instances and to move applications to the public cloud automatically in the case that the workload is too high in the private data center. We implemented the proposed framework in a prototype of a hybrid cloud environment, and conducted an experiment using three different migration policies to evaluate the proposed framework. The preliminary result demonstrated that the framework is able to handle a short-term workload peak in the private data center by moving the workload to the public cloud. In the future, we plan to improve the automatic migration policy and the self-provisioning mechanism by considering different public cloud pricing models and the issues of quality of service. We also plan to build a larger hybrid cloud environment to evaluate the proposed framework. Acknowledgments This work was partially supported by the Nation Science Council of Republic of China under Grant No. 100-2218-E-008-005- and by Industrial Technology Research Institute of Republic of China under Grant No. B0-A0007.

References 1. Pallis G (2010) Cloud computing: the new frontier of internet computing. IEEE Internet Comput 14(5):70–73 2. Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M (2009) Above the cloud: a Berkeley view of cloud computing. Electrical Engineering and Computer Sciences, University of California at Berkeley, 10 Feb 2009

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3. Forter I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared. Grid computing environment workshop 4. Mell P, Grance T (2009) The NIST definition of cloud computing, version 15, NIST. Retrieved Jan 2010, from http://csrc.nist.gov/groups/SNS/cloud-computing/ 5. Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22 6. Van den Bossche R, Vanmechelen K, Broeckhove J (2010) Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: 2010 IEEE 3rd international conference on cloud computing (CLOUD), pp 228–235 7. Zhang H, Jiang G, Yoshihira K, Chen H, Saxena A (2009) Intelligent workload factoring for a hybrid cloud computing model. In: Proceedings of the 2009 congress on services—I, pp 701–708 8. Amazon Virtual Private Cloud (VPC), retrieved from http://aws.amazon.com/vpc/ 9. Nick JM, Cohen D, Kaliski BS Jr (2010) Key enabling technologies for virtual private clouds. In: Furht B, Escalante A (eds) Handbook of cloud computing. Springer Science+Business Media, LLC, New York, pp 47–63 10. Wood T, Shenoy P, Gerber A, Ramakrishnan KK, Van der Merwe J (2009) The case for enterprise-ready virtual private clouds. In: Proceedings of HotCloud’09 workshop on hot topics in cloud computing, San Diego, CA, USA, June 2009 11. Cloud Computing Center for Mobile Applications, retrieved from http://www.itri.org.tw/chi/ ccma/ 12. Foster I, Jennings NR, Kesselman C (2004) Brain meets brawn: why grid and agents need each other, AAMAS’04, 19–23 July 2004, New York, NY, USA 13. Lange DB, Oshima M (1999) Seven good reasons for mobile agents. Commun ACM 42:88–89 14. Spanoudakis N, Moraitis P (2006) The Gaia2JADE process for multi-agent systems development. Appl Artif Intel J 20(2):251–273 15. Amoretti M, Laghi MC, Tassoni F, Zanichelli F (2010) Service migration within the cloud: code mobility in SP2A. In: 2010 international conference on high performance computing and simulation, pp 196–202, June 2010 16. Java Agent Development Framework (JADE), retrieved from http://jade.tilab.com/ 17. Olfati-Saber R (2006) Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Auto Cont 51(3):401–420 Mar 18. Jung JJ (2009) Consensus-based evaluation framework for cooperative information retrieval systems. Knowl Inf Syst 18(2):199–211 19. Varela CA, Agha G (2001) Programming dynamically reconfigurable open systems with SALSA (OOPSLA 2001 ACM Conference on Object-Oriented Systems, Languages and Applications). ACM SIGPLAN Not 36:20–34 20. Kaisler S, Money WH (2011) Service migration in a cloud architecture. In: 44th Hawaii international conference on system sciences (HICSS), pp 1–10 21. Hao W, Yen I-L, Thuraisingham B (2009) Dynamic service and data migration in the clouds. In:33rd annual IEEE international computer software and applications conference, pp 134–139

Multi-Band OFDM and OFDM Simulation Software Using MATLABÒ Graphical User Interface Roslina Mohamad, Wan Mohd Hafeez, Wan Mohd Salleh and Nuzli Mohamad Anas

Abstract This paper presents the conventional and multiband Orthogonal Frequency Division Multiplexing (OFDM) simulation software named as Signal Modulation Simulator (SiMiSIM). A simulation system with Graphical User Interface (GUI) is built to interface user friendlier than contemporary interface or command line interface. Three basic frequency modulation techniques are given as options which are Binary Phase Shift Keying (BPSK), Quadrature Phase-Shift Keying (QPSK) and Quadrature Amplitude Modulation (QAM). Each of the signals has to face Forward Error Correction (FEC) encoder to enhance the signal robustness at the receiver end. The simulation is able to simulate Bit Error Rate (BER) and Power Spectral Density (PSD) for research purpose as well as educational purpose.





Keywords Multiband OFDM Additive White Gaussian Noise Bit Error Rate Forward Error Correction



1 Introduction In early parallel transmission systems, a few non-overlapping sub-channels share the whole frequency band as shown in Fig. 1. Apparently the existence of guard band between two adjacent sub-channels is to provide non-overlapping subR. Mohamad (&)  W. M. Hafeez (&)  W. M. Salleh  N. M. Anas (&) Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia, 40450 Shah Alam, Selangor, Malaysia e-mail: [email protected] W. M. Hafeez e-mail: [email protected] N. M. Anas e-mail: [email protected]

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Fig. 1 Conventional nonoverlapping multi-carrier modulation [2]

Fig. 2 Overlapping multicarrier modulation [2]

channels as to eliminate the possible interference among adjacent sub-channels, which is known as Inter-Carrier Interference (ICI). This guard band constitutes a waste of spectrum. However in mid-1960s, spectral efficiency was improved by overlapping the sub-channels as shown in Fig. 2 had saved up to 50% of the spectrum used which was developed using OFDM technology. OFDM is not only a frequency multiplexing technique that mandates orthogonally among subchannels but also a special case for multi-carrier modulation. Moreover, OFDM can be regarded as either multiplexing technique or modulation scheme [1]. As compared in Figs. 1 and 2 respectively, apparently that bandwidth can be saved and be used for other sub-channels. In addition, another advantage of OFDM is multiple orthogonal carriers are transmitted simultaneously. By transmitting several symbols in parallel, symbol duration is increased proportionately, which reduces the effects of Inter Symbol Interference (ISI) caused by a dispersive Rayleigh-fading environment [2]. This paper objective is to describe the simulation functioning software called Signal Modulation Simulator, in short SiMiSIM, which developed using MATLAB Graphical User Interface Development Environment (GUIDE) to provide better easy access. SiMiSIM cater not only for OFDM transmission but also simulate the Multiband OFDM system. Various results concerning to the system performances are calculated such as the BER and other relevant information such as PSD and the signals constellation. Hence, it allows user to display multiple graph concurrently for easier comparisons among available modulation options which include PSK and QAM. The rest of this paper is organized as follow: A literature review of the project is explained in Sect. 2, emphasizes on both conventional and multiband OFDM system together with the introduction of SiMiSIM. Then, in Sect. 3, described system model and explained each module involved in baseband signal processing. Thorough explanation includes the data randomizer, random interleaver, channel coding and signal modulation used in this simulation. Section 4 discussed on methodology towards this simulation tool called SiMiSIM and briefed on the simulation flows. Section 5 discussed the result obtained from several examples and continued in the next section with the conclusion and future work recommendation on this project.

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2 Literature Review The motivation towards this simulation tool development, so-called SiMiSIM, is to ease the calculation of complex equation involved in bit error rate (BER) alongside the power spectral density (PSD) using GUI manner. Despite of conventional command line interface (CLI) approach, users are able to display various system performance results concurrently.

2.1 Conventional OFDM Orthogonal Frequency Division Multiplexing (OFDM) is a well-known technique to mitigate inter-symbol interference (ISI) due to multipath effects. For highly dispersive channels, OFDM is more efficient at capturing multipath energy provides higher spectral efficiency and inherent resilience to narrowband radio frequency (RF) interference. It also has excellent robustness in multipath environment while preserving the orthogonally among subcarriers. The concept is to divide a bandwidth into several parallel streams, one for each sub-carrier. Each sub-carrier is modulated with a conventional modulation scheme at a lower rate.

2.2 Multiband OFDM The principle idea of multiband approaches is to divide the Ultra-Wideband (UWB) frequency band from 3.1 to 10.6 GHz into multiple smaller frequency bands or sub bands and uses multiple carrier frequencies to transmit the information. Each sub band has a bandwidth greater than 500 MHz to comply with the Federal Communication Commission (FCC) definition of UWB signal [3]. Multiband OFDM spectrum in UWB channel is shown in Fig. 3. Apparently it had been divided to S sub band and each sub band may have N subcarriers each.

2.3 Signal Modulation Simulator Signal Modulation Simulator (SiMiSIM) is a tool to simulate the performance of variants coding and modulation techniques in the context of OFDM transmission, both conventional and multiband OFDM system. SiMiSIM gives easy access and attractive user interfacing built using Graphical User Interface Development Environments (GUIDE) tool in MATLAB, where users are able to program the software themselves provided in m-file script format. Furthermore, ones can

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Fig. 3 Overlapping multicarrier modulation [2]

visualize the system performance in terms of BER, PSD and also the signal constellation graphically at both ends.

3 System Model This section discussed the transceiver module used in constructing SiMiSIM which includes the data randomizer, Forward Error Correction (FEC), bit-interleaving, signal modulation and also OFDM transmission. Each module is thoroughly explained in short while. Figure 4 depicted the system model where each module is representing in block diagram concatenate. Noted that, Additive White Gaussian Noise (AWGN) and Saleh–Valenzuela (S–V) has been used representing the channel modeling. Signal source enters the randomizer to have more evenly distributed power density and also to avoid too high peak power that may lead to distortion. Also in this process, it breaks up any adjoining long strings of zeros or ones and brings movement to the data stream [4]. The data are scrambled to convert the data bit sequence into a pseudo random sequence which is free from long repeated strings. Polynomial generator of pseudo random binary sequence (PRBS) used in data randomizer as shown in (1) [3]. g ¼ 1 þ D14 þ D15

ð1Þ

In (1), D represents a single bit delay. xn in PRBS is generated as in (2) and the scrambled data bit stream is then obtained using (3) [3] as derived below. xn ¼ xn14  xn15

ð2Þ

s n ¼ bn  x n

ð3Þ

In information theory, FEC used as an error control system of data transmission, whereby systematically generated redundant data are added to its original messages. It is to increase the robustness of the signal due to the compression process and also makes the signal vulnerable to channel noise and interference [4, 5]. In

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Fig. 4 Transceiver architecture of OFDM

SiMiSIM, Convolutional encoder is used concatenated with Reed Solomon (RS) code to improve the BER and for more accurate decoding at the receiver [3, 4]. Reed Solomon (RS) code is non-binary cyclic codes with symbols made up of multiple bit sequences [6]. The codes are very useful for burst error correction where errors occur in a large sequence. RS code with symbols made up of m bit sequences where m corresponds to elements of the Galois Field, GF(qm) and is any positive integer greater than 2 [4]. The parameters of RS codes are as the following [4]: • Code length n ¼ 2m  1

ð4Þ

• Number of parity check symbols n  k ¼ 2t

ð5Þ

K ¼ 2m  1

ð6Þ

Dmin ¼ n  k þ 1

ð7Þ

• Number of data symbols

• Minimum distance

Random interleaving is used to rearrange data sequence using a fixed random permutation order. It enhances the error correcting capability of coding by constructing a long block code from small memory convolutional codes. By doing so, errors typically occurs in bursts rather than uniformly distributed in long codes can approach the Shannon capacity limit. In multi-carrier transmission, cyclic prefix of an exact copy of a segment of the OFDM symbol is located toward the symbol end [1] as to mitigate the ISI effects. Despite of redundancy it can effectively be avoided at the cost of power loss and bandwidth expansion but this inserting of cyclic prefix guard interval before each block of parallel data symbols will decreases the spectral efficiency of the OFDM system [7, 8].

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Fig. 5 SiMiSIM operations

Fig. 6 SiMiSIM after the simulation is completed

4 Methodology Initially, the baseband signal processing described in previous section is designed using MATLAB m-file prior converting to GUI based software. Figure 5 depicted the architecture and software flows. It involves displaying the transmitting power spectrum either both in time and frequency domain along with the

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Fig. 7 PSD in time domain

Fig. 8 PSD in frequency domain

signals constellation. Towards the end, the error rate analysis is visualized using semi-log plotting format in range of given signal-to-noise power ratio. In this simulation, either AWGN only or S–V channel modeling may be chosen as to simulate the realistic data transmission. Note that conventional or multiband OFDM transmission also can be simulated with various coding and modulation schemes. Options include both binary and quaternary PSK or multi-amplitude QAM of 16 and 64-level for the signal modulations while code rate of 1/2, 3/4 and 5/6 can be chosen for the convolutional encoder. Users also able to determine the length of cycle prefix and number of symbols involved. Ones would be able to compare various results obtained in the simulation with theoretical performance. BER plots of each type of modulation as well as their signal constellations can be easily visualized for academic and research purposes.

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Fig. 9 16-QAM bit error rate

Fig. 10 16-QAM scatter plot

Those visualizing figures are displayed side by side simultaneously in the Graph Preview section which includes the power spectrum of respective signal in both frequency and time domains. Figure 6 shows a snapshot of the SiMiSIM after a simulation is completed.

5 Results and Discussion Performance of 16-QAM with 3/4 Convolutional Encoder is used to discuss in this section. Guard period of rate 1/32 with 100 OFDM symbols is used throughout discussion. Hence in Figs. 7 and 8, the power spectrum density in both time and

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frequency domain is shown respectively. A multi-carrier modulation with different power is transmitted within the OFDM signal. While in Fig. 9, shown the BER calculated in SiMiSIM using semiology representation which follows the waterfall-like behavior for Shannon theory. Scatter plot of the 16-QAM transmission with AWGN corrupted signal is shown in Fig. 10. As a 16-QAM modulation is used, thus 4 constellations exist at each of quartet of the graph and making the total of the constellations is 16.

6 Conclusion SiMiSIM is a tool to simulate baseband signal processing involved in conventional and multiband OFDM systems in a graphical user interface manner. This tool able to simulates the error performance and other visualize figures relate to the signal processing which includes the signal constellation and the power spectrum. Easy access and attractive interface provided makes users able adapt and learn in short time. Furthermore, results obtained from simulations can be compared to theoretical or any other previous work for easy understanding. We would like to recommend of the future works of implementing the conventional and multiband OFDM system on a DSP hardware platform in order to gauge real-time system performances. Acknowledgments The authors would like to thank Ministry of Higher Education, Malaysia (MOHE) for supporting this research through grant number 600-RMI/ST/FRGS 5/3/Fst (120/2010) and 600-RMI/ST/FRGS 5/3/Fst(164/2010).

References 1. Chiueh T-D, Tsai P-Y (2007) OFDM baseband receiver design for wireless communications. Wiley, Singapore 2. Ghavami M et al (2007) Ultra wideband signals and systems in communication engineering, 2nd edn. Wiley, West Sussex 3. Siriwongpairat WP, Liu KJR (2008) Ultra-wideband communications systems multiband OFDM approach. Wiley, New Jersey 4. Vengadasalam JAL (2007) Performance analysis of digital television systems. Master of Engineering (Telecommunication), Faculty of Engineering, University Malaya 5. Mohamad R, Anas NM (2010) Performance analysis of convolutional interleaver on TMS320C6711 digital signal processing kit. In: Proceedings of the 2010 international conference on computer applications and industrial electronics, 5–8 December 2010 6. Sklar B (2001) Digital communications fundamental and applications, 2nd edn. Prentice-Hall, Upper Saddle River 7. Kattoush AH et al (2009) The performance of multiwavelets based OFDM systems under different channel conditions. Dig Sig Proc 20:472–482 8. Batra A et al (2004) Multi-band OFDM: a new approach for UWB. Presented at the 2004 IEEE international symposium on circuits and systems, Sheraton Vancouver Wall Centre Hotel, Vancouver, Canada

A Comparison on PSO Variants in Calculating HEPWM Switching Angles for a Cascaded H-Bridge Multilevel Inverter Norkharziana Mohd Nayan, Naziha Ahmad Azli and Shahrin Md. Ayob

Abstract This paper presents a comparison study on the application of various PSO algorithms in calculating the Pulse Width Modulation (PWM) switching angles of a cascaded H-bridge multilevel inverter. It aims to investigate the behavior of the PSO algorithm in three different variants. The results are compared to determine the most suitable algorithm for optimizing the multilevel inverter output voltage through Harmonic Elimination PWM (HEPWM) switching technique.





Keywords Particle Swarm Optimization Harmonic Elimination PWM Inverter

1 Introduction Multilevel inverter plays an important role in medium to high power conversion application. The applications include active power filters, static var compensator, unified power flow controller (UPFC), electric vehicles, and industrial motor drives [1–4]. Harmonic Elimination Pulse Width Modulation (HEPWM) offers N. M. Nayan (&) School of Electrical System Engineering, Universiti Malaysia Perlis, 02000 Kuala Perlis, Perlis, Malaysia e-mail: [email protected] N. A. Azli  S. Md. Ayob Department of Energy Conversion (ENCON), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia e-mail: [email protected] S. Md. Ayob e-mail: [email protected]

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elimination of dominant lower order harmonics and filters higher residual frequencies in a multilevel inverter output voltage. In general, this method produces a good harmonic performance for the inverter output. The elimination of the unwanted harmonics is obtained via the substitution of the nonlinear transcendental equations with a set of systems of linear equations. Recent research on power converter control using PSO shows that the optimization algorithm can be successfully used to control a multilevel inverter [5, 6]. Moreover, PSO algorithm is easy to implement and has been empirically shown to perform well on many optimization problems. Involvement of the PSO algorithm has improved the overall system performance, especially on the aspect of speed processing and the quality of the output power. Multilevel inverters with HEPWM switching controls are known to inherent characteristics such as nonlinearity, unavailability of a precise model and excessive complexity that make them suited for PSO control. Thus, it is expected to be able to reduce the computational burden associated with the solutions of the non-linear transcendental equations relevant to the harmonic elimination problem. The known problem of HEPWM switching technique for inverters in general is computing the switching angles as it involves the solving of transcendental equations. The problem gets worse when the technique is applied to a multilevel inverter topology. In order to solve this problem, a solution using PSO method is proposed in this work. The advantage of this method is its capability in solving the non-linear equations associated with HEPWM with fast generation of the switching angles due to faster convergence when searching for the solutions. The calculated switching angles are also accurate to any desired value of modulation index, which makes the angle resolution higher. The produced output voltage is expected to be of high-quality with precision in the PWM switching angles while the unwanted harmonics are eliminated. Furthermore, via PSO method, the memory capacity can be reduced due to its online computational capability, which makes the system more flexible and interactive. In addition, using this method the elimination of selected harmonics could minimize the THD and motor losses [7]. Thus, the filtering process can be omitted from the system. Since the inception of PSO in 1995 [8], several researchers have modified the algorithm in order to make it adaptable to their research application and perform faster than the original version. Common variants of the PSO algorithm are standard PSO, Inertia Weight PSO, Constriction Factor PSO and also Mutative PSO [9, 10]. This work aims to investigate the behavior of the PSO algorithm in three different variants. The algorithms are used to compute the multilevel inverter switching angles. The results are compared to determine an optimized algorithm for optimizing switching control performance of multilevel inverter implementation. It is expected that a suitable PSO algorithm that will produce a high resolution of optimized PWM switching angles and low THD voltage for a multilevel inverter can be achieved.

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1

0 ααα 1 2 3

360

αN

ωt

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Fig. 1 Harmonic elimination PWM waveform

2 Harmonic Elimination PWM A typical output voltage waveform of a HEPWM is shown in Fig. 1 contains harmonics and the output function f(t) can be expressed in Fourier’s series as: 1 X ðan sin nan þ bn cos nan Þ ð1Þ f ðtÞ ¼ n¼1

Due to the quarter wave symmetry of the output voltage, the even harmonics are absent and only odd harmonic are present [11]. The amplitude of the nth harmonic an is expressed only with the first quadrant switching angles a1, a2,…, aN #  " N X 4 k ð1Þ cos nak Þ ð2Þ 1þ2 an ¼ np k¼1 and the solution must satisfy: 0\a1 \a2 \    \aN \

p 2

ð3Þ

For any odd harmonics, Equation (2) can be expanded up to the kth term. Where N is the number of variables corresponding to switching angles a1 through aN of the first quadrant. In order to eliminate the selected harmonic, an is assigned the desired value for fundamental component and equated to zero for the harmonics to be eliminated. Nonlinear transcendental equations are formed. a1 through aN are calculated by solving this equation. It is evident that (N-1) harmonic can be eliminated with N number of switching angles. #  " N X 4 k ð1Þ cos ak Þ ¼ M 1þ2 a1 ¼ np k¼1 #  " N X 4 k ð4Þ 1þ2 ð1Þ cos 5ak Þ ¼ 0 a5 ¼ 5p k¼1 #  " N X 4 k ð1Þ cos nak Þ ¼ 0 1þ2 an ¼ np k¼1

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where M is the amplitude of the fundamental component. The main issue with regard to the HEPWM method is computation the switching angles. Several methods for computing optimal PWM switching angles includes Newton–Raphson, Walsh function, Resultant Theory, Genetic Algorithm (GA), curve fitting techniques and PSO [4, 9, 16–18]. Newton–Raphson method is widely used in this area, but it is not appropriate for a large number of switching angles if good initial guesses are not available and may end in local optima. A second approach is Walsh’s functions commonly used to solve a linear equation. An alternative technique based on GA is very effective in solving optimal PWM switching angle. Unfortunately this method is complicated and has a mutation probability. The Resultant Theory’s method has been used to solve non-linear transcendental harmonic elimination equations by converting them into polynomial equations. However, this method is unattractive and has a limitation when the number of inverter level increases as the degrees of the polynomial will also increase that may lead to numerical difficulty and substantial computational burden. Curve fitting techniques present a solution for the optimal PWM switching angle’s trajectories with polynomial equations. However, the problem with curve fitting techniques arises when a large look-up table needs to be stored on the control system memory. In addition the sampling interval for the PWM switching angle is low when digitally implemented. Neural network control, fuzzy logic and PSO are parts of the latest trends on computing optimal PWM switching angles. Neural network and fuzzy logic control are quite complicated since the parameters need repetitive training before being implemented on the system and this may become a time-consuming task. PSO has similar characteristics with GA, but it is easy to implement as it does not have genetic operators such as cross over and mutation which may drag to longer processing time.

3 PSO: The Theory and its Variants PSO is relatively another type of an optimization algorithm originated from Kennedy and Eberhart [12]. Theory of this stochastic optimization procedure is based on the movement and intelligence of swarms, which has the ability to solve optimization problems with social interactions. Research conducted by Al-Othman [4] on the harmonic elimination problem of a cascaded H-bridge inverter with nonequal DC sources proves that PSO have reduced the computational burden associated with the solution of the non-linear transcendental equations. The main reason for choosing PSO in this work is that PSO is very simple in concept, easy to implement and computationally efficient. In power converter areas, PSO mainly involves inverter control, AC/AC choppers, photovoltaic controller and motor drives systems [9, 11, 16, 21]. It is proven that PSO algorithm is very efficient relative to other evolutionary computation techniques in order to solve optimization problems.

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3.1 PSO: The Theory PSO is inspired by social behaviour of birds flocking or fish schooling. PSO conducts the searching process using a population of particles. Each particle is a potential solution to the problem under investigation. Each particle in a given population adjusts its position by flying in multi-dimensional search space until an unchanging position of the fittest particle is encountered. The concept; at each time step changing the velocity and position of each particle toward its pbest and gbest is according to Equations (5) and (6) vid ðt þ 1Þ ¼ ½w  vid ðtÞ þ ½c1  r1  ðpid ðtÞ  xid ðtÞÞ þ ½c2  r2  ðpid ðtÞ  xid ðtÞÞ

ð5Þ

xid ðt þ 1Þ ¼ xid ðtÞ þ vid ðt þ 1Þ

ð6Þ

For a target problem which has n-dimensions; xi = (xi1, xi2, …, xin)T; position vector of the ith particle Vi = (vi1, vi2, …, vin)T; velocity vector of the ith particle Pi = (pi1, pi2, …, pin)T; best position of each particle/pbest Pg = index of best particle among all particle in the population/gbest w = inertia weight c1 and c2 = acceleration constant r1 and r2 = random number in a range of 01 Pi is the best fitness so far that particles have achieved. The first part of Equation (5) is the inertia weight part whereas w represents the degree of the momentum of the particles. The second part is the cognition part, which represents the independent behaviour of the particle itself. The rest parts of the equation represent the social part, which represents the collaboration among the particles. c1 and c2 represent the weighting of the cognition and social parts that pull each particle towards pbest and gbest positions. Variable xi is the solution found by each particle. The goodness of the solution is evaluated based on its fitness value.

3.2 The PSO Variants Since its inception in 1995, several researchers have modified the algorithm in order to make it adaptable to their research application and perform faster than the original version. The common variants of the PSO algorithm are as follows: i) The original PSO or standard PSO—An explorative PSO algorithm. Unfortunately, it may suffer from late convergence as it goes closer to the maximum iterations. The equation for the standard PSO is given by Equation (7);

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Vid ¼ Vid þ C1 ðpBest  Xid Þ  rand1 þ C2 ðgBest  Xid Þ  rand2

ð7Þ

ii) Inertia weight PSO—Good for focus but lacks in solution quality. The equations for the standard PSO are given by Equations (5) and (6). The inertia weight is usually decreased linearly from 0.9 to 0.4 to improve convergence. iii) Constriction factor PSO—best for focus and solution quality, better performance in continuous value. Unfortunately lacks in discrete and binary problems. Vid ¼ xðVid þ C1 ðpBest  Xid Þ  rand1 þ C2 ðgBest  Xid Þ  rand2 Þ

2 v ¼  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; 2  u  u2  4u

u ¼ c1 þ c2; u [ 4

ð8Þ

ð9Þ

iv) Mutative PSO—Rapid convergence but may be premature. This algorithm kills off non-performing particles and replaces them with mutated version of gbest.

4 Simulation The simulation is done by implementing a PSO algorithm on calculating the PWM switching angles of the power switches in a multilevel inverter to produce the its output voltage. Through PSO algorithm, optimum PWM switching angles of the multilevel inverter can be determined. With that, a high processing speed and resolution of the generated switching angles can be established. A single-phase cascaded H-bridge multilevel inverter PSO algorithm is applied to this system. The PSO control algorithms are designed and simulated using MATLAB/Simulink. In addition, the training of the PSO control algorithm on a representative set of input and targets pairs have been done using the same simulation software.

4.1 The Software Development The development and implementation stages of the PSO algorithm on the system are shown in Fig. 2. It begins with the determination of the number of angles per quarter cycle, N for the HEPWM equations. With that, Equation (4) is used to calculate the switching angles by adapting the equation into an m-file programming command with PSO algorithm. Subsequently, this program is trained to determine the correct weighting parameter solutions for the HEPWM switching angles through a training simulation. Then, the algorithm is integrated with the multilevel inverter simulation system to verify the control parameters. After

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PSO

DSP

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Gate Driver

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Fig. 2 The system block diagram

Table 1 Comparison of the PSO variants Standard PSO Best fitness Last iteration Reference angle a1 = 15.2985 a2 = 20.7779 a3 = 30.8777 a4 = 41.5097 a5 = 47.0563 a6 = 62.2443 a7 = 64.3129 a8 = 89.8477

8.324 9 10-6 350 15 20.1 30 38 57 63 64 90

Inertia weight PSO

Constriction factor PSO

1.1102 9 10-16 302 Calculated angle 15 21 30 42 48 57 64 87.6362

0 126 15 21 30 38 49.2042 57 64 90

achieving the optimized PWM switching angles, another program in C is developed to generate the pulse widths for the HEPWM. In order to drive the power switch on the inverter’s side, the pulse generation algorithm is programmed on a DSP to produce the PWM pulses.

5 Results In order to select the suitable PSO algorithm for the PWM switching angles implementation, several simulations using various types of PSO algorithm as discussed in the previous section have been done. Computation of the PWM switching angles for eight angles per quarter cycle (N = 8 and) amplitude modulation, ap1 M = 1 is made using Equation (4). The PSO equation parameters are: Swarm size = 50, Dimension = 8, Maximum iteration = 1000. Table 1 presents the comparison made on the PSO algorithm variants based on the computed angle value, best fitness and number of maximum iteration with the switching angles obtained from Newton–Raphson’s method [13] used as the reference angle.

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6 Discussions The simulation result shows that implementation of the standard PSO to calculate the PWM switching angles of the multilevel inverter suffers from late convergence and immature value. It can be seen that the calculated angles do not converge to stable values and they are also inaccurate. Meanwhile, from the Inertia Weight PSO best fitness, there is a significant difference on maximum iteration compared to the standard PSO. The Inertia Weight PSO converges faster than the Standard PSO. The computed angle values are much closer to the reference angles but not very accurate. Constriction Factor PSO has the fastest convergence performance based on its final iteration to achieve best fitness and have stable angle values. The calculated angles are closer to the reference angles compared to Inertia Weight PSO and standard PSO. Therefore, it is concluded that the Constriction Factor PSO is the most suitable algorithm for implementing in a multilevel inverter system. However, in order to achieve more optimal results further training should be done by changing the PSO parameters. Apart from that, the objective function of the PSO algorithm can also be changed according to the programmer’s creativity as it may give different way of iteration and best fitness value. Nevertheless, Standard PSO and Inertia weight PSO can as well be used to give an optimal result only if the processing time is not the work constraint.

7 Conclusions In this paper, three types of PSO algorithms namely standard PSO, Inertia Weight PSO and Constriction Factor PSO have been used to calculate the PWM switching angles of a multilevel inverter. A brief summary on HEPWM and PSO has been given. Based on the simulation result, PSO algorithm can be successfully used to calculate the HEPWM switching angles of a multilevel inverter. At the same time, the selected harmonics in the multilevel inverter output voltage are eliminated with low THD percentage. Implementation of the PSO based system on a multilevel inverter switching control has produced a high accuracy of switching angles generation with high quality output voltage waveform. From the simulation with three types of PSO algorithm, it is found that PSO implementation performances according to the selected application give a distinct characteristic. Implementation of Standard PSO suffers from late convergence and immature value. While with Inertia weight PSO the computed results are good for focus, but lacks in solution quality. Whereas, with Constriction PSO algorithm the computed angle values is best for focus and solution quality. Simulation results also show that Constriction Factor PSO is the potential method in producing an optimize pulse generation for the multilevel inverter. It is expected that with the implementation of the PSO algorithm, a high processing speed system with less complexity in the control part is produced.

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References 1. Tolbert LM, Member S, Chiasson JN, Peng FZ (2003) Modulation index regulation of a multilevel inverter for static var compensation East, pp. 1–6 2. Lai J, Member S, Peng FZ (1996) Multilevel converters—A new breed of power converters, vol 32(3), pp 509–517 3. Cunnyngham T (2001) Cascade multilevel inverters for large hybrid-electric vehicle applications with variant DC sources. University of Tennessee, Knoxville 4. Al-Othman AK, Abdelhamid TH (2009) Elimination of harmonics in multilevel inverters with non-equal dc sources using PSO. Energy Convers Manag 50(3):756–764 5. Ray RN, Chatterjee D, Goswami SK (2009) An application of PSO technique for harmonic elimination in a PWM inverter §. Appl Soft Comput 9:1315–1320 6. Kaviani AK, Fathi SH, Farokhnia N, Ardakani AJ (2009) PSO, an effective tool for harmonics elimination and optimization in multi-level inverters. In: 2009 4th IEEE conference on industrial electronics and applications, pp 2902–2907 7. Kouzou A, Saadi S, Mahmoudi MOO, Boucherit MSS (2009) Particle swarm optimization applied for the improvement of the PWM AC/AC choppers voltage. In: 2009 compatability and power electronics, pp 146–152 8. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95— International conference on neural networks, pp 1942–1948 9. Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. System, no. 7, pp 84–88 10. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No.98TH8360), vol 49(5), pp 69–73, May 11. Holmes DG, Lipo TA (2003) Pulse width modulation for power converters—Principles and practice. Wiley, USA, p 724 12. Hagh MT, Taghizadeh H, Razi K (2009) Harmonic minimization in multilevel inverters using modified species-based particle swarm optimization, October, vol 24(10), pp 2259– 2267 13. Azli NA (2001) Online optimal pulsewidth modulation (PWM) multilevel control of a modular structured multilevel inverter (MSMI) for high power AC power supply applications, Universiti Teknologi Malaysia

A Hybrid Artificial Potential Field: Genetic Algorithm Approach to Mobile Robot Path Planning in Dynamic Environments Yanping Liu and Kamal K. Bharadwaj

Abstract In this paper, a hybrid Artificial Potential Field—Genetic Algorithm approach is developed and implemented for mobile robot path planning in dynamic environments. The hybrid approach first uses Grid Method where the mobile robot environment is represented by orderly numbered grids, each of which represents a location in the environment. Then, it applies Genetic Algorithm (GA), a global planner, to find an optimal path according to the current environment. The GA proposed here uses an evolutionary population initialization and genetic operators, which make the evolutionary process converge very efficiently. Finally, a new Artificial Potential Field method, a local planner, is applied to follow the path obtained by GA from one intermediate node to next intermediate node avoiding the obstacles. Experimental results clearly illustrate that the proposed hybrid approach works well on large scale dynamic environments. Keywords Artificial Potential Field Dynamic Path Planning



Genetic Algorithm



Mobile Robot



Y. Liu (&)  K. K. Bharadwaj School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067 India e-mail: [email protected] K. K. Bharadwaj e-mail: [email protected]

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1 Introduction In the control of mobile robot, path planning is one of the key issues. The robot path planning problem can be typically described as follows: given a robot and a description of its working environment, plan a collision free path between two specified locations that satisfies certain optimization criteria [1]. Path planning research of mobile robot in static environment has reached mature stage, but the path planning in dynamic environment is a big challenge. In practice, it is often no complete knowledge about the environment. Having a detailed map with all the obstacles marked seems to be unrealistic for most of the situations. Whether the robot path planning belongs to which category, the path should meet the following conditions [2]: • The path should be collision-free; • The path should be as short as possible and search time should be as little as possible; • The path should be as smooth as possible. Many algorithms for path planning have been studied and developed over the past few years. The main methods of path planning for mobile robot can be divided into two categories—Artificial Potential Field (APF) approaches and Artificial Intelligence (AI) approaches. The main AI-based approaches for robot path planning are Genetic Algorithm (GA), Fuzzy Logic Control (FLC) and Artificial Neural Network (ANN) [3]. Recently, many researchers have applied the hybrid approaches, such as Fuzzy-Genetic approach, on path planning. This paper develops and implements a hybrid Artificial Potential Field— Genetic Algorithm approach to mobile robot path planning in dynamic environments. The approach first uses Grid method to represent the environment. Then, it applies Genetic Algorithm, a global planner, to find an approximate optimal path according to the current environment. At last, Artificial Potential Field method, a local planner, is applied to follow the path from one intermediate node to next intermediate node avoiding the obstacles.

2 Hybrid Approach Based on Artificial Potential Field and Genetic Algorithm 2.1 Overview The mobile robot environment is represented by orderly numbered grids, each of which represents a location in the environment. The boundary of obstacles is formed by their actual boundary plus minimum safety distance considering the size of the mobile robot, which makes it possible to treat the mobile robot a point in the

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Fig. 1 The outline of hybrid approach

Goal GA (Global Planner)

Start

APF (Local planner& Obstacles Avoidance)

environment. In a large scale environment, suppose the environment is 10 9 10 meters with a robot size 10 9 10 cm. We can model the environment into 100 9 100 grids, and treat the robot as a point in the environment. Figure 1 shows the outline of the hybrid approach. In this hybrid approach, a Genetic Algorithm is proposed first. It uses a simple effective path representation that combines grids and coordinates representations. This approach makes it possible to have one number for each gene and to use integer numbers instead of real numbers in chromosomes. As we know, the time cost of Genetic Algorithm increases exponentially when the dimension of configuration space increases. So the Genetic Algorithm will not work properly on the environments with large scale dimension. The suggested path length should be constrained to be less than 20. After applying Genetic Algorithm, a global optimal or near-optimal path has been got according to the current environment. However, as the environment is dynamic, some obstacles are moving, we can not follow the fixed path obtained by GA simply. How to follow the path and avoid the obstacles? It is not efficient to apply GA again when the environment is changed. Therefore, a local planner is proposed. We need an effective local path planner that follows the path obtained by global planner and avoid the moving obstacles properly. Since the predefined path is obtained before the movement of robot. Artificial Potential Field, a local planner, is proposed to follow the path from one intermediate node to next intermediate node avoiding the obstacles.

2.2 Genetic Algorithm (GA) as Global Planner GA first searches the optimal path or near-optimal path based on the current environment. In this paper, GA uses a chromosome with variable length. As the dimension of the environment is very large, the chromosome size (the number of nodes included in the path) should not be as long as the environment size, otherwise GA will not work efficiently. Therefore, the proposed chromosome size is less than 20. The following will discuss the chromosome, fitness function and genetic operators in details. Representation and Initial Population. A chromosome represents a path as a sequence of nodes, where each node indicates a grid number representing a location in the environment. The first node is the start and the last node represents the

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Fig. 2 A sample chromosome

goal. A feasible path is a collision free path, i.e. no nodes fall on any obstacles. The length of a chromosome is variable and between 2 and maximum length 20. As an example, Fig. 2 shows a sample chromosome. The initial population can be generated randomly, however, by experiment, it is shown that there are many infeasible paths in random initial population, which makes the evolutionary process converge very slowly. In some complicated environment which contains many obstacles, it is usually not applicable to get a feasible path. In this paper, the generation of initial population should satisfy one condition—all of the individuals should be feasible path. By doing this, the initialization will be slower than random initialization, but the evolutionary process is improved a lot. The simulation result will show that. Evaluation. The design of fitness function is one of the key issues in GA evolution. In this paper, the fitness function not only considers the length of the path, but also considers the smoothness, since the smoother path will make the mobile robot move more easily. As mentioned previously, all the paths generated initially are feasible, hence we do not need consider the path security. The fitness function is composed of two sub-functions: a. Sub-function of Path Length Fit1 ¼

N X

di

ð1Þ

i¼1

Where N is the number of line segment of a path, di is the Euclidean distance of the two nodes forming the line segment. b. Sub-function of Smoothness Fit2 ¼

N 1 X

gðli ; liþ1 Þ

ð2Þ

i¼1

Wheregðli ; liþ1 Þdenotes the inclination of two adjacent line segments li and liþ1 : To make sure the value is non-negative, we can define gðli ; liþ1 Þ ¼ cosðli ; liþ1 Þ þ 1. The fitness function can be given as Fit ¼ w1  Fit1 þ w2  Fit2

ð3Þ

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Where wi ði ¼ 1; 2Þ respectively stands for the weighted values of length, smoothness in the fitness function. According to the fitness function, we can see that the fitter individual will have smaller fitness value, which indicates the path is shorter and smoother. Reproduction and Genetic Operators. In this approach a Tournament Selection method is used as a selection strategy. N individuals are selected randomly and the fitter individual will be selected as the first parent. N is the selection pressure according to the size of population. The same process is repeated to select the second parent. After the selection process, four operators are used to evolve the selected parents. Each operator is controlled by its probability. These operators are: a. Crossover This operator combines two selected paths (parents) to generate two offspring. Compared with traditional crossover operation, which randomly selects a mating intermediate node on each parent, the crossover operation proposed here is under one condition: the mating intermediate nodes selected from two parents should assure that two offspring are feasible. The mating intermediate nodes are randomly selected several times. If these two nodes can not be found, the crossover operation for these two parents is skipped. b. Mutation There are three types of mutation—Replacement, Addition and Deletion. The position is randomly selected for Replacement, Addition and Deletion several times. It should make sure that after Replacement, Addition and Deletion, the new path is feasible. Otherwise, this mutation operation will be skipped.

2.3 Artificial Potential Field (APF) as Local Planner In this paper, a new potential field method is proposed for path planning of a mobile robot in a dynamic environment where the goal and obstacles are moving. The attractive potential is defined as a function of the relative position and velocity of the goal with respect to the robot. The repulsive potential is also defined as the relative position and velocity of the robot with respect to the obstacles. Accordingly, the virtual force is defined as the negative gradient of the potential in terms of both position and velocity rather than position only. Attractive Potential Function. The attractive potential field function is presented as follows [4]  m  n Uatt ðp; vÞ ¼ ap pg ðtÞ  pr ðtÞ þav vg ðtÞ  vr ðtÞ

ð4Þ

Where pr ðtÞ and pg ðtÞ denote the positions of the robot and the goal at time t, respectively; pr ¼ ½x y zT in a three-dimensional space or pr ¼ ½x yT in a twothe velocities of the robot and the goal at dimensional space; vrðtÞ and vg ðtÞ denote  time t, respectively; pg ðtÞ  pr ðtÞ is the Euclidean distance between the robot

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  and the goal at time t; vg ðtÞ  vr ðtÞ is the magnitude of the relative velocity between the goal and the robot at time t; ap and av are scalar positive parameters; m and n are positive constants which satisfy m; n [ 1. The new attractive potential Uatt ðp; vÞ is a function of both the position p and velocity v of the robot. Therefore, we shall define the corresponding virtual attractive force as the negative gradient of the attractive potential in terms of both position and velocity. When pr 6¼ pg and vr 6¼ vg , we will have Fatt ðp; vÞ ¼ Fatt1 ðpÞ þ Fatt2 ðvÞ

ð5Þ

 m1 Fatt1 ðpÞ¼map pg ðtÞ  pr ðtÞ nP RG

ð6Þ

 n1 Fatt2 ðvÞ¼nav vg ðtÞ  vr ðtÞ nV RG

ð7Þ

Where

With nP RG being the unit vector pointing from the robot to the goal and nV RG being the unit vector denoting the relative velocity direction of the goal with respect to the robot. The attractive force Fatt consists of two components: the first component, Fatt1 ðpÞ; pulls the robot to the goal and shortens the distance between them, the second component, Fatt2 ðvÞ; drives the robot to move at the same velocity of the goal. Repulsive Potential Function. Similarly, we can get the repulsive potential Urep ðp; vÞ as a function of both the position p and velocity v of the robot with respect to the obstacles. The repulsive force function used here is referred in [4]. After the calculation of the attractive and repulsive forces, the total virtual force can be obtained by Ftotal ¼ Fatt þ Frep

ð8Þ

For the case where there are multiple obstacles, the repulsive force is given by Frep ¼

no X

Frep i

ð9Þ

i¼1

Where no is the number of obstacles and Frep i is the repulsive force generated by the ith obstacle. The total virtual force Ftotal will be used for local path planning. When employing the potential functions for dynamic path planning, local minimum problems do exist and should be taken care of. To solve the problem, the simplest method is to keep the robot move according to the total virtual force as usual and wait for the obstacles to change their positions. Since the environment is highly dynamic where some obstacles are moving, the situations where the configuration of the obstacles keeps static are rare. Therefore, the waiting method is often adopted.

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Fig. 3 One typical run of path planning in the simulation environment: (a) The best initial path (fitness: 86.73); (b) The best solution in generation 56 (fitness: 71.81); (c) The best solution in generation 62 (fitness 50.28); (d) The near-optimal path in generation 132 (fitness: 46.04)

3 Simulation In this simulation, the environment is of size 50 9 33 grids. The start is grid number 64 and the goal is grid number 1,304. The population size is 80, crossover rate is 0.8, mutation rate is 0.03 and the maximum generation is kept 200. In the fitness function, w1 ; w2 are chosen as 0.6 and 0.4, respectively. The proposed Genetic Algorithm can easily deal with the obstacles and obtain a near-optimal path. Figure 3 shows the evolution process. The best solution in the initial population is shown in Fig. 3a, b shows the best solution after 56 generations. The genetic algorithm continues to evolve better solution (Fig. 3c) until the maximum generation is reached. A near-optimal path is obtained after 132 generations as shown in Fig. 3d). The near-optimal path is obtained by GA after 132 generations. Then the APF approach is used to follow the optimal path and avoid the obstacles. As a local planning, every time the robot chooses one intermediate node as the start and its next intermediate node as the goal. It moves from one intermediate node to next intermediate node repeatedly till the robot reaches the global goal. The robot tries to reach the desired goal which attracts it. When it moves towards the obstacles, the repulsive force prevents it from colliding with the obstacles.

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4 Conclusion and Future Work The novelty of this work is the combination of a global planner and a local planner for robot path planning in dynamic environments with moving obstacles. GA is employed as global planner to obtain a global optimization based on the current environment while the APF is used as local planner that is good at obstacles avoidance while following the optimal path. The hybrid approach is able to quickly determine the optimal feasible path for robot in dynamic environments. In this work, we have developed and implemented a hybrid Artificial Potential Field and Genetic Algorithm approach to deal with path planning problem for mobile robots in dynamic environments. Compared with the genetic based approach, the hybrid approach provides a better performance in processing time, and that is indeed crucial for a robot to quickly respond to avoid collision with the dynamic obstacles. Though the hybrid Artificial Potential Field and Genetic Algorithm approach solves the issue of obtaining the optimal path without collision with obstacles in dynamic environments, some further extensions need to be considered as future work: a. The environment in this simulation is described in a two-dimensional surface. For modeling a more realistic environment, a three-dimensional modeling needs to be considered. b. The hybrid approach is applied on a single robot currently. Extension of our proposed schema to handle on multi-robot system is a more interesting future research work.

References 1. Prahlad V, Kay Chen T, Mingliang W (2000) Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceeding of the IEEE congress on evolutionary computation, pp 256–263 2. Pu S, Yujie C (2010) Dynamic path planning for mobile robot based on genetic algorithm in unknown environment. In: 2010 Chinese control and decision conference, pp 4325–4329 3. Qixin C, Yanwen H, Jingliang Z (2006) An evolutionary artificial potential field algorithm for dynamic path planning of mobile robot. In: Proceeding of IEEE international conference on intelligent robots and systems, pp 3331–3336 4. Ge SS, Cui YJ (2000) New potential functions for mobile robot path planning. In: IEEE transactions on robotics and automation, pp 615–620 5. Jianping T, Simon XY (2003) Genetic algorithm based path planning for a mobile robot. In: Proceeding of IEEE international conference on robotics & automation. IEEE Press, Canberra, pp 1221–1226 6. Yong Z, Lin Z, Xiaohua Z (2008) Mobile Robot path planning base on the hybrid genetic algorithm in unknown environment. In: Eighth international conference on intelligent systems design and application, pp 661–665

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7. Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: Proceeding of IEEE international conference on robotics and automation, pp 500–505 8. Koren Y, Borenstein J (1991) Potential Field methods and their inherent limitations for mobile robot navigation. In: Proceeding of IEEE international conference on robotics and automation, pp 1398–1404 9. Scott M (2004) An introduction to genetic algorithms. J Comput Sci Coll 20:115–123 10. Wang Y, Sillitoe P, Mulvaney J (2007) Mobile robot path planning in dynamic environments. In: IEEE international conference on robotics and automation, pp 71–76 11. Lbszlo S, Annamiria K (2003) Autonomous navigation in a known dynamic environment. In: The 12th IEEE international conference on fuzzy systems, vol 1, pp 266–271 12. Tom MM (1997) Machine learning. McGraw-Hill, 13. Xio J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/ navigator for mobile robots. In: IEEE transactions on evolutionary computation, vol 1, pp 18–28 14. Ahmed E, A.Abdullah H, Shawki A (2004) Genetic algorithm for dynamic path planning. In: CCECE 2004—CCGEI 2004, Niagara Falls 15. Yanrong H, Simon XY (2004) A knowledge based genetic algorithm for path planning of a mobile robot. In: Proceeding of the 2004 IEEE international conference on robotics & automation, pp 4350–4355

Energy-Constrained Scheduling for Weakly-Hard Real-Time Tasks on Multiprocessors Yeonhwa Kong and Hyeonjoong Cho

Abstract In this paper, we consider energy constrained scheduling for weakly hard real-time constrained tasks running on multiprocessors. Rather than simply saving as much energy as possible, we consider energy as hard constraint under which the system remains functional and delivers an acceptable performance at least during the prescribed mission time. We propose a class of Energy-constrained Multiprocessor Real-Time Scheduling algorithms for (m, k)-firm deadline constrained tasks (EMRTS-MK). We prove that EMRTS-MKs provide assurance in terms of quality of multimedia services under a certain condition. We also evaluate the proposed algorithms in several experiments, which quantitatively show that EMRTS-MKs achieve the aforementioned scheduling objectives. Keywords Real-time systems

 (m, k)-firm deadline  Multicore  DVS

1 Introduction Several known advantages of multiprocessors, e.g., low power consumption, low cost per performance, etc. have recently made them pervade into portable computing and communication devices. These technological trends increasingly offer better platforms in which users can experience a variety of services including

Y. Kong (&)  H. Cho Department of Computer and Information Science, Korea University, Seoul, South Korea e-mail: [email protected] H. Cho e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_32,  Springer Science+Business Media B.V. 2012

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multimedia streaming. One unique feature of multimedia services is that they allow occasional deadline misses to a certain acceptable level, differently from traditional hard real-time services that do not allow any deadline misses. Thus, to guarantee the quality required by multimedia services while utilizing their reduced strictness of timeliness requirement, sophisticated real-time scheduling is crucial particularly on multiprocessors. Moreover, portable devices are battery-powered, and therefore energy efficiency is another significant design factor to consider. Rather than simply saving as much energy as possible in portable devices, we consider the problem of ensuring that the system with finite energy budget remains functional and delivers an acceptable performance at least during the prescribed mission time, where energy turns to be a hard constraint. In this paper, we aim at designing an energy-constrained scheduling algorithm for weakly-hard real-time constrained tasks running on multiprocessors, which is the first research effort to the best of our knowledge. Weakly-hard real-time systems. Real-time systems are classified into hard and soft depending on the degree of deadline satisfaction. In contrast to hard realtime systems, which do not allow any deadline miss, tolerance to deadline misses of soft real-time systems has traditionally been expressed as a maximum allowable loss percentage. However, this probabilistic specification is not sufficiently clear to represent the quality of service of soft real-time systems. For example, a video stream can be represented to tolerate a 10% loss rate. When a video stream has 100 frames, a 10% loss rate can be obtained either by first the 10 frames’ deadline misses out of 100 frames or by one deadline miss per every 10 frames. Even if two systems accomplish the same loss rate, their qualities of service are different with respect to the number of deadline misses within a given time period. Thus Hamaoui et al. [1] proposed the (m, k)-firm deadline constraint, where at least m deadlines should be met in every consecutive k deadlines. A violation of the given (m, k)-firm deadline is called dynamic failure, and the number of dynamic failure occurrences is utilized as a metric to evaluate performance of the systems. Later, Bernat and Burns proposed a concept of weakly hard real-time constraint that can tolerate a specified degree of missed deadlines, which encompasses several alternative (m, k)-firm deadline models [2]. Real-time scheduling for multiprocessors. Real-time scheduling algorithms for multiprocessors can be divided into two approaches, partitioned and global. Partitioned scheduling algorithms restrict task migration between processors after allocating each task to a processor for running. Partitioned scheduling is known to be an NP-hard problem. On the other hand, global scheduling algorithms allow task migration, which has made several optimal algorithms in this category appear, e.g., P-Fair [3], EKG [4], LNREF [5]. Our EMRTS-MKs are designed based on PFair to inherit its timeliness guarantee under under-load situations. DVS for weakly hard real-time system. Several attempts for using the DVS technique to reduce energy consumption for (m, k)-firm deadline task scheduling have been proposed. To guarantee the (m, k)-firm deadline, Ramanathan et al. [6] proposed a strategy to statically determine two types of jobs: mandatory and optional job. The mandatory jobs are the jobs that must meet their deadline to

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satisfy the (m, k)-firm deadline constraints, while the optional jobs can be executed to further improve the quality of service or simply skipped to save energy consumption. Later, Niu and Quan designed a static/dynamic algorithm for weakly hard real-time systems [7]. They statically determined the mandatory job but dynamically updated it during run-time. An energy constrained algorithm for weakly hard real-time system was proposed by Alenawy and Aydin [8]. They defined the energy-constrained scheduling problem for weakly–hard real-time tasks on a single processor. However, to the best of our knowledge, studies on energy efficiency of multiprocessor real-time scheduling algorithms for (m, k)-firm constrained tasks do not exist. Contribution. We consider energy constrained scheduling for weakly hard realtime constrained tasks running on multiprocessors. Rather than simply saving as much energy as possible, we consider energy as a hard constraint under which the system remains functional and delivers an acceptable performance at least during the prescribed mission time. Our scheduling objectives are to (1) meet a prescribed mission time as much as possible; (2) minimize the number of dynamic failure occurrences to satisfy the demand on quality of multimedia services and; (3) satisfy as many deadlines as possible to maximize the quality of multimedia services. To achieve these goals, we design a class of Energy-constrained Multiprocessor RealTime Scheduling for (m, k)-firm deadline constrained tasks (EMRTS-MK). In this paper, we show that EMRTS-MKs provide guaranteed performance and energy constrained scheduling for weakly hard real-time tasks on multiprocessors.

2 System Model 2.1 Task Model We consider a periodic real-time task set T = {T1,….TN} where N is the number of tasks. Each task is independent and characterized by Ti = (Ci, Pi, mi, ki), where Ci is Ti’s execution time, Pi is its period, and mi and ki are its (m, k)-firm deadline parameters. A set of N periodic tasks are executed on M homogenous processors P = {P1,…, PM}. When Ti is subject to (m, k)-firm deadline constraint, its distance, Disti, represents the minimum number of consecutive deadline misses to fall into a dynamic failure. With respect to the distance, we classify tasks into urgent and non-urgent. The Disti of urgent task Ti is one while that of non-urgent task Ti is greater than one. As Ramanathan et al. proposed in [6], we consider two types of jobs: mandatory and optional. We use the concept of (m, k)-pattern. Definition 1 (Quan and Hu [9]) The (m, k)-pattern of task Ti, denoted by Q i ¼ fpi0 ; pi1 ; . . .. . .piðki1Þ g; which satisfies the following: (i) Tij,is a mandatory kP i 1 pij ¼ mi : job if pij = 1 and optional if pij = 0, and (ii) j¼0

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The deeply-red pattern is one of (m, k)-patterns that was proposed by Koren et al. [10]. According to this scheme, a job Tij, the jth job of task Ti, is determined to be mandatory if 0 B j mod ki \ mi.

2.2 Power and Energy Consumption Model We consider the real-time tasks running on battery-powered multiprocessors, where energy recharge is not possible at runtime. To describe system behaviors related to energy, we use the following terminologies. • Emax: maximum energy capacity of the system. • Emin: energy threshold below which safe operation of the system is not guaranteed. • Ebudget (t): total amount of energy that the system can consume for running from time t. When the battery is fully charged, Ebudget(t) is defined as Emax-Emin. • Etotal(t, S0 ): total amount of energy required to run all jobs of tasks within a time interval [t, X] at processor speed S0 , where t is the current time and X is its mission time. • Elimit(t, S0 ): total amount of energy required to run tasks within a time interval [t, X] at processor speed S0 to schedule all mandatory jobs. The power consumption of CMOS-based processors consists of dynamic power consumption Pdynamic and stand-by mode power consumption Pstd. Pdynamic is a function of the processor speed s, as denoted by g(s), where g(s)  S3 as explained in [11]. We assume that all processors share the same clock signal and therefore all processors’ speeds are uniformly determined [10, 12]. We also assume that the processors support continuous frequency scale so that the normalized processor speed is in the range of (0, 1).

3 Problem Definition In this paper, we consider a scheduling problem for (m, k)-firm deadline constrained real-time tasks running on multiprocessors. It is distinct from [8] since we additionally consider two important design factors, i.e., multiprocessor environment and computational overload. The scheduling problem can be defined as How can all tasks running on battery-powered multiprocessors remain functional during their mission time (0, X) while satisfying their (m, k)-firm deadline constraints and, if necessary, satisfying as many deadlines as possible in both computational under-load and over-load cases?. Here the system is under-loaded if the total utilization demand of tasks, defined as the sum of Ci/Pi of all Ti, is less than or equal to M. Otherwise, the system is overloaded. This problem can be more

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Table 1 Scheduling objectives Ebudget C Etotal

Ebudget C ELimit

Ebudget \ ELimit

Under-load

Overload

MTS: 100% DFR: 0% DSR: 100% MTS: maximizing DFR: 0% DSR: maximizing DSR: maximizing DFR: minimizing

DSR: maximizing DFR: minimizing

specifically described with respect to the target system’s energy budget and computational load as in Table 1. In Table 1, each cell is filled with the corresponding scheduling objectives. We use three metrics in order to quantitatively evaluate system performance, mission time satisfaction (MTS), dynamic failure ratio (DFR) defined as the number of dynamic failure occurrences per total number of job releases, and deadline satisfaction ratio (DSR) defined as the number of deadline satisfactions per total number of job releases.

4 Proposed Algorithm 4.1 Speed Determination With a single processor, it is known that the normalized processor speed that is equal to total utilization of the periodic real-time task set minimizes its energy consumption without missing any deadlines [11]. For multiprocessors, on the other hand, it is complicated to determine a proper speed. In [13], Funaoka et al. introduced max{Umax, U/M} as an optimal uniform frequency for multiprocessors. U is the total utilization, M is the number of processors, and Umax is the maximum value of individual task utilizations. However, this speed determination requires modification to be applied directly to the quantum-based scheduling algorithms, e.g., P-Fair, that makes their scheduling decisions every time quantum. In the quantum-based scheduling, all task parameters including Ci and Pi are defined as multiples of the time quantum, i.e., defined as integer values. Therefore, after applying the speed determination above, task parameters may become real numbers, which makes a task finish earlier than the next time quantum boundary. Example 1 Suppose that a task’s execution time is given as one. When the normalized processor speed is reduced to 0.8, the execution time accordingly changes from 1 to 1/0.8 and thus the task occupies two time quanta although its extended execution time is smaller than two. If real-time constraints permit, task execution

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Fig. 1 An example of relation between processor speed and occupied time quanta

time can be more extended up to the second time quantum’s boundary, which implies lower processor speed and reduced energy consumption. The latter indicates that in order to obtain a proper processor speed in quantum-based scheduling, we should consider the number of time quanta that tasks occupy at a given processor speed (Fig. 1). Definition 2 S0 is the minimum processor speed at which all tasks meet their deadlines under P-Fair scheduling algorithms. Our processor speed determination algorithm is described in Algorithm 1. A set of tasks where each task Ti is characterized by (Ci, Pi, mi, ki) is the input of this algorithm and a determined speed S0 is its output. If the processor speed changes, then the parameters of the task also change to (Ci0 , Pi, mi, ki). Example 2 Consider three tasks constrained by their (m, k)-firm deadlines as shown in Table 2. The number of processors is two and the mission time is given as 12 time quanta. For simplicity, the rests that are less than 10-3 are rounded. Algorithm 1 first determines a tentative S0 for the given task set. As the total utilization is 1.083 in the given task example, the processor speed is determined to be 0.54. When Stemp is 0.54, the time quanta occupied by each task is C10 = d1=0:54e = 2, C20 = d2=0:54e = 4, and C30 = d1=0:54e = 2, respectively. And the total utilization is 2.166. It is greater than the number of processors and therefore 0.54 for Stemp is not feasible. Then, Algorithm 1 attempts to find a task having the least difference between its occupied time quanta and its actual extended execution time. In the example, T2 has the least difference (Cleast = C2), so we reduce T2’s occupied time quanta from 4 to 3(S0 = 0.667). Consequently, S0 is computed as max{0.667,0.5} = 0.667. Algorithm 1. Calculate Speed (Q) P Stemp=calculate ( CPii )/M for(i=0;i\N;i++) Ci0 =Ci/S Ptemp Utemp= (Ci/Stemp)/Pi while(Utemp[M){ for(i=0;i\N;i++){   if( Ci0  Ci0 ! ¼ 0Þf   if ð Ci0  1Þ \  0  - 1)) C’least-( Cleast

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Table 2 A task set T1 T2 T3

Ci

Pi

Ui

mi

ki

1 2 1

2 6 4

0.5 0.333 0.25

2 1 2

3 2 3

{ least = i;} } }  0   1Þ; Stemp ¼ Cleast =ð Cleast Utemp=0; for(i=0;i\N;i++){ Ci0 =Ci/Stemp   Utemp ¼ Utemp þ Ci0 =Pi; }} if(Stemp[1) Stemp=1; S0 = max{Stemp, Umax}

4.2 Static DVS Algorithm We first propose static EMRTS-MK running tasks at a fixed processor speed. Once processor speed is determined when the system starts, and it never changes at runtime. To determine processor speed, the static EMRTS-MK invokes Algorithm 1 once before the system starts. Algorithm 2 is a hierarchical scheduling algorithm consisting of both high-level scheduling and low-level scheduling. High-level scheduling is invoked at every boundary of each task in order to select and dispatch tasks to low-level scheduling. On the other hand, low-level scheduling is invoked at every time quantum to schedule the tasks selected by the high-level scheduling. Thus, whenever highlevel scheduling is invoked, low-level scheduling is also subsequently invoked. We use a P-Fair scheduling as the low-level scheduling. Algorithm 2 has three queues for scheduling tasks. QA is a queue containing tasks admitted for low-level scheduling. QU is a queue containing non-admitted and urgent tasks. QNU is a queue containing non-admitted and non-urgent tasks. EA: sum of energy required to run the tasks in QA UA: sum of utilization of tasks in an admit queue UTi: individual utilization of a task Ti ETi: energy required to run Ti

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In Algorithm 2, getTask() obtains the task at its boundary. updateDist(Ti) updates Ti’s distance. Sort(Q, A) sorts all tasks in queue Q in the order of A. move(Ti, Q) pops and inserts Ti into queue Q. Algorithm 2. Static EMRTS-MK 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28: 29: 30: 31: 32: 33: 34: 35: 36:

At start CalculateSpeed() At boundary of each task Tk, a present time t Tk=getTask(); updateDist(Tk); if Tk is urgent then insert(Tk, QU) else insert(Tk, QNU) calculate ETotal calculate ELimit calculate EA if(Etotal B Ebudget) { if(U B M){ Sort(QU, LeastUtilizationFirst) Sort(QNU, LeastDistanceFirst) for(Ti[ QU)do if (UA ? UTi B M){ move(Ti[ QU, QA) UA = UA ? UTi} else break for(Ti [ QNU)do if (UA ? UTi B M){ move(Ti[ QNU, QA) UA = UA ? UTi;} } } else if(ELimit B Ebudget){ Sort(QNU, LeastEDFirst) Sort(QU, LeastEDFirst) for(Ti[ QU)do if(UA ? UTi B M){ move(Ti[ QU, QA) UA = UA ? UTi } if(QU ==NULL){ for(Ti[ QNU){ if(ELimit+ETemp B Ebudget &&

UA ? UTi B M){ 37: move(Ti[ QNU, QA)

Energy-Constrained Scheduling

38: 39: 40: 41: 42: 43: 44: 45: 46: 47:

343

ETemp = ETemp ? ETi UA = UA ? UTi} else break } } else if(ELimit [ Ebudget){ Sort(QU, LeastEDFirst) Etemp = EA; for(Ti[ QU)do if(ETemp B Ebudget &&

UTi ? UA B M){ 48: 49: 50: 51: 52: 53: 54:

move(Ti[ QU, QA) ETemp = ETi ? Etemp UA = UA ? UTi} else break } At every quantum Pfair(QA)

P Theorem 1 Algorithm 2 satisfies all tasks’ deadlines if Ebudget C Etotal and Ci/ Pi B M(0 B i\N), where M is the number of processors and N is the number of tasks. Proof At a time instant of each boundary of a task, the task is inserted into either QU or QNU. Since the energy budget is larger than or equal to Etotal (not Elimit), it goes to the first if branch. If the total utilization is less than or equal to M, then all tasks are admitted to low-level scheduling. The low-level scheduling, P-fair, is optimal in the sense that it satisfies all tasks’ deadlines when the total utilization is less than or equal to M. Therefore, all tasks meet their deadlines and consequently no dynamic failure occurs. In Algorithm 2, Energy Density (ED) is used to sort urgent tasks in QU and non-urgent tasks in QNU in the order of the least ED first. Definition 3 (AlEnawy and Aydin [8]) The energy density EDi of a task Ti is the weighted ratio of energy consumption Ei of the mandatory instances of the task Ti divided by the number of dynamic failures DFmax that this task can cause during i . the mission time, that is, EDi = Ei/wiDFmax i The wi is a weight indicating the relative impact of Ti’s dynamic failures on the overall system performance. To ensure that important tasks are given priority in allocating the energy needed to meet their constraints, the designer can assign large weights to those tasks. The wi is a weight indicating the relative impact of Ti’s dynamic failures on the overall system performance [8].

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Fig. 2 EMRTS. (a) Tasks run by static EMRTS-MK when Ebudget(0) equals Elimit(0). (b) Tasks run by dynamic EMRTS-MK when Ebudget(0) is 70% of Elimit(0)

Fig. 3 DSR and DFR when the total utilization is 2.5

4.3 Dynamic DVS Algorithm The static EMRTS-MK runs at fixed processor speed S0 that is determined as the minimum speed. However, if Ebudget is lower than Etotal, then we cannot run all tasks due to the limited energy and instead, Algorithm 2 selects tasks to execute. The fact that only the selected tasks run implies that we can determine new processor speed just to run those tasks. The new processor speed, denoted by S*, is lower than S0 , and it consumes less energy. To determine S*, dynamic EMRTSMK invokes a dynamic speed determination algorithm, a simple modification of Algorithm 1, for tasks in QA at every boundary of each task before running Algorithm 2. Thus, dynamic EMRTS-MK can dynamically adjust the processor

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Fig. 4 DSR and DFR when the total utilization is 4.5

speed at every boundary of each task. In the modification of Algorithm 1, the parameter Ci is replaced with Ri where Ri is Ti’s remaining execution time. Example 3 Consider the three (m, k)-firm deadline constrained tasks shown in Table 1. The mission time is also given as 12 time quanta and M is 2. In Example 2, S0 is determined as 0.667. In Fig. 2, the grey rectangles are selected jobs. Example 3 shows how EMRTS-MKs schedule tasks. In Fig. 2a, the static EMRTS-MK schedules urgent tasks setting S0 to 0.667 and S0 does not change at runtime. In Fig. 2b, we see that the processor speed changes at every boundary.

5 Experiment Result We evaluated the performance of our proposed algorithms using the OMNeT++ simulator. Two metrics DSR and DFR were used for the evaluation. We first randomly generated real-time tasks constrained by (2, 3)-firm deadlines. For setting task periods, we consider the task’s parameters used in [14] that show an example of mobile multimedia application parameters. One of our target applications is the mobile multimedia service, and thus we set the task periods to 30, 25, or 400. The mission time X is given as 4 times of the hyper-period of all tasks. Our experimental setting including (m, k)-firm deadline parameters and mission time follows that of [8]. We assume the number of processors is four. We varied the total utilization within the range of (2.5, 4.5), which covers both underloaded and overload situations. Ebudget is set to range from 20 to 100% of Elimit at time 0. For each experimental result, we repeated the same experiments 10 times to obtain the average value. Figure 3 shows when the system is under-loaded, static and dynamic EMRTSMKs decrease DSR and increase DFR as the ratio of Ebudget to Elimit decreases. Figure 3 also shows that the dynamic EMRTS-MK outperforms static EMRTS-

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MK in terms of both DSR and DFR. Especially, the dynamic EMRTS-MK incurs no dynamic failure even with 80% of Elimit to Ebudget. It implies that when the system is under-loaded, EMRTS-MK can satisfy the minimum quality of multimedia service even under a little shortage of energy by dynamically adjusting the processor speed. Figure 4 shows EMRTS-MK’s graceful performance degradation even in an overload situation. In the case of an overload, if the energy budget is sufficient to execute a mandatory job, then the dynamic algorithm achieves the same performance as the static algorithm. Since the system schedules the maximum number of tasks, the system cannot decrease the processor speed. We consider energy constrained scheduling for weakly hard real-time constrained tasks running on multiprocessors. Rather than simply saving as much energy as possible, we consider energy as a hard constraint under which the system remains functional and deliver an acceptable performance during the least prescribed mission time We prove that EMRTS-MKs provide assurance in quality of multimedia services under a certain condition. We also evaluate the proposed algorithms in several experiments, which quantitatively show EMRTS-MKs achieve the aforementioned scheduling objectives.

6 Conclusion Aiming at multimedia services on mobile devices, we consider energy constrained scheduling for (m, k)-firm deadline constrained tasks running on multiprocessors. We propose static and dynamic EMRTS-MKs to maximize the quality of multimedia services under both energy and weakly-hard real-time constraints. We prove that EMRTS-MKs provide assurance in the quality of multimedia services when the system with sufficient energy is under-loaded. Our experimental evaluation validates that EMRTS-MKs achieve our scheduling objectives. Acknowledgments This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No. 2011-0011534).

References 1. Hamdaoui M, Ramanathan P (1995) A dynamic priority assignment technique for streams with (m,k)-firm deadlines. IEEE Trans Comput 44(12):1443–1451 2. Bernat G, Burns A, Llamosi A (2001) Weakly hard real-time systems. IEEE Trans Comput 50(4):308–321 Apr 3. Baruah SK, Cohen NK, Plaxton CG, Varvel DA (1996) Proportionate progress: a notion of fairness in resource allocation. Algorithmica 15(6):600–625 4. Andersson B, Tovar E (2006) Multiprocessor scheduling with few preemptions. In: Proceedings of the 12th IEEE international conference on embedded and real-time computing systems and applications, pp 322–334, Aug 2006

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5. Cho H, Ravindran B, Jensen ED (2006) An optimal real-time scheduling algorithm for multiprocessors. In: Proceedings of the 27th RTSS, IEEE Computer Society, pp 101–110 6. Ramanathan P (1999) Overload management in real-time control applications using (m,k)firm guarantee. IEEE Trans Paral And Dist Sys 10(6):549–559 Jun 7. Niu L, Quan G (2005) A hybrid static/dynamic DVS scheduling for real-time systems with(m,k)-guarantee. In: Proceedings of the 26th RTSS 2005, pp 356–365, Miami, Florida, Dec 8. Alenawy TA, Aydin H (2005) Energy-constrained scheduling for weakly-hard real-time systems. In: Proceedings of the 26th IEEE RTSS’05, pp 376–385 9. Quan G, Hu X (2000) Enhanced fixed-priority scheduling with (m,k)-firm guaranteed. In: RTSS, pp 79–88 10. Koren G, Shasha D (1995) Skip-over: algorithm and complexity for overloaded systems that allow skips, In: RTSS 11. Aydin H, Melhem R, Mosse D, Alvarez PM (2004) Power-aware scheduling for periodic real-time tasks. IEEE Trans Comput 53(5):584–600 May 12. Magkils G, Semeraro G, Albonesi DH, Dropsho SG, Dwarkadas S, Scott ML (2003) Dynamic frequency and voltage scaling for multiple-clock-domain microprocessor. IEEE Micro 23(6):62–68 13. Funaoka K, Kato S, Yamasaki N (2008) Energy-efficient optimal real-time scheduling on multiprocessors. In: Proceedings of the 11th IEEE international symposium on object/ component/service-oriented real-time distributed 14. Yuan W, Nahrstedt K (2003) Energy-efficient soft real-time cpu scheduling for mobile multimedia systems. In: SOSP, October

A New Software Process Model: The Insect Model Mahmood Vahedi Moghaddam and Mehdi Zand Akbari

Abstract Process models are common topic in the software engineering world. While there are many of these models, just one of them is used very commonly in the real cases which is classical model. In this article a new model that is called ‘‘Insect model’’ is presented which has new suggestions for use in real operational environments.




Analysis Keywords Process models Planning Implementation Test










Design




Coding




Development




1 Introduction The existence of various amount of software process models, seem to bring no further increase in software quality and costs. An evidence of this is the common use of one of the most primitive process models: ‘‘the water fall model’’. This is caused by the simplicity and the low budget requirement of that waterfall model. But this model has great disadvantages which make it an unwilling choice for software projects. This leads us to use more dynamic and flexible models like DSDM. But flexibility and dynamic behavior of these kinds of models make some problems in

M. V. Moghaddam (&)
M. Z. Akbari Department of Computer Engineering Science and Technology, University of Mazandaran, Babol, Iran e-mail: [email protected] M. Z. Akbari e-mail: [email protected]

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feasibility phase. Experienced software project managers claim the difficulty of Cost evaluation and effort estimation for these kinds of models. The other problem with existing process models is the way that we adopt our software to the end user and costumer needs. For instance, as Hughes says in his ‘‘software project management’’ book about XP process model: ‘‘test cases and expected results are devised before the design take place. To do this, the developer and user must agree exactly what should happen given specific circumstances’’, it is obvious that our users are not such professional to help us to create precise test cases. Insect model is away to think and plan more realistic. It insists on having a complete knowledge about end-users needs before starting the implementation phase. This strategy leads to (a) Risk rates reduction (b) Much easier cost evaluation and effort estimation (c) Lower production costs caused by having very small amount of code changes (almost no code changes).

2 What is a Software Process Model? Software process models often represent a networked sequence of activities, objects, transformations, and events that embody strategies for accomplishing software evolution. Such models can be used to develop more precise and formalized descriptions of software life cycle activities. Their power emerges from their utilization of a sufficiently rich notation, syntax, or semantics, often suitable for computational processing. Software process networks can be viewed as representing multiple interconnected task chains [1, 2]. Task chains represent a non-linear sequence of actions that structure and transform available computational objects (resources) into intermediate or finished products. Non-linearity implies that the sequence of actions may be non-deterministic, iterative, accommodate multiple/parallel alternatives, as well as partially ordered to account for incremental progress. Task actions in turn can be viewed a non-linear sequences of primitive actions which denote atomic units of computing work, such as a user’s selection of a command or menu entry using a mouse or keyboard. Winograd and others have referred to these units of cooperative work between people and computers as ‘‘structured discourses of work’’ [3], while task chains have become popularized under the name of ‘‘workflow’’ [4]. Task chains can be employed to characterize either prescriptive or descriptive action sequences. Prescriptive task chains are idealized plans of what actions should be accomplished, and in what order. For example, a task chain for the activity of object-oriented software design might include the following task actions:

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Develop an informal narrative specification of the system. Identify the objects and their attributes. Identify the operations on the objects. Identify the interfaces between objects, attributes, or operations. Implement the operations. Clearly, this sequence of actions could entail multiple iterations and non-procedural primitive action invocations in the course of incrementally progressing toward an object-oriented software design. Task chains join or split into other task chains resulting in an overall production network or web [1]. The production web represents the ‘‘organizational production system’’ that transforms raw computational, cognitive, and other organizational resources into assembled, integrated and usable software systems. The production lattice therefore structures how a software system is developed, used, and maintained. However, prescriptive task chains and actions cannot be formally guaranteed to anticipate all possible circumstances or idiosyncratic foul-ups that can emerge in the real world of software development [5, 6]. Thus, any software production web will in some way realize only an approximate or incomplete description of software development. Articulation work is a kind of unanticipated task that is performed when a planned task chain is inadequate or breaks down. It is work that represents an open-ended non-deterministic sequence of actions taken to restore progress on the disarticulated task chain, or else to shift the flow of productive work onto some other task chain [7, 8, 9, 10]. Thus, descriptive task chains are employed to characterize the observed course of events and situations that emerge when people try to follow a planned task sequence. Articulation work in the context of software evolution includes actions people take that entail either their accommodation to the contingent or anomalous behavior of a software system, or negotiation with others who may be able to affect a system modification or otherwise alter current circumstances (Bendifallah 1987, Grinter 1996, Mi 1990, Mi 1996, Scacchi and Mi 1997). This notion of articulation work has also been referred to as software process dynamism.

3 New Insect Model This model has three main steps. In the first step that can be consider as a head of the model, we have project evaluation with the foundation user agreement. In this step project manager must has several meetings with end-users and also costumers for having their opinion and using it in the feasibility study of the project. As illustrated in Fig. 1 pay attentions that after finishing this step, it is not possible to come back to this part again. If, for any reason, you decided to go backward in the model, it would be considered as a new project.

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Fig. 1 Insect model Table 1 Comparing different factors that have effect on software process model Models Speed of Sharing Cost Effort Total development Stakeholders estimation estimation Insect model The linear sequential model Incremental model The spiral model Win–win spiral model Dynamic systems development method (DSDM) Extreme programming (XP)

2 2 1 2 3 1

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The dynamic step that can use the floatability of the project effort estimation is the middle part. In this step first the requirements of the user must be determined and according to them the analysis and design of the project must be occurred. These two part (analysis and design) must have an internal test for confirmation of the user requirements. After that, it is the time for creating demo to show to the

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end-user.So with constructing demo codes (which is mostly just a user interface) we can have the ideas, new requirements and view point of non-software-professional end users. This loop will be continued until the user confirms that his requirements are completely involved in the project. The state of ‘‘having a compete vision on the project goals’’ should also be confirmed by the analysis group and project manager. Then the project members can start the coding phase with free mind of having any serious code changes. And after coding it is the time of final test and V&V phase and at last the maintenance. As mentioned before, this will reduce the project budget requirement dramatically. Notice that in this model, although we have a dynamic behavior, but it is fully predictable and under control, so the harmful face of dynamicity, which usually lets the whole project down (because of unpredictability of costs, efforts and delivery time), is completely vanished.

4 Compare with Other Models There are some factors that effectively influence on performance of a process model. So comparing these factors can lead us to best model. Some of these factors are Speed of development, sharing stakeholders in duration of project, and some project management factors as cost estimation and effort estimation. We have get a priority between number one and three to each of these factors for a special process model according of our experience and studies. It is obvious that a model which has the lowest total priority is a good suggestion to take. But notice that according to different teams and different managers these priorities may have change a little. According to above results it seems that insect model is a best and intermediate model in various factors and is a good suggestion to take (Table 1).

5 Conclusion In this article, we produced a new process model that seems to have an undercontrol dynamic behavior that leads to a better software project management method, which is much easier to implement and manage. Because in this model we have just low cost changes, it introduces a high level of efficiency.

References 1. Kling R, Scacchi W (1982) The web of computing: computer technology as social organization, ‘‘advances in computers’’, vol 21. Academic Press, New York, pp 1–90 2. Garg PK, Scacchi W (1989) ISHYS: design of an intelligent software hypertext environment. IEEE Expert 4(3):52–63

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3. Winograd T, Flores F (1986) Understanding computers and cognition: a new foundation for design. Ablex Publishers, Lexington 4. Bolcer GA, Taylor RN (1998) Advanced workflow management technologies. Softw Process Improv Pract 4(3):125–171 5. Bendifallah S, Scacchi W (1989) Work structures and shifts: an empirical analysis of software specification teamwork. In: Proceedings of 11th international conference on software engineering, IEEE Computer Society, pp 260–270 6. Mi P, Scacchi W (1990) A knowledge base environment for modeling and simulating software engineering processes. IEEE Trans Knowl Data Eng 2(3):283–294 7. Bendifallah S, Scacchi W (1987) Understanding Software Maintenance Work. IEEE Trans Softw Eng 13(3):311–323 8. Grinter R (1996) Supporting articulation work using software configuration management. J Comput Support Coop Work 5:447–465 9. Mi P, Scacchi W (1996) A meta-model for formulating knowledge-based models of software development. Decis Support Syst 17(4):313–330 10. Scacchi W, Mi P (1997) Process life cycle engineering: a knowledge-based approach and environment. Intell Syst Account Finance Manag 6(1):83–107

Improving Recommendations in Tag-Based Systems with Spectral Clustering of Tag Neighbors Rong Pan, Guandong Xu and Peter Dolog

Abstract Tag as a useful metadata reflects the collaborative and conceptual features of documents in social collaborative annotation systems. In this paper, we propose a collaborative approach for expanding tag neighbors and investigate the spectral clustering algorithm to filter out noisy tag neighbors in order to get appropriate recommendation for users. The preliminary experiments have been conducted on MovieLens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach and naive tag neighbors expansion approach in terms of precision, and the result demonstrates that our approach could considerably improve the performance of recommendations. keywords Tag neighbors Social tagging

 Recommender system  Spectral clustering 

This research is supported by EU FP7 ICT project M-Eco: Medical Ecosystem Personalised Event-based Surveillance (No.247829). R. Pan (&)  P. Dolog Department of Computer Science, Aalborg University, Aalborg, Denmark e-mail: [email protected] P. Dolog e-mail: [email protected] G. Xu Centre for Applied Informatics, Victoria University, Melbourne, Australia e-mail: [email protected]

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1 Introduction The success of Web 2.0 applications has faced a new era for sharing and organizing documents in online social communities. A large number of social tagging sites, like Delicious, CiteUlike, Digg, or Flickr, has sprung up in a short period. Tag, as one kind of specific lexical information that is user-generated metadata with uncontrolled vocabulary, plays a crucial role in such social collaborative tagging systems. With the help of tagging data, user annotation preference and document topical tendency are substantially coded into the profiles of users or documents for further web data management. However, one serious problem in social tagging systems is the sparseness of tagging data. Users typically like to annotate a small fraction of the shared documents only that they are really interested in, leaving most of the other documents with incomplete annotations. As the tags are of syntactic nature, in a free style and do not reflect sufficient semantics, the problems of redundancy, ambiguity and less semantics of tags are often incurred in all kinds of social tagging systems [8, 9]. Due to the problems of less semantics and sparse quality of tagging data, the tag-based recommender systems which rely simply on the lexical similarity of tags alone are likely to neglect the retrieval of some closely related documents. Therefore, our motivation in this paper is to expand the tag vector expression of user and document by employing the statistical learning in tagging data. One technical solution we present in this paper is the expansion of tag expression with co-occurred tag neighbors, with which we are able to facilitate the organization of information documents in search and navigation. However, there is another problem emerging: not all the social tagging systems proposed so far maintain high quality and quantity of tag data, even the tag elements in tag expression can be expanded, such tag expression also contains lots of inappropriate tags, we particularly call them as ‘‘noisy tags’’. Thus another challenge is how to expand the tag expression in an appropriate way and filter out the noisy tags to enhance the recommendations. We propose a new recommendation approach by combining the tag neighbor expansion and noisy tag filtering techniques, where the former is realized by the collaborative approach and the latter is manipulated by the spectral clustering algorithm. With the enhanced tag expression by appropriate tag neighbors, we remeasure the similarity between the expanded user profile and document profile to improve the tag-based recommendations. The main contributions made in this paper are: 1. The collaborative tag neighbor expansion approach via co-occurrence analysis. 2. The filtering of noisy tag neighbors by utilizing the clustering algorithm. 3. The improved tag-based recommendations by enhancing the quality and quantity of tag neighbors. The rest of the paper is organized as follows: Sect. 2 presents the related work in the field of tag neighbors and tag clustering. In Sect. 3, we describe the

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preliminaries for the data model, user profile and document profile. Section 4 discusses the detailed process of extending the tag neighbors. The experiment design, evaluation measures and the comparison of the results are in Sect. 5 We conclude the paper in Sect. 6.

2 Related Work Tags have been recently studied in the context of recommender systems due to various reasons. Recommendations of relevant documents should be based on the sufficient occurrences for similar signals expressed by tags. We review the related literatures from the perspectives of tag expansion and tag clustering. Tag Expansion in Recommendation: Bayyapu and Dolog in [1] try to solve the problems of sparse data and low quality of tags from related domains. They suggest using tag neighbors for tag expression expansion. However the tag neighbors are based on the content of documents. We propose another approach to extend the tag set for the user profile by collaborative filtering approach. In [2], an interesting approach was proposed to model the documents in social tagging systems as a document graph. The relevance of tag propagated along edges of the documents graph is determined via a scoring scheme, with which the tag prediction was carried out. Tag Clustering: The purpose of tag clustering is the ability of aggregating tags into topic domains. In [4] topic relevant partitions are created by clustering documents rather than tags. By clustering of documents, it improves recommendation by distinguishing between alternative meanings of a query. While in [3], clusters of documents are shown to improve recommendation by categorizing the documents into topic domains.

3 Preliminaries 3.1 Folksonomy The folksonomy is a three-dimensional data model of social tagging behaviors of users on various documents. It reveals the mutual relationships between these threefold entities, i.e. user, document and tag. A folksonomy F according to [5] is a tuple F = (U, T, D, A), where U is a set of users, T is a set of tags, D is a set of web documents, and A  U  T  D is a set of annotations. The activity in folksonomy is tijk  fðui ; dj ; tk Þ : ui  U; dj  D; tk  Tg; where U ¼ fU1 ; U2 ;    ; UM g is the set of users, D ¼ fD1 ; D2 ;    ; DN g is the set of documents, and T ¼ fT1 ; T2 ;    ; TK g is the set of tags. tijk ¼ 1 if there is an annotation ðui ; dj ; tk Þ; otherwise tijk ¼ 0:

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3.2 User Profile and Document Profile The constructed folksonomy data model is actually a three-dimensional array (or called tripartite hyper-graph). In real applications, we often decompose the tagging data into two two-dimensional matrices, i.e., user profile and document profile. User profile can be used to store the descriptive tags of the user’s characteristics and preferences. The document profile is represented by the tags generated by the group of users tagging the documents. In the context of social tagging systems, the user profiles and document profiles thus are expected to be represented by the representative tags. Therefore the process of user and document profiling is to capture the significant tags from a large volume of tagging data in a social collaborative environment. We will utilize them based on our previous work [7].

3.3 Similarity Measure for Tags The similarity is quantity that reflects the strength of relationship between two objects. In the previous part, each user profile and document profile can be represented by the pair of tags and frequencies. In this manner, we perform the transformation of above two matrices and utilize the cosine function to measure the similarity between two tags. Its value ranges from 0 to 1, the higher value of the similarity, the more similar the objects are.

4 Expanding Tag Expression with Tag Neighbors for Recommendation To deal with the challenge of of low tag quality that commonly incurred in social tagging information systems, we propose an approach of expanding tag expression with tag neighbors. Based on the tag neighbor expansion, we devise a new tag-based recommendation. In this section, we give the details.

4.1 Collaborative Approach for Tag neighbor The basic idea of proposed approach is to utilize the tag neighbors to extend the users’ or documents’ profiles which are represented by the tags. In the tag similarity matrix, each tag has different similarity weights with other tags, we assume the higher the weight, the more similar the tags are to the target tag. To realize the task of expanding the tag, the major difficulty is how to define the tag neighbors and how to locate them from the total tags. Here we adapt a statistical definition of

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tag neighbor—the tags which are co-occurred most frequently or they have the higher similarity weight in the similarity matrices to the target tag, are the neighbors for each other. So the N tags according to the top-N similarity weight can be defined as the tag neighbors of an individual tag. After such steps, each tag will have an additional neighboring tag set which will help to improve the quantity of the tag expression. Given an arbitrary tag T, its neighboring tags are defined as: NbðTi Þ ¼ fTj : Tj 2 TopNfSMðTi ; Tj Þgg where TopNfSMðTi ; Tj Þg is the tags which possess the top-N highest similarity values to tag Ti :

4.2 Tag Neighbor Filtering based on Spectral Clustering In this section we use tag clustering to find the tag aggregates with similar functions or topics.The spectral clustering is based on the graph partition which maps the original co-occurrence observations onto a new spectrum space with a reduced dimensionality. The obtained partition guarantees the disjoint clusters with minimum cut optimization. Especially in the context of social tagging systems, we know each tag can be expressed a column vector of user profile and document profile, i.e., Ti ¼ TUi and Ti ¼ TDi : Then we stack up TUi and TDi and form a new tag vector over users and documents, Ti ¼ TUi [ TDi : At last, we employ spectral clustering on the integrated tag vectors to get tag clusters. The pseudo codes of the spectral tag clustering are listed in Algorithm 1. From the above tag clustering, we obtain a set of tag clusters: TC ¼ fTC1 ;    ; TCC g ¼ fðT11 ;    T1i1 Þ;    ðTC1 ;    ; TCiC Þg

Algorithm 1: Spectral Tag Clustering Input: The tag-user matrix and tag-document matrix, i.e., TU ¼ fTUi ; i ¼ 1;    ; Kg; TD ¼ fTDi ; i ¼ 1;    ; Kg Output: A set of C tag clusters TC ¼ fTCc ; c ¼ 1;    ; Cg such that the cut of C-partitioning of the bipartite graph is minimized 1 Construct the integrated tag-user-document matrix by stacking up the above two matrix, Ti ¼ TUi [ TDi ; 2 Calculate the diagonal matrices D of T; 3 Form a new matrix RT ¼ D1=2 TD1=2 ; 4 Perform SVD (Singular value decomposition) operation on RT, and obtain k singular vectors Ls to create a new projection matrix RV;

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5 Execute a clustering algorithm on RV and return clusters of tags in the integrated vectors: TC ¼ fTCc ; c ¼ 1;    Cg: The naive tag neighbors will be calculated by collaborative approach. However, the problem is that not all of the expanded tag neighbors are appropriate for the target tag since there are some noisy tags in the tag neighbors. From the above way, all of the tags are clustered into the several individual clusters where each cluster is a set of tags sharing the similar functions or topics. Bearing this phenomenon in mind, we thereafter propose to utilize tag clusters to determine whether the naive tag neighbors are included in the expansion of tags. The basic idea of clustering filtering is: the neighboring tags from the same tag cluster of the target tag contribute collaboratively to specific function or topic, being kept as the appropriate tag neighbors for tag expression expansion; otherwise be discarded. So the next processing step is to filter out the noisy tags according to the discovered tag clusters. In the previous sections, each tag has an expanded tag neighborhood, which might belong to different clusters. To ensure all neighboring tags are from the same tag cluster, each tag in the expanded neighborhood will be compared with all the tags from the tag cluster where the target tag is assigned. If the expanded neighbor appears in the same cluster, it then can be considered as the appropriate neighbor of the tag tag, making it kept in the expanded tag set; otherwise, it should be filtered out. After such steps above, the left elements could be defined as the tag neighbors for the target tag, and the quality of the tag neighborhood will be accordingly improved. Also in such way the density in the integrated tag-user-document matrix could be increased substantially. For example, given the Tag Ti belongs to a certain cluster Cj ; and the Ti has a expanded tag neighborhood as TN ¼ fTN1 ;    ; TNk ;    ; TNK g; then we can compare each tag TNk with the tags in Cj ; if TNk is existed, it can be defined as the tag neighbor; otherwise it will be discarded. After such steps, the tag neighborhood will be updated.

4.3 Improved Recommendation with Tag Neighbor Expansion After the tag neighbor expansion is completed, we get updated user profiles and document profiles in the forms of tag vector expression with expanded tag neighbors. We then utilize the similarity measure between users and documents to make tag-based recommendations. The whole algorithm for improved recommendation with tag neighbor expansion is described as Algorithm 2. Algorithm 2: Tag Neighbor Expansion for Recommendation Input: A collected tagging data Output: A list of top-N documents for the candidate user

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1 Pre-process the tagging data to construct a user profile matrix and a document profile matrix. 2 Represent the tag in the user profile vectors and document profile vectors. 3 Calculate the tag similarity matrix. 4 Get the top-N tags according to the highest values in the similarity matrix for each tag. 5 Partition tags into different clusters by spectral clustering algorithm. 6 Check all the tag neighbors generated in the previous steps whether they belong to the same cluster with the original tag, and filter out noisy tag neighbors. 7 Update the tag vectors of user profiles and document profiles with tag neighbors. 8 Calculate the similarity between the candidate user’s tag vector and the document tag vector, and rank the documents according to the similarity values in a descending order. 9 Select the top-N documents with the first N highest similarities as the recommendions to the candidate user.

5 Experimental Evaluations The experiments are performed on the ‘‘MovieLens’’ dataset. Our experiments focus on results conducted on the implemented tag-based recommender system with tag neighbor expansion. The goal of such experiments is to show that the document recommendations derived by using our proposed approach would result in an improvement of document recommendation performance.

5.1 Dataset and Experimental Setup As for experiment dataset, we utilize the part of the ‘‘MovieLens’’ data, which contains tags provided by users on movies. It includes 114 users, 1006 documents and 1589 tags. The setting of the dataset is that each user and document has at lest 5 annotated tags. Following the traditional protocal which chooses 20–30% data for the testing data, we divide 75% data as the training data and the rest 25% as the testing data.

5.2 Modularity Metric Modularity is originally proposed to assess the division quality of a network. The modularity of a particular division of a network is calculated based on the

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differences between the actual number of edges within a community in the division and the expected number of such edges if they were placed randomly. Consider a particular division of a network into k communities. We can define a k  k symmetric matrix SM whose element smij is the fraction of all edges in the network that link vertices in community p to vertices in community q. The similarity of smCpq between the two clusters Cp and Cq ;is defined as [6], P P cp 2Cp c 2C cpq Pp q smCpq ¼ P ; p; q ¼ 1; 2. . . m; cp 2C cq 2C cpq where cpq is the element in the similarity matrix SM. When p = q, the smCpq is the similarity between the elements inside the clusters, while p 6¼ q; the smCpq is the and the cluster Cq : So the similarity between the cluster Cp P P condition of a high quality cluster is argmaxð p smCpp Þ and argminð p;q smcpq Þ; p 6¼ q; p; q ¼ 1; 2; . . . m: This quantity measures the fraction of the edges in the network that connects vertices of the same type minus the expected value of the same quantity in a network with the same community divisions. Here we compare the result of Q values by using Spectral Clustering, Single Linkage Clustering and Random Clustering. The result below is based on the average result of executing the same experiment ten times over the same dataset. Of the entire 114 user profiles and 1006 document profiles constructed, we employ various clustering algorithms to build up the tag groups. The number of cluster varies from 2 to 800. The upper number is set to 800 because it is half of the total amount. When the number of cluster is higher than 800, the average number of members in each cluster is lower than 2, which will not provide reasonable clustering information. The results are shown in Fig. 1. From the figure, we observe that the value of Q for Spectral Clustering consistently outperforms bettwer than the other two algorithms, which also justifies the rationale of choosing Spectral Clustering. When the cluster number reaches 120 the Q gets the maximal as 0.344. With the growth of the number of clusters, the value of Q is gradually decreasing to 0.03. It is concluded that for this dataset 120 clusters is the best choice.

5.3 Precision Evaluation In the experiment we have 246 existing documents for testing. We compared the precision for the recommended documents to the individual user. The traditional way is to calculate the similarity between the user profile and the document profile in the tag vector, and the system will recommend the N documents to the user according to the top-N similarity values. Another approach is to calculate such similarity based on the naive tag neighbor expansion by collaborative filtering

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Fig. 1 Comparison of three clustering algorithms on 1589 tags to select optimal number of clusters

Fig. 2 Precision comparisons on three different strategies

approach. However, the disadvantage is that such tag neighbors include many noisy tags, degrading the precision of recommendation. Our proposed approach is to filter out the noisy tags by utilizing the clustering. We will calculate the precision in such step: The top-N documents will be recommended to the user by ranking the similarity values derived with various approaches, the recommended documents are compared with the existing documents in the test data. If there are k documents appeared out of the N recommended documents in the test data, the precision for the individual user is defined as t. t¼

Ki 100% N

We average the whole precision for all of the users, and compare the recommendations form top 1 to 40 documents to the users in the experiments. Other two comparable approaches used in the experiments are pure tag vector approach and naive tag neighbor expansion approach. We denote these three approaches as TagNeighborhood with Clustering, TagNeighborhood and Pure Tag Vector. The precision comparison of three approaches for top 40 recommendations is shown as Fig. 2. The ‘‘conditional recommended documents’’ in Fig. 2 means that the compared documents in testing data are with the rating value higher than or equal 4. Because

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the MovieLens dataset contains the users’ evaluations for the movies by rating value from 1 to 5, with 1 denoting the lest preferred and 5 the best preferred. In the experiments, the average precision of TagNeighborhood with Clustering is 84.1, the TagNeighborhood is 78.3, the pure tag vector is 37.4. In summary, the experimental results validate the advantage of the tag neighbor expansion with clustering in recommendations.

6 Conclusion In this paper, we have proposed a collaborative filtering approach for expanding the tag neighbors. Such expansion helps to enhance the tag expression quality in social tagging systems. We have also introduced to utilize the clustering algorithm to filter out the noisy tag neighbors. We have carried out evaluation experiments on the MovieLens dataset. The evaluation results have shown the improved recommendations by our proposed approach.

References 1. Bayyapu KR, Dolog P (2010) Tag and neighbour based recommender system for medical events. In: Proceedings of MEDEX 2010: the first international workshop on web science and information exchange in the medical web colocated with WWW 2010 conference, 2010 2. Budura A, Michel S, Cudré-Mauroux P, Aberer K (2009) Neighborhood-based tag prediction. The semantic web: research and applications, pp 608–622 3. Chen H, Dumais S (2000) Bringing order to the web: automatically categorizing search results. In: CHI ’00: proceedings of the SIGCHI conference on human factors in computing systems, New York, NY, USA, ACM, pp 145–152 4. Hayes C, Avesani P (2007) Using tags and clustering to identify topic-relevant blogs. In: International conference on weblogs and social media, March 2007 5. Hotho A, Jäschke R, Schmitz C, Stumme G (2006) Folkrank: a ranking algorithm for folksonomies. In: Althoff K-D, Schaaf M (eds), LWA, Hildesheimer Informatik-Berichte, vol 1. University of Hildesheim, Institute of Computer Science, pp 111–114 6. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):26113 7. Pan R, Xu G, Dolog P (2010) User and document group approach of clustering in tagging systems. In: Proceeding of the 18th international workshop on personalization and recommendation on the web and beyond. LWA 2010 8. Xu G, Gu Y, Dolog P, Zhang Y, Kitsuregawa M (2011) Semrec: a semantic enhancement framework for tag based recommendation. In: Proceedings of twenty- fifth AAAI conference on artificial intelligence (AAAI-11), 2011 9. Xu G, Zong Y, Pan R, Dolog P, Jin P (2011) On kernel information propagation for tag clustering in social annotation systems. In: Proceeding of 15th international con- ference on knowledge-based and intelligent information and engineering systems (KES2011), 2011

Event-Centric Test Case Scripting Method for SOA Execution Environment Youngkon Lee

Abstract Electronic collaboration over the Internet between business partners appear to be converging toward well-established types of message exchange patterns that involve both user-defined standards and infrastructure standards. At the same time, the notion of event is increasingly promoted for asynchronous communication and coordination in SOA systems. In collaboration between partners or between components is achieved by the means of choreographed exchanges of discrete units of data—messages or events—over an Internet-based protocol. This paper presents an event-centric test case scripting method and execution model for such systems. Keywords SOA


Event-driven
Process invocation

1 Introduction While current Web Service technologies show much progress, current services are mainly limited to atomic services. Thus, they are not adequate to handle the autonomous and complex services in realistic settings. In dealing with this problem, some research works have developed languages to compose the individual Web Services into transactions or workflows. Web Services Flow Language (WSFL) [1] was designed for service compositions in the form of a workflow, and XLANG [2] for the behavior of a single Web Service. However, these works are not sufficient for providing the adaptive web Services generated from a particular context. Y. Lee (&) e-Business Department, Korea Polytechnic University, Jeongwangdong, Siheung, 2121 Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_35, Ó Springer Science+Business Media B.V. 2012

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Electronic collaborations over the Internet between business partners (e-Business/e-Government) appear to be converging toward well-established types of message exchange patterns that involve both user-defined standards and infrastructure standards. At the same time, the notion of event is increasingly promoted for asynchronous communication and coordination in Event-Driven Architectures (EDA) that are considered as either complementary to or part of SOA systems. In both cases collaboration between partners or between components is achieved by the means of choreographed exchanges of discrete units of data—messages or events—over an Internet-based protocol. Such systems require an event-centric test case scripting markup and execution model. In e-Business transactions as in EDAs, partners or components must agree on the use of a combination of standards in order to interoperate with each other. Typically, these standards can be classified into three layers: – Messaging infrastructure standards, ranging from transport level to higher-level messaging protocols and quality of service (QoS) including reliability and security, such as those defined as SOAP extensions, or REST (Representational State Transfer). – Multi-message exchange standards as manifested in business processes and choreographies. – Business document standards may be business content structure and semantics, taxonomies in use, code lists, semantic rules, or the XML schema modeling style. They are industry-specific (e.g. RosettaNet PIP schemas, AIAG Inventory Visibility and Interoperability schemas), horizontal document standards, or regional guidelines. There have been conformance and interoperability test suites and testing tools for above layer individually. But the testing of integrations of standards has been ad-hoc, or limited mostly to standards in the messaging infrastructure. Although the need for testing some form of integration of standards has been well recognized for infrastructure standards, there has been little support for testing integrations that extend to the use of standards specific to a business—e.g. for documents or choreographies. Such integrations can be construed as userdefined profiles. For example, the level of QoS required for a business transaction may depend on the nature of business data being exchanged, or on some property defined by the related business process. Testing and monitoring these infrastructure layers and their integration also requires that test cases access a combination of contracts—agreements, policies or business transaction patterns—represented by meta-level documents. This compliance objective goes beyond quality assurance for the messaging function: it requires the monitoring of live transactions in production environments, as well as verifying conformance of business endpoints in operation conditions. This calls for a flexible test execution model that can accommodate performance constraints as well as different timeliness constraints—e.g. where tests are either deferred over log data, or executed on live exchanges in a monitoring mode.

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Consequently, the execution model of such test cases or monitoring cases, must accommodate dynamic conditions requiring real-time or near real-time error detection or measurement, allowing to correct and report on business exchanges as they proceed. The output of a monitoring script also must provide more information than a report of the type pass/fail. Different ways of ‘‘passing’’ or ‘‘failing’’ must be reported on, as well as identifying the types of business transactions. The output must be easy to format and feed to another decision engine, such as a rule engine that will process this input in real-time. For example, a rule may decide to generate an alert if a business transaction lasts too long, depending on the nature of the transaction and on the SLA associated to these business partners. This paper defines a testing and monitoring model, as well as a test script markup, so that test cases or monitoring cases can be fully automated, and portable across test environments. In Sect. 2, we summarize the related works regarding the web service flow language for testing. Section 3 presents the concept of EventCentric Test Case Script (EVEC), Sect. 4 describes the implementation of EVEC, and we conclude in Sect. 5.

2 Related Works In fact, the automatic or semi-automatic management of service flows over the Web has not been achieved yet. In the Web Services model that is quite different from traditional one, there are a large number of similar or equivalent services which user can freely select and use for their application. Since the service is developed and deployed by the third party, the quality of service is not guaranteed. The services may not be adequate as per service requestor’s requirements and kept evolving, without notification to service requestors, according to the provider’s requirements and computing environment. Thus, it is important to provide adaptability to evolving services as well as diverse context of services. Kammer et al. [3] suggested workflow to be dynamic, which allows changes with minimal impact to the ongoing execution of underlying workflow, as well as be reflexive, which provides knowledge about a workflow’s applicability to the context and the effectiveness of its deployment evaluated over time. Understanding constraints and context associated with services may affect the quality of service. From this perspective, optimization may occur through the evaluation and refinement of a previous service flow. Automatic composition of services is challenging, because it is difficult to capture semantics and context of services and measure the quality of services. One exemplary effort that aims for this function is DAML-based Web Service Ontology (DAML-S) [4], which describes the properties and capabilities of Web services. Workflow technology has been around since a decade ago and has been successful in automating many complex business processes. A significant amount of work has been done in this field, which deals with different aspects of workflow

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technology process modeling, dynamic workflows, and distributed workflows. Process modeling languages such as IDEF, PIF, PSL or CIMOSA [5] and frame based models of services were used to design process typing, resource dependencies, ports, task decomposition and exception. Current research on web services paves way for web service based workflows, which has obvious advantages pertaining to scalability, heterogeneity, reuse and maintenance of services. Major issues in such inter-organizational service based workflows are service discovery, service contracts and service composition. Web Services Flow Language (WSFL) was proposed to describe compositions of services in the form of a workflow, which describes the order of service invocation. Service composition aids such as BizTalk [6] were proposed to overcome the limitations of traditional workflow tools which manually specify the composition of programs to perform some tasks. Other industrial initiatives such as BPEL4WS [7], and XLANG concentrates on service representation issues to tackle the problems of service contracts, compositions and agreements. Current efforts are to automate the complete process of service discovery, composition and binding, using the machine understandable languages. Some other recent advances are WS-Transaction and WS-Coordination which define protocols for executing Transactions among web services. There is a research for modeling QoS of workflows [8], and defining a QoS based middleware for services associated with the underlying workflow [9], but it doesn’t take into account QoS factors related to Internet based services. Some researchers describe QoS issues related to web services from the provider’s perspective [10]. We believe that the current research has not delved into QoS issues related to Web Service based workflows, and many critical issues related to the availability, reliability, performance and security of Web Services need to be handled. Our approach tactfully utilizes and monitors these QoS parameters to provide a consistent service interface to other applications in the workflow through adaptive QoS based selection, binding and execution of Web Services.

3 Event-Centric Test Case Script Event-centric test case script (EVEC) is designed so that the same scripts can be used either in live monitoring mode, or in analysis of past events from a log (referred to as deferred mode in this paper called hereafter the ‘‘deferred mode’’) or yet in mixed situation. Testing and Monitoring of Business Processes as well as more generally of systems the behaviour of which can be traced by events, fall in the following three categories: – Compliance with specifications. Such specifications may be of a business transaction, business process definition, documents exchanged, or of infrastructure behaviour (e.g. messaging protocol). Enabling the automatic generation of EVEC scripts from such specifications when these specifications are formal—e.g. process definition, choreographies, document schema or rules—is

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part of the requirements although the methodology to achieve this is out of scope of this document. Some test assertion design and best practices, such as those in Test Assertions Guidelines [11] may be used for deriving scripts from such representations even when automatic generation is not possible. – Compliance with agreements. Such agreements may be business agreements such as SLAs, or regulatory compliance rules. They may also be infrastructure configuration agreements (e.g. ebXML CPA, WS-Policy). This category of application includes SLA monitoring, business metrics and aspects of business activity monitoring (BAM) that are closest to operations, e.g. for regulatory compliance. – Business Operation intelligence. Such monitoring is not directly related to compliance, but primarily intended for generating reports and various analytics of business activities. This includes analyzing the logs of processes and business transactions for reports and BI. This category of application includes BAM (business activity monitoring). In its dynamic aspect, this monitoring is addresses the need for visibility in business processes and service-oriented systems, which include problem detection/anticipation, diagnostics and alarm generation. Each one of the above categories may be considered both in a realtime context (e.g. generation of alarms and notifications during operation) and a deferred, off-line analysis context (periodic generation of reports or metrics with no direct, automatic feedback loop to operations). In both cases, the same input—in form of events—is assumed. From the viewpoint of script execution semantics, ‘‘live’’ and ‘‘deferred’’ are not distinguished: the same script is executable on input that is either live or logged. To ensure flexibility for handling various monitoring contexts and situations, mixing of both execution modes must be supported: – A script may start executing ‘‘deferred mode’’ with its events already partially logged, and then catch-up with the on-going logging of events and continue ‘‘live’’. – Conversely, a script may start live, and if its execution engine is interrupted for some reason, may resume its analysis of events that have already been logged while the test engine was stopped, in deferred mode. Then it may catch-up with events and eventually go live again. When events are consumed in a publishsubscribe mode, a simple queuing mechanism is sufficient to provide the above flexibility. However, EVEC must be able to correlate with past events.

4 Implementation of EVEC The EVEC script language is designed for testing and monitoring processes or business transactions of various kinds, and more particularly for analyzing and validating event patterns that are generated by these processes. To this extent,

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Fig. 1 Blocking and non-blocking modes of scriplet invocation

EVEC may be described as an event-processing language. The content and structure of these events may be quite diverse, but a common XML wrapper is assumed. The top-level constructs are the script package and the scriplet: – The script package, or ‘‘script’’: This is the main unit of execution. The script package contains an ‘‘execution context’’ (\ execution-context [ element) that defines various global constructs and bindings, e.g. for declaring event boards. The script package also contains one or more ‘‘scriplets’’. The execution context in a script package defines which scriplet to start execution with—or main scriplet. In case other scriplets are defined, the main scriplet is expected to invoke these directly or indirectly. – The scriplet: A scriplet defines a flow (or thread) of execution for a sequence of atomic operations. Scriplets can execute either concurrently or not (see detailed meaning in the next section), and can be started in a blocking or non-blocking way. EVEC is designed so that it leverages existing XML script languages for special features such as logical conditions and event selection. The default language for all logical expressions over XML content is XPath, along with its existing function libraries (e.g. advanced set of functions for time and duration management). The concept of concurrency in EVEC is entirely dependent on the notion of ‘‘virtual present time’’ (VPtime). When a scriplet starts to execute, it is assigned a VP-time which will condition its event consumption and timestamp its event production. The default VP-time assignments are:

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Fig. 2 Fully past backward blocking invocation

– The first scriplet of a script package is assigned the initial VP-time of this script, the default of which is in turn the actual present time (AP-time). – The VP-time of a scriplet S2 started by a scriplet S1, is the value of VP-time in S1 when [start S2] is executed. These default values can be overriden by the \ start [ operation, which allows to set the VP-time of the started scriplet (see the start/@vptset attribute in Sect. 4). Inside a scriplet, the VP-time may be progressed by two operations: – \wait [ : will add some predefined duration to the VP-time, or wait until some date, or yet until some other scriplets complete. – \catch [ : when waiting—virtually or really—for some event to occur, will advance the VP-time to the occurring date of events being caught. Event catching in a scriplet is only considering (by default) events occurring at or after the current VP-time. Besides \ wait [ and \ catch [, the execution duration of other EVEC operations is considered as negligible, as far as the VP-time is concerned: in other words, these operations do not affect the VP-time. The VP-time window of a scriplet execution is defined as the [starting VP-time, ending VP-time] time interval of the execution. Intuitively, concurrent execution is achieved when the VP-time windows of two scriplets overlap. Concurrency is entirely determined by the start VP-time assigned to these scriplets. When a scriplet S1 starts a scriplet S2, it can do so either in a blocking or non-blocking way:

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– Blocking invocation: intuitively, the invoking scriplet (S1) will wait until the invoked scriplet (S2) terminates. The next statement in the invoking scriplet S1 (after \ start [), will execute at a VP-time that is same as the VP-time set at the end of the invoked scriplet (S2). In other words, the VPtimes of S1 and S2 are ‘‘synchronized’’ after S2 completes (see start/@vptsync = ‘‘true’’ in Sect. 4). More generally, to accommodate the case where a starting time is set at a date/ time anterior to the invoking time (@vptset = ‘‘a past date/time’’), the VP-time of the next statement in S1 is either the last VP-time value of S2 or the last VPtime value in S1 (just before invoking S2), whichever occurs the latest. – Non-blocking invocation: intuitively, the invoked scriplet (S2) will not cause the invoking scriplet (S1) to wait. In other words, the VP-times of S1 and S2 are not ‘‘synchronized’’ after S2 completes. (see start/@vptsync = ‘‘false’’ in Sect. 4). The next statement in the invoking scriplet S1 will execute at a VP-time that is same as the VP-time value just before executing the \ start [ statement, this regardless of the value of start/@vptset. Non-blocking invocations should not be seen as only useful for actual concurrent (or multi-threaded) processing. In many cases, it makes scripting easier and more intuitive, even when execution is entirely deferred on past (logged) events that could otherwise be processed serially in a single-threaded way. Various cases of blocking and non-blocking invocations are illustrated below. The following figure illustrates both modes of scriplet invocations, and how the VP-time is affected—or not—in the invoking scriplet. When a scriplet S1 does a blocking invocation of a scriplet S2, the VP-time of the invoked scriplet S2 is initiated at the current VP-time of the invoking scriplet S1 (unless a different VPtime value is given using start/@vptset as illustrated in the next figures). The scriplet S1 is then ‘‘blocked’’ until the VP-time at the end of S2 is known and assigned as current VP-time in S1. In the non-blocking invocation (see Fig. 1), S1 will ignore the ending time of S2. A single-threaded execution may still execute S2 first before executing the next statement in S1. The execution semantics would still allow ‘‘concurrent’’ catching (in terms of virtual present) of same events by S1 and S2, as both will select events starting from VP-time t1. In the above figure, S1 is catching an event at time t1b while S2 is catching an event at time t2. Depending on their respective selection expressions, these catches could capture either the same or different event, causing the new VPtime in S1 (t1b) to be either prior or after the new VPtime in S2 (t2). In a single-threaded execution of the non-blocking case that started ‘‘live’’ (VPtime = present time), S2 could be executed first live, then the remaining part of S1 can be executed ‘‘deferred’’ on the log of events, starting from time t1 now in the past. Clearly, more fine-grain serialization of S1 and S2 executions would be required if these two scriplets communicate with each other, e.g. if S1 is consuming an event posted by S2 or vice versa (Fig. 2).

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5 Conclusion In this paper, we present an event-centric test case scripting method and execution model, EVEC, for such systems. EVEC enables testing and monitoring of applications and business processes, the behavior of which can be traced and monitored via events. The notion of event in EVEC is broad, thus, it could cover all of the type of SOA business processes. Further study is required to define and classify the detailed test case metadata or artifacts that would complement EVEC in test environments.

References 1. Leymann F (2001) Web services flow language, TR WSFL 1.0, IBM Software Group, May 2001 2. Thatte S (2001) XLANG Web services for business process design. http://www.gotdotnet.com/ team/xmlwsspecs/xlang-c/default.htm 3. Kammer P, Bolcer GA, Taylor RN, Bergman M (2000) Techniques for supporting dynamic and adaptive workflow. J Comput Support Coop Work 9:269–292 4. DAML-S specifications. http://www.daml.org/services/ 5. Kosanke K (1993) CIMOSA—open system architecture for CIM; ESPRIT consortium AMICE. Springer, Berlin 6. Biztalk. http://www.microsoft.com/biztalk/ 7. Business process execution language for Web services, version 1.0.http://www-106.ibm.com/ developerworks/webservices/library/ws-bpel/. Accessed July 2002 8. Cardoso J, Sheth A, Miller J (2002) Workflow quality of service 9. Sheth A, Cardoso J, Miller J, Koch K (2002) QoS for service-oriented middleware (2002) ‘‘web services and grid computing.’’ In: Proceedings of the Conference on Systemics, Cybernetics and Informatics, Orlando, FL, July 2002 10. Mani A, Nagarajan A, Understanding quality of service for Web services. http://herzberg.ca. sandia.gov/jess/ 11. OASIS Test Assertions Guidelines (TAG) TC, ‘‘test assertions guidelines.’’ http://www.oasisopen.org/committees/tag/

bQoS (Business QoS) Parameters for SOA Quality Rating Youngkon Lee

Abstract With Web services starting to be deployed within organizations and being offered as paid services across organizational boundaries, quality of service (QoS) has become one of the key issues to be addressed by providers and clients. While methods to describe and advertise QoS properties have been developed, the main outstanding issue remains how to implement a service that lives up to promised QoS properties. This paper provides the service level agreement (SLA) parameters for QoS management applied to Web services and raises a set of research issues that originate in the virtualization aspect of services and are specific to QoS management in a services environment—beyond what is addressed so far by work in the areas of distributed systems and performance management. Keywords SOA


Business context
SLA parameter

1 Introduction Whether offered within an organization or as a part of a paid service across organizational boundaries, quality-of-service (QoS) aspects of services are important in a service-oriented computing environment. Dealing with QoS is a sign of a technology going beyond its stage of initial experimentation to a production deployment and many recent activities related to QoS of Web services indicate that this is becoming an increasingly relevant topic.

Y. Lee (&) e-Business Department, Korea Polytechnic University, Jeongwangdong, Siheung 2121, Korea e-mail: [email protected]

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Efforts in the past years mainly focused on describing, advertising and signing up to Web and Grid services at defined QoS levels. This includes HP’s Web Services Management Framework (WSMF) [1], IBM’s Web Service Level Agreement (WSLA) language [2, 3], the Web Services Offer Language (WSOL) [4] as well as approaches based on WS-Policy [5]. These efforts enable us to describe quality metrics of services, such as response time, and the associated service level objectives flexibly and in a way that is meaningful for the business needs of a service client. However, one of the challenging issues is to associate or derive a system configuration that delivers the QoS of a described Web service using the above mentioned approaches. In many cases, this is non-trivial. Sometimes we can rely on experience with tested, dedicated system configurations to decide, for example, the size of a cluster for a particular workload guaranteeing a particular response time for a given percentile of requests. In addition, managing a service at different QoS levels on the same infrastructure is not easy. While managing QoS in distributed systems is not a novel problem, a number of additional issues arise in the context of a service-oriented computing environment. Those issues arise from the specific properties of Web services. For example, cross-organizational Web services may be accessed through the public Internet and client side QoS metrics have to include network properties in addition to properties of the service-implementing application itself. In addition, composite and recursively aggregated services—and the ability to aggregate is seen as a key benefit of Web services—gain new QoS properties that are not always easily derived from their parts. The objective of paper is to analyze the main QoS factors of Service-oriented Architecture (SOA) in a business context and to provide the service level agreement (SLA) parameters that affect critically the business performance. According to OASIS Reference Model for Service Oriented Architecture [SOA-RM] [6], the Service Oriented Architecture (SOA) is a paradigm for organizing and utilizing distributed capabilities that may be under control of different ownership domains. The service within SOA is a mechanism to enable access to one or more capabilities, where the access is provided using a prescribed interface and is exercised consistent with constraints and policies as specified by the service description. This specification further defines the business service level agreement (bSLA) between the service requester and the service provider for the service which is defined in SOA-RM, within the end-to-end resource planning (EERP) technology [7]. The applications of EERP are any kind of business services, and they are not limited to Web Services only. This applies the well-known technique for service discovery and optimization in a novel way to improve business results. It models the business process and the range of potential services, and then guides the selection and deployment of services based on the end-to-end business value. Modeling the business service-level agreements to manage and evaluate services and establishing agreements about the business service is essential to long-term value chain improvement. The bSLA is different from the SLA in the software/IT world. The bSLA is the contact between the service

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Fig. 1 Points of measurements defining semantics of metrics

requester and the service provider, and the SLA is the contract between the service provider and the network/system provider. The SLA is network/system oriented agreement that deals with network performance and system availability. The bSLA is a business oriented agreement that deals with price, time to deliver, and the quality/rating of the service. In Sect. 2, we summarize the related works about web service selection based on the QoS metrics. Section 3 presents the service process model which enables service performance optimization in the business respect and Sect. 4 details the bSLA model including parties, parameters and obligations, and we conclude in Sect. 5.

2 Related Works The first step to manage Web service’s quality is to define it. While this is important for Web services as well as in traditional distributed systems, explicit definition is particularly important in an environment transcending organizational boundaries. Quality is expressed referring to observable parameters relating to a non-functional property, for example, the response time of a request. A level of quality is agreed upon as a constraint over those parameters, potentially dependent on a precondition. Hence, the party offering a Web service, in agreement with its customers and users, will define the QoS parameters and the particular instances of the service to which these parameters relate. In the case of a Web service, a parameter such as response time can relate to an individual invocation of an operation or a class of operations, all having the same (individual) quality properties of having an aggregate property, e.g., the average response time of this class of operations or another stochastic metric. A further step in managing Web services QoS is the definition of the semantics of the QoS parameters. A Web service and its subscribers and users must understand what it is meant. It is important what is measured where. For performance-oriented metrics this can be at different points, as Fig. 1 illustrates. The definition of QoS parameters corresponds to the establishment of ontology between a service provider and its clients. Ontology can be established in two approaches. (1) It can be a definition of terms and, potentially, or the semantics of the relationships between them, as facilitated by DAML and OIL [8]. This approach results in a fixed set of well understood terms—in our case the QoS parameters. (2) Another approach uses constructive ontology. Based on a set of well-know defined terms (as in 1) and a set of well-know composition operators,

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Fig. 2 Contracts defining the scope of quality guarantees

new terms’ (QoS) parameters can be defined by composing new parameters out of existing ones using the operators. Having established common understanding of quality of service parameters and the associated guarantees given by the provider, it also has to be established to which relationships between a client and a server a QoS guarantee applies. A service may provide the same quality to all requesting clients, to each client individually or to a defined set of clients that a provider organization and an organization requiring a QoS level for multiple clients agree upon in a contract, which is also called an SLA (Fig. 2). Clients will refer to the contract when requesting the service according to a particular quality level. The different scoping approaches of QoS guarantees require different means of establishing a particular quality level for a client: If a QoS level is associated with a service, a client searches for a suitable service in a directory, e.g., UDDI and retrieves its quality definition, e.g., stated as a WS-Policy expression. In the case of an individual client or a contract, a negotiation mechanism, which can be very simple, must be provided. Once the contract is established, the provider organization must provision a service-implementing system such that it behaves as it has been agreed upon. This involves deriving the amount of resources needed and the runtime management of resources. However, this is not simple. While we have developed—improvable— approaches to the issues raised above, the issue of provisioning and runtime managing a Web service-implementing system is not equally well understood yet. In the next section, we discuss what distributed systems and performance management approaches can provide. A number of performance management technologies, such as workload managers and network dispatchers, have been developed to control response times of individual systems and clusters and various availability management approaches. However, it is not straight-forward to configure, for example, workload managers to satisfy response time goals for a set of different scopes of quality—for Web services as well as for any distributed system. In this section, we outline some typical approaches of how today’s QoS parameters are managed in distributed systems.

3 Service Process Model This section describes the service process model conceptually. Figure 3 shows the conceptual model, and of messages flows with brief descriptions. We also include timeline and sequence diagrams Fig. 4 to show how an implementation would use

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Fig. 3 Service process model

Fig. 4 Service message sequence without optional messages

service messages and build a continuous business process improvement loop. In Fig. 3, the business quality of Service is abbreviated as bQoS, business rating is abbreviated as Rating, and business service level agreement is abbreviated as bSLA.

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Fig. 5 XML schema for bSLA

The service requester is the client system who tries to find an optimal solution provided by service optimization portal (SOP). Service providers provide business services. Each service provider may offer the same service but with different bQoS and Ratings. Services may be running on different platforms with different implementations, but they all support message exchanges of bQoS, Rating, and BSLA information in the XML formats. The SOP accepts the request from the Service requester, performs bQoS and rating queries, calculates optimal solution(s), and then returns the result to the service requester. The Rating Provider is a party unaffiliated with either the requester or the target of the rating request, such as a third party rating organization, given a reference to a particular business service and provider, issues either a number or a classification description. There can be another way to implement the service optimization without the SOP. For example, there can be a case for some services providers and service consumers using SOA Registry-Repository to find each other, to exchange business quality of services information among them, and to begin negotiations for establishing Service Level Agreements (SLAs). The results of messages 2 through 9 in Fig. 4 are used to calculate the optimal deployment for a given set of services requests. A list of alternatives might be returned in message 10. Each step in the process would have a service provider determined for each service and for each alternative. Messages 11 and 12 are exchanged between the service requester and the selected service providers to define the BSLA.

4 bSLA Model The bSLA model is for representing SLA parameters in the respect of business value. The BSLA is the root element for EERP–Business Service-level agreement (bSLA). The bSLA is a formal contract between a service provider and a client guaranteeing quantifiable business quality of service (bQoS) at defined levels. It can have one or more of the following elements (Fig. 5): The following describes the attributes and elements listed in the schema outlined above: – /sla:BSLA is a root element of Business Service-level agreement (bSLA) for EERP. – SLAParties is a required element in bSLA that defines parties invoked in this bSLA for the service. SLAParties element has both the service provider and services requester elements.

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sla:ServiceProviderType sla:SlaUriType sla:ServiceProviderNameType sla:ServiceRequesterType sla:SlaUriType sla:ServiceRequesterNameType ...

Fig. 6 XML schema for bSLA parties

– /sla:BSLA/sla:SLAParties/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to the SLAParties element in the future. Unrecognized attributes may cause a fault or be silently ignored. – /sla:BSLA/sla:SLAParameters is defined monitoring of bQoS metrics, including service profile uri, operations and other optional elements. It is a required element that uses sla:SLAParametersType. – /sla:BSLA/sla:SLAParameters/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to the SLAParameters element in the future. – /sla:BSLA/sla:SLAObligations is agreed bSLA obligations aspect of the service, including obligations, action guarantees. It is a optional element that uses sla:SLAObligationsType. – /sla:BSLA/sla:SLAObligations/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to the SLA Obligations element in the future. – /sla:BSLA/sla:SLATerms is agreed bSLA terms aspect of the service, including bSLA term elements. – /sla:BSLA/sla:SLATerms/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to the SLATerms element in the future. – /sla:BSLA/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to the root BSLA element in the future. – /sla:BSLA/sla:BSLAExtension is an optional element that keeps different (extensible) elements to be specified in the future. – /sla:BSLA/sla:BSLAExtension/{any} is an extensibility mechanism to allow different (extensible) elements to be specified in the future. The SLAParties describes the list of parties invoked in the bSLA for the service. There should be one SLAParties element present in the bSLA of service. The following describes the attributes and elements listed in the schema outlined above:

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– /sla:SLAParties, bSLA Parties aspect of the service, is for parties invoked in the bSLA for the service, including both service provider and service requester elements. – /sla:SLAParties/sla:ServiceProvider represents the provider for parties. It is a required element for bSLA Parties. – /sla:SLAParties/sla:ServiceProvider/sla:ServiceUri is a required element for Service Provider. – /sla:SLAParties/sla:ServiceProvider/sla:ServiceProviderName is the name of the service provider. It is also a required element for Service Provider. – /sla:SLAParties/sla:ServiceProvider/sla:ServiceProviderName/@languageID is an optional attribute in the ServiceProviderName element, using xsd:language type (Fig. 6). – /sla:SLAParties/sla:ServiceProvider/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to ServiceProvider element in the future./sla:SLAParties/sla:ServiceRequester represents requester for the service, including requester’s name and the URI that represents the requester. It is a required element for bSLA Parties. – /sla:SLAParties/sla:ServiceRequester/sla:ServiceRequesterUri represents the requester’s identifier in URI format for the service requester. It is a required element for Service Requester. – /sla:SLAParties/sla:ServiceRequester/sla:ServiceRequesterName is requester’s name for the service requester. It is a required element for Service Requester. – /sla:SLAParties/sla:ServiceRequester/sla:ServiceRequesterName/@languageID is an optional attribute in the ServiceRequesterName element. – /sla:SLAParties/sla:ServiceRequester/@{any} is an extensibility mechanism to allow additional attributes, based on schemas, to be added to the serviceRequester element in the future. – /sla:SLAParties/{any} is an extensibility mechanism to allow different (extensible) elements to be specified in the future.

5 Conclusion In this paper, we proposed a new concept of bSLA, whose items are proper to evaluate SOA service performance in the respect of service business benefit. The bSLA includes the service actor information, bSLA parties and the quality information, bSLA parameters, and bSLA obligations of the service parties. We also devised a service optimization portal which provides the best service composition by evaluating the value of bQoS of each service chain. Further study is required to define and classify the quality factor group for business case by case.

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References 1. Catania N, Kumar P, Murray B, Pourhedari H, Vambenepe W, Wurster K (2003) Web services management framework, version 2.0, Hewlett-Packard. http://devresource.hp.com/drc/ specifications/wsmf/WSMF-WSM.jsp. Accessed 16 July 2003 2. Ludwig H, Keller A, Dan A, King R, Franck R (2003) A service level agreement language for dynamic electronic services, vol 3. Electronic Commerce Research Kluwer Academic Publisher, Norwell, pp 43–59 3. Ludwig H, Keller A, Dan A, King R, Franck R (2003) Web service level agreement (WSLA) language specification, version 1.0, IBM corporation. http://www.research.ibm.com/wsla/ WSLASpecV1-20030128.pdf. Accessed 28 Jan 2003 4. Tosic V, Pagurek B, Patel K (2003) WSOL—a language for the formal specification of classes of service for web services. In: Proceedings of ICWS’03 (The 2003 international conference on web services), CSREA Press, Las Vegas, USA, pp 375–381, 23–26 June 2003 5. Box D, Curbera F, Hondo M, Kale C, Langworthy D,. Nadalin A, Nagaratnam N, Nottingham M, von Riegen C, Shewchuk J (2003) Web services policy framework (WSPolicy). http:// www.ibm.com/developer-works/library/ws-policy. Accessed 28 May 2003 6. Mackenzie CM et al (2006) Reference model for service oriented architecture 1.0. OASIS committee draft, 2006.8 7. William C et al (2010) SOA-EERP business quality of service version, OASIS committee draft, 2010.11 8. Connolly D, van Harmelen F, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein LA (2001) DAML ? OIL (March 2001) reference description, W3C. http://www.w3.org/TR/ daml+oil-reference. Accessed 18 Dec 2001

Business-Centric Test Assertion Model for SOA Youngkon Lee

Abstract This paper presents a design method for business-centric SOA test framework. The reference architecture of SOA system is usually layered: business process layer, service layer, and computing resource layer. In the architecture, there are so many subsystems affecting the system’s performance, which relates with each other. As a result, in respect of overall performance, it is meaningless to measure each subsystem’s performance separately. In SOA system, the performance of the business process layer with which users keep in contact usually depends on the summation of the performance of the other lower layers. Therefore, for testing SOA system, test cases describing business process activities should be prepared. We devised a business-centric SOA test assertion model which enables to semi-automatic transform test assertions into test cases by the concept of prescription level and normalized prerequisite definition. The model also minimizes the semantic distortion in the transformation process. Keywords SOA


Business process
Test assertions
Test cases

1 Introduction Service Oriented Architecture (SOA) is generally defined as a business-centric IT architectural approach that supports integrating businesses as linked, repeatable business tasks, or services. SOA enables to solve integration complexity problem and facilitates broad-scale interoperability and unlimited collaboration across the

Y. Lee (&) e-Business Department, Korea Polytechnic University, 2121, Jeongwangdong, Siheung city, Korea e-mail: [email protected]

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enterprise. It also provides flexibility and agility to address changing business requirements in lower cost and lesser time to market via reuse. SOA has a lot of promises of interoperability, however, at the cost of: lack of enterprise scale QoS, complex standards which are still forming, lack of tools and framework to support standards, and perform penalty. Recently, as SOA has been widely adopted in business system framework, performance issues in SOA are raised continuously from users and developers. SOA system is generally composed of various subsystems, each of which relates intimately with others. Therefore, if performances are issued, it is very difficult to find out the reason clearly. For example, if a business process in SOA system has longer response time than before, there could be various reasons: cache overflow in a business processor, wrapping overhead in service interface, or exceptions in computing resources, etc. One thing clear is that the performance of business process layer depends on the lower layer and measuring the performance of business layer includes indirectly measuring the performance of all the lower layers. But, most of the test frameworks developed focus on measuring SOA messaging performance, as we present in Chap. 2. They almost adopt batch-style testing where all the test cases are executed in a sequence. OMG recommended a standard SOA reference model, MDA (Model Driven Architecture) [1]. It is widely adopted in real world because it is normative and enables SOA system to be implemented in a business-centric approach. In the MDA, a business process is designed firstly in a way for satisfying business requirements and later services are bounded to the activities in the business process. Business processes are described in a standardized language (e.g. WSBPEL) and they are executed generally on a business process management (BPM) system. For testing SOA systems implemented according to the MDA reference model in business-centric way, test harness should have business process simulation functionality so that it can behave as BPM and test overall performance at the same time. This means that the test harness can execute business process, perform tests, and gather metric values. The performance of the business process layer with which users keep in contact usually depends on the summation of the performance of the other lower layers. Therefore, for testing SOA system, test cases describing business process activities should be prepared. In SOA system, test assertions may help develop tighter test cases which could be used as an input for SOA test harness. Any ambiguities, contradictions and statements which require excessive resources for testing can be noted as they become apparent during test assertion creation. Test assertions should be reviewed and approved to improve both the quality and time-to-deployment of the test cases. Therefore, best results are achieved when assertions are developed in parallel with the test cases. Test assertions provide a starting point for writing conformance test cases or interoperability test cases. They simplify the distribution of the test development effort between different organizations while maintaining consistent test quality. Test assertions improve confidence in the resulting test and provide a basis for coverage analysis.

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In Sect. 2, we present some related works. Section 3 provides the concept of test assertion. In Sect. 4, we describe a test assertion model. Section 5 presents cases of the test assertion and Sect. 6 shows complex predicates of test assertions. Conclusions are presented in the last section.

2 Related Works This section presents some test frameworks and script languages developed or proposed for SOA system. Web Services Quality Management System This system has been developed by NIA in order to measure Web services’ quality on the criteria of WSQM (Web Services Quality Model) quality factors [2]: interoperability, security, manageability, performance, business processing capability, and business process quality. This system contributes to consolidate the quality factors of SOA. However, it requires expanding its architecture to apply SOA, system, because it targets only to Web services system. ebXML Test Framework This framework has been implemented by NIST and KorBIT for testing ebXML system according to OASIS IIC Specification [3]. It could test packaging, security, reliability, and transport protocol of ebXML messaging system implemented by ebMS specification [4]. The main purpose of this framework is to test conformance and interoperability of ebXML messaging system, and it is not proper for testing service oriented systems. Besides, it cannot test ad hoc status resulting from various events, because it is not event-driven but batch-style test framework. JXUnit and JXU JXUnit [5] and JXU [6] is a general scripting system (XML based) for defining test suites and test cases aimed at general e-business application testing. Test steps are written as Java classes. There is neither built-in support for business process test nor support for the event-driven features. However, as a general test scripting platform that relies on a common programming language, this system could be used as an implementation platform for general e-business tests. Automatic Test Mark-up Language (ATML) In its requirements, ATML provides XML Schemata and support information that allows the exchange of diagnostic information between conforming software components applications [7]. The overall goal is to support loosely coupled open architectures that permit the use of advanced diagnostic reasoning and analytical applications. The objective of ATML is focusing on the representation and transfer of test artifacts: diagnostics, test configuration, test description, instruments, etc.

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Test Choreography Languages These are standards for specifying the orchestration of business processes and/ or transactional collaborations between partners. Although a markup like XPDL [8] is very complete from a process definition and control viewpoint, it is lacking the event-centric design and event correlation/querying capability required by testing and monitoring exchanges. Also, a design choice has been here to use a very restricted set of control primitives, easy to implement and validate, sufficient for test cases of modest size. Other languages or mark-ups define somehow choreographies of messages and their properties: ebBP, WS-BPEL, WS-Choreography [9]. The general focus of these dialects is either the operational aspect of driving business process or business transactions, and/or the contractual aspect, but not monitoring and validation. Although they may express detailed conformance requirements, they fall short of covering the various aspects of an exhaustive conformance check, e.g. the generation of intentional errors or simulation of uncommon behaviors. In addition, the focus of these languages is mainly on one layer of the choreography—they for instance, ignore lower-level message exchanges entailed by quality of service concerns such as reliability, or binding patterns with the transport layer.

3 Concept of Test Assertion A test assertion is a testable or measurable expression for evaluating the adherence of an implementation (or part of it) to a normative statement in a specification. A set of test assertions may be associated with a conformance clause in order to define more precisely what conformance entails. Test assertions lie between the specification and any suite of tests to be conducted to determine conformance (See Fig. 1). Such a test suite is typically comprised of a set of test cases. These test cases may be derived from test assertions which address the normative statements of the specification. Judging whether the test assertion is testable may require some knowledge about testing capabilities and resource constraints. Sometimes there is little knowledge of what actual testing conditions will be. In such cases the prime objective of writing test assertions is to provide a better understanding of what is expected from implementations, in order to fulfill the requirements. In other cases, the test assertions are designed to reflect a more precise knowledge of testing conditions. Such test assertions can more easily be used as a blueprint for test suites.

4 Test Assertion Model This section aims to cover the simpler aspects of test assertions. Some more complex aspects are covered later in this section. Figure 2 below shows the anatomy of a typical test assertion, and how its parts relate to the specification

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Fig. 1 Role of test assertion

being addressed, as well as to the implementations under test. Some optional parts are not shown in the figure. Some of the elements which comprise a test assertion are considered core while others are optional. A test assertion includes, implicitly or explicitly: Identifier A unique identifier of the test assertion facilitates tools development and the mapping of assertions to specification statements. It is recommended that the identifier be made universally unique. Normative Sources These refer to the precise specification requirements or normative statements that the test assertion addresses. Target The target categorizes an implementation or a part of an implementation of the referred specification, which is the main object of the test assertion and of its Normative Sources. Predicate A predicate asserts, in the form of an expression, the feature (a behavior or a property) described in the specification statement(s) referred by the Normative Sources. If the predicate is an expression which evaluates to ‘‘true’’ over the test assertion target, this means that the target exhibits this feature. ‘‘False’’ means the target does not exhibit this feature. In addition, a test assertion may optionally include following components. Description This is an informal definition of the role of the test assertion with some optional details on some of its parts. This description must not alter the general meaning of the test assertion and its parts. This description may be used to annotate the test assertion with any information useful to its understanding. It does not need to be an exhaustive description of the test assertion.

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Fig. 2 General anatomy of a test assertion

Prescription Level This is a keyword that indicates how imperative it is that the Normative Statement referred to in the Normative Source, be met. See possible keyword values in the Glossary. Prerequisite A test assertion Prerequisite is a logical expression (similar to a Predicate) which further qualifies the Target for undergoing the core test (expressed by the Predicate) that addresses the Normative Statement. It may include references to the outcome of other test assertions. If the Prerequisite evaluates to ‘‘false’’ then the Target instance is not qualified for evaluation by the Predicate. Tags Test assertions may be assigned ‘tags’ or ‘keywords’, which may in turn be given values. These tags provide you with an opportunity to categorize the test assertions. They enable you to group the test assertions; based on the type of test they assume or based on their target properties. Variables Test assertions may also include variables for convenience in storing values for reuse and shared use, as well as for parameterization. As a test assertion has parts that can be evaluated over a Target instance (i.e. the Prerequisite and the Predicate), the following semantics apply to a test assertion: – ‘‘Target not qualified’’: if the Prerequisite (if any) evaluates to ‘‘false’’ over a Target instance. – ‘‘Normative statement fulfilled [by the Target]’’: if the Prerequisite (if any) evaluates to ‘‘true’’ over a Target instance, and the Predicate evaluates to ‘‘true’’.

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– ‘‘Normative statement not fulfilled [by the Target]’’: if the Prerequisite (if any) evaluates to ‘‘true’’ over a Target instance, and the Predicate evaluates to ‘‘false’’.

5 Case Study of Test Assertion Consider the following statement in the widget specification: [requirement 101] ‘‘A widget of medium size MUST use exactly one AA battery encased in a battery holder.’’ There are actually two requirements here that can be tested separately: (requirement 101, part 1) A medium-size widget MUST use exactly one AA battery. (requirement 101, part 2) A medium-size widget MUST have a battery holder encasing the battery. Because of this it is possible to write two test assertions: – TA id: widget-TA101-1a Normative Source: specification requirement 101, part 1 Target: medium-size widget Predicate: [the widget] uses exactly one AA battery. Prescription Level: mandatory and – TA id: widget-TA101-1b Normative Source: specification requirement 101, part 2 Target: medium-size widget Predicate: [the widget] has a battery holder encasing the battery. Prescription Level: mandatory The granularity of a test assertion is a matter of judgment. A single test assertion instead of two can be written here, with the predicate: ‘‘[the widget] uses exactly one AA battery AND has a battery holder, encasing the battery’’. This choice may later have an impact on the outcome of a test suite written to verify the conformance of widgets. With a single test assertion, a test case derived from this test assertion will not be expected to distinguish between the two failure cases. Using two test assertions—one for each sub-requirement—will ensure that a test suite can assess and report independently about the fulfillment of each subrequirement. Other considerations such as the different nature of tests implied or the reuse of a test assertion in different conformance profiles [VAR], may also lead to the adoption of ‘‘fine-grained’’ instead of ‘‘coarse-grained’’ test assertions. Usage considerations will dictate the best choice.

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6 Complex Predicates Recall the previous example of [requirement 101]. The target can be defined as ‘‘a medium-size widget’’ or as just ‘‘a widget’’. The latter is a natural decision if the specification requirement uses the wording: ‘‘[requirement 101] If a widget is medium size, then it MUST use exactly one AA battery and be encased in a battery holder.’’ For the simplicity of this example, if the two test assertion predicates for widget-TA101-1a and widget-TA101-1b are combined into one example, one possible outcome is: TA id: widget-TA101-2a Normative Source: requirement 101 Target: widget Predicate: if [the widget] is medium-size, then [the widget] uses exactly one AA battery AND the battery is encased in a battery holder. Prescription Level: mandatory The target category is broad, but the predicate part is really of interest only for a subset of this category (the medium-size widgets). Usage considerations should again drive the decision here: a test suite that is designed to verify all widgets, and does not assume a prior categorization of these into small/medium/large sizes, would be improved with test assertions that only use ‘‘widget’’ as the target, such as widget-TA101-2a. A test assertion predicate may, then, be a Boolean expression—a composition of atomic predicates using logical operators AND, OR, NOT. A test assertion predicate may also be of the kind: ‘‘if (condition) then (expression)’’. The predicate is worded in an abstract way, still close to the wording of the specification. No indication of what kind of test procedure will be used, such as how to determine the number and type of batteries, is given. Detailed criteria for the condition evaluation, such as what kind of battery holder is acceptable, are also not provided. These details are normally left to the test cases that can be derived from the test assertions. These test cases will determine the precise criteria for conforming to the specification. However, if a precise criterion for interpreting the battery holder requirement is provided in an external specification, either referred to directly by the widget specification or by a related conformance clause, then a test assertion must use this criterion in its predicate. Such a test assertion must then refer not only to the specification requirement in its reference property, but also to the external specification or to the conformance clause that refers to this specification. Another case where a predicate is more complex is when its conditional expression involves more than one part of an implementation (or implementations). In some cases it is clear which one of these objects must be considered the target, while others are just accessory objects. Consider the following predicate: ‘‘the [widget price tag] matches the price assigned to the widget in its [catalog entry]’’, where price tags and catalog entries are both items that must follow the store policy (in effect the specification). In this case it may be reasonably assumed that the ‘‘catalog’’ content is authoritative over the price tag. The price tag can then

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be considered as the test target, while the accessory object may be identified by a variable which is then used in the predicate.

7 Conclusion We presented a SOA test assertion model, which facilitates to make test cases in normalized form. In the model, we devised the concept of prescription level and normalized prerequisite for preparing test cases. By the concepts, test assertion can be transformed into test cases without semantic distortion. The model minimizes the human intervention in preparing test cases by automating some processes for translating test assertions into test cases. We showed two cases of complex predicates. Further studies are required to develop a test framework to check out automatically that test cases are conformed to test assertions.

References 1. Miller J, Jishnu M (2003) MDA Guide Version 1.0.1.http://www.omg.org/docs/omg/03-0601.pdf. OMG, June 2. Lee Y et al (2008) Web Services Quality Model 1.1.OASIS WSQM TC, October 3. Durand J et al (2004) ebXML test framework v1.0. OASIS IIC TC, October 4. Peter W et al (2007) ebXML messaging services 3.0, OASIS ebMS TC, July 5. Java XML Unit (JXUnit). http://jxunit.sourceforge.net 6. JUnit, Java for Unit Test. http://junit.sourceforge.net 7. ATML (2006) Standard for automatic test markup language (ATML) for exchanging automatic test equipment and test information via XML. IEEE, Dec 8. XPDL (2005) XML process definition language (workflow management coalition) document number WFMC-TC-1025: version 1.14, 3 October 2005 9. OASIS (2006) Business process specification schema 1.0.1, May 2001 and ebBP, v2.0.4, October

A Classification of Model-Driven Approaches to Develop Semantic Web Service Alaeddin Kalantari, Suhaimi Ibrahim and Hamed Taherdoost

Abstract Nowadays, the number of infrastructure strategies based on Serviceoriented architecture (SOA) is growing and demands for reusing and composing available Web services have increased significantly. Therefore, a rich Semantic Web service description is required to reach an automated Web service discovery, composition, and invocation. Due to the complexity of the Semantic Web service languages as well as lack of enough knowledge in the field of knowledge engineering and knowledge representation among developers, the adoption of Semantic Web service is yet slow down. There are several Model-driven architecture (MDA) based approaches to tackle that issue. In order to evaluate the model-driven approaches which are used to develop Semantic Web services, it is vital to have a proper classification of these approaches. The aim of this paper is to classify several model-driven approaches for developing semantic Web services into two main categories namely software methodology and UML formalist based approaches. The results of the paper give an essential perspective to do research work on Model-driven approaches for Semantic Web service development.







Keywords Web service Semantic web service Model driven architecture (MDA) Software methodology-based UML formalist-based







A. Kalantari (&) Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia e-mail: [email protected] S. Ibrahim Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia e-mail: [email protected] H. Taherdoost Faculty of Computer Science, Islamic Azad University, Semnan Branch, Tehran, Iran e-mail: [email protected]

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1 Introduction Presently, the number of available Web services based on renowned standards such as Simple Object Access Protocol (SOAP), Universal Description, Discovery and Integration (UDDI), and Web Service Description Language (WSDL) are increasing. Thus, organizations are embarking to use infrastructure strategy based on Service Oriented Architecture (SOA) in a platform-independent manner to facilitate interoperability, reusability, and flexibility among B2B enterprise applications [1]. Regardless of the benefit of SOA, discovery and integration of Web services are not fully automated because, each service in the Internet may have several different descriptions [1, 2]. One of the most possible solutions to overcome this matter is using Semantic Web for describing Web services in unambiguous and machine-interpretable fashion. Some proposals for Semantic Web services have been submitted in W3C such as OWL-S [3], WSMO [4], and SAWSDL [5] to facilitate the automation of Web service tasks such as discovery, selection, composition, and execution. Indeed, writing Semantic Web service descriptions through aforementioned submissions are not easy for developers and need additional knowledge in the field of knowledge engineering and knowledge representation. Although, several tools and editors such as OWL-S Editors, WSMO studio [6], and WSMOViz [7] have been proposed to facilitate writing Semantic description, developers have to learn the syntax and concepts of the Semantic Web service languages and how to write them. This lack of knowledge and also the complexity of these languages cause the adoption of Semantic Web services slow down [8]. In order to overcome this problem, several Model Driven Architecture (MDA) [9] based approaches have been proposed to generate Semantic description of Web services from a set of models. MDA focuses on modeling instead of coding and is based on several OMG standards such as Meta-Object Facility (MOF), Unified Modeling Language (UML) [10], Common Warehouse Meta-model (CWM), and Meta-Data Interchange (XMI). MDA defines an architecture based on computation independent models (CIMs), platform independent models (PIMs), platform specific models (PSMs) and model transformation. The transformation model is written based on a model transformation language such as eXtensible Stylesheet Language Transformation (XSLT) [11], Atlas Transformation Language (ATL) [12]. In this paper, the state-of-the-art MDA-based Semantic Web service description approaches are classified into two categories: (a.) software methodology based approaches that propose the creation of Semantic annotations in the way of software engineering methodology and specify Semantic descriptions during the Web services development lifecycle and (b.) UML formalist based approaches that only extract Semantic description of Web services from a set of diagrams provided by UML tools. The rest of this paper is organized as follows: Sect. 2 offers a survey of the most important model-driven approaches for providing Semantic Web service which is

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MDA-based semantic Web service description approaches

Software Methodology base

Torres et al (2006)

Brambila et al (2007)

MIDAS-S (2006, 2010)

UML formalist base

Gronmo et al (2005)

Yang and Chung (2006)

Lautebacher and Bauer (2007)

Timm and Gannod (2008)

Bensaber and Malki (2008)

Sun et al (2009)

Kim and Lee (2009)

Belouadha et al (2010)

Fig. 1 Taxonomy of MDA-based semantic web service description approaches

followed in Sect. 3, by providing a summary of these approaches into the appreciable table. Finally, Sect. 4 concludes the paper.

2 Classification of MDA-Based Approaches This section will present state-of-the-art MDA based approaches that provide Semantic Web service descriptions. These approaches are classified into two categories namely: software methodology-based and UML formalist–based. Fig. 1 shows the taxonomy of MDA-base Semantic Web service description approaches.

2.1 Software Methodology-Based Software methodology-based approaches are methodological solution based on the Software Engineering disciplines. In these approaches, the Semantic annotations are created during the service developments using concrete methodology which is able to combine the best practices in Semantic specification and Web service development. Torres et al. 2006. Torres et al. [13] approach extends a Web Engineering method such as Object Oriented Web Solution (OOWS) with an ontology model to specify processes and data that are published for external usage. In addition, an OOWS-based ontology model is created by using the application of a set of transaction rules. This approach specifies the functionalities and operations of the

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services using a set of structural and behavioral models such as class, state-chart, and activity diagrams. The service profile is not supported and should be created manually. In order to model OWL and OWL-S based constructs, a profile with state-chart and activity diagrams along with the domain ontology representation that uses UML class diagram are specified. Brambila et al. 2007. Brambila et al. approach [14] uses Business Process Model and Notation (BPMN) [15] and Web Modeling Language (WebML) [16] to develop WSMO-based Semantic Web services in the way of model-driven methodology. The main aim behind this approach is to alleviate the difficulty of Semantic Web service description using current Semantic and Web application technologies. In addition, Semantic descriptions are extracted from application design models via a semi-automated generator. This approach adopts WSMX as the Semantic execution environment. Indeed, all diagrams of BPMN and WebML are serialized in an XML serialization such as XMI. Then, this approach uses XSLT to map models into WSMO-based descriptions. Furthermore, CASE tool WebRatio [17] converts WebML skeletons into codes before they are being executed by WSMX at run-time. MIDAS-S (2006, 2010). MIDAS-S approach [18, 19] is based on a modeldriven software methodology for developing Web Information System (WIS) like MIDAS [20] framework. The Semantic aspect is added into the PIM and PSM levels of model to develop Semantic Web service in a MDA manner in conjunction with WSML specification using UML models. Hence, other aspects of this approach such as hypertext, content, and behavior are able to integrate with the development of Semantic Web service. The four main concepts of WSMO such as ontologies, goals, mediators, and Web services for generating the WSML description are provided into the PSM level. In this approach Goal, Web service, and ontology model are divided into two different models namely content and context models. The ontology context model collects information about namespaces, used mediators, and imported ontologies and enlarges the package diagram of UML while the ontology content model is designed for enlarging the UML class diagram and representing other elements such as concepts, attributes, axioms. The Web service context and content models are defined similar to the aforementioned ontology models in the scope of Web service with the difference that the content models represent the elements of Web service such as capabilities, interfaces, and the like.

2.2 UML Formalist-Based UML formalist-based approaches extract the Semantic description of Web services from a set of diagrams provided by UML tools. In fact, these approaches keep the elaboration of Semantic descriptions separate from the actual development of the underlying services [14].

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Gronmo et al. 2005. Gronmo et al. approach [21] provides a UML profile that supports OWL-S, WSMO, and able to design an independent service model. In addition to the UML profile, a transformation mechanism is provided to transfer OWL-S into UML diagrams and vice versa using UML Model Transformation Tool (UMT). This approach uses XSLT script to transfer one model to another model. In addition, it takes advantage of OCL in UML models to represent the conditions. In this approach, matchmaking algorithms support Semantic matching of categories, input, and output. This approach consists of three main steps: modeling the composition, discovery, and selection. In the first step, the composition model is designed without considering particular Web services. Next, from the available service registry, the discover process is handled using a matchmaking algorithm. It uses the semantically described composition that is converted from composition design in the first step. In this step, the selected services are defined semantically based on OWL-S and they should be converted to UML diagrams using an automated tool. Finally, the appropriate services are selected to perform desired tasks in a UML model. Once the Semantic composite model of Web services is provided, it is transformed to OWL-S description. Yang and Chung 2006. This approach [22] generates OWL-S service model ontology using UML class and state-chart diagram. The focus of this approach is only the OWL-S service model ontology that describes the information about service interoperation. The process of generating service model ontology is divided into two sub-processes: generating atomic services with extracting information from UML class diagram and generating information that are extracted from statechart diagrams for composite services. In addition, a set of mapping rules are provided to carry out the mapping between UML diagrams and OWL-S. This approach takes advantage of GUL for representing conditions in UML model instead of OCL. Moreover, SWRL is used for representing conditions in OWL-S specifications. SWRL atom name is described using stereotyped class and SWRL atom type is represented using stereotyped dependency. This approach uses XSLT as transformation language for converting XMI to OWL-S service model ontology. Lautebacher and Bauer 2007. This approach [23] presents a meta-model and UML-profile for Semantic Web services. The meta-model is provided based on Ontology Definition Meta-model (ODM) to support OWL-S, WSMO, WSDL-S, and SWSF. In addition, a set of informal transformation rules along with the openArchitectureWare-language Xpand [24] are used to extract codes from the meta-model. The meta-model in this approach consists of five packages that interact with each other. All concepts of ontology are represented in ontology package similar to ODM. The interface package represents WSDL service elements model and its Semantic description. The non-functional descriptions of one or more Semantic Web services are modeled in ServiceProvider package. The functional package includes all aspects to annotate every single step. ProcessFlow package merges the extended concepts that are defined in ServiceProvider package. In this approach, Semantic Web service grounding and WSDL documents are modeled using UML diagrams. Practically, UML class diagrams are used to represent non-Semantic and WSDL-based Web services and UML activity

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diagrams model Web service composition. Output and Input pins are used to achieve data binding between processes. Moreover, constraints are used to model conditions on control constructs. Timm and Gannod 2008. Timm and Gannod approach [8] provides a modeldriven architecture Semantic Web service development for OWL-S based Semantic using UML class and activity diagrams along with their UML profiles for OWL-S. The approach is able to use WSDL and Semantic based Web services. The workflow of this approach consists of four steps: modeling, conversion, grounding, and execution. The UML class diagram is used to model the structure of the service and the UML activity diagram aids the modeling of composition. This approach uses OCL for representing conditions in PIM and takes advantage of XSLT scripts for transformation. In addition, an automated SPesification and EXecution (SPEX) tool is provided to load any number of WSDL documents and facilitate description, grounding, and execution. Bensaber and Malki 2008. This approach [25] first, convert WSDL document into the UML profile model in the reverse engineering manner to gain high level graphical models and then, uses imported ontologies to define UML model semantically. Finally, the UML models are converted to the OWL-S description. This approach divided into three main steps namely, Reverse Engineering Process, Annotation Process, and Conversion Tool respectively. The automatic conversion from WSDL to UML is provided via implemented conversion rules. The UML profile is used to define stereotypes and tagged values. The approach takes advantage of OCL for specifying effects, pre and post conditions in UML modeling. Finally, the OWL-S ontology is obtained from an XMI document generated from UML model. In this approach the access URIs and bindings are transformed from WSDL documents to UML activity diagrams before the grounding. Therefore, the grounding process is performed automatically. Sun et al. 2009. Sun et al. approach [2] is based on Gronmo et al. [26] and Bensaber and Malki [25] and uses existing Web services available in the internet to create composite Web service. The purpose of this approach is to increase the degree of automation for service composition. This approach includes five steps: Discovery Web Service, Reverse Engineering, Model Composite Web Service, Transform Composite Web service, and Publish Composite Web service. The Discovery Web Service is used to find relevant Web services from the registries and extract the equivalent WSDL. The output of this step is WSDL documents. The Reverse Engineering provides two UML diagrams like Class diagram for service model and Activity diagram for workflow model. In Model Composite Web Service, a UML tool is used to integrate imported ontologies, extracted UML diagrams, and information from UML to create a model of composite Web service. The obtained UML model is transformed into XMI document through an adequate mapping rules and UML profiles in Transform Composite Web Service. Finally, the composite web service is published in the internet. In this approach, the ontology model is based on ODM. All UML models are transformed into XMI document through a set of adequate mapping rules. This approach uses ATL for creating transformation rules.

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Kim and Lee 2009. Kim and lee approach [1] is a method to moderate the complexity of UML diagrams for developing Semantic Web service descriptions. The method follows three steps, ontology, process, and transformation modeling. First step provides the capability to extract a set of class diagrams from imported ontologies. In the next step, sequence and activity diagrams are used along with UML profile and its elements like stereotype, tagged value, and constraint to accomplish the process tasks. The objects of sequence diagrams are related to a relevant class diagram. In the process of designing sequence diagrams, interaction fragments are used to describe OWL-S control constructs. Activity diagrams are used to represent OWL-S composite process because their provided notations can describe various control constructs related to OWL-S. Finally, the UML diagrams are exported to an XMI document which is transformed into the OWL-S description via an XSLT script. In addition, the conditional statements are transformed into SWR syntax in OWL-S descriptions. Belouadha et al. 2010. Belouadha et al. [27] provides a method to create a model-driven SAWSDL Semantic Web service composition. The approach provides an independent UML profile for modeling Web services. In addition, transaction rules are used to generate the SAWSDL interface files. Furthermore, the BPMN notation is used to model the behavior of Web service composition and for generating its executable BPEL file. In this approach, a transformation tool is created to map WSDL to SAWSDL and vice versa. Practically, their approach is based on semantic web service composition meta-model which is independent of the SAWSDL description. The interface of this meta-model is realized by a business service which is modeled as web service. This interface contains a set of operations that have some parameters like input, output and faults. Each parameter specified by specific data type. In this meta-model, each parameter, operation, and interface can associate with a semantic concept which can be an ontological concept. Two type of mapping are specified in this meta-model between given parameter data types and its semantic concept: LiftingSchema and LoweringSchema attributes. Thus, mapping class is specified using these attributes as its properties. This approach takes advantage of ATL to implement the transformation rules.

3 Summary of Approaches In this section, the aforementioned approaches are represented in terms of MDA standard properties such as modeling language, transformation languages, and Semantic Web service languages as shown in Table 1. Indeed, each approach must have these properties to be considered ‘‘model-driven approach’’.

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Table 1 A summary of approaches in terms of MDA standard properties Modeling language UML Approaches CD AD SQD STD UP UML formalist-based Gronmo H H H et al. [21] Yang & H H Chung [22] Lautebacher H H H & Bauer [23] H H H Timm & Gannod [8] Bensaber & H H H Malki [25] Sun et al. [2] H H Kim & Lee H H H H [1] Belouadha H H et al. [27] Software methodology-based Torres et al. H H H H [13] Brambila et al. [14] MIDAS-S H H [18, 19]

Non-UML

Semantic Transformation web service language standards

Condition BPMN WebMl OCL

OWL-S

XSLT

GUI

OWL-S

XSLT

CNT

OWL-S

Xpand

OCL

OWL-S

XSLT

OCL

OWL-S

XSLT

N/A CNT

OWL-S OWL-S

ATL XSLT

SAWSDL

ATL

OWL-S

M2T

WSMO

XSLT

WSMO

N/A

H

OWL H OCL

H

CD class diagram, AD activity diagram, SQD sequence diagram, STD statechart diagram, UP UML profile, CNT constraints, M2T model to text, N/A not applicable

4 Conclusion and Future Work In this paper, we have provided an overview of most prominent model-driven approaches that describe Web Services based on current W3C submissions such as OWL-S, WSMO, and SAWSDL. Moreover, we have classified these approaches into two main categories namely, software methodology and UML formalist based. This classification helps to evaluate the approaches for further researches and investigations. The initial evaluation shows that almost all approaches that are placed in the UML-formalist based category are based on OWL-S. Instead, all approaches that provide semantic Web service based on WSMO have been provided in software methodology manner. Most approaches use UML as their modeling language and in most of them the UML class diagrams are used to represent atomic process. There are still some issues that we are working on.

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Currently, we are comparing these approaches and categories based on some known criteria such as independency, expressiveness, completeness, readability, and etc.

References 1. Kim I-W, Lee K-H (2009) A model-driven approach for describing semantic Web services: from UML to OWL-S. IEEE Trans Sys Man Cybern Part C Appl Rev 39(6):637–646 2. Sun W et al (2009) A model-driven reverse engineering approach for semantic web services composition. IEEE Comput Soc, Xiamen 3. David M, Burstein M (2004) OWL-S: semantic markup for Web services, [cited 2010 August 4]. http://www.w3.org/Submission/OWL-S/. Accessed 22 Nov 2004 4. Jos de Bruijn CB (2005) Web service modeling ontology (WSMO), [cited 2010 Feb 21]. http://www.w3.org/Submission/WSMO/. Accessed 3 June 2005 5. Semantic Annotations for WSDL and XML Schema, [cited 2010 September 12]. http:// www.w3.org/TR/sawsdl/. Accessed 28 August 2007 6. WSMO Studio (2009) [cited 2010 May 18]. http://www.wsmostudio.org/ 7. Kerrigan M (2006) WSMOViz: an ontology visualization approach for WSMO. Institute of Electrical and Electronics Engineers Inc, London, United kingdom 8. Timm JTE (2008) A model-driven framework for the specification, grounding, and execution of semantic Web services. Arizona State University, p. 170 9. Miller J, Mukerji J (2003) MDA Guide Version 1.0.1, [cited 2010 Jan 15]. www.omg.org/ mda/mda_files/MDA_Guide_Version1-0.pdf 10. Group OM (2010) OMG unified modeling language (UML), [cited 2010 September 21]. http://www.omg.org/spec/UML/2.3/. Accessed May 2010 11. Clark J (1999) Extensible stylesheet language transformations (XSLT), [cited 2009 October 27]. http://www.w3.org/TR/xslt. Accessed 16 November 1999 12. ATL User Guide (2010) [cited 25 November 2010]. http://wiki.eclipse.org/ATL/User_Guide 13. Torres V, Pelechano V, Pastor O (2006) Building semantic web services based on a model driven web engineering method. Springer, Tucson 14. Brambilla M et al (2007) Model-driven design and development of semantic Web service applications. ACM Trans Internet Technol 8(1): 15. Business Process Model and Notation (BPMN) (2009) Version 1.2, [cited 2010 August 6]. http://www.omg.org/spec/BPMN/1.2 16. Stefano C et al (2002) Designing data-intensive web applications, vol 500. Morgan Kaufmann Publishers Inc., San Francisco 17. Webratio site development (2009) [cited 2010 March 14]. http://www.webratio.com 18. Acuna CJ, Marcos E (2006) Modeling semantic web services: a case study. Association for Computing Machinery, Palo Alto 19. Sanchez DM et al. Toward UML-Compliant semantic web services development. Int J Enterp Inf Sys 6(1):44–56 20. Cáceres P, Marcos E, Vela B (2003) A MDA-based approach for web information system development. In: Workshop in software model engineering 21. Grnmo R, Jaeger MC, Hoff H, (2005) Transformations between UML and OWL-S. Springer, Nuremberg 22. Yang JH, Chung IJ (2006) Automatic generation of service ontology from UML diagrams for semantic web services. Springer, Beijing 23. Lautenbacher F, Bauer B (2007) Creating a meta-model for semantic web service standards. INSTICC Press, Barcelona 24. Xpand, [cited 2010]. http://www.eclipse.org/workinggroups/oaw/

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25. Bensaber DA, Malki M (2008) Development of semantic web services: Model driven approach. Association for Computing Machinery, Lyon 26. Grnmo R et al (2004) Model-driven Web services development. IEEE Comput Soc, Taipei 27. Belouadha F-Z, Omrana H, Roudies O, A model-driven approach for composing SAWSDL semantic Web services

A Conceptual Model Explaining IT/IS Usage Behavior Sabine Hoesch and Siegfried Voessner

Abstract Information Systems and Information Technology have become a crucial success factor for business environments. To ensure optimal use of Information Systems it is especially important to understand usage behavior and factors influencing their adoption by an organization. In recent decades some models have been developed to describe information technology and information systems (IT/IS) usage behavior under voluntary or mandatory conditions focusing mainly on technological or task-related perspectives. These models however can not explain observed phenomena like underperformance of technologically optimal information systems or over-performance of suboptimal systems. In an attempt to close this gap we present a causal model integrating both human and social change processes as well as feedback factors affecting the usage of IT/IS on a regular basis. We further show implications for the system adoption process in a business environment. Keywords IT/IS usage Causal loop




Acceptance




Usage behavior




Commitment




S. Hoesch (&)
S. Voessner Department of Engineering- and Business Informatics, Graz University of Technology, Graz, 8010 Austria e-mail: [email protected] S. Voessner e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_39, Ó Springer Science+Business Media B.V. 2012

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1 Introduction In the field of IT/IS, to predict acceptance, usage and success many models were proposed [1–6]. Ajzen’s theory of planned behavior (TPB) explains motivational based behavior [1], the technology acceptance model (TAM) is intended to predict user’s acceptance towards technology in both volitional and mandatory settings. TAM is able to predict about 40% of system use. It misses out negative emotions regarding technology and emotions related to the process of implementing a new technology [1] as well as TPB. Additionally, most of the models do not provide information about the types of relationships.

2 Research Objectives With this study we aim to explain the IT/IS usage behavior at an individual level with respect to the possibility of enforcing IT/IS usage in organizations. Moreover even the technological and social change processes are taken into account. In a business environment users usually are not decision makers. Users have to get along with the available system or perhaps have to decide whether to use it or not. Furthermore changing organizational structures and processes resulting from IT/IS implementation often unsettle employees. With the proposed model we show implications for the individual IT/IS adoption process in a business environment.

3 Methodology In a literature review we considered articles related to the various models explaining IT/IS usage, acceptance and success. A synthesis of these models and studies which focus mainly on specific relationships led to the main behavior model. Based on the behavior theories Motivation Model, Theory of Planned Behavior [7, 8] and the behavior theories in relation to IT/IS usage and acceptance TAM [4] and IS Success Model [3], we developed an Extended Intention Model. We use System Dynamics to put components into perspective and add loops to explain business IT/IS usage at an individual level.

4 Existing Behavioral Models and its Factors In this paper we build on the relevant main behavior models explaining either behavior in general or especially targeting IT/IS usage behavior and will therefore review them in brief. These models are Theory of Planned Behavior, Technology

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Acceptance Model, IS Success Model and the Motivation Model. In the following we give a brief explanation of the models and its factors.

4.1 Behavioral Models The Theory of Planned Behavior suggests that an individual’s intended behavior is determined by the attitudes toward a specific behavior, subjective norms and control beliefs regarding the behavior [1]. TAM [4] examines the effect of perceived usefulness and perceived ease of use on intention to system use. It includes task and technology related determinants. References [2, 3] proposed the IS Success Model. It suggests that any organizational or individual benefits are determined by usage and user satisfaction. This model neither includes any individual differences nor emotions related to the process of change. The Motivation Model [8] suggest that any behavior is related to extrinsic or intrinsic motivation [8, 9].

4.2 Factors in Behavioral Models In these existing models the main factors are: intention, attitude, subjective social norms, motivation, perceived behavioral control, system usage, satisfaction, usefulness, ease of use and enjoyment. Additionally several studies show a relevant effect of commitment [7, 10, 11]. The core element is the intention to perform a specific behavior. It results from motivational factors indicating the willingness how hard to try and how much effort to spend to perform a behavior [1]. Attitude is the individual’s evaluative or affective response to a specific object [7] or a behavior in question and affects the intention to perform a behavior [1]. Intrinsic and extrinsic sources motivate to perform an intended behavior. The more motivated an individual is the more likely is the behavior in question [8]. Intrinsic motivation covers the engagement for pleasure and satisfaction. Extrinsic motivated behavior is performed due to instrumental reasons [8], e.g. attaining rewards or avoiding punishments [9]. Someone can even be motivated by subjective social norms. These refer to the degree to which an individual perceives that most people who are important to him think he should or should not use the system [1, 6, 7, 12]. Perceived behavioral control (PC) is the emotional response to the difficulty of performing a behavior [1, 13–15]. Reference [16] found self-efficacy, requisite resources and technical support to determine PC. The absence of PC control refers to any negative emotional attachment regarding the task in question that requires a specific behavior, e.g. lack of resources. User commitment describes the user’s psychological attachment to system use [10]. Rather than just complying with the beliefs of other’s the user wants to comply following its own beliefs. There are three types of commitment, each satisfying different personal goals. Internalization

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relates to behavior adopted because of its content, that is evaluated to be consistent to the user’s own personal goals [10]. Identification occurs when the user adopts a behavior not because of the content, but he wants to achieve a satisfying, selfdefining relationship to other persons or groups. Identification and Internalization are subsumed to affective commitment. In compliance, the content and the salience of relationships are irrelevant. The adopter of a particular behavior wants to achieve a favorable reaction [3, 8, 10, 11, 17, 18]. According to the definition of motivation [8] this kind of commitment pertains to extrinsic motivation that is regulated from outside the individual user [10]. System usage is defined as the amount of system use by the individual user. As satisfaction refers to prior usage of IT/IS, from the perspective of a causal model system usage only acts as an enabler for satisfaction. In consumer behavior satisfaction is determined by user’s experiences/perceptions and the expectations the consumer established [20–22]. In the field of IT/IS user satisfaction is defined as the net feeling of pleasure or displeasure that results from a specific interaction with the information system [23]. According to this definitions user satisfaction requires usage prior to the evaluation. Satisfaction reflects the hierarchy of consumer needs: Functionality, Usability and Pleasure [24] and is mediated by the expectation of success [20, 25]. Usefulness is considered as the perceived usefulness from the user’s perspective and is always related to the user’s tasks. It includes the items Work More Quickly, Job Performance, Increase Productivity, Effectiveness, Makes Job Easier and Is Useful. Reference [26] proposed task-technology fitness as an appropriate evaluator for meeting user’s needs. Therefore we conclude that perceived usefulness copes with the ability to support user’s tasks. Ease of use, refers to ‘‘the degree to which a person believes that using a particular system would be free of effort.’’ This follows from the definition of ‘‘ease’’: ‘‘freedom from difficulty or great effort’’ [4]. Enjoyment is related to stimulating, beautiful and pleasurable system attributes[24]. Expertise is related to the level of education [27] and describes an individual’s characteristics of domain experience [27, 28], prior similar experience with and knowledge about technologies and systems in general and especially regarding the IT/IS in question. These models do not provide any insight into individual reactions to social and technological change processes. Therefore we have to extend these models and factors described above to be applicable to a business IT/IS context. They provide the basis for our extended behavior model which we present in the following.

5 An Extended Intention Model as a Basis for Business IT/IS Usage Based on the TPB factors, IT/IS usage intention underlies subjective norms, attitude and perceived behavioral control (Fig. 1). Subjective norms are formed by the extrinsic reinforcement of compliance; another form of subjective norm is

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Fig. 1 An extended IT/IS usage intention model based on Ajzen’s TPB [1]

Identification [12]. Additionally to this extrinsic type of motivation employees can be intrinsically motivated, that is, when users use the system because usage of the system satisfies them and helps them to achieve goals, e.g. accomplish tasks. With respect to user’s uncertainty and self-efficacy the intention model is extended with the perceived control of change which is subsumed with perceived behavioral control to perceived control of behavior and change (PCBC). The uncertainty when introducing new IT/IS is expressed in its opposite, selfefficacy, which refers to the ability to deal with uncertainty in a changing sociotechnical system breaking up processes and organizational structures. Based on Sydow’s model of a socio-technical system [29], that includes the items Task, Technology, User and Role in the primary work environment, we define the uncertainties: Task Self-Efficacy (User—Task): The user is aware that he has the required skills. He is sure to be able to accomplish the new or changing tasks with the available resources. Technology Self-Efficacy (User—Technology): The user is sure that he will be able to use the new IT/IS. Structure Self-Efficacy (User— Role): The user is sure, that although tasks and technologies are changing he will hold his role within the organizational structure. Process Self-Efficacy: Process self-efficacy refers to the overall control of task execution in the primary work environment. The user is sure that he will be able to transform the given input into the desired output within the business process. As all these self-efficacies regard to both control beliefs and fears of change we combine these to the perceived control of behavior and change (PCBC). Similar to perceived behavioral control directly related to the behavior in question, PCBC affects the psychological attachment regarding a specific behavior and therefore the intention to use.

6 An Extended Behavior Model for Business IT/IS Usage By putting all building blocks together (factors, motivation models and polarities of relationships) we can finally construct an extended behavior model for business IT/IS usage. Figure 2 presents relationships between the system variables and the type of relationship, which is related to the causal loop diagram notation. ‘‘+’’ refers to a positive relationship: increasing the value of a variable increases the value of the related variable. On the other hand ‘‘–’’ represents a negative

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Reeinforcing loop Balancing loop Direction of relationship

+ -

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Positive relation Negative relation

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Compliance

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Expectations Intention to Use

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Feedback

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Affective Commitment

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Fig. 2 The Business IT/IS usage behavior model shows the factors and the feedback loops

relationship, indicating that a modified value of the independent variable causes the value of the related variable to change in the other direction [30]. Based on the presented intention model the types of relationships and feedback loops are added to form the causal model. Additionally we add the following relationships: Using the system enhances user’s expertise with the IT/IS and in general with technology through the cognitive learning process (Usage is positively related to Expertise). This familiarity and experience [1] with IT/IS improve task and technology self-efficacy (Expertise is positively related to PCBC). Similar prior experiences and expertise with the new IT/IS enable greater learning and a more positive perception of ease of use [12, 27, 31] (Expertise is therefore positively related to Ease of Use). [24] defined the overall needs to be functionality, usability and pleasure (enjoyment). The fulfillment of personal needs results in satisfaction. Thus the higher usefulness and ease of use the higher the satisfaction [23]. According to this concept enjoyment improves satisfaction too [32–34] (Usefulness, Ease of Use and Enjoyment are positively related to Satisfaction). A good non-subjectively defined task-related system quality will by trend lead to high usefulness and ease of use. Even enjoyment can result from a good task-related system quality (Task-related System Quality is positively related to Usefulness, Ease of Use and Enjoyment). According to [4] ease of use enhances usefulness (Ease of use is positively related to Usefulness). Missing satisfaction in the best case brings users to complain (Satisfaction is negatively related to Feedback). Another possibility to gain feedback is to drive it through affective commitment. When users commit to a good system quality they are engaging more in system

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improvement (Affective Commitment is positively related to Feedback). In case of the availability of resources this feedback can be transformed into system improvement leading to a better task-related system quality and a higher commitment. Due to affective commitment users will invest greater effort in learning and using the system and find it more useful [10]. Even compliance can enforce intention to use [35]. On the other hand it was found to reduce perceived usefulness and ease of use [9, 10].

7 Analyzing the Model The model consists of five reinforcing and four balancing loops. In a reinforcing loop starting from one factor and going around the loop ends up in increasing the value of the factor. E.g. increasing Intention to Use enhances Usage of the system, which in turn enables Satisfaction, which will increase Intention to Use. The second reinforcing loop includes Intention to Use, Usage, Expertise, PCBC. Intention to Use enhances system Usage, that leads to improved Expertise. As Expertise reduces the anxieties regarding the new technology and the modified task, PCBC is improved, which in turn leads to higher Intention to Use. Expertise also occurs in the third reinforcing loop: better Expertise leads to the perception that the system is easier to use, improving Satisfaction, which leads to a higher Intention to Use, Usage and improved Expertise. As Ease of Use is improving Usefulness, a fourth reinforcing loop can be identified (Expertise, Ease of Use, Usefulness, Satisfaction, Intention to Use, Usage). From the fifth loop Affective Commitment turns out to be a driver for System Improvement. In a balancing loop increasing one factor and going around the loop ends up in decreasing the original value of the factor (or vice verse). E.g. high Expectations tend to lower Satisfaction, which in turn over time leads to reduce expectations. The other balancing loops encourage feedback from dissatisfied users. Providing that there are enough monetary and human resources and knowledge for system development, feedback can yield to system improvement activities, which improve the Task-related System Quality. As Usefulness, Ease of Use and Enjoyment result from Task-related System Quality and end up in Satisfaction, three balancing loops can be identified. Identifying these reinforcing loops it is obvious that there are several factors driving IT/IS usage and few factors limiting IT/IS usage. PCBC represents the anxiety and uncertainties regarding the change in a socio-technical system. The lack of PCBC may inhibit the intention to use and therefore usage of the system and is therefore absolutely required for intention to use. Even usage is limited to the amount that is useful to accomplish tasks in the primary work environment. Bringing users to comply decreases the perceptions about usefulness of a system and therefore lowers the impact of one main driver of Intention to Use, which is Satisfaction. As we found the relationship between Satisfaction and Feedback to be rather weak, Affective Commitment enables greater System Improvements than

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it can be achieved by dissatisfied users. As Usage only acts as an enabler to Satisfaction, the reinforcing loop obviously is a rather weak one.

8 Future Work The model presented in this paper integrates technological and task related issues as well as the human and social change processes. Feedback factors were identified demonstrating that continuous usage of IT/IS is a consequent evaluation process of individual positive and negative perceptions. The causal model enables the identification of barriers to IT/IS implementation success and therefore offers the opportunity to soften impeding factors and reinforce motivating factors. Nevertheless IT/IS usage in business environment remains a complex field of investigation. Future work has to be done to validate the conceptual model further using comparative, detailed case studies throughout industries. This will provide possibilities to support an optimal IT/IS implementation process. Further strategies have to be identified to ensure the individual adoption of IT/IS in an business environment.

References 1. Ajzen (1991) The theory of planned behavior. J Organ behav hum decis process 50(2): 179–211 2. Delone W, McLean E (1992) Information systems success: the quest for the dependent variable. J Inf Sys Res 3(1):60–95 3. Delone W, McLean E (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Sys 19:9–30 4. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–339 5. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478 6. Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315 7. Fishbein M, Ajzen I (1975) Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley Reading, Boston, pp 89–114 8. Vallerand RJ (1997) Toward a hierarchical model of intrinsic and extrinsic motivation. Academic Press, Cleveland, pp 271–360 9. Deci EL (1971) Effects of externally mediated rewards on intrinsic motivation. J Per Soc Psychol. 18:105–115 10. Malhotra Y, Galletta D (2005) A multidimensional commitment model of volitional systems adoption and usage behavior. J Mgmt. Inf Sys. 22(1):117–151 11. Kelman HC (1961) Processes of opinion change. Public Opin Q. 25:57–78 12. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci. 46(2):186–204 13. Bandura A, Adams NE, Hardy AB, Howells GN (1980) Tests of the generality of selfefficacy theory. Cogn Ther Res. 39–66

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14. Bandura A (1982) Self-efficacy mechanism in human agency. Amn Psychol. (37):122–147 15. Bandura A (1978) Self-efficacy: Toward a unifying theory of behavioral change. Adv Behav Res Ther 1(4):139–161 16. Huang E, Chuang MH (2007) Extending the theory of planned behaviour as a model to explain post-merger employee behaviour of IS use. Comput Human Behav 23(1):240–257 17. Engel P (1998) Comments on logical and psychological partitioning of mind: depicting the same map?. New Ideas Psychol 16(3):107–113 18. Wixom BH, Todd PA (2005) A theoretical integration of user satisfaction and technology acceptance. Inf Sys Res 16(1):85–102 19. Seddon PB (1997). A respecification and extension of the DeLone and McLean model of IS success. Inf Sys Res 8(3):240–253 20. Oliver RL (1981) Measurement and evaluation of satisfaction processes in retail settings. J Retail 57(1):25–48 21. Liao C, Palvia P, Chen J (2009) Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). Int J Inf Manag 29(4):309–320 22. Brown SA, Venkatesh V, Kuruzovich J, Massey AP (2008) Expectation confirmation: an examination of three competing models. organ behav human decis process 52–66 23. Seddon PB, Kiew M (1994) A partial test and development of the DeLone and McLean model of IS success. In: Proceedings of the Fifteenth International Conference on Information Systems. 99–110 24. Jordan PW (2000) Designing pleasurable products: An introduction to the new human factors. CRC 25. Bhattacherjee Aab (2001) Understanding information systems continuance: an expectationconfirmation model. MIS Q 25(3):351–370 26. Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance, MIS Q 19(2):213–236 27. Agarwal R, Prasad J (1999) Are individual differences germane to the acceptance of new information technologies?. Decis Sci 30(2):361–390 28. Olfman L, Mandviwalla M (1994) Conceptual vs. procedural software training for graphical user interfaces: a longitudinal field experiment. MIS Q 18(4):405–426 29. Sydow J (1985) Der soziotechnische Ansatz der Arbeits—und Organisationsgestaltung. Campus-Verl, Frankfurt 30. Sterman J (2000) Business Dynamics. McGraw-Hill, New York 31. Gick ML, Holyoak KJ (1987) The cognitive basis of knowledge transfer. Trans Learn: Contemp Res Appl 9–46 32. Diefenbach S, Hassenzahl M (2009) The beauty dilemma: beauty is valued but discounted in product choice. ACM, New York, pp 1419–1426 33. Karapanos E, Hassenzahl M, Martens J (2008) User experience over time. ACM, New York, pp 3561–3566 34. Igbaria M, Schiffman SJ, Wieckowski TJ (1994) The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behav Inf Technol 13:349–361 35. Díez E, McIntosh BS (2009) A review of the factors which influence the use and usefulness of information systems. Environ Model Softw 24(5):588–602 36. Al-Gahtani SS, King M (1999) Attitudes, satisfaction and usage: factors contributing to each in the acceptance of information technology. Behaviour and Information Technology, Taylor and Francis Ltd, London, pp 277–297 37. Barki H, Hartwick J (1994) Measuring user participation, user involvement, and user attitude. MIS Q 18(1):59–82 38. He J, King W (2008) The role of user participation in information systems development: implications from a meta-analysis. J Manag Inf Sys, M. E. Sharpe, Inc, Armonk, New York, 301–331

Offering a Conceptual Model of Technology Acceptance in e-Banking Based on the Customers’ Satisfaction and Trust Alireza Dehghan, Parham Arjomand, Amirahmad Nayyeri and Masoud Tabatabaey

Abstract By facilitating the relationship with customers and increasing its speed and effectiveness, the Information Technology paves the way to improve the functions and innovations of offering services. One of the issues that is always raised when investigating a new technology is the amount of the Acceptance of that technology by users and customers. Getting highly improved in the two recent decades, electronic banking is not an exception. The Technology Acceptance Model is one of the models which has been widely used to describe the influential factors in the Acceptance of information systems and new technologies. The two factors of conceived using facility and conceived profitability have been regarded as basic factors in determining the Acceptance of various information technologies in the last two decades. In this article, while investigating the technology Acceptance model, efforts have been made to offer a combined model based on two significant factors, including customers’ satisfaction and trust, as other influential factors in e-banking Acceptance. Keywords e-Banking


Technology Acceptance Model
Satisfaction
Trust

A. Dehghan (&)
P. Arjomand Faculty Member of Department of Information Technology Engineering, Kazeroun, Iran e-mail: [email protected] P. Arjomand e-mail: [email protected] A. Nayyeri Faculty Member of Department of Computer Science, Kazeroun, Iran e-mail: [email protected] M. Tabatabaey Student of Information Technology Engineering, Kazeroun Higher Education Complex, Fars, Kazeroun, Iran e-mail: [email protected]

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1 Introduction One of the most important elements to develop e-commerce is an e-banking system that facilitates transactions of e-commerce instantaneously with global financial system changes. In fact, e-commerce is not independent of e-banking. Use of electronic systems in finance and credit institutions is increasing fast all over the world and customers of e-banking services are growing. According to Data Monitor research, number of e-banking services customers in 8 countries, France, Germany, Italy, Sweden, Switzerland, The Netherlands, Spain and England from 4.5 million in 1999 got up to 22 millions in 2004. In 2005 more than 75% of companies in developed countries use at least one of the e-banking services [19]. E-banking is one of the most successful services in e-commerce based on increase of customer satisfaction which is relied to access to value-added services. Actually, this method has transformed the way access to accounts and type of transactions. In this type of banking, customer needs should be identified and ranked to know the importance and influence of each of these needs on customer satisfaction. It can work with banks to determine what factors and with what degree of influence, have an impact on customer satisfaction and as a result try to satisfy that factors. One of the most significant results of customer satisfaction is their loyalty. Regarding to rapid growth of communication technology and emerging electronic banking system, pioneer banks in this field that make customer satisfaction their highest priority, can get up great benefits. In the past, because of the state banking system and the same services provided by banks, customers paid no fixation to any particular bank. But in recent years by growth of nongovernmental banks and having more competition among banks, all banks try to gain competitive advantage, particularly through better satisfying customers’ needs. In this regard, one of the most strategic services is electronic banking and customer satisfaction of that. There is always a concern in the implementation of electronic banking whether this new technology and new services to their users and customers will be admitted or not. The Technology Acceptance Model (TAM) is one of the models which has been widely used by users and customers to describe the influential factors in the Acceptance of information systems and new technologies [22]. In this paper in addition to examine each of the satisfaction and trust factors, will pay attention to the relationship between this two factors in e-banking and at the end we will provide a conceptual model of technology Acceptance in electronic banking based on satisfaction and trust of customers.

2 Acceptance of IT Acceptance of IT systems plays an important role in investing in this technology. Acceptance of this technology by the users is one of the factors affects the success and has positive results for banking organizations. Impact of technology, has

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positive relationship with its acceptance. So, if the potential users of this technology show resistance against it, the desired objectives can’t be achieved. Low level of acceptance of information technology causes low-level use. Low level of computer use at work is one of the main problems that affect the effectiveness and efficiency of banking institutions and has negative effects. Since the new banking system based on computer use, factors affecting customers and users of information technology should be known to increase the use of computers.

3 The Technology Acceptance Model (TAM) The Technology Acceptance Model is one of models which widely used to describe the factors affecting the Acceptance of information systems and communication technology by users and customers [22, 30]. This model should be regarded as a powerful model for predicting technology acceptance by users [30]. Understanding the factors that cause the Acceptance of a technology and make information technology to be accepted is important researches in the field of information technology [9, 11]. Technology Acceptance model was made by Davis In 1989 to understand the relationship between attitudes, behavior of IT consumer and also to determine the factors affecting IT Acceptance by users of information systems and in order to clarify and predict the behavior of computer users was developed [8].(see Fig. 1). As shown in Fig. 1, technology Acceptance model derived from the Theory of Reasoned Action (TRA) (Fishbein-Ajzen). The logical operation theory that is a general model expresses that: attitude determines the social behavior [23] and attitude is a function of the beliefs about the consequences of the behavior and evaluates the result [1]. In the technology Acceptance model an implementation of the logical operation theory is done to model the Acceptance of information systems by users. To beliefs, Perceived Ease of use (PEOU) and Perceived Usefulness (PU) has been considered as two basic structures in the Acceptance of IT in the past decades [12]. According to The technology Acceptance model, perceived usefulness of the system by users is influenced by the fact that the system is easy to use. This model also assumes that the perceived ease of use and perceived usefulness has a direct impact on people’s attitude to use of information systems and ICT [2]. The aim of technology model is to provide a description of the computer acceptance parameters which is general and has the ability to describe the behavior of users in a wide range of computing technology and its users are of different types [20]. The model has descriptive approach, in addition to predictive aspects. Thus, researchers and administrators can identify why a specific system may not be accepted and the appropriate corrective steps to be done. In 2000, Davis and Venkatesh [30] expanded the TAM model theoretically and described the usefulness and desires and intentions to use words the social impact and Cognitive instrumental processes. The model developed is known as TAM II

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Fig. 1 Original technology acceptance model

Fig. 2 Technology acceptance model II

[21] (see Fig. 2). Using collected data from four different systems that in two examples voluntary use and in two others compulsory use was analyzed. Model structures at three different times: before deployment, a month after deployment, and three months after system implementation in any organization was measured. So, the developed model was confirmed in all organizations at all three times [16]. By using TAM I as starting model, in TAM II new theoretical structures including social impacts (subjective norms, voluntary and imagine) and cognitive instrumental processes (job relationship, quality of output, the ability to prove the results and ease of use) was added to the model [20].

4 The First Principle of e-Banking, Customer Orientation Because the economic cycle of all financial institutions will continue with the support and investment of customers of in e-banking systems should pay special attention to the customer. Research shows that customer retention is how crucial for businesses and service quality is a factor that is likely to affect customer retention rate. Even a 5% increase in customers, raises the profit level 25–30% and if a institution 5% decrease rate of losing customers, the final net value of each customer up to 75% increase for the Institute [15]. Because of today’s competitive business world, the organization will succeed in attracting customers that provides

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the best and most to services to its customers. Therefore, the role of customers in the development of economic activities should be a principle and E-banking applications regarding to this principle of structures [18]. One of the major flaws in the current banking system is that the customer’s is classified by the bank branch where he has account and other branches do not know him as one of users of their bank’s services. This causes the customer refers to several banks for performing banking activities and incurs costs and wastes a lot of time [19]. Development of ebanking creates the possibility that customers be serviced in any branch of that bank and not just a specific branch [18]. In other words, the extensor branch of account will register his signature and his name and through connected systems all the branches of the bank, and if all systems are connected, all the banks knew him and will service him [19].

5 Customer Satisfaction According to this research, the concepts related to customer satisfaction and its measurement, the first time was expressed in 1977 by ‘‘Oliver’’. He proposed customer satisfaction as pleasant experience of shopping of last product or service [10]. In other words, customer satisfaction means that customers are satisfied with treatment of employees and services of organizations and the bank has succeeded in attracting and retention of them. These satisfied customers, how spend more time and money, to will expect services with higher quality [7]. In other words, Kotler defines customer satisfaction as a scale to which a company’s actual performance to satisfy customer expectations is measured. In his opinion If the performance of the company meets customer expectations, customer is satisfied and otherwise make him sense discontent [25]. Customer satisfaction provides many benefits for the company and higher levels of customer satisfaction leads to loyalty. The customers, who are satisfied a lot, tell their good experiences to others. High customer satisfaction is a kind of insurance for the company against possible mistakes that because of changes associated with their services is inevitable. Permanent customers in dealing with such situations are more tolerant and due to previous good experiences, easily ignore these few mistakes. Therefore, it is not surprising that customer satisfaction is the most important function of organizations and institutions [17]. In accordance with principles of communication marketing, customer satisfaction is the beginning of relationship between customer and seller. Communication satisfaction is the prerequisite for quality of communication. But in electronic banking there is no relationship between customer and seller. Therefore for satisfying customers other solutions such as giving special discounts to customers using electronic banking, giving services to customers conveniently, providing high-speed internet for transaction should be used.

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Fig. 3 Technology acceptance model and its impact on trust

6 Trust The first step in describing a structure is to understand its meaning. The important element among organizations and transactions is trust which facilitates risk and significantly eases the economic transactions. Trust facilitates human interactions. Trust helps the economy and helps people to do business transactions smoothly. In other side, Lack of Trust is a beneficial mental state that makes us able to avoid the systems or individuals or organizations which are unreliable and unsafe [6]. Berry mentioned trust as the most powerful marketing tools based on relationship [3]. According to studies of Fui-Hoon [5] Customers decide to buy online just because of trust [28]. Meyer has defined trust as a person willing to be vulnerable in front of the opponent, Based on this assumption that the other side will perform the expected activity without his direct supervision and control [26]. In another definition trust given as a belief or expectation to which the seller states are reliable and seller is not exploiting vulnerability of customer [6]. Trust and risk completely relevant together and in other words, risk is core of trust [26].

7 The Impact of Trust on Electronic Banking Today, security and distrust are the main concerns of users of electronic banking. For users of electronic banking that connect to the Internet to use services of this type of banking, trust has a great importance. Trust happens when a person believes that the other side has useful factors in addition to profitability [27]. It is essential that customers know the product or service provider because without reducing the risk customers can’t continue to interact with the product or service providers. Trust is one of the most effective methods of reducing risk for customers [14]. Trust in electronic banking is more important than in offline banking [26]. When it is important to make others to trust us that uncertainty and risk is

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Fig. 4 Factors affecting customer satisfaction and loyalty

widespread [24]. In the Internet, users all around the world have the ability to find important files and information on the computers and transfer information through internet. Marketing researchers consider trust as a key structure in relationship marketing [4], [14]. The researchers believe that customer trust has a significant effect on customer loyalty. This could be considered as customer willingness to maintain an ongoing relationship with the seller. Bashiri and Joneidi in an article titled ‘‘Effect of Trust on customer acceptance of Internet banking, based on TAM model, Mellat Bank case’’ studied the basic model of TAM (see Fig. 3). The researchers by adding another structure to the basic model, Concluded that the Trust as a third influential structure in the basic model will increase the acceptance of Internet banking by customers [20].

8 The Relationship Between Satisfaction and Customer Trust Since different models for the study of loyalty, customer satisfaction and Trust in the marketing issues are discussed. For example Deng and Zhaohua in an article titled ‘‘Understanding customer satisfaction and loyalty’’ offered a model where factors such as Trust, service quality, perceived value (functional, emotional, social and financial) and the costs for change are considered as factors influencing customer loyalty and satisfaction but these factors are not necessarily affected by the companies trade marks (see Fig. 4) [13].

9 Technology Acceptance Model Based on Trust and Customer Satisfaction Based on discussed models and cases and after studies done in this research, these models were combined and eventually a conceptual model for acceptance of technology with emphasis on satisfaction and Trust was provided (see Fig. 5).

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Fig. 5 TAM with an emphasis on customer satisfaction and trust

As was stated in the previous sections, TAM model has two basic structures which are perceived usefulness and perceived ease of use. These two structures are always influenced by external variables such as organizational factors, social factors, mental perceptions and system features such as computer hardware, training method and etc. After upgrade of this model to TAM II, new theoretical structures were added to the TAM model. Regarding to interplay of these two basic structures on Trust and effect of trust on tend to use technology we can introduce trust as the third affecting structure on this model. According to research done by Zhohavi Dong and his colleagues, factors such as Trust, service quality, perceived value (functional, emotional, social and financial) and costs for are considered as factors influencing customer loyalty and satisfaction (see Fig. 4) Hence the usefulness of Trust and Perceived ease of use and perceived value creates customer satisfaction that this trust has a significant impact on customer satisfaction and tend to use technology and Finally leads to two different sense of loyalty and technology adoption ore on the other side, will make customer to complain that causes customer temporarily dismissal or possibly will Completely separate him of technology.

10 Conclusion Economic life of all financial institutions continues with investment and support of customers. In launching e-banking systems should pay special attention to the customers. Due to global extent of business competition, an Institution will be

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successful in attracting more customers that provides its services in high quality and quantity. But, to fulfill this need, these institutions should respect important principles that always focuses on creating Satisfaction and confidence among customers. Decades of electronic banking passed, and there is still the concern whether this technology can be fully accepted by customers. For this reason, various researches has been done about the electronic banking. One of the ways to evaluate acceptance of new technology is the TAM model. In theoretical part of this research relationship between technology acceptance and the satisfaction and trust was widely described and then the relationship between satisfaction and trust introduced. These issues show that there is a close relationship between these two structures and this model. Finally, Due to studies done by researchers in other researches, expressed a combined model obtained from trust and satisfaction which are important in acceptance of electronic banking.

References Journal 1. Al-Hajri S, Tatnall A (2008) Technological innovation and the adoption of internet banking in Oman. Electron J Virtual Organ Netw (eJOV) 10:59–83. Special issue on living labs, August 2008 2. Alsajjan B, Dennis C (2010) Internet banking acceptance model: across-market examination. J Bus Res 63:0148–2963, forthcoming. http://www.arraydev.com/commerce/JIBC/031101.htm 3. Berry LL (1995) Relationship marketing of services-growing interest: emerging perspectives. J Acad Mark Sci 23(4):236–2450 4. Crosby LA, Evans KR, Cowles D (1990) Relationship quality in services selling: an interpersonal influence perspective. J Mark 54(3):68–81 5. Nah F, Davis S (2002) HCI research issues in electronic commerce. J Electron Commer Res 3(3):98–113 6. Geyskens I, Steenkamp JBEM, Scheer LK, Kumar N (1996) The effects of trust and interdependence on relationship commitment: a trans-Atlantic study. Int J Res Mark 13(4):303–317 7. Kanthawongs P (2011) A structural model of student satisfaction in web-based ERPsimulated learning environments. Int J Educ Inf Technol 5(2): 8. Klopping IM, Mckinney E (2004) Extending the technology acceptance model and tasktechnology fit model to consumer e-commerce. Inf Technol Learn Perform J 22(1)35–48. Spring 9. Rigopoulos G (2007) A TAM framework to evaluate user’s perception towards online electronic payments. J Internet Bank Commer (JIB) 12(3):1–6. December. http:// www.arraydev.com/commerce/jibc/2007-12/George_final.pdf 10. Schoder D, Madeja N (2004) Is customer relationship management a success factor in electronic commerce? J Electron Commer Res 5(1):38–53 11. Yousafzai SY, Foxall GR, Pallister JG (2010) Explaining internet banking behavior: theory of reasoned action, theory of planned behavior, or technology acceptance model? Cardiff Univ J Appl Soc Psychol 40(5):1172–1202

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Book 14. Gefen D (2000) E-commerce: the role of familiarity and trust. Omega 28(6):725–737 15. Hapson J, Logari B (2005) Service management, customer-oriented culture. In: Irannezhad Parizi M (ed) Interpreter, 2nd edn. Modiran, Tehran 16. Kripanont N (2007) Examining a technology acceptance model of internet usage by academics within Thai business schools. Victoria University, Melbourne March 17. Venoos M, Safariyan D (2001) Marketing banking services. Negahe Danesh, Iran

Conference Paper or Contributed Volume 18. Abdolah Poor M, Kaviyani A (2010) Explain the role of online service quality in Internet banking and its impact on customer satisfaction. The set of articles for 2nd international conference on financial services marketing, Tehran, Iran 19. Ashrafi Joo B, Shojaei M (2008) Elimination infrastructure challenges for the development of e-Banking. The set of articles for 2nd international conference on e-Banking, Tehran, Iran 20. Bashiri M, Joneidi M (2007) Effect of trust on customer acceptance of internet banking, based on TAM model, Mellat bank case. The set of articles for 2nd international conference on eBanking, Tehran, Iran 21. Chismar WG, Wiley-Patton S (2002) Does the extended technology acceptance model apply to physicians. The 36th Hawaii international conference on system sciences (HICSS’03), vol 36, no 03, 2002 IEEE 22. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340 23. Deng Z, Lu Y, Chen Z (2007) Exploring Chinese user adoption of mobile banking. The sixth Wuhan international conference on e-business. e-Business track, Wuhan, China, pp 176–182, May, pp 26–27 24. Grazioli S, Jarvenpaa SL (2000) Perils of Internet fraud: an empirical investigation of deception and trust with experienced internet. IEEE Trans Syst man Cybern A Syst Hum 30(4):395–410 25. Kotler P, Armstrong G, Saunders J, Wong V (2001) Principles of marketing, 3rd edn. Veronica, Prentice Hall 26. Mayer RC, Davis JH, Schoorman FD (1995) An integrative model of organizational trust. Acad Manag Rev 20(3):709–734 27. McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 13(3):34–59 28. Misztal BA (1996) Trust in modern societies: the search for the bases of social order. Polity Press, Cambridge 29. Ratnasingham P (1998) The importance of trust in electronic commerce, internet research: electronic networking applications and policy 8(4):313–321 30. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46(2):186–204

Part II

Computer Convergence

Architecture for Secure SCADA System Based on IF-MAP Scheme Chuan Deng, Lin Yang, Linru Ma and Yuan Cao

Abstract SCADA system is the brain of ICS. Along with more and more interconnection, many security challenges have risen up. Protection SCADA system is significant critical to national infrastructure. Based on IF-MAP scheme published by TCG, we research the integration of IF-MAP and SCADA system, present an architecture for secure SCADA system by utilizing IF-MAP function of publish, subscribe and search network metadata in real-time, to unify and coordinate all the potential loosing security and safety entities, implementing defense-in-depth and defense-in-coordination. Keywords SCADA


IF-MAP
Coordination
Security

C. Deng (&) Institute of Communication Engineering, University of Science and Technology of PLA, Nanjing, People’s Republic of China e-mail: [email protected] L. Yang
L. Ma Institute of Electronic Equipment System Engineering Corporation of China, Beijing, People’s Republic of China e-mail: [email protected] L. Ma e-mail: [email protected] Y. Cao National Defense Technology University, Changsha, People’s Republic of China e-mail: [email protected]

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1 Introduction SCADA systems, as the brains of the national critical infrastructures, such as water distribution and wastewater collection systems, oil and gas pipelines, electrical utility transmission and distribution systems, and rail and other public transportation systems, have become a major security concern recently. The widespread interconnectivity of these industrial control systems with enterprise IT networks, combined with legacy systems that can’t be easily patched due to operational considerations and protocols never designed for security, have triggered a potential attacks from various adversaries. A sophisticated targeted ICS attack started by Stuxnet [1] worm in July 2010 has greatly shocked ICS engineers and IT security professionals. Even though there has been numerous articles and guidelines published to aid SCADA system, considering the significant role of it, now available security schemes for SCADA are far from preventing system from all the discovered vulnerabilities and potential penetration. Complicating the situation, most of these SCADA devices and protocols have little or no security capability on their own and will take decades to be replaced due to cost factor. Common security solutions applied to SCADA system are limited to paired firewall between corporate network and control network, simple authentication by password, VPN for remote access, IDS for abnormal flow and others. What worse, in order to mitigate influence with routine operation, some of these security solutions are configured as weak as possible. For example, password in HMI cannot be set very long or complex for sake of emergent cases. And firewall policy is configured to avoid blocking any instruction data flow. These practical paradigms may take the essential difference between SCADA and legacy IT system into consideration. But it is obviously disobey the original principles for SCADA security protection. How to secure SCADA and still leave the system reliable enough to provide critical services such as power or water 24 9 7 9 365 is worthy of endeavor. IF-MAP Binding for SOAP specification version 2.0 [2] and IF-MAP Metadata for Network Security version 1.0 [3] published by TCG in July 2010, provide a possible robust solution to satisfy both reliability and security, which can be used seamlessly with legacy SCADA equipment. By introducing a MAP server, IFMAP enhanced equipments can provide rapid information sharing and notification of problems (metadata of security) around whole the system. Utilizing these metadata of security, all security assets and even physical safety equipments can coordinate to establish a real Defense-in-Depth. It is an ideal match for industrial control systems where time is of the essence. While our framework is a Proof of concept, Byres Security teamed with Juniper Networks to secure SCADA with Tofino [4] solution has made some practical progress. This paper briefly shows new implication of IF-MAP introduced by TCG in Sect. 2, presents a novel framework applying IF-MAP to protect SCADA system in Sect. 3, and demonstrate some possible use cases in Sect. 4 to prevent ‘‘Possible

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Incident Scenarios’’ by some use cases. At last we conclude our paper and future work.

2 IF-MAP Background In 2008, the Trusted Computing Group’s Trusted Network Connect Working Group (TNC-WG) released its initial IF-MAP (Interface for Metadata Access Points) specification. This open standard extended the TNC architecture for network security to support standardized, dynamic data sharing through Simple Object Access Protocol (SOAP) exchanges among a wide variety of networking and security components. The initial version of IF-MAP enabled integration of network security functions such as network access control (NAC), remote access, intrusion detection, endpoint profiling, behavior monitoring, data leak detection, etc. With several suppliers shipping IF-MAP support in their products, a number of end users have successfully piloted and/or deployed production systems based on IF-MAP 1.1. But in the last two years, customers and suppliers have discovered new uses for IFMAP. Because the protocol is highly extensible, they’ve been able to apply its publish and subscribe semantics to fields such as cloud computing, Industrial Control Systems and SCADA security, physical security, and more. To enable and ease new applications of IF-MAP, TCG has now separated IFMAP into a base protocol (IF-MAP 2.0) and a set of network security metadata (IF-MAP Metadata for Network Security 1.0). Now innovators can employ the base protocol without having to worry about the network security aspects. Because MAP servers are metadata-independent, they can be used for any application. Innovators only need to develop IF-MAP client code for their application. Products with IF-MAP support currently available from suppliers include the Great Bay Beacon endpoint profiler; Juniper Networks Unified Access Control (UAC) and SSL VPN appliances (SA); Infoblox DHCP Server and Orchestration Server; Insightix BSA Business Security Assurance suite; Lumeta IPsonar active network discovery solutions; Hirsch Electronics Velocity Physical Access Control System and Byres Security Tofino industrial security gateways. Following Fig. 1 describes MAP role in TNC v1.4 architecture. Here Metadata Access Point acts as a MAP sever, supporting IF-MAP protocol and real-time ‘‘meta-database’’. While all the other elements interacting with MAP server using IF-MAP protocol to publish, search or subscribe metadata, are classified as MAP clients, which include firewall, IDS, AAA, VPN gateway, GPS, badge readers and so on. Of course, MAP scheme can extend beyond TNC architecture, such as integrated into SCADA system, only if there are network elements supporting IFMAP protocol [5]. It is really a big challenge to coordinate all the security and safety elements in the SCADA system.

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Fig. 1 IF-MAP scheme used in TNC architecture

• Security and safety infrastructure is complex, heterogeneous, and usually distributed. • Large real-time data flows between infrastructure components. • Needed for coordination between Sensors, Flow Controllers, PDP, physical safety assets etc. • Components often interested in different patterns and events. • Timely routing and reliability of delivery of metadata is critical for coordination. • Coordination data is loosely structured and changes frequently. MAP is specifically designed to fit the security coordination use case. MAP models all system elements into identifiers (all objects are represented by unique identifiers), links (Connote relations between pairs of objects), and Metadata (attribute containers attached to Identifiers or Links). All identifiers and links exist implicitly, but have no meaning until metadata is attached to them. IF-MAP operations and data types are represented as XML documents. Here in Fig. 2, identifiers are represented by ovals, metadata is represented by rectangles, and links are represented by lines connecting identifiers. It describes all the data related with a ‘‘access request’’ originated by an ‘‘identity’’ using a ‘‘device’’ with ‘‘MAC-address’’ and a leased ‘‘IP-address’’, through ‘‘switch device’’ and ‘‘PDP device’’, authorized with a ‘‘capability’’. All MAP clients publish their own related metadata to MAP server, which in turn integrated them into such like a paradigm, supported other element’s subscription and search operation.

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Fig. 2 Paradigm of IF-MAP data model

3 IF-MAP in SCADA System Industrial control systems such as SCADA that control critical infrastructure operation has evolved from original isolated systems running proprietary control protocols using specialized hardware and software into nowadays more and more interconnecting systems running standard control protocols using commercial hardware and COTS. The Smart Grid is an excellent example of industrial systems connecting using Internet protocols. In this case, power plants and the electrical distribution grid are connected to the internet. Unfortunately, an unprotected system on a network is vulnerable to attack by hackers. Even industrial systems that are connected internally but not connected to the Internet still use Internet protocols or others that are even more vulnerable. This makes it relatively easy for someone to hack those systems. As a result, industrial controls systems and the Smart Grid environment can benefit from IF-MAP. IF-MAP can ensure that the only device on the network that can control a particular critical load such as a chemical pump is the device that is supposed to control that pump. IF-MAP provides rapid information sharing and notification of problems; it is an ideal match for industrial control systems where time is of the essence. As a possible approach to integrate IF-MAP into SCADA system, a MAP server should exist in control system LAN, which is responsible for storing published metadata and responding clients’ search, subscribe and poll operation. Infoblox’s

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IBOS (Infoblox Orchestration Server) and Juniper Network’s IC Series Unified Access Control Appliance have been available as a MAP server, while IRON project from FHH and OMAPD project support open-source MAP server. Then MAP clients can be any entity in the control system, as long as they support IFMAP. In fact, MAP client may include IDS, Firewall, SEM/SIM, DHCP, physical safety devices and so on. ARUBA, Great Bay, Juniper, Lumeta, Tofino have provide related products as MAP client. While there also exists several opensource IF-MAP client developed in language of JavaScript, Perl, and C/C ++ [9]. Thus we have designed a framework of SCADA system integrated with IFMAP scheme, and implemented a prototype to demonstrate how effective it can be as to coordinate with every IF-MAP-ware entity to improve security protection of SCADA system. Figure 3 describes this framework, in which MAP clients are embedded with an IF-MAP protocol enabled client agent. To entity in legacy SCADA system, this IF-MAP protocol enabled client agent can be implemented as an independent End box working together with host entity; while in new designed system, entity can be designed with IF-MAP protocol enabled client agent from the ground.

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Considering the trend of interconnection of enterprise network and SCADA control network, we equip MAP server in SCADA control access network, which is responsible for analysis of all the access requests from enterprise network (or even from Internet) to SCADA control network. When there is a new access request from a relative new agent (periodically), AAA server in access control network will analyze this agent according to security policy, based on Directory Server, Posture Validation Server, and Audit Server. Actually, this architecture forms a Trusted Network Access, as the TCG TNC v1.4 specification demonstrated. In this architecture, AR means access requestor from enterprise network and internet. PEP (Policy Enforcement Point) is the switch with firewall. PDP (Policy Determination Point) stands for AAA server and following Directory Server, Posture Validation Server, and Audit Server. MAP server is a new added server. MAP clients can be all the Sensor and Flow Controller in the scope of whole architecture, like IDS, DHCP, SEM, AAA, Data Historian, wireless access point and so on. Details about how to establish a trusted SCADA system is out of scope of this paper, which will focus on integration IF-MAP protocol with SCADA system and some use cases. We can also refer some advance in the related research topics, like a Scalable Trusted Software Dissemination System [10], to help the collection of proof of system status, Trusted Computing Dynamic Attestation Using a Static Analysis based Behavior Model [11], to collecting dynamic proof of system status, Integrated management platform for seamless services provisioning in converged network [12], to converge multi-system information to get integrated cooperation, and a secure communication in integrated heterogeneous wireless networks [13] to enhance the communication between wireless access point and central control point.

4 Some Use Cases Our new IF-MAP-based SCADA security system allows corporate-wide security policy to be easily deployed for SCADA devices that simply don’t know what security is. It lets the information driving these policy decisions be dynamically determined from a large number of real-time sources, like Active Directory servers, SCADA equipment positions or network security events. Use Case I: AAA server cooperates with IDS AAA server can leverage abnormal activity information discovered by IDS for real-time adjustment of access control policy. First, AAA establishes a subscription session with MAP server by IF-MAP protocol, to subscribe all the potential access requestor’s abnormal activity information. In the same way, IDS establishes a publication session with MAP server. During the running of SCADA system, IDS works to detect intrusion. Given an intrusion actually being discovered, IDS publish this information in an XML format file to MAP server. MAP server then relay this message to all the interesting party, including AAA server. Now AAA

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server knows there bursts an intrusion based on a specific entity or from such a platform. Then AAA server informs switch to deny any new access request from this entity or platform, to prevent any potential undermining activity to control system. Use Case II: Firewall cooperates with Physical Safety By IF-MAP scheme, traditional industrial physical safety equipment can work together with new information security equipment, like physical badge reader cooperating with firewall. The way to establish communication session with MAP server is the same as Use CaseI. Physical badge reader publishes metadata about employee who has passed the physical badge reader using their eID card to enter control center. When establishing a new control session, firewall searches the eID in the control session is or is not included in the eID list that physical badge reader has published. Then firewall can determine whether to allow or deny this control session establishment. Thus, this is a novel way to integrate physical safety and network security into a coordination fashion for control system protection.

5 Conclusion IF-MAP schema, as this paper demonstrates above, can bring a new sight of pervasive security and unified security, only if more and more network entity began to adopt IF-MAP-ware equipment. Based on IF-MAP sophisticated adoption, many more novel applications can be surely developed. Until now, Juniper and Infobox Corporation have developed MAP server, and Arcsight, ARUBA, LUMETA, have already IF-MAP-supported productions as MAP client. Acknowledgments This project work is sponsored by the National High Technology Research and Development Program of China (863 Program), under Grant No. 2009aa01z426.

References 1. Stuxnet (2010) A breakthough, http://symentac.com/connect/blog/stuxnet. Accessed 29 Nov 2010 2. IF-MAP Binding for SOAP specification version 2.0 (2010) http://trustedcomputinggroup.org . May 2010 3. IF-MAP Metadata for Network Security version 1.0 (2010) http://trustedcomputinggroup.org. July 2010 4. Tofino, Byres Security teams with Juniper Networks to Secure SCADA with Tofino, http:// tofinosecurity.com/blog/tags/tofino 5. TNC-RSA-2010 workshop, TNC everywhere, Pervasive Security 6. Eric Byres, Scott Howard, Metadata Access Point (IF-MAP), New Standards Protecting Old Technology, Oct 2010 7. Clifford N (2010) Understanding trust and security in SCADA systems. In: Second workshop on advances in trusted computing (WATC’10), Tokyo, Japan, Nov 2010

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8. Okhravi H (2010) Trusted_and_High_Assurance_Systems. Dissertation, University of Illinois at Urbana-Champaign 9. Open source IF-MAP project, http://www. ifmapdev.com 10. Chuan D, Lin Y, Linru M, Yuan C Towards a practical and scalable trusted software dissemination system. JoC 2(1):53–60 11. Li T, Yu F, Lin Y, Kong X, Yu Y Trusted computing dynamic attestation using a static analysis based behaviour model. JoC 2(1):61–68 12. Kryvinska N, Van Thanh D Strauss C (2010) Integrated management platform for seamless services provisioning in converged network. IJITCC 1(1):77–91 13. Xie B, Kumar A, Zhao D, Reddy R, He B On secure communication in integrated heterogeneous wireless networks. IJITCC 1(1):4–23

Research on the Model of the Survivable Storage System (SSS) and Its Architecture Xiao-yuan Yang, Jing-jing Wang and Xiao-feng Chen

Abstract In view of the present inconsistent understanding of the model definition of survivable storage network, a seven-tuple model definition and a kind of architecture of SSS were given, taking the characteristics, such as wide-open style and high intricacy, of the actual network environment into account. In addition, some detailed analysis to the key elements of the model and the modules of the architecture was made separately. Our research can provide guidelines for design and construction of SSS, besides, it can also provide basic methods for evaluating the survivability of a storage system. Keywords SSS

 Seven-tuple model  Architecture

1 Introduction Since 1993 Neumann et al. proposed the definition of network system survivability until now, research on the survivable storage has been lasted for nearly two decades. However, there are lots of issues of network system survivability still X. Yang (&)  J. Wang (&) Key Laboratory of Network and Information Security of CAPF, Engineering College of CAPF, Xi’an 710086, People’s Republic of China e-mail: [email protected] J. Wang e-mail: [email protected] X. Yang  X. Chen (&) Key Laboratory of Computer Networks and Information Security of the Ministry of Education, Xidian University, Xi’an 710071, People’s Republic of China e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_42, Ó Springer Science+Business Media B.V. 2012

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need further and more researches. Yang et al. [1] found that there are only a few of the relevant thesis, which about the survivable storage, have studied the problem of the definition of the network systems survivability by using the methodology proposed in paper [2]. Furthermore, they drew a conclusion that a unified, standardized definition of network systems survivability has not been formed in the academic world. In the outcomes already achieved in the researches till now, Knight Research Group of the university of Virginia proposed the formalized definition of the network survivability, which is respectively a four-tuple model [3] and a six-tuple model [4]. But they didn’t give the genericity of the system survivability. An eight-tuple formalized definition of the survivable system was proposed in paper [2]. However, it didn’t take the time factor into account. In paper [5], the time factor was considered, then a seven-tuple formalized definition and its application examples were given. But the key elements such as actual work environment of the network, different users’ requirements and so on. Furthermore, a five-tuple formalized definition was put forward in paper [6], combining with the thoughts of intrusion masking and defense-in-depth. It’s a pity that the author didn’t pay attention to the characteristics, open-wide style and high intricacy, of the practical network environment. What’s more, the indication of the work environment, the analysis of survivability requirements and the vague trust relationships between the nodes didn’t considered in its model definition. Therefore, the model definition of SSS is worthy of further study, and its corresponding architecture also needs to be developed and perfected. A seventuple formalized definition of SSS and its architecture are given in this paper based on the previous research outcomes. Besides, both of the definition and architecture are analyzed respectively.

2 A Seven-Tuple Model of SSS 2.1 The Characteristics of the Open Network Environment At present, the bounded network is no longer the mainstream. The open large-scale network system which has no boundary has become the major data service object. Compared with bounded network environment, open large-scale storage network basically has the features as follows: 1. The number of the storage nodes is huge and changing. Besides, faults are unavoidable for the storage nodes; 2. The storage environment always be threatened and attacked. Even some devastating disasters will happen; 3. The network is highly distributed, and there is no one, neither any organizations or any individuals, have the entire management authority;

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4. Each node doesn’t fully know the situation of the whole network or the other nodes; 5. The nodes are not all completely credible. The mutual-operations between different nodes are decided by the protocol between them.

2.2 The Seven-Tuple Model Definition Focus on the characteristics of the open network environment, here we give the seven-tuple model definition of SSS as follows, based on the five-tuple model definition [6] which was proposed in 2008 by Zhang. Definition 1 A survivable storage system can be defined as a 7-tuple. The Definition of the seven-tuple \ E, S, D, C, F, A, R [. E is the specific description of the storage system’s work environment, including the sets of storage nodes (servers) and users, resources distribution and the system or the users’ requirements or needs, etc. S is the set of services which are provided for users by storage system. D is stands for the technologies and algorithms of data processing, such as the data dispersal algorithm used in the process of data shares storage. C is the strategy of choosing storage nodes. F is the set of different system-fault types, including the disposal methods. A is the protocols of access control and authentication between storage nodes (servers) and users. R is the protocol of data-reconstruction. In the definition above, the elements which belong to the tuple model interact with each other, so as to realize the survivability of the data services in the storage system. In addition, the importance of some elements should be stressed under control, according to the practical requirements of the system and users.

2.3 The Elements Analysis 2.3.1 Work Environment E In the storage system, E is the description of its work environment, including the set of user-nodes, the set of server-nodes, practical natural conditions of the environment, software and hardware configuration, the specifications of system and users’ requirements and needs, etc. All these factors can be expressed by different sets, of which there are many concrete factors. Good description of the work environment is helpful for the designer to achieve an overall and detailed analysis for the features of the storage system, but also helpful to refine the requirements definition of the system and users accurately. At the same time, good description is conducive to carry on

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targeted tests of the storage system, providing better basis so as to realize its survivability further.

2.3.2 Services Provided by System S Form the service-orientated perspective, the system mainly provides data services to its users, such as accessing, deleting, updating the files and so on. The data files can be text, image, audio, video and other multimedia files. If S ¼ fs0 ; s1 ; . . .; sn gis the set of all the services can be provided by the storage system, each element si ði  0; i 2 Z Þin S stands for one kind of service provided by it. Because of the users’ different identities in reality, their corresponding services have different priorities, which denoted as G ¼ fg0 ; g1 ; . . .; gm g. Each element gi ði  0; i 2 ZÞin G stands for the priority of one certain user’s service provided by the system. For every service provided by the system, there is a particular probability to it according to the user’s requirement or need for the survivable system and the priority of it. Supposing the probability is denoted as P : jpi 2 Rj0  pi  1; i  0; i 2 Z j, and R stands for the real number. So the reliability of the certain service ended can be defined as Pi. P is one of the standards to judge whether the system realizes its survivability. From the perspective of system maintenance, the services are composed by a series of non-basic and basic services [7]. When the system is attacked, it needs to suspend some non-basic services, release the resources occupied by them, and start some appropriate services to replace them. So as to weaken the influences brought by the attacks, provide uninterrupted services to the users, and maintain the system’s survivability.

2.3.3 Technologies and Algorithms of Data Processing D In the open network environment, the users always take care of the quilities of the data services much more. In order to provide timely and sustainable services, we should first process the mass data files, then stored them distributely in the netwrok. At present, the data processing algorithms usually adopted are as follows: (1) Backup and data-mirror Backup is the most basic and direct method [8]. Remote backup can ‘save the life’ of the data files at the critical moment. However, it still has disadvantages of information leak and occupying too big storage space. If combining backup with secret sharing and erasure code, etc., we can enhance the survivability of data services greatly. (2) Secret sharing scheme Secret sharing scheme is proposed by Blakley [9] and Shamir [10] first, including threshold secret sharing scheme and non-threshold secret sharing scheme, which are all common methods to protect data files used in the research of SSS.

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Threshold secret sharing scheme has the advantage that once some shares have been modified, the probability of obtaining semantically meaningful information is very small if these modified shares participate in the recovery process. Take (k, n) threshold secret sharing scheme for example, the original secret only can be recovered by any k or more than tðm  tÞ participants sharing their secret shares. And the secret couldn’t be recovered by less than k participants. In addition, we always adopt short-share secret sharing scheme to adapt the mass ocean-store. If consider the unreliability of the secret distributor and the share keepers, we may adopt the verifiable secret sharing schemes or other distributed authentication schemes, so that the user can find out which nodes’ shares have been modified. Thus he can avoid obtaining shares from these nodes and start updating promptly. (3) Erasure coding First, erasure coding divides the data file into m segments. Then it transforms the m segments into nðm  nÞ shares, in which any tðm  tÞ shares can restore the original data file. Compared with the threshold secret sharing scheme, erasure code more emphasize the data file’s reliability. Till now, there are lots of data processing algorithms based on erasure codes, such as Information Dispersal Algorithm (IDA) [11] proposed by Rabin, which is very classical. Above all, when we design a data processing algorithm, it is necessary to consider the efficiency of data distributed storage, but also prevent the information from leaking or losing. In the research of SSS, it is an important problem that designing a safe and high-efficiency data processing algorithm, combining with the knowledge of complexity theory and information theory.

2.3.4 Strategies of Choosing Storage Nodes C Data services are provided together by the storage nodes in the distributed storage system, so all the services relate to the problem of nodes selection [6]. The key point of data storage is the selection of storage nodes, and the strategy of nodes selection should take the factors such as nodes’ reliabilities, whether the operating systems have the same structure, geographical environment, hardware configuration, whether cross different networks, and the remaining available storage space etc. into account comprehensively. In addition, during the process of providing data services, the storage network should change constantly the node selection strategy according to the quality of services requested and the number of updated share as have errors. What’s more, four common strategies of storage nodes selection were proposed in paper [12]. And it obtained a conclusion through experiments of the survival rate and space utilized rate. Namely, the first strategy, choosing nodes randomly, is the best storage strategy. Combining with the actual situation of the network to choose the appropriate storage nodes, so as to reduce the burden of updating shares, improve the efficiency and provide the protection for continuous and timely data services.

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In the storage system of network, besides the text files, the plentiful multimedia files, such as images, are also very important. Through the conclusion above, in paper [13], we put forward a new kind of image survivable storage scheme combing with hyperchaotic. Because there are four pesudo-random sequences fX; Y; Z; U g in the hyperchaotic system, and after quantification they can be denoted as fx; y; z; ug. In order to realize the image survivable storage, this scheme first uses the x-sequence to scramble the rows and columns of binary-layers of the image so as to achieve its encryption. Then it uses the y-sequence to divide the encrypted image into a random number of sub-blocks, uses the z-sequence to choose the sub-blocks randomly, and uses the u-sequence to choose the storing nodes randomly. At last, all the sub-blocks of the image are stored respectively in the random selected storing nodes. The experiment simulations in our paper [13] can prove this scheme can fully realize choosing storing nodes randomly, providing the security and the disater recovery of the stored images at the same time. In addition, some optimization analysis were also done by two different cases. What’s more, according to the conclusions of optimization analysis given, not only can the server determine how many subblocks should be divided into for an image, but also can judge the storage network whether meets the basic survivable requirements.

2.3.5 System Fault Types and Disposal Methods F In the research of survivability, the faults in the system are always divided into three types [6]: crash faults, symmetric faults and Byzantine faults. Crash faults generally mean faults of the hardware or the communication lines, leading the data be transmitted abnormally. Symmetry faults are not easy to be discovered by users. Although the normal service provided by the system still can be carried out continuously, the data provided is malicious or invalid information, because the data has been modified by the behavior of attack or intrusion. Byzantine faults stand for random faults, namely the behavior of the nodes which have faults is random. Thus they can be regarded as software faults, including the disruption of the normal services brought by the attacker’s intrusion. Because when symmetric faults happen, the data is modified, and the user couldn’t obtain accurate information, which is similar to the situation that the system couldn’t provide normal data restore services. Therefore, in the storage system, the symmetry faults are same as Byzantine faults, which means the faults can be divided into two types directly: Byzantine faults and crash faults. In SSS, if (k, n) threshold secret sharing scheme is adopted to data dispersal, then the user must send requests of read to k storage nodes so as to restore the data. Supposing there are only two types of faults in SSS, and there are t nodes which have faults, of which there are c crash faults and b Byzantine faults. Then, the worst situation is that the t nodes have faults are all included in the nodes which the user has sent request of read to. Thus, the accurate information cannot be recovered. At this moment, the user should re-choose t nodes from the rest

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n-k nodes, and send access request again. Therefore, for this kind of system, we must request n  k  t [6]. Besides, the situation of the system adopting erasure codes is similar to the one adopting threshold secret sharing schemes. In the open network environment, malicious attacks, natural disasters, accidents, computer viruses, the ageing hardware of the node itself and so on all may lead the storage nodes go wrong in the system. Ideally, the mechanism of intrusion or fault detection should be able to forecast the possibility of the fault which will happen in the storage system, and be able to repair the system or eliminate the possibility before the fault happens. However, such kind of technique still needs to be researched urgently, and the rate of wrong pre-alarm is still very high. Therefore, under normal circumstances, the key point of the system is to discover the faults already happened in time, determine the fault type, and start the appropriate disposal measures on its own initiative before the fault disrupting data services. So as to remove the fault promptly, and the data services can still be completed correctly in the tolerance-time set by the system and user. When some fault happens, one kind of fault can determine its own disposal scheme according to its fault type identified by the fault identification mechanism and the relationship between different nodes’ faults. Whether this disposal scheme can eliminate the influence brought by the fault timely and effectively, is the key of reducing the risk rate of faults happening in the future in order to realize SSS.

2.3.6 Access Control and Authentication Protocols A The attacks to the storage system can come from various aspects. They may come from the storage node itself, may come from a user, who may be authorized already or even is an illegal user. The purpose of attacks can be illegally gain other users’ private secret information, or can be simply disrupt the network by leading faults. Therefore, SSS must carry out access control to all the participants of the data service and the applications started by them. What’s more, access control always pays more attention to the problem of access authorization, so as to improve the quality of data services through mutual authentication among the participants. Compared with the general authentication schemes, besides the basic features of general authentication schemes, the identity authentication schemes of SSS should also have the features as follows: 1. The authentication schemes should have high efficiency. Because the system has to store mass data, and the quality of data service is very important for it, so the authentication schemes must be efficient. 2. The authentication schemes should be able to self-updating smoothly, which can be realized by self-renewal hash chains. 3. The authentication should be mutual, not only limited to the one-way authentication of the servers to the users.

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4. The authentication should be anonymous, and ensure that any other malicious third party is not able to obtain the true private information of the users or servers. Both sides of the authentication should keep certain concealment during the process of communication. Anonymous authentication can be realized by the method of combining hash chains with one-time signature or zero-knowledge proof. In our paper [14], we proposed a self-renewal hash chain scheme based on fair exchange idea (SRHC-FEI) as the identity authentication scheme of SSS. It combines with One-Time Signature (OTS) so as to realize mutual-authentication, anonymous-authentication and self-renewal of hash chain simultaneously. Besides, the analysis results of our paper [14] compared with ILHC [15], Zhang’s [16, 17], Zhang’s [18], may indicate our scheme has many good properties that a good authentication scheme should have.

2.3.7 Data-Reconstruction Protocols R SSS not only requires data services can be carried out continuously even when fault happens, but also the function of self-adaptive re-configuration [6, 19]. Reconfiguration is one kind of redundant management scheme, which is the behavior of recovery and counterattack after the system being attacked or having faults. The main function of re-configuration is using the other nodes’ correct shares to restore the date, when some nodes’ shares have been lost or destroyed. By this way, the lost or destroyed shares of the nodes can be re-constructed. Then the reconstructed shares can be re-distributed to the other reliable storage nodes, or can be used to replace the shares which are not available in the original nodes. This is data reconstruction. Data re-construction provides the possibility of realizing the storage system’s survivability, it focuses on the recovery of the destroyed data in the nodes which have faults to maintain the consistency of the data, but not the updating of all the data shares in all the nodes. Once some storage node’s fault or intrusion has been detected, data re-construction should be started simultaneously, so as to recover the original data. Many systems are designed to have the function of data reconstruction, such as OceanStore [19, 20], etc.

3 The Architecture of SSS SSS should consider many factors overall, such as efficiency, security and quality of services and so on. Every module in the system should not only give full play to its function, but also integrate with each other, together providing services harmoniously, so as to provide protection of the system’s survivability. What’s more,

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survivability is the entire feature of the network system, and any parts of the system all may have faults or intrusions [21]. If the architecture is designed well, it is possible that the survivability of the whole system are better than some parts of it, even sometimes, it may have no survivability in some functions. In this case, the system may still has good survivability, which is noteworthy in qualitative or quantitative analysis of system’s survivability [22].

3.1 Design of the Architecture The survivable network system is different from the traditional security network system, the latter more emphasizes the resistance and prevention against the attacks. Once the attacks succeed, the system will collapse, losing its original significance. And in the open network environment, SSS should be service- oriented, it pays much more attention to the protection and recovery of itself and basic services, not only the prevention against the attacks. From the view of dialectics, the survivable network systems are more scientific and reasonable. Therefore, when we design the architecture of SSS, we must consider this specially. Here, the architecture of SSS is designed, which is shown as Fig. 1, based on the ideas of resistance, detection and intrusion tolerance [6]. According to Fig. 1, we describe the work process as follows: 1. The user asks for a data service, and start the corresponding application. 2. The user’s identity is authenticated, the requests and needs of the system and user are purified, the corresponding service type is determined, and the application is offered a legal identification. 3. The application with legal identification enters into the read/write-process through the access control module. During the write-process, the data files need to be processed by some certain algorithms. 4. Intrusion, fault and environment detection modules interact with the reliability analysis module, providing the basis of node selection strategy together with the quality of service evaluation module. 5. After choosing the nodes, carry out the read/write-protocols, and provide corresponding data service. 6. The quality of service evaluation module interacts with the reliability analysis module according to the final situation of the data service provided. Then choose and start some mechanisms such as risk remediation, remote backup, fault nodes replacement, self-adaptive evolution, etc. In order to improve the architecture and the configuration of the system, recover the data files and enhance the survivability of the system. As shown in Fig. 1, a large number of different types of data files are stored in SSS safely and intactly. The important point of the system lies on evaluate the

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Fig. 1 The architecture of SSS (The real lines in the figure stand for the data channels, the dashed lines stand for the metadata channels.)

influences on the system and the service brought by different factors (including attacks and accidents, etc.), so as to evaluate the quality of the corresponding service. Furthermore, appropriate remedial measures according to the evaluation results should be adopted, and the strategies, schemes even architecture of the system should be adjusted, in order to provide continuous as well as high-quality services, which makes the system has stronger survivability.

3.2 Analysis of the Architecture 3.2.1 Data Processing Module After the application passing the access control, the read/write protocol is started. The data files must be processed before storing in the nodes, so as to ensure they can be recovered when fault happens. The data processing module includes encryption and decryption to the mass data, coding and decoding, and other data processing methods, which is shown in Fig. 2. Specific operations can be selected according to the purified requests and needs of the system and user.

Research on the Model of the SSS Fig. 2 Data processing module

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Fault detection module Fault recognition Recovery from fault

3.2.2 Fault Detection Module As shown in Fig. 3, the main function of fault detection module is to recognize the fault, determine its type and start appropriate self-adaptive disposal measures to recovery from the fault. First, the fault detection mechanism finds the node with fault. Then it carries out some certain data re-construction algorithm, and roll back the data stored in the node to the state before having the fault or choose a new node to store the data, replacing the fault node, according to the node’s fault type. And not only the fault recognition mechanism can determine the fault type of the node, but also can trace some wrong share to locate the node with fault accurately when it is necessary (Fig. 4).

3.2.3 Storage Environment Detection Module In view of the features of storage nodes’ natural environment, the storage environment detection module can test the factors of the storage environment such as temperature, humidity, vibration, pressure and so on selectively and regularly. When the storage is being threatened, it is necessary to start remote backup mechanism or hand over the unfinished service to the other nodes to complete after rolling it back the to the original state. In this way, it is possible to minimize the destruction to SSS brought by the accidents, so as to provide continuous services.

3.2.4 Intrusion Tolerance Module An ideal intrusion detection system (IDS) should have a large range covered and a low rate of wrong alarm. However, the present IDS are far away from this request. So it is very necessary to improve the present IDS, so that the simple intrusion

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Fig. 5 Intrusion tolerance module

Intrusion tolerance module Intrusion detection Intrusion response Intrusion masking

detection mechanism can evolve into the intrusion tolerance mechanism, which including the function of intrusion detection [22]. Because the idea of intrusion tolerance accepts the existence of the weak points in the system, so its aim designed as keeping the system working normally, providing basic key services continuously, and maintain the basic functions of the system [23] by the way of starting some mechanisms that can prevent the system’s security from losing, even when some parts or components of the system have already been destroyed by attacks, or have been controlled by the malicious intruders. Just because of the thought above, we add intrusion tolerance module into the design of the architecture. This module includes three parts: intrusion detection module, intrusion response module and intrusion masking module, which is shown as Fig. 5. In this module, the main function of intrusion detection module is finding out the behaviors without access authorization to the system resources or the behaviors with the aim to destroy them by detecting the network flow, the running state of the host computers, and the quality of services. The main function of the intrusion response module is judging and recognizing the intrusion behaviors reported. The main function of the intrusion masking module is taking some appropriate measures according to the features of the intrusion behaviors, so as to reduce the destructions and impacts on the system brought by them. Therefore, the system can tolerate the intrusion behaviors which have already happened. Just as paper [24] said, the intrusion tolerance module should pay much more attention to the survival ability of the system under the circumstances that the intrusion behaviors exist. Before the intrusion leading great damage to the system, suitable protection and remediation measures should be adopted first. Taking advantage of the fault-tolerance techniques of the soft/hardware in the distributed system to mask the impacts on the system’s function brought by intrusions or attacks, so as to realize the security and continuity of the system’s key functions.

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3.2.5 Reliability Analysis Module The main function of the reliability analysis module is collecting other modules’ information, integrating multi-factors, and judging the reliabilities of storage nodes and data services, which is shown in Fig. 6. The quantitative evaluation here includes the worst survival rate [12], the usage rate [12], the rate of having faults, and the rate of successful services, etc. Once the system runs into the abnormal state, the first problem is not to find out the reason why the system’s state changed, but to evaluate the impacts brought by intrusions and faults, so as to make the system can recover its services successfully and promptly. At the same time, the impacts are reduced to the minimum [25]. The reliability analysis module interacts with the quality of service evaluation module to get information, which can provide abundant basis for the establishment of storage node selection strategy in the data services.

4 Conclusion At present, mature theory outcomes in the research of formalized definition of SSS have not proposed yet. And the architectures already designed out are still need to perfect. In addition, the traditional research on the security techniques has a most common hypothesis, namely, the system being researched and protected is bounded and closed but not open. However, the prevalent systems in today are all open-style systems, which has broken this hypothesis in the traditional security techniques completely. Therefore, in the further research on the model definition and architecture, we should take the open style and complexity of the practical network environment into account fully. In this paper, the defects and disadvantages of the present formalized definition of SSS are first analyzed. Then a kind of seven-tuple model definition is given, and every element of the model is analyzed separately. Further, a kind of architecture of SSS is designed based on the ideas of resistance, detection and intrusion tolerance. Then, every module is expounded in detail, too. This paper may provide a reference for the further research on SSS, and a basic method to design or evaluate a survivable storage system.

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References 1. Yang C, Ma J (2004) Formalized definition of network system survivability (in Chinese)[J]. Electron Sci Technol 4(175):1–2 2. Westmark R (2004) A definition for information system survivability. In: Proceedings of the 37th Hawaii international conference on system sciences. Springer–LNCS: IEEE, pp 2086–2096 3. Knight JC, Sullivan KJ (2000) On the definition of survivability. Technical Report: CS-TR33-00, Department of Computer Science, University of Virginia, pp 33–48 4. Knight JC, Strunk EA, Sullivan KJ (2003) Towards a rigorous definition of information system survivability. In: Proceedings of DARPA information survivability conference and exposition, vol 1, Washington, USA, pp 78–89 5. Yuan D, Zhang Y (2006) Research on definition of network survivability (in Chinese). J Comput Res Develop 43:527 6. Zhang W (2008) Theory and key technology research on survivability of information (in Chinese). Xidian University, Xi’an, pp 22–24, 27–28, 32–38 7. Ellison RJ, Fisher DA, Linger RC et al (1999) Survivability: protecting your critical systems. IEEE Internet Comput 3(6):55–63 8. Joseph Cooley A, Jeremy Mineweaser L, Leslie Servi D et al (2003) Software-based erasure codes for scalable distributed storage. In: Proceedings of the 20th IEEE/11th NASA Goddard conference on mass storage systems and technologies (MSS’03) 9. Blakley GR (197) Safeguarding cryptographic keys. In: Proceedings of the national computer conference. American Federation of information processing societies proceedings, vol 9, pp 313–317 10. Shamir A (1979) How to share a secret. Comm ACM 22(11):612–613 11. Rabin MO (1989) efficient dispersal of information for security, load balancing, and fault tolerance: Harvard Universty, Cambridge, Massachusetts. J Assoc Comput Mach 36(2):335–348 12. Hayashi D, Miyamoto T, Doi S, Kumagai S (2002) Agents for autonomous distributed secret sharing storage systrem. In: Proceedings of the 2002 international conference on circuit/ systems computers and communications, pp 482–485 13. Yang X, Wang J, Zhong W et al (2011) An image survivable storage scheme based on hyperchaotic. In: Proceeding of ICCSN 2011, EI (00000000000000) 14. Yang X, Wang J, Chen J, Pan X (2010) A self-renewal hash chain scheme based on fair exchange idea (SRHC-FEI). In: The 3rd IEEE international conference on computer science and information technology (ICCSIT 2010), EI (20104413338326) 15. Bicakci K, Baykal N (2002) Infinite length hash chains and their applications. In: Proceedings of IEEE 11th international workshops on enabling technologies (WETICE 2002). IEEE Computer Society, Washington, DC, USA, pp 56–61 16. Zhang H, Zhu Y (2006) A self-renewal hash chain mechanism. Trans Wuhan Univ (Edition: Science) 52(5):596–599 17. Zhang H, Li X, Ren R (2008) A novel self-renewal hash chain and its implementation. In: IEEE/IFIP international conference on embedded and ubiquitous computing, pp 144–149 18. Zhang M, Dong B, Yang X (2009) A new self-updating hash chain scheme. In: International conference on computational intelligence and security, IEEE, pp 315–318 19. Kubiatowicz J, Bindel D, Chen Y et al (2000) OceanStore: an architecture for global-scale persistent storage. In: Proceedings of the ninth international conference on architectural support for programming languages and operating systems (ASPLOS 2000), pp 190–201 20. Rhea S, Eaton P, Geels D et al (2003) Pond: the OceanStore prototype. In: Proceedings of the 2nd USENIX conference on file and storage technologies 21. Krings AW, Harrison W, Azadmanesh A et al (2004) Scheduling issues in survivability applications using hybrid fault models. Paral Proc Lett 14(01):6

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22. Lejun Z, Lin G, Wei W et al (2007) The research summarization of technology of network system survivability evaluation and enhancement. Comput Sci 34(8):30–32 23. Guo Y, Ma J (2005) the analysis of in-abroad research actuality and existing issues for tolerance inbreak. China Inf Secur 07:337 24. Guo Y (2005) Theory and methods of intrusion tolerance: research and applications. Xidian University, Xi’an, pp 8–9 25. Chao W (2006) Theory and key technology research on survivable network system. Xidian University, Xi’an, pp 5–7

New Improved Impossible Differential Attack on Reduced-Round AES-128 Ya Liu, Dawu Gu, Zhiqiang Liu, Wei Li and Weihao Kong

Abstract In this paper, we propose a novel impossible differential attack on 7-round AES-128. Firstly, we construct some new 2-round impossible differentials of AES, which allow us to distinguish the wrong keys from the correct key more efficiently. Based on them, we present an impossible differential attack on 7-round AES-128. The data complexity is about 280 chosen plaintexts. Compared to the best known result, the data complexity of our attack is reduced by nearly 226:2 times. Keywords Block Cipher

 AES  Impossible differential attack

Y. Liu (&)  D. Gu  Z. Liu  W. Kong Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China e-mail: [email protected] D. Gu e-mail: [email protected] Z. Liu e-mail: [email protected] W. Kong e-mail: [email protected] W. Li School of Computer Science and Technology, Donghua University, Shanghai 201620, China W. Li Shanghai Key Laboratory of Integrate Administration Technologies for Information Security, Shanghai 200240, China

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_43,  Springer Science+Business Media B.V. 2012

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1 Introduction Block cipher is used in wireless network to protect the security of communication [1–4]. The Advanced Encryption Standard (AES) [5] is a SP-network block cipher with 128-bit block size. It supports three kinds of variable key sizes and the number of rounds depends on the key size, i.e., 10 rounds for 128-bit keys, 12 rounds for 192-bit keys and 14 rounds for 256-bit keys. For simplicity, they are denoted as AES-128, AES-192 and AES-256, respectively. Since it was selected as the standard by NIST in 2001, AES gradually became one of the most widely used block ciphers and its security has also gotten a great deal of attentions. When it was proposed, the designers carefully considered to resist many classic attacks such as differential cryptanalysis and linear cryptanalysis. Then, during the AES process and after its selection, more and more researchers reevaluated its security under various cryptanalytic methods, e.g., algebraic attack, collision attack, meetin-the-middle, related-key attack, impossible differential attack and so on [6–13]. Impossible differential cryptanalysis was independently proposed by Knudsen [14] and Biham et al. [15]. Its main idea is to use impossible differentials that hold with probability zero to discard the wrong keys until only one key left. Up to now, impossible differential cryptanalysis had received much attention and was used to attack a variety of well-known block ciphers. In the following, we will illustrate some previous impossible differential attacks on 7-round AES-128. The first attacks were presented independently in [10, 11], which required about 2115:5 chosen plaintexts and 2119 7-round encryptions. Then, Lu et al. improved their results in [12]. They required 2112:2 chosen plaintexts and 2117:2 memory accesses. The latest result on impossible differential cryptanalysis of AES-128 was proposed by Mala et al. [13]. They adopted some techniques to attack on AES-128 with about 2106:2 chosen plaintexts and 2110:2 7-round encryptions. In this paper, we present sixty 2-round impossible differentials of AES and mount a novel impossible differential attack on 7-round AES-128 successfully. Compared to the previously known results, our attack has achieved the best data complexity. As a matter of fact, we only require about 280 chosen plaintexts, which is nearly 226:2 times as much as the best results on impossible differential cryptanalysis of AES-128. Meanwhile, the time and memory complexities are approximately 2127 7-round encryptions and 265 128-bit blocks of memory. In Table 1, we summarize our results along with the previously known results on impossible differential cryptanalysis of AES-128. The remaining of this paper is organized as follows. Section 2 gives a brief description of AES and some notations. Section 3 first presents a lot of new 2-round impossible differentials, and then proposes an impossible differential attack on 7-round AES-128 successfully. Section 4 concludes this paper.

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Table 1 Summary of previous impossible differential attacks and our new attack on AES-128 Rounds Data (Chosen plain texts) Time Memory (Byte) Source 5 7 7 7 7 7

229:5 2115:5 2115:5 2112:2 2106:2 280

231 E 2119 E 2119 E 2112:3 E 2110:2 E 2127 E

242 2109 245 293:2 294:2 269

[5] [6] [7] [8] [9] This paper

2 Preliminaries In this section, we first describe AES-128 briefly. Then we introduce some notations.

2.1 Overview of AES-128 AES-128 [5] is a SP-network with 10 rounds. Each 128-bit block is treated as a byte matrix of size 4  4, and the round function has four operations including SubBytes(SB), ShiftRows(SR), MixColumns(MC) and AddRoundKey(ARK). In the key schedule, the secret key is expanded into eleven 128-bit subkeys denoted byW½0;    ; 43. Each word of W½ consists of 32 bits and forms a column of a round subkey. The first 4 words are loaded with the user supplied key and the remaining words are updated according to the following rules: • For i=4,…,43, do • If i  0 mod 4 then W½i ¼ W½i  4  SBðW½i  1\\\8Þ  RCON½i=4 • Otherwise W½i ¼ W½i  1  W½i  4; where RCON½ is an array of predetermined constants and \ 8 is the rotation of a word by 8 bits to the left.

2.2 Some Notations Before introducing our attack, we first give the following notations used in this paper. • xIi ,xO i : the input and output of round i; MC • xSB ; xSR and xARK : the intermediate values after the application of SB, SR, i i ; xi i MC and ARK operations of round i;

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• DxIi and DxO i : the input and output differences of round i; SB SR and DxARK : the differences of the intermediate values after the • Dxi ; Dxi ; DxMC j i application of SB, SR, MC and ARK operations of round i; • k0 ,ki : the whitening key and the subkey of round i; • xi ¼ MC 1 ðki Þ: an equivalent key obtained by applying MC-1 on ki ; • xi;4nþm : the byte located in m-th row and n-th column of xi ; • xi;ðj1 ;j2 ;Þ : the bytes located in positions j1 ; j2 ; . . . of some intermediate state xi ; • xi;colðzÞ : the z-th column of the intermediate state xi , where z 2 f0; 1; 2; 3g; • xi;colðy;zÞ : columns y and z of the intermediate state xi , wherey; z 2{0, 1, 2, 3}; • xi;SRðcolðzÞÞ , xi;SR1 ðcolðzÞÞ : the bytes in xi corresponding to the places after applying the SR or SR-1 operation on column z of xi , where z 2 f0; 1; 2; 3g; • ajb: the concatenation of two bit strings a and b;

3 Improved Impossible Differential Attack on 7-Round AES-128 In this section, we present sixty new 2-round impossible differentials of AES. Based on them, we mount an impossible differential attack on 7-seven-round AES-128 successfully. At last, we analyze the whole complexity.

3.1 New 2-Round Impossible Differentials of AES In this section we present sixty 2-round impossible differentials, which can be used to attack 7-round AES-128. These 2-round impossible differentials allow us to distinguish the wrong key more efficiently. We illustrate them by a proposition. Proposition 1 DxIi and DxSR iþ1 are defined as before. If the following two conditions hold: (1). The 0-th and 2-nd columns of DxIi are zero only in bytes 0 and 10; (2). In DxSR iþ1 , at least one of four bytes 0, 7, 10 and 13, is non-zero. Then DxIi ! DxSR iþ1 is an impossible differential. There are fifteen impossible differentials. If we interchange the order of the ARK and MC operations in round i þ 1, then DxIi ! DxARK iþ1 are also impossible differential. Similarly, if the 0-th and 2-nd columns of DxIi are zero only in bytes 1 and 11 (or bytes 2 and 8, or bytes 3 and 9) and at least one of the bytes in DxSR iþ1 corresponding to the places after applying SR-1 operation on column 3 (or column 2, or column 1) is non-zero, then DxIi ! DxSR iþ1 are impossible differentials.

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Fig. 1 A new 2-round impossible differential of AES

In Fig. 1, we list one of our 2-round impossible differentials. Among it, the bytes denoted by # of the intermediate state xiþ1 are non-zero. If all bytes of the column 0 in xIiþ1 are zero, then it is possible not to meet a contradiction in the middle. Although the length of our impossible differentials is not the maximal, we can still present an impossible differential attack on 7-round AES-128 based on them. The data complexity is with very low compared with the previously known results. In the following, we will elaborate our attacking algorithm.

3.2 New Impossible Differential Attack on 7-Round AES-128 In this sub-section, we will propose a novel impossible differential attack on 7-round AES-128 by the use of sixty 2-round impossible differentials in Sect. 3.1. We put two additional rounds on the plaintext side and three additional rounds on the ciphertext side of the 2-round impossible differentials. Some previously known techniques, such as the early abort technique and key schedule consideration, are collected in order to reduce the whole complexity. Meanwhile, we divide the key space into some small sets and swap two linear operations, the ARK operation and the MC operation, in the 4th and 6th rounds. This attack is illustrated in Fig. 2. Based on sixty 2-round impossible differentials above, we mount an impossible differential attack on 7-round AES-128 successfully. The precise attacking algorithm can be presented as follows: 1. Select a set of 264 plaintexts which have certain fixed values in all but the bytes (0,2,5,7,8,10,13,15). Call this special set a structure. Clearly, each structure can form 2127 plaintext pairs such that the differences of the bytes (1,3,4,6,9,11,12,14) are zero. Take 2n structures. In total, there are 2nþ64 plaintexts and 2nþ127 plaintext pairs ðP; P0 Þwith the desired difference DP ¼ P  P0 . 2. Choose only those plaintext pairs whose ciphertext pairs have zero difference in all bytes except the (0,7,10,13) bytes. The expected number of such pairs is 2nþ127  296 ¼ 2nþ31 . Implementing this step, we require 264 blocks of memory for the hash tables and 4  2nþ31 blocks of memory for the proper ciphertext pairs.

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Fig. 2 New improved impossible differential attack on 7 rounds of AES-128

3. Based on the key schedule of AES-128, we can obtain the following equations: k1;ð0Þ ¼ k0;ð0Þ  SBðk0;ð13Þ Þ  C; k1;ð2Þ ¼ k0;ð2Þ  SBðk0;ð15Þ Þ  C0 ; k1;ð8Þ ¼ k0;ð4Þ  k0;ð8Þ  k1;ð0Þ ¼ k0;ð4Þ  k0;ð8Þ  k0;ð0Þ  SBðk0;ð13Þ Þ  C; k1;ð10Þ ¼ k0;ð6Þ  k0;ð10Þ  k1;ð2Þ ¼ k0;ð6Þ  k0;ð10Þ  k0;ð2Þ  SBðk0;ð15Þ Þ  C0 ; Therefore, we only guess each possible value k0;ð0;2;4;5;6;7;8;10;13;15Þ in order to get all possible values of k0;ð0;5;10;15Þ jk0;ð2;7;8;13Þ jk1;ð0;2;8;10Þ . 0MC 4. Guess each of 232 possible k0;ð0;5;10;15Þ , and then compute xMC 0;colð0Þ and x1;colð0Þ . If MC MC DxMC 1;ð1;3Þ ¼ ð0; 0Þ, Dx1;ð0Þ 6¼ 0 and Dx1;ð2Þ 6¼ 0, then these plaintext pairs are left. 2

16 . Therefore, the expected The probability for that happens is q1 ¼ 255 2554 2 nþ31 16 nþ15 number of the remaining pairs is 2 2 ¼2 . 5. For each of the remaining 2nþ15 pairsðP; P0 Þ, continue to guess all possible 0MC MC MC values of k0;ð2;7;8;13Þ . Compute xMC 1;colð2Þ andx1;colð2Þ . If Dx1;ð9;11Þ ¼ ð0; 0Þ, Dx1;ð8Þ 6

¼ 0 and DxMC 1;ð10Þ 6¼ 0, then these plaintext pairs continue the following steps. The probability of this event is q2 216 . Thus, the number of the remaining pairs is about 2nþ15  216 ¼ 2n1 .

New Improved Impossible Differential Attack Table 2 Complexity of our attack

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232  2  2nþ31 ¼ 2nþ64 264  2  2nþ15 ¼ 2nþ80 280  2  2n1 ¼ 2nþ80 2112  2  2n15 ¼ 2nþ98 2130

6. Guess each possible value k0;ð4;6Þ . By the key schedule, we can calculate k1;ð0;2;8;10Þ . For each of the remaining 2n1 pairs ðP; P0 Þ, we compute xMC 2;colð0;2Þ and MC 0MC MC 0MC MC 0MC x0MC 2;colð0;2Þ . If x2;ð0;10Þ ¼ x2;ð0;10Þ (or x2;ð1;11Þ ¼ x2;ð1;11Þ , or x2;ð2;8Þ ¼ x2;ð2;8Þ , or 0MC xMC 2;ð3;9Þ ¼ x2;ð3;9Þ ), then these plaintext pairs are left. The probability for that occurs  3 2 14 is q3 ¼ 255 . So the expected number of the remaining pairs is 2554 4 2

about 2n1  214 ¼ 2n15 . 7. Continue to guess k7;ð0;7;10;13Þ . Decrypt the remaining 2n15 ciphertext pairs to 0ARK ARK obtain xARK 6;colð0Þ and x6;colð0Þ . If all bytes of Dx6;colð0Þ are non-zero, then these pairs n15 251 can be left. The probability for that happens is 251  255

255. There are about 2 n15:00228 2 plaintext-ciphertext pairs left. 8. Because k7;ð0;7;10;13Þ provides four bytes information on the user supplied key, we can use it to set up four equations on some bytes of the secret key. Combining the guessed key k0;ð0;2;4;5;6;7;8;10;13;15Þ , we obtain fourteen equations on some bytes of the secret key. As a result, we only need to guess two bytes of k0 (e.g. k0;ð1;3Þ ) so as to recover the whole cipher key. Furthermore, all bytes of x6;colð0Þ and k5;ð0;5;10;15Þ can be calculated. For each of the remaining 2n15:00228 pairs ðC; C 0 Þ, if it can be decrypted to the corresponding output differences of impossible differentials, the guessed key was removed. We expect that the plaintext pairs are removed with the probability 1  232 .

Up to now, we have presented an impossible differential attack on 7-round AES-128 successfully. In the following, we will discuss the whole complexity. In step 8, for each of 216 possible k0;ð1;3Þ , the probability for that happens is 1  232 . Denote the number of the wrong keys for two-byte k0;ð1;3Þ bye. Thus n15:00228

e ¼ 216 ð1  ð1  232 ÞÞ2 . Take n ¼ 18, then e ¼ 2240 . At this moment the data complexity is 2nþ64 ¼ 282 chosen plaintexts. The time complexity can be calculated in table 2. Thus the overall time complexity is about 2130 1-round encryptions, which is 130 equivalent to 2 7 2127 7-round encryptions. Meanwhile, the memory complexity is about 265 128-bit words of memory. We can further reduce the data complexity. Take n ¼ 16. At this time, the data, time and memory complexities are about 280 chosen plaintexts, 2127 7-round encryptions and 265 128-bit of memory, respectively.

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4 Conclusion In this paper, 7-round AES-128 has been broken with relatively low data complexity by the use of sixty 2-round impossible differentials. In contrast with the previous known results on impossible differential cryptanalysis of AES-128, our proposed attack allows us to make full use of the redundancy of the key schedule. Consequently, the data complexity is reduced greatly. As a matter of fact, to break 7-round AES-128 we require about 280 chosen plaintexts, which is nearly 226:2 times as much as the best results on impossible differential cryptanalysis of AES128. Furthermore, the time and memory complexities are 2127 7-round encryptions and 265 128-bit blocks of memory, respectively. Acknowledgments This work has been supported by the National Natural Science Foundation of China (No. 61073150 and No. 61003278), the Opening Project of Shanghai Key Laboratory of Integrate Administration Technologies for Information Security, and the Fundamental Research Funds for the Central Universities.

References 1. Hsu P, Tang W, Tsai C, Cheng B. Two-layer security scheme for AMI system. J Converg 2(1):47–52 2. Imani M. Taheri M, Naderi M. Security enhanced routing protocol for ad hoc networks. J Converg 1(1):43–48 3. Kumar D, Aseri TC, Patel RB. Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks. Int J Inform Technol Commun Converg 1(2):130–145 4. Rahman MZ, Pathan A. A case study: establishing redundant access networks in the telecommunication sector of a developing country. Int J Inform Technol Commun Converg 1(1):108–126 5. Daemen J, Rijmen V (2002) The design of Rijndael: AES—The advanced encryption standard. Springer, Heidelberg 6. Murphy S, Robshaw MJB (2002) Essential algebraic structure within the AES. In: Yung M (ed) CRYPTO 2002. Lecture notes in computer science, vol 2442. Springer, Heidelberg, pp 1–16 7. Gilbert H, Minier M (2000) A collision attack on 7 rounds of Rijdael. In: The third AES candidate conference, pp 230–241 8. Demirci H, Taskin I, Coban M, Baysal A (2009) Improved meet-in-the-middle attacks on AES. In: Roy B, Sendrier N (eds) INDOCRYPT 2009. Lecture notes in computer science, vol 5922, Springer, Heidelberg, pp 144–156 9. Biryukov A, Khovratovich D, Nikolic I (2009) Distinguisher and related-key attack on the full AES-256. In: Halevi S (ed) CRYPTO 2009. Lecture notes in computer science, vol 5677, Springer, Heidelberg, pp 231–249 10. Bahrak B, Aref MR (2008) Impossible differential attack on seven-round AES-128. IET Inform Security 2:28–32 11. Zhang W, Wu W, Feng D (2007) New results on impossible differential cryptanalysis of reduced AES. In: Nam K-H, Rhee G (eds) ICISC 2007, Lecture notes in computer science, vol 4817, Springer, Heidelberg, pp 239–250

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12. Lu J, Dunkelman O, Keller N, Kim J (2008) New impossible differential attacks on AES. In: Chowdhury DR, Rijmen V, Das A (eds) INDOCRYPT 2008, Lecture notes in computer science, vol 5365, Springer, Heidelberg, pp 279–293 13. Mala H, Dakhilalian M, Rijmen V, Modarres-Hashemi M (2010) Improved impossible differential cryptanalysis of 7-Round AES-128. In: Gong G, Gupta KC (eds) INDOCRYPT 2010, Lecture notes in computer science, vol 6498, Springer, Heidelberg, pp 282–291 14. Knudsen LR (1998) DEAL—A 128-bit Block Cipher, Technical report, Department of Informatics, University of Bergen, Norway 15. Biham E, Biryukov A, Shamir A. Cryptanalysis of skipjack reduced to 31 rounds using impossible differentials. In: Stern J (ed) EUROCRYPT 1999, Lecture notes in computer

Smart Space Model for Facilitating Dynamic Service Binding Changgyu Bak, Euna Choi, Jung-Won Lee and Nammee Moon

Abstract In ubiquitous smart space, identification of the device’s location as well as human is essential to provide dynamic services. Dynamic service binding depends on what service is available, where the service is requested, and which devices can support the service. However, most of research considers that the space is just the plane and the device’s location is represented by the coordinates (x, y) rather than the spaces such as a living room or a kitchen. Therefore, the postprocessing is needed to integrate the above three kinds of information. In this paper, we propose a smart space model for dynamic service providing and the method for managing the device’s location. For this, we developed an embedded system called a space manager and deployed it to our test bed for u-Home. As a result, we showed that our smart space model enabled to manage the multiple spaces and devices effectively.



Keywords Intelligent space Device location awareness Ubiquitous computing Dynamic service binding





C. Bak (&)  E. Choi  J.-W. Lee Deptartment of Electrical and Computer Engineering, Ajou University, Suwon, Korea e-mail: [email protected] E. Choi e-mail: [email protected] J.-W. Lee e-mail: [email protected] N. Moon Deptartment of IT App. Tech. GSV, Hoseo University, Seoul, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_44,  Springer Science+Business Media B.V. 2012

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1 Introduction Ubiquitous Smart Space (USS) is a user-centered convergence space which has characteristics of situation-awareness, autonomic cooperation, and self-growing [1]. The current issue in USS is dynamic service providing by real-time context [2]. The context includes three kinds of information—where the service is requested, what service is available within that space, and which devices can support to the service. That means the Three—the space, the service and the device—in One. Existing location-aware applications and systems are mainly focused on tracking the human’s location and providing location-based services. Even if the location of a device is considered, it is an IP or GPS-based mobile device for providing the seamless services such as ‘TV watching service’ with a DMB phone and a TV monitor. However, in ubiquitous smart space, a various kinds of devices (i.e. a cell phone, a laptop, a humidifier, a lamp, a refrigerator and so on) are participating in service providing. Some devices are mobile and others are static or semi-dynamic. Therefore, identification of the device’s location as well as human is essential to provide dynamic services. There are some techniques for the device’s location awareness with indoor GPS, tagging, sensing, and grouping with IP information [3, 4]. Since they identify the device’s location as coordinates (x, y), we face the following problems for providing dynamic services. First, the space which they deal with is not practical. Until now, the requested service has bound the devices ‘close to the user’ or ‘near here’ by the service discovery. Therefore, the coordinates are enough to compute the distance between a human and a device. However, USS is a convergence space with services, devices, and real-time context. Second, they have traced the movement of devices but they do not consider the way how managing the devices in the aspect of the service execution and the service space. Dynamic service binding depends on what service is available, where the service is requested, and which devices can support the service at that time. Therefore, we need to manage systematically various devices and services distributed in the multiple spaces [5–8]. In this paper, we propose a smart space model for dynamic service providing and the method for managing the device’s location. We first classify the space into two types, physical and logical space, and define seven device types according to the change of device’s location. For this, we developed an embedded system called a space manager and deployed it to our test bed for u-Home. As a result, we showed that our smart space model enabled to manage the multiple spaces and devices effectively. The remainder of this paper is organized as follows. Section 2 compares the related work and Sect. 3 presents USS requirements for dynamic service providing. Section 4 proposes a smart space model and the method for managing the device’s location and Sect. 5 shows the implementation of the embedded system called space manager and the result of testing to the u-Home. Finally, Sect. 6 concludes it.

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Table 1 Comparison between characteristics of the location awareness methods Items Indoor Location Methods BIM Diversity of space Autonomy of management Prior knowledge Subject of events Mobility of objects Space algorithm Location tracking Complexity of systems

Indoor GPS

Tagging

Sensing

Non Non Necessity Objects High Existence Available High

Non Non Necessity Tags High * Low Insufficiency Available High * Low

Non Insufficiency Necessity Objects High Insufficiency Available High

Insufficiency Insufficiency Necessity Careless Careless Non Non Medium

2 Related Work The USS has distinct characteristics which differ from the general building structure. Since USS is a user-centered convergence space, the role of the services and the devices is to provide ‘user-customized’ services. Recently, the locationbased service (LBS) is rapidly emerging as user-customized applications [9]. Therefore, identification of the device’s location as well as human is essential to provide the LBS. The following Table 1 shows a comparison between methods for the location awareness. Recent researches make the localization data by using indoor GPS [3], tracking with various sensors, and RSSI system [10]. Since the subject of events can make changes in a service space, these events are used for the circumstance information. The indoor GPS system improves the problem of not being possible to take signals inside from GPS satellites. To solve this, it experimentally mounts pseudolites to develop an indoor GPS which detects an accurate position of objects. Sensor detecting systems use ultrasonic sensors and beacons [4]. They estimate the direction of a mobile object by using a digital compass also. For localization in a wide space, some researches implement a wireless sensor network based on the ZigBee standard to measure RSSI. These methods generate knowledge that is only simple positions where a focused target is. Since there are only location tips without any information or consideration related to provide services in USS, we need to develop a new research for contemplating offering services to users. Additionally, some studies make the indoor location-oriented service realized by combining a location technology and hierarchical room-based location mode [11]. However, they cannot fulfill conditions to satisfy users by operating autonomic services even they consider a relationship between service spaces and devices. In other words, a new technique is necessary to connect space elements and service information tightly. It lets services be autonomic in USS. The BIM (Building Information Modeling) of architect is tried to consider the information of geometric areas [12]. It makes database about a facility to build and to repair this. However, by the BIM, users can receive only a few services

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restricted in already built areas because it focuses on buildings only without offering services. Since a service should content user’s request with no limitation given by geometric spaces, a new approach is needed to manipulate autonomic service space. Furthermore, most of methods have difficulties to achieve an aim of service which is autonomic and self-binding by connecting organically between spaces and users in USS. Therefore, we should design a new space management model for managing and providing devices and user-centered services considering characteristics of USS.

3 Requirements of Smart Space Most of the space concepts, which have been accepted, are not proper to operate ubiquitous services. Generally the space is divided physically by the size, the area, or its own purpose. Each space is separated by some solid dividers such as walls or pillars. Every event and operation is not able to effect on outside of one defined individual space. When a user requests some services, he gets physical limitation by where he is even in USS. However since the goal of USS service is to satisfy user’s requirements by using unconstrained binding with all potential resources, the novel concept of space should include the following specification. • A concept of space is needed for providing dynamic services. • A standard to divide a space should be defined by a range of where the service affects rather than physical barriers (walls, pillars) or a size. • It has to be free to merge multiple spaces or separate them. Dynamic service binding depends on what service is avalilable, where the service is requested, and which devices can support the service at that time. The following figure shows an example of the dynamic service environment (we have already develop u-Service ontology and this is a part of them [13]). A user requests ‘manageCooler’ service. The ‘Service Discoverer’ discovers and binds appropriate services according to the run-time context in the case of abstract goal descriptions. There are six candidate services—‘CoolerPower’, ‘CoolerControl’, and so on. At this time, if ‘CoolerControl’ service is determined, the appropriate device among devices which support the service is selected. We can think devices such as an air-conditioner, a fan-cooler, and an electric-fan as candidate devices for providing a service. One of them is finally selected by considering where the service is requested. Identification of the device’s location as well as human is essential to provide dynamic services. Although many existing researches suggest indoor location awareness methods, these do not fit into the service environment in USS. These have a system which produces some coordination of objects on pre-informed maps or blueprints, so the complex post-processing is needed to compute the service space. Thus, the new method for the device’s location should satisfy following requirements.

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Fig. 1 The space type: PS and LS

• It should offer the information related to services. The device’s location has to be the result after checking whether the device can support the requested service or not rather than the raw coordinates (x, y). • It enables to query what services are available in the specific space and which devices are ready to support the specific service. • I should be managed the change of the device’s location. The change affects directly on the service execution and halting. Therefore, we propose a smart space model to those requirements in the next section.

4 Smart Space Model In this section, we propose a smart space model and the method for managing the device’s location. We first classify the space into two types, physical and logical space, and define seven device types according to the change of device’s location. Then we manage the change of the device’s location by two events—registration to and withdrawal from the space and find out which device satisfies with the requested service on the specific space in real-time.

4.1 The Space Type We suggest the novel concept of spaces for providing services without any physical limitation. We define the space types as a physical space and a logical space. • Physical Space (PS) Type: this type means the unit of a minimized space for service providing. It is similar to the building structure and the physical division of a blueprint. For instance, they are living room, kitchen and so on as the following Fig. 1. • Logical Space (LS) Type: if two physical spaces are merged into one or one physical space is divided into two spaces, we called them logical spaces. For example, let’s see the following Fig. 1. There are originally six PSs. Let’s

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assume that an air-conditioner exists in PS4. If it can make cooling down the bedroom 1 and 2, a LS can be generated. Therefore, the air-conditioner can operate in LS1—PS4 and PS5.

4.2 The Device Type Under the space types, it is necessary to define the device types for dynamic service binding. We define some functions as the following to reflect the time duration and to query where the device is. • Where (i, j) means a function for finding where an object ‘i’ exists at this point of time = j. The result is a set of PS (including LS). • d is increasing amount of time. It is a window size to examine the change of the device’s location. S • dt¼0 where ðDk ; tÞ : Let’s assume that the relative time to start checking the device’s location is t = 0. After time ¼ d; the change of the kth device’s location is expressed as a set of PS and LS. NA (Not Available): If Sd t¼0 where ðDk ; tÞ ¼ ;; a device, Dk, does not exist at this point of time = t in the USS. • NEW: if the type of Dk was ‘NA’ before time = t and where ðDk ; tÞ ¼ fPSi g; a device (Dk ) has never been registered to any place before t and it is newly entered into ith PS of the USS when time = t. S • STATIC: if dt¼0 whereðDk ; tÞ ¼ fPSi g; the device has never moved to the other : For PS during d: The type of Dk is ‘STATIC’ in PSi and expressed as PSstatic i static = (when PS3 = living room), it means example, if the air conditioner PS3 that the location of the air conditioner is fixed in a living room during d. In Fig. 2 (a), USS has two physical spaces, PS1 and PS2. A device, Dk, is entered into the PS1 and other devices are static.   S • MOVED: if dt¼0 where ðDk ; tÞ ¼ PSi ; PSj when PSi = PSj, the type is S ‘MOVED’. If dt¼0 where ðCD player; tÞ ¼ fPS1; PS2 g ¼ PSmoved when PS1 = 1;2 living room and PS2 = kitchen, the CD player was moved from the living room to the kitchen as shown in Fig. 2(b). S • MOBILE: if dt¼0 where ðDk ; tÞ ¼ fPS1 ; . . .; PSn g; ðn  3Þ; the type is ‘MOBILE’. It seems to offer seamless services for a user with a mobile device such as a cellphone or a DMB in Fig. 2(d).   • OVERLAP: if whereðDk ; tÞ ¼ PSi ; PSj when PSi = PSj, the type of Dk is ‘OVERLAP’. This type is differentiated from the above ‘MOVED’. PSmoved 1;2 means the historical change of Dk and it exists in PS2, currently. But PSoverlap 1;2 means that Dk participates in both of PS2 and PS3 at this point of time. A further

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Fig. 2 The device types: a NEW and STATIC, b MOVED, c OVERLAP, d MOBILE, e FAIL

Fig. 3 The event-transition for the location awareness of Dk

important consideration is that LS can be newly generated by overlapped Dk as shown in Fig. 2(c). Actually, Dk exists in PS2 but it acts as if it is also in PS3. Therefore, LS1 including two spaces, is generated. For example, where ðCD Player; tÞ ¼ fPS2 ; PS3 g when PS2 = living room and PS3 = kitchen, the CDPlayer can provide a service in both of two spaces. Sd S2d • FAIL: if t¼0 where ðDk ; tÞ ¼ fPS1 ; . . .; PSn g and t¼d where ðdevk ; tÞ ¼ ; (n C 1), the type of Dk is defined as ‘FAIL’. The difference of ‘NA’ type is

470 Table 2 Event strings for device location awareness

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Description

a b c d e f

Dk inflows into USS Dk leaves a certain PSi Dk enters into a certain PSi Dk moves other space without any registration Dk ’s location is not changed Location awareness is failed

whether to consider the previous record of the space or not. If a device which operates well in some spaces is disappeared or out of order, it is ‘FAIL’.

4.3 The Event-Transition Diagram for Managing the Device’s Location Now, we got the space type and the device type. Based on these types, we have to monitor the change of the device’s location. From now on, we present an eventtransition diagram for the location awareness of Dk in the following Fig. 3. Each node means the events of Dk such as an initial state (Init), registering to a specific space (Registration), and withdrawing from a space (Withdrawal). We don’t need to check the location periodically because it is important to check whether the device exists in the space or not. Therefore, we focus on the events—registration to the space and withdrawal from it. Table 2 shows the meaning of each transition as behaviors of Dk : In our space model, since the location is time-variant, we do not specify the final state explicitly. Dk can start from an initial state by entering into the smart space but it can stop in any states and any time. Every event string is created by the diagram which is defined in Table 3. (Here, ‘+’ means that the event could occurs one or more times). When a device (Dk ) is entered into the USS for the first time, it is recognized as a smart device of USS by the initial ‘registration’ step. After this, the Dk moves from one PS to another PS and the history of the location is managed to providing dynamic services.

5 Implementation and Experimental Results 5.1 Space Manager To materialize the space model in USS, we designed and implemented the space manager(SM) in Fig. 4. It enables a device to register to PS and withdraw from it.

Smart Space Model Table 3 Correspondence between event string and the types of PS Event string Description a [a|c]e+ bc d e f

Dk inflows into USS Dk leaves a certain PSi Dk enters into a certain PSi Dk moves other space without any registration Dk ’s location is not changed Location awareness is failed

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Types of device status NEW STATIC MOVED MOBILE OVERLAP FAIL

Fig. 4 Space manager

It also sends all kinds of information of the movement history to a home gateway. We have already developed an USA (Universal Service Actuator) which is a kind of adaptor for making a device to compute and communicate the other devices [14]. It is largely composed of five blocks—a human detection sensor, an infrared module, an IEEE802.15.4 module, a Bluetooth module, and a microprocessor (Atmega 128). As you see the next Fig. 5, we deployed SM to our real test bed, u-Home, it shows a device with USA (a kind of an adaptor for communication), SM, and a home-gateway.

5.2 Experimental Result In Fig. 5, it presents home domain environment which our space model is deployed. There are three space managers and they are assigned to each PS (living room, bed room, kitchen). In the left-side picture, since there is no space model adapted, a user can be allowed to use only the defined services limited by installation conditions of the devices. By binding a certain space manager to PS, the registered devices are participating in the service operation based on our space model and the location awareness technique.

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Fig. 5 u-Home service space applied the Space Model

• ‘‘Registration’’ and ‘‘Withdrawal’’ for the device’s location awareness: when all service suppliers (D1 * D7) are entered in USS for the first time, the event, ‘Registration’, makes each device bound with a space manager (SM1 * SM3). When a device is entered in a certain space at the beginning, its inflow and user’s movement are detected by a human detection sensor of a SM. The SM transports device communication settings. After this, the status type of the device is defined as ‘‘NEW’’. The ‘‘NEW’’ device is only available its own registered SM without any interruption from the others. As shown in Fig. 5, a humidifier (D4) is moved from the bed room where it had already been registered to the living room. It will be ready to operate a service in the new service space when it is registered under SM1 (of the living room). D4 has ‘‘MOVED’’ type in SM1 and the list of device management in SM2 is updated that D4 is N/A (Not Available). • ‘‘Sharing’’ by the ‘‘LS’’ type: the audio (D6) was registered in the kitchen for the first time. Since the ‘‘Reading mode service’’ is requested in the living room, the device should be defined as ‘‘OVERLAP’’ status type and takes both space IDs of SM1 and SM2.

6 Conclusion In this paper, we propose a smart space model for dynamic service providing and the method for managing the device’s location. For this, we devleoped an embedded system called a space manager and deployed it to our test bed for u-Home. As a result, we showed that our smart space model enabled to manage the multiple spaces and devices effectively. The space manager is for managing service environments. We realize that our space model can provide zero-configuration services to achieve an intelligent and autonomic operation by ubiquitous computing. Further research should be carried out to establish service policy and information management guideline about

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devices with users. These aim to make ubiquitous service tightly coupled with users by learning about user preference and usage of service. Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0005305).

References 1. Feng Z, Mutka MW, Lionel MN (2005) Service discovery in pervasive computing environments. IEEE Pervasive Comput 4(4):81–90 2. Chakraborty D, Perich F, Joshi A, Yesha Y (2002) A reactive service composition architecture for pervasive computing environments. In: Proceedings of the 7th Personal Wireless Communications Conference, pp 53–62 3. Haruhiko N, Kerni K, Yoshihiro S, Masaumi O, Seiji K, Kenjirou F., Yuki K., Shugeki S (2008) GPS-based indoor positioning system with multi-channel pseudolite. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 905–910 4. Kim H, Choi J (2008) Advanced indoor localization using ultrasonic sensor and digital compass. In: Proceedings of the International Conference on Control, Automation and Systems, pp 223–226 5. Huang Chiming, Cheng Rei-Heng, Chen Shau-Ruei, Li Chung-I (2011) Enhancing network availability by tolerance control in multi-sink wireless sensor networks. JoC 1(1):15–22 6. Imani Mohsen, Taheri Mahdi, Naderi M (2011) Security enhanced routing protocol for ad hoc networks. JoC 1(1):43–48 7. Kryvinska Natalia, Thanh Do Van (2011) Christine strauss, integrated management platform for seamless services provisioning in converged network. IJITCC 1(1):77–91 8. Kumar Dilip, Aseri Trilok C, Patel RB (2011) Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks. IJITCC 1(2):130–145 9. Malkani YA, Dhomeja LD (2009) Location aware device discovery for physically contains environments. In: Proceedings of the 2nd International Conference on Computer, Control and Communication, pp 1–5 10. Masashi S, Tomonori K, Yoshikazu O, Masatuki M (2006) Indoor localization system using RSSI measurement of wireless sensor network based on zigbee standard. In: Proceedings of the IASTED International Conference on Wireless Sensor Networks, pp 188–193 11. Cheng Y (2009) Using ZigBee and room-based location technology to constructing an indoor location based service. In: Proceedings of 2nd International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp 803–806 12. Whole Building Design Guide a program of the National Institute of Building Science, http://www.wbdg.org/ 13. Lee M, Lee J, Park S, Cho W (2010) SOA-based service layering for facilitating dynamic service composition and alternative service discovery. J Inf Sci Eng 26(3):991–1010 14. Choi E, Bak C, Kim S, Lee J (2009) Development of a space manager for ubiquitous smart space. In: Proceedings of the 36th Korea Computer Congress 36(1):256–261

Functionality Evaluation of Ubiquitous Service Ontology for Dynamic Service Composition Meeyeon Lee, Jung-Won Lee, Nammee Moon and Seung Soo Park

Abstract Effective service description and modeling methodologies for dynamic service composition are essential techniques for providing autonomous services in ubiquitous computing environments. In our previous paper, we proposed ‘‘u-Service’’ as an abstract and structured concept for operations of devices in ubiquitous environments. In addition, we established the mechanism to structure u-Services in ontology and the description specification to represent the attributes of u-Services. However, we did not present methods or standards that are sufficient to analyze and evaluate the effectiveness of the u-Service ontology during the design time. Since the existing quality models for software products or computing systems cannot consider the characteristics of u-Services, they are not suitable for a u-Service ontology. Therefore, in this paper, we propose an evaluation model to assess the functionality of u-Service ontologies, based on our u-Service ontology building process. We extract a modeling goal and evaluation indicators according to characteristics of u-Service ontology and establish quality metrics to quantify them. The experimental results for u-Service ontologies that were constructed in our previous studies show that we can comprehensively analyze the design of M. Lee (&)  S. S. Park Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea e-mail: [email protected] S. S. Park e-mail: [email protected] J.-W. Lee Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea e-mail: [email protected] N. Moon Department of IT App. Tech. GSV, Hoseo University, Seoul, Korea e-mail: [email protected]

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u-Service ontology. Further, on the basis of the evaluation results, improvement in design quality can be achieved.





Keywords Ubiquitous computing Service ontology Evaluation metric Dynamic service composition Service-oriented architecture





1 Introduction Ubiquitous computing is aimed at achieving the specific goals of users by providing intelligent and autonomous services according to variable context. In ubiquitous environments where the context like user’s location and available devices keep changing, a system should provide smart services such as creating environmental conditions for a particular user action or detecting/handling a specific event. The core techniques for the service systems are context-awareness and dynamic service discovery/composition [1, 2]. Moreover, it is important to effectively describe and model the information about services as well as the context. Based on these points of ubiquitous services, in our previous studies [3, 4], we abstracted operations (functions) of devices deployed in ubiquitous environments as ‘‘ubiquitous service (u-Service)’’. Further, we proposed the ubiquitous service ontology (u-Service ontology) classifying u-Services by the degree of abstraction and structuring them by using the service layer method of SOA (Service-Oriented Architecture) [5]. However, to guarantee the quality of a u-Service ontology, some design principles or evaluation methods are required. A u-Service ontology is considered to have good quality if it has an effective service description and if it can fully support dynamic service discovery and composition of service systems, which are the ultimate purposes of a u-Service ontology. In the research field of general ontology, many studies on ontology evaluation have been conducted in order to select a good ontology. That is because the quality of an ontology affects the effective reusing/sharing of knowledge and performance of applications such as inference systems [6]. Most of the studies on ontology selection or evaluation [6, 7] have been conducted for domain ontology, which represents declarative knowledge. This type of ontology describes vocabulary or concepts, and their semantic relations in a specific domain. On the other hand, the main description unit of service ontology is ‘‘service’’ which is a set of procedures or actions and includes information about its sub-processes, their execution order, and input/ output parameters [8, 9]. For these two types of ontology, their targets and factors to describe are different from each other; therefore, there are two standard languages; OWL (Web Ontology Language) [10] and OWL-S (OWL for Services) [9], respectively. The evaluation models for domain ontology check the efficiency

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of concept hierarchies, the semantic relationships between concepts, a quantity of represented knowledge, etc. These factors are not sufficient and helpful for assessing the capability of dynamic service composition of service ontology. Therefore, we analyzed some models that have been intensively studied or have already been established as standards in various fields. ISO/IEC 9126 [11–14] is a standard for software products and has been used as the basis for creating other quality models. Based on this model, QMOOD (Quality Model for Object-Oriented Design) [15] was developed for object-oriented software. The model considered elements of object-oriented systems such as objects, classes, and methods. These two models cannot be directly applied to ontology, which is a type of knowledge base, but we can refer to their development methodology or the form of quality model. Some studies on SOA focused on assessing whether a system is designed on the basis of serviceoriented characteristics. In the design quality model of SOA [16], This model defined the evaluation factors from SOA design components such as web services, their operations, and messages used in operation calls. It appears to be similar to our model with respect to the evaluation time because the evaluation is conducted during the design time. However, this model could not consider features of ubiquitous environments and u-Services like context-awareness, because its target is business systems consisting of web services. UCQM [17] is a quality model for ubiquitous computing systems. It adopted some quality characteristics from ISO/IEC 9126 model and created some new ones based on eight key characteristics of ubiquitous computing systems such as sensing environmental changes, ubiquitous accessibility, and n-to-n connection. It can evaluate performance (functionality, usability, portability, mobility) of ubiquitous systems in ubiquitous environments such as u-home, u-company, and u-mart. Since this model can evaluate just execution performance of systems, it cannot check the design of u-Service ontology also. To select the optimal Web service by evaluating the performance of many ones, QoS (Quality of Service) for Web services [18, 19] becomes very important. However, QoS deal with just execution performance of an individual Web service such as the service error rate, the failure rate, availability for use, and the processing time on networks. In other fields, there are also many studies to assess their target [20–23], however, they are not suitable for a u-Service ontology. We cannot use these existing quality models to evaluate the functionality of u-Service ontology, but they are well-established and widely used models in various areas. Therefore, we can refer to them including the development process, while extracting, classifying, and naming new factors. In this paper, we propose a model that helps in designing a good u-Service ontology and in evaluating its quality with respect to dynamic service composition during the design phase and not during the run-time. In particular, we focused on the functionality of a u-Service ontology to support dynamic service discovery and composition. Furthermore, we define evaluation metrics for u-Service ontology.

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Fig. 1 The structure of u-Service ontology (a group related to ‘‘E-Temperature’’ Effect to control temperature)

2 u-Service Ontology The target of typical service ontology is Web services or business services, not services in ubiquitous environments. Therefore, in our previous studies [3, 4], we proposed a methodology to extract u-Services and represent their properties. The u-Service ontology is a type of service ontology that describes and structures u-Services. ‘‘U-Services’’ are unit that abstracted operations of various devices deployed in ubiquitous environments. We classified the u-Services into three levels according to their degree of abstraction. Next, we describe their properties, which are checked for dynamic service discovery and composition, using u-Service description specification. Followings are the main factors among 14 factors of our u-Service description specification [3, 4]. • The level of abstraction (AbsLevel): three abstraction levels of u-Services. ‘‘ABSTRACT’’ indicates the highest level, ‘‘COMPOSITE’’ indicates the middle levels, and ‘‘ATOMIC’’ is for atomic u-Services in the lowest level. • Pre-condition to run u-Service (Precondition-Status): status of devices. • Effects occurring after running u-Service (Effect - Functional/Environmental Effect): functional (‘‘showing images’’, ‘‘warning of danger’’, etc.) or environmental (‘‘controlling noise’’, ‘‘rising temperature, etc.) effects that are produced after the execution of the u-Service. U-Service ontology has a hierarchical structure in which the u-Services are grouped in three abstraction levels based on functional/environmental Effects. Fig. 1 shows the structure of our u-Service ontology that exhibits a tree-like form, and it is a group of u-Services related to an environmental Effect for controlling temperature (E-Temperature).

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3 Evaluation Model with Metrics for Ubiquitous Service Ontology To define the evaluation metrics for the u-Service ontology, we set its design goal and selected features that should be considered in the design phase for achieving the goal. A good u-Service ontology must be capable of fully supporting service discovery, alternative service discovery, and the u-Service overloading method for dynamic service composition [3, 4]. With dynamic service composition, a service system developer or a user describes context and service information. By using this knowledge, a system searches, selects, and composes appropriate services for the run-time context. It is applicable to ubiquitous, pervasive, mobile environments because of its high adaptability and flexibility according to context (user’s location, time, etc.). Therefore, we set the ultimate goal of u-Service ontology as ‘‘dynamic service composition’’. This goal implies that u-Service ontology should be the basis for dynamically selecting the appropriate u-Services and/or alternative ones according to the run-time context. Therefore, we establish the evaluation factors for designing a u-Service ontology that can accomplish this goal. Evaluation factors are the explicit factors that the target must comply with so that they can be used for assessing the target. The evaluation factors for u-Service ontology can be derived from above mentioned three base areas, and we are mainly concerned with SOA service design principles for service modeling. SOA asserts that it is very important to produce, describe, and model high quality services. It defines a set of eight design principles and recommends creating services based on them [24]. In this section, we analyze each SOA principle in terms of the difference between SOA services and u-Services. Then, we chose two (Abstraction, Autonomy) among eight principles after modifying them to be more proper for u-Service ontology. The other six were excluded because they are the nature of u-Service ontology that does not need to evaluate (Statelessness, Discoverability, Service contract). Some of them (Reusability, Loose coupling, Composability) are more related to the structure of u-Service ontology than to functional aspects. Therefore, a functionality evaluation model proposed in this paper is grounded on the assumption that u-Service ontology is well-formed with u-Service description specification. Besides these, in order to reflect characteristics of u-Service ontology, we added new factors based on the three base areas. A new factor ‘‘Replaceability’’ can consider that we have to diversify services which can take the similar effect in order to replace a service with a similar one according to the context, because ubiquitous environments are dynamically changeable and unpredictable. In addition, since user goals in ubiquitous environments are relatively ambiguous and abstract, the service system should be able to discover and select the best service for the run-time context with u-Service ontology. The factor ‘‘Polymorphism’’ evaluates these points.

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Table 1 Evaluation factors of u-Service ontology with metrics for dynamic service composition Evaluation factors Evaluation metrics (conceptual) # of ’dynamic uServices0 Abstraction total number of ’uServices’ ð1Þ # of ’dynamic effects’ Replaceability totoal number of ’effects’ (2) P # of ’polymorphic u - services of each u - Service’ Polymorphism total number of compositions (composite uServices)’ (3) # of ’u - services without prerequisite u - Services’ Autonomy (4) total number of ’uServices’

Our four evaluation factors are selected to achieve an efficient design, so they do not consider the running performance such as the service time, the service success rate, and the cost of execution. These factors are listed in Table 1, and we explain in detail each factor with their evaluation metrics in sub-sections,

3.1 Abstraction A u-Service ontology is a set of u-Services that are the abstracted unit provided by various devices or platforms in diverse environments; and not a simple list of device operations [3, 4]. If a u-Service ontology can allow a u-Service to be executed by different devices, then it is possible to decrease the failure rate of service execution. In other words, if a device breaks down or it is already running the other service, then it should be replaced by the other device to successfully execute the service. The ‘‘Object’’ factor of our u-Service description specification designates the devices performing the u-Service. Domain knowledge in the u-Service ontology should include abundant device information that needs be used for u-Service description. As shown in Fig. 2 (b), u-Services AS-5 and AS-6 are unavailable when the device is not deployed in environments or has troubles because a single device (D-1 or D-2, respectively) is assigned to them. On the other hand, AS-7 or AS-8 can be executed by replacing unavailable devices in a class with others in another class. In the case of u-Service AS-7 and AS-8, these two u-Services are abstract, and we call them dynamic u-Service. The definition of ‘‘dynamic u-Service’’ in metric (1) is ‘‘u-Services that can be run by multiple (more than two) device classes or platforms’’. In Fig. 2, AS-7, AS-8, CS-4, CS-3, and CS-2 are ‘‘dynamic u-Services’’. If the value is close to 1, it implies the high abstraction (between 0 and 1).

3.2 Replaceability Ubiquitous environments have variable factors such as location and available devices. Even when there is no device to execute the service or when running services cannot satisfy a user, ubiquitous service systems should be capable of

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Fig. 2 Examples of u-Service ontology

providing services seamlessly. To achieve this, u-Service ontology organized all u-Services with (functional/environmental) Effects. Here, Effect implies the effects or certain circumstances caused by running services, as explained in Sect. 3. It is recommended that u-Service ontology has this structure so that the u-Services with the same Effect can be recognized and then the unavailable u-Services can be substituted with available one. If, for example, the u-Service ontology describes no Effect information (Fig. 2 (a)) or if the relation between an Effect and a u-Service is 1-to-1, then the service provision rate and user satisfaction will de-crease because the u-Service ontology cannot support the replacement between u-Services. In other words, if a service required for a goal is unavailable, then user satisfaction will decrease owing to the failure of just one service. In the case of Fig. 2 (b), u-Services are grouped based on their Effects so that a system can identify u-Services that can cause the same Effect. Consequently, when a u-Service (AS-6 in Fig. 2 (b)) is not executable, it can be replaced by another (AS-8) to achieve the Effect (E5). The definition of ‘‘dynamic Effect’’ in metric (2) given in Table 1 is ‘‘the Effects that can be achieved by multiple (more than two) u-Services’’. The Effects E1 and E5 shown in Fig. 2 (b) represent the dynamic Effect. The range of this measurement is from 0 (low replaceability) to 1 (high).

3.3 Polymorphism This factor is from object-oriented paradigm. We redefine the meaning of polymorphism for u-Service ontology as ‘‘composite u-Services (ABSTRACT or COMPOSITE) will be bound and executed by the most appropriate atomic u-Service among sub-u-Services according to the run-time conditions’’. Even if a user does not specify the atomic services to be run or ambiguously and abstractly

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describes his/her goal, a system can search the structure of the u-Service ontology to discover the most suitable one. For example, assume that a user want to control temperature. Using the u-Service ontology shown in Fig. 1, the u-Service ‘‘turning on heater’’ may be run under certain conditions, and ‘‘opening curtain’’ is selected under other conditions. In Fig. 2 (b), the composite u-Service CS-2 can be bound to AS-5 or AS-6 or AS-8 depending on the circumstances or user preference. We created the term of ‘‘Polymorphic u-Service’’ used in metric (3). Polymorphic u-Services of each composite u-Service refer to u-Services in its sub-layers, that is, all sub-u-Services. The one u-Service among them will be chosen to perform based on the run-time context. With this definition, polymorphic u-Services of CS-3 in Fig. 2 (b) are AS-5 and AS-6, and CS-2 has six polymorphic u-Services. The Polymorphism value for this u-Service ontology is (6 + 2 + 2)/3 = 3.33. The result value obtained by using metric (3) ranges from 0 to the number of all u-Services, excluding itself. A high value implies effective polymorphism.

3.4 Autonomy A u-Service has no autonomy for execution if there are some other u-Services that have to be executed prior to executing the u-Service. As an example, in our u-Service ontology, we assumed that the u-Service ‘‘HeaterUP’’ can be performed after completing ‘‘HeaterON’’, so ‘‘HeaterUP’’ is less autonomous. Hence, certain u-Services necessarily require preceding u-Services, but it is useful to design u-Services that can run independently. With metric (4) in Table 1, a high value indicates good autonomy.

4 Results To verify whether our model is useful for overall assessment of the functionality of u-Service ontology, we applied our evaluation model to two versions of our u-Service ontology constructed in our previous studies. Our u-Service ontology has been developed for u-Home and public safety management domain by using the ontology editor Protégé [25]. Version 1.0 was constructed at the beginning of our research to establish the building process of the u-Service ontology and the description specification. In version 3.0, we added more domain knowledge (device classes, etc.) and as many u-Services, and Effects as possible in order to increase its size and to make it applicable in the case of more situations (Table 2). Table 3 shows the results. • Abstraction: version 3.0 has weak abstraction. This implies that most of relations between u-Services and their execution platform are 1-to-1 because we simply listed operations of devices appeared in sample situations. To improve

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Table 2 Two versions of our u-Service ontologies for experiment Target comparison u-Service ontology (v.1.0) u-Service ontology (v.3.0) Description Domain # of sample situations # of services # of effects # of device classes

The first version of our u-Service ontology Ubiquitous 12 125 91 61

The latest version to cover additional sample situations Ubiquitous 52 264 217 94

Table 3 Experiment results for two u-Service ontologies Target factors u-Service ontology v.1.0 u-Service ontology v.3.0

Scope

(1) (2) (3) (4)

0 0 0 0

Abstraction Replaceability Polymorphism Autonomy

64/125 = 0.51 21/91 = 0.23 315/45 = 7 29/125 = 0.23

106/264 = 0.4 24/217 = 0.11 741/99 = 7.48 159/264 = 0.6

B B B B

x x x x

B B B B

1 1 b 1

the quality for this factor, we need to vary device classes or form one abstract u-Service unit by combining u-Services that are performed by similar types of devices. • Replaceability: the score for version 3.0 is low. Even though the number of Effects in version 3.0 increased more than twice, a large portion of them are not ‘‘dynamic Effect’’. It seems to be a side effect of u-Service extraction based on sample situations. Since u-Services were extracted from operations of devices that appeared in sample situations, we did not abstract them or derive hidden u-Services. For instance, with a situation of ‘‘tracing a getaway stranger by CCTV’’, we just made u-Services such as ‘‘CCTVZoomIn’’ and Effects such as ‘‘E-ZoomIN’’, but did not diversify u-Services for the Effects. For improvement in replaceability, it is necessary to derive many u-Services, or modify the granularity of Effects, etc. • Polymorphism: version 3.0 has improved polymorphism because of the efficient grouping and structuring of u-Services. Version 3.0 contains more polymorphic u-Services; therefore, it can bind a u-Service to various executable atomic u-Services for the run-time context and efficiently support abstract goal descriptions. • Autonomy: this result implies that u-Services added into version 3.0 are autonomous. They will be performed independently. The results indicates that version 3.0 is focused on creating many u-Services and forming many layers, but not representing the useful relationships between u-Services such as similar Effects produces or being run by similar type of devices, compared to version 1.0. Therefore, version 3.0 will be applicable in the case of relatively simple situations, but probably fail the replacement of u-Services or

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devices in dynamically changeable situations. Overall, it is difficult to state that version 3.0 complied fully with the evaluation factors for supporting dynamic service composition. These experiments prove that the proposed evaluation model is useful to assess the quality of a u-Service ontology.

5 Conclusion Ubiquitous service ontology should efficiently describe domain knowledge and information about u-Services to support dynamic service discovery and composition, which are essential techniques for ubiquitous computing. Therefore, in this paper, we proposed not only the factors to guarantee the design quality of knowledge base in terms of the functionality, but also the metrics for quantitative analysis. We set the goal of the u-Service ontology as dynamic service composition and defined four evaluation factors and their metrics. They were established by referring to SOA design principles and by reflecting characteristics of u-Service ontology. We proposed them to evaluate the capability of u-Service ontology to support dynamic service composition in the design time and not performance in the execution time. Moreover, we believe that our evaluation model will assess whether the u-Service ontology is developed effectively for dynamic service composition. Future work will focus on the validity and completeness of the evaluation model. Acknowledgments This research is supported by the ubiquitous Computing and Network (UCN) Project, the Ministry of Knowledge and Economy (MKE) Knowledge and Economy Frontier R\D Program in Korea and a result of subproject UCN 11C3-T3-10 M. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0005305).

References 1. Chakraborty D, Perich F, Joshi A, Yesha Y (2002) A reactive service composition architecture for pervasive computing environments. In: 7th Personal Wireless Communications Conference, pp 53–62 2. Chakraborty D, Joshi A (2001) Dynamic service composition: state-of-the-art and research directions. Technical Report TR-CS-01-19, University of Maryland 3. Lee M, Lee JW, Park SS, Cho W (2008) Ontology-based service description and overloading method for ubiquitous computing. J Korea Inf Process Soc 15B(5):465–476 4. Lee M, Lee JW, Park SS, Cho W (2010) SOA-based service layering for facilitating dynamic service composition and alternative service discovery. J Inf Sci Eng 26(3):991–1010 5. Erl T (2005) Service-oriented architecture: concepts technology and design. Prentice Hall, New Jersey 6. Gomez-Perez A (1995) Some ideas and examples to evaluate ontologies. In: 11th Conference on Artificial Intelligence for Applications, pp 299–305

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7. Tartir S, Arpinar IB, Sheth AP (2008) Ontological Evaluation and Validation Theory and Applications of Ontology 2. Springer-Berlin, 8. O’Sullivan J, Edmond D, Hofstede AHM (2002) Service description: a survey of the general nature of services Technical Report. Queensland University of Technology, 9. Martin D, Burstein M, Hobbs J, Lassila O, McDermott D, Mcllraith S, Narayanan S, Paolucci M, Parsia B, Payne T, Sirin E, Srinivasan N, Sycara K (2004) OWL-S: semantic markup for web services. W3C Member Submission 10. McGuinness DL, Hamelen F (2004) OWL web ontology language overview. W3C Member Submission 11. ISO/IEC: ISO/IEC 9126-1 Software engineering-product quality-part 1: quality model. ISO/IEC Technical Report (2001) 12. ISO/IEC: ISO/IEC 9126-2 Software engineering-product quality-part 2: external metrics. ISO/IEC Technical Report (2003) 13. ISO/IEC: ISO/IEC 9126-3 Software engineering-product quality-part 3: internal metrics. ISO/IEC Technical Report (2003) 14. ISO/IEC: ISO/IEC 9126-4 Software engineering-product quality-part 4: quality in use metrics. ISO/IEC Technical Report (2004) 15. Bansiya J, Davis CG (2002) A hierarchical model for object-oriented design quality assessment. IEEE Trans Software Eng 28(1):4–17 16. Shim B, Choue S, Kim S, Park S (2008) A design quality model for service-oriented architecture. In: 15th Asia-Pacific Software Engineering Conference, pp.403-410 17. Oh S, Kim S, Rhew S (2007) UCQM: a quality model for practical evaluation of ubiquitous computing systems. J Korean Inst Inf Sci Eng Softw Appl 34(4):342–358 18. Lee S, Shin D (2008) Web service QoS in multi-domain. In: 10th International Conference on Advanced Communication Technology, pp 1759–1762 19. Ko JM, Kim CO, Kwon IH (2008) Quality-of-service oriented web service composition algorithm and planning architecture. J Sys Softw 81(11):2079–2090 20. Prahmkaew S (2010) Performance evaluation of convergence ad hoc networks. J Converg 1(1):101–106 21. Malakuti S, Aksit M, Bockisch C (2011) Runtime verification in distributed computing. J Converg 2(1):1–10 22. Shah SNM, Mahmood AKB, Oxley A (2011) Development and performance analysis of grid resource allocation methods. Int J Inf Technol Commun Converg 1(3):331–355 23. Halim Z, Baig AR, Mujtaba H (2010) Measuring entertainment and automatic generation of entertaining games. Int J Inf Technol Commun Converg 1(1):92–107 24. Erl T (2007) SOA Principles of service design, Prentice Hall, New Jercy 25. Protégé, http://protege.stanford.edu/

OO Divide and Conquer Pattern Suitable for Parallel, Grid and Cloud Computing Manouchehr ZadahmadJafarlou, Bahman Arasteh and Parisa YousefzadehFard

Abstract The advancement of processors, parallel processing and wireless networking technologies and also growing trend to netsourcing, enable developers to design and implement software that may soon lead to true pervasive and distributed computing. Design patterns help software engineers to tackle the challenges related to these issues by presenting comprehensive documents that address problems and their solutions in any related area. To deal with these problems, this study presents an object-oriented divide and conquer pattern along with dynamics, implementation and discussions that can be used in parallel, grid and cloud computing programming models. Paper also contains related works.







Keywords Design pattern Parallel programming Divide and conquer processor Thread Grid computing Cloud computing











Multi-

M. ZadahmadJafarlou (&) Young Researchers Club, Department of Computer, Ilkhchi branch, Islamic Azad University, Ilkhchi, Iran e-mail: [email protected] B. Arasteh Department of Computer, Tabriz branch, Islamic Azad University, Tabriz, Iran e-mail: [email protected] P. YousefzadehFard Department of Computer, Payame Noor University, Tabriz Center, Tabriz, Iran e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_46,  Springer Science+Business Media B.V. 2012

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1 Introduction Divide and conquer (D&C) is an algorithm design paradigm based on breaking a problem into sub-problems that are themselves smaller instances of the same type of problem, recursively solving these sub-problems and appropriately combining their answers. Patterns help developers to build on the collective experience of skilled software engineers. They capture existing, well-proven experience in software development and help to promote good design practices. Every pattern deals with a specific, recurring problem in the design or implementation of a software system [1]. The necessity of replacing the original problem by a more general or complicated problem in order to get the recursion going, and absence of systematic method for finding the proper generalization make mastering to understand and design of D&C algorithms time consuming. As a result this study capture the essence of how expert parallel, grid and cloud computing programmers think about implementing D&C parallel algorithms and communicate that essential under standing in a way professional programmers can readily master. The remainder of this article is organized as follows. First, we present the related works (Sect. 2). Then we will introduce documentation for D&C pattern (Sect. 3). And finally Sect. 4 contains conclusions.

2 Related Works Any introductory algorithms text will have many examples of algorithms based on the D&C pattern, most of which can be parallelized with this pattern. [2] introduced a collection of patterns along with D&C that can be used in parallel programming. However issues such as structure, dynamics, implementation and consequences of patterns are considered less. [3] noted that using OO programming helps developers to develop parallel programs for shared and distributed memory parallel computers without code modification. The subject of [4] is the computer simulation of transient processes in strongly radiative plasma that refers to solving the problems of radiative magnetohydrodynamics and where the application of OOP and parallel computing is emphasized. [5] describes D&C pattern as an example that can be used to introduce students in a Java software design course to the construction of program families using software frameworks. Some algorithms frequently parallelized with this strategy include the BarnesHut [6] and Fast Multipole [7] algorithms used in N-body simulations; signalprocessing algorithms, such as discrete Fourier transforms; algorithms for banded and tridiagonal linear systems, such as those found in the ScaLAPACK package

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[8]; and algorithms from computational geometry, such as convex hull and nearest neighbor. A particularly rich source of problems that use the D&C pattern is the FLAME project [9]. This is an ambitious project to recast linear algebra problems in recursive algorithms. [15–18] also are suitable to apply D&C pattern.

3 Documentation of D&C Pattern Patterns must be presented in an appropriate form. A good description helps us grasp the essence of a pattern immediately-what is the problem the pattern addresses, and what is the proposed solution? A good description also provides us with all the details necessary to implement a pattern, and to consider the consequences of its application. Our pattern description template is adopted from [1]: Name. Divide and conquer (D&C) Context. Recursively breaking down a problem into two or more sub-problems of the same type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem. Problem. How do you best replace original problem by a recursive and comprehensive one that can be broke to sub-problems from that type and recursively combine solutions of lower sub-problems to make solution to upper sub problems as tree like and finally solve original problem? Forces. When using D&C paradigm, you must reconcile the following forces as you consider a solution to the problem: • The comprehensive recursive problem must be easy to divide, initialize and combine. • The methods for initializing, partitioning and combining the result relatively lower in cost than the overall time of processing sub-problems. • For some problems, the branched recursion may end up evaluating the same sub-problem many times over. In such cases it may be worth identifying and saving the solutions to these overlapping sub-problems, a technique commonly known as memorization. Followed to the limit, it leads to bottom-up D&C algorithms such as dynamic programming and chart parsing. • Sub-problems must have the ability of independent and parallel processing. Solution must decreases the movement cost of data and solutions between processing elements in distributed memory systems. • D&C algorithms can also be implemented by a non-recursive program that stores the partial sub-problems in some explicit data structure, such as a stack, queue, or priority queue. This approach allows more freedom in the choice of the sub-problem that is to be solved next and also memorization. Solution. Introduce a coordination instance between clients of the problem and the processing of individual sub-problems. In each phase a problem (or sub-problem)

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Fig. 1 Structure of object oriented DandC pattern

that is condensed in an object is solved directlyif it is simple enough- or divided into equal sub-problems and encapsulated in individual condreteDandC objects and all this objects of the same problem (sub-problem) divided or solved parallelly or concurrently. All the solutions of individual objects combined to create ultimate solution. This general principle is found in three application areas: • Dealing with communication costs in parallel, grid and cloud computing. In a distributed-memory system, a higher-level sub-problem will wait to result of its sub-problem. So a heuristic entire solution and also dividing strategy must be considered whether it makes sense to replicate some data at the beginning of the computation. • Dealing with dependencies in parallel, grid and cloud computing. In DandC Pattern (Fig. 1), sub-problems can be solved independently from each other. Less commonly, the sub-problems require access to a common data structure. In this case dividing strategy can be executed in one system until it reach to base sub-problems, then this sub problem objects delegated to parallel processors so in this method all processors have their own data. For previous level the coordination must be done for all ConcreteDandC objects of that level. • Fault tolerance. The execution of a problem is delegated to several replicated sub-problems. Failure of executions can be detected and handled. • Computational accuracy. The execution of a problem is delegated to several replicated sub-problems. Inaccurate results can be detected and handled.

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Structure. The concreteDandC component provides a problem that can be solved by applying the D&C paradigm. It offers an interface that allows clients to access this problem. Internally, the concreteDandC class implements functions for create and delete a concreteDandC object for sub-problems, assigning encapsulated concreteProblem_Solution object to concreteDandC object, determining the appropriateness of problem to direct solving or dividing, dividing a problem to fine-granular sub-problems, solving each of the sub-problems and combining the returned solutions of sub-problems from lower level. Figure 1, illustrates the structure of object oriented concreteDandC pattern using UML class diagram (Enterprise Architect 7th version). This structure helps to straight handling, distribution and assiging of concreteDandC object in distributed, multi-thread and multi-core processing environments. Dynamics. In the following scenario sub-problems are called parallelly or concurrently. The concreteDandC pattern unleashes its full power when sub-problems are called parallelly or concurrently, for example by assigning them to several separate threads and allotting to processors in multi-core or in distributed systems. The scenario comprises following phases: • Client creates the mainProblem object as instance of ConcreteProblem_Solution class and initializes it. Client creates the mainDandC pattern object as instance of ConcreteDandC class and initializes it by assigning the mainProblem object. isSmallEnough function called and determine the appropriateness of problem to direct solving or dividing to sub-problems. If it was simple enough, it solved directly by applying solveSimpleEnoughSubP function. If problem was not simple enough, divideAndSetSubPs function was called and divide it to finegranular sub-problems and return sub-problems in an array. All sub-problems initialized. For all sub-problems; an instance of concreteDandC pattern is created, all object are assigned with appropriate sub-problems and solved parrallell or concurrently. Finally combineSolutionsOfSubPs function combines all solutions, create solution of that level and return it to upper level or to client when it is the mainDandC (Fig. 2). Implementation. To illustrate the implementation of D&C Pattern, following pseudo code for concreteDandC is presented. The D&C pattern solves a problem by recursively dividing it into one or more sub-problems of the same type, solving each sub-problem independently, and then combining the sub-problem solutions to obtain a solution for the original problem. Since this algorithmic strategy can be applied to a whole set of problems of a similar type, D&C, in addition to its meaningful influence in algorithms, serves well for the purpose of examining a algorithm design pattern. In following pseudo code, function solve() represents a template method because its implementation is the same for all algorithms in the family of D&C pattern. However, functions solveSimpleEnoughSubP(), divideAndSetSubPs(), isSmallEnoug(), and combineSolutionsOfSubPs() represent hook methods, because their implementations vary among the different algorithms. For example,

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Fig. 2 Dynamics of object oriented DandC pattern

the solveSimpleEnoughSubP() function for quick sort is quite different from that for merge sort. For merge sort, the combineSolutionsOfSubPs() function performs the major work while divideAndSetSubPs() is simple. The opposite holds for quick sort and binary search [5]. namespace DandCPattern { public class ConcreteDandC : AbstractDandC { private ConcreteProblem_Solution problem; private ConcreteDandC sub_Problems_Solver; public ConcreteDandC(){} *ConcreteDandC(){} public override void Dispose(){} public override void assignProblem(ConcreteProblem_Solution PS){}

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public override void combineSolutionsOfSubPs(){} public override void delete(){} public override ConcreteProblem_Solution[] divideAndSetSubPs(){} protected override bool isSmallEnough(){} public override void new(){} public override void solve(){} protected override void solveSimpleEnoughSubP(){} }/*end ConcreteDandC*/}//end namespace DandCPattern In [10–13] an implementation and design method for pattern presented and applied that use patterns combination to modeling system and transformation between different models of MDA. Quantitative and qualitative evaluations of it illustrate the ease of reuse, accurate automation, and large granular transformation among modeling levels of MDA. Known Uses. Sometimes algorithms such as the binary search that produces only one sub-problem in each recursion phase was included in the list of D&C pattern. While these algorithms can be implemented more efficiently than general D&C pattern in particular using simple loops, it can be considered that the name D&C pattern should be used only when each problem may generate more than one subproblem. Some parallelized form of D&C paradigm as specified in related works section include the Barnes-Hut [6] and Fast Multipole [7] algorithms used in Nbody simulations; signal-processing algorithms, such as discrete Fourier transforms; algorithms for banded and tridiagonal linear systems, such as those found in the ScaLAPACK package [8]; and algorithms from computational geometry, such as convex hull and nearest neighbor. A particularly rich source of problems that use the D&C pattern is the FLAME project [9]. This is an ambitious project to recast linear algebra problems in recursive algorithms. Consequences Benefits Exchangeability and extensibility. By providing an abstract AbstractDandC Pattern class, it is possible to exchange existing class implementations or add new ones without major changes. Clients are not affected by such changes, and Pattern Development Automation (PDA) process is enhanced and also it helps to creation of accurate implementation of patterns. Separation of concerns. The introduction of the abstractDandC Pattern class separates client code from the code for partitioning work, delegating work to subproblems and combining the results from the sub-problems. Solving conceptually difficult problems. The D&C pattern solves conceptually difficult problems, such as the classic Tower of Hanoi puzzle. Create efficient algorithms. The D&C pattern helps in the discovery of efficient algorithms. It was the key, for example, to Karatsuba’s fast multiplication method, the quick sort and merge sort algorithms, the Strassen algorithm for matrix

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multiplication, and fast Fourier transforms. In all these examples, the D&C approach led to an improvement in the asymptotic cost of the solution. Adapted for execution in multi-processor machines. The D&C pattern is suitable for execution in multi-processor machines, especially shared-memory systems where the communication of data between processors does not need to be planned in advance, because distinct sub-problems can be executed on different processors. Efficient use of memory caches. The D&C pattern tends to make efficient use of memory caches. Once a sub-problem is small enough, it and all its sub-problems can be solved within the cache. An algorithm designed to make use of the cache is called cache-oblivious, because it is small enough to fit in majority of caches [14]. Liabilities Feasibility. The D&C pattern is not always feasible. You must partition work, copy data, launch sub-problems, control their execution, wait for the subproblem’s results and combine the results. All these activities consume processing time and storage space. Machine dependency. The D&C pattern for parallel, grid and cloud computation strongly depends on the architecture of the machine on which the program runs. This may decrease the changeability and portability of a D&C pattern structure. Hard to implement. Implementing the D&C pattern is not easy, especially for parallel, grid and cloud computation. Many different aspects must be considered and carefully implemented, such as how work is subdivided, how problems and their sub-problems should collaborate, and how the final result should be computed. You also must deal with errors such as the failure of sub-problem’s execution, failure of communication between problems and their sub-problems. Implementing the D&C pattern for parallel, grid and cloud computation usually requires sound knowledge about the architecture of the target machine for the system under development. Portability. Because of the potential dependency on underlying hardware architectures, the D&C pattern structures are difficult or impossible to transfer to other machines.

4 Conclusion This article presented an object oriented D&C pattern along with context that in which the pattern may apply, problem that it must solves, solution to the problem, an object oriented structure and dynamics using UML diagrams, implementation, known uses and consequences. Object oriented structure of pattern makes it suitable to be used in many concurrent, parallel, Grid and distributed programming methods. Comparing proposed documentation of D&C pattern with existent similar works shows that the presented work can finely supports developers to success in tackle related challenges.

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References 1. Buschmann F, Meunier R, Rohnert H, Sommerlad P, Stal M (1996) Pattern-oriented software architecture. A System of Patterns, Vol 1. Wiley, New Jersey 2. Mattson GT, Sanders AB, Massingill LB (2004) A pattern language for parallel programming. Addison Wesley, Boston 3. Devloo P (1998) Object oriented programming applied to the development o scientific software. In: Computational MechanicS New Trends and Applications 4. Gasilov V, D’yachenko S, Olkhovskaya O, Boldarev A, Kartasheva E, Boldyrev S (2008) Object-oriented programming and parallel computing in radiative magnetohydrodynamics simulations. In: Parallel Computing: Architectures, Algorithms and Applications 5. Cunningham H, Liu Y, Zhang C (2004) Using the divide and conquer strategy to teach java framework design. In: Principles and Practice of Programming in Java (PPPJ) Conference 6. Barnes J, Hut P (1986) A hierarchical O(N log N) force calculation algorithm. Nature 324(4) 7. Greengard L, Gropp WD (1990) A parallel version for the fast multipole method. Computers Math Applic 20(7) 8. Cleary A, Dongarra J (1997) Implementation in ScaLAPACK of divide-and-conquer algorithms for banded and tridiagonal linear systems. Technical Report CS-97-358, University of Tennesee, Knoxville, TN 37996, USA 9. Gunnels AJ, Gustavson GF, Henry MG, van de Geijn AR (2001) FLAME Formal linear algebra methods environment. ACM Trans Math Soft 27(4):422–455 10. Jafarlou ZM, Moeini A, Fard YP (2011) New process: pattern-based model driven architecture. Procedia Comput Sci J, In Press 11. Zadahmad Jafarlou M, Yousefzadeh Fard P (2011) Heuristic and pattern based Merge Sort. Procedia Comput Sci J 3:322–324 12. ZadahmadJafarlou M, YoushefzadehFard P, Arasteh B (2011) A pattern-oriented PIM for resource management of educational services in mobile networks. Procedia—Social and Behavioral Sciences, In Press 13. Jafarlou ZM, Fard YP, Arasteh B (2011) A pattern-oriented and web-based architecture to support mobile learning software development. Procedia—Social and Behavioral Sciences, In Press 14. Frigo M, Leiserson CE, Prokop H, Ramachandran S (1999) Cache-oblivious algorithms. In: 40th IEEE Symposium on Foundations of Computer Science (FOCS 99), pp 285–297 15. Li Y, Xiao L, Chen S, Tian H, Ruan L, Yu B. Parallel point-multiplication based on the extended basic operations on conic curves over ring Zn. JoC 2(1):69–78 16. Sberg M, Nolte T, Pettersson P. Prototyping and code synthesis of hierarchically scheduled systems using TIMES. JoC 1(1):75–84 17. Xie B, Kumar A, Zhao D, Reddy R, He B. On secure communication in integrated heterogeneous wireless networks. IJITCC 1(1):4–23 18. Chang J, Yang H, Chao H, Chen J. Multipath design for 6LoWPAN ad hoc on-demand distance vector routing. IJITCC. 1(1):24–40

A Dynamic and Reliable Failure Detection and Failure Recovery Services in the Grid Systems Bahman arasteh, Manouchehr ZadahmadJafarlou and Mohammad Javad Hosseini

Abstract Fault tolerance and resource monitoring are the important services in the grid computing systems, which are comprised of heterogeneous and geographically distributed resources. The reliability and performance must be considered as a major criterion to execute the safety–critical applications in the grid systems. Since the failure of resources can leads to job execution failure, fault tolerance service is essential to satisfy dependability in grid systems. This paper proposes a fault tolerance and resource monitoring service to improve dependability factor with respect economic efficiency. Dynamic architecture of this method leads to reduce resource consumption, performance overhead and network traffic. The proposed fault tolerance service consists of failure detection and failure recovery. A two layered detection service is proposed to improve failure coverage and reduce the probability of false alarm states. Application-level Checkpointing technique with an appropriate graining size is proposed as recovery service to attain a tradeoff between failure detection latency and performance overhead. Analytical approach is used to analyze the reliability and efficiency of proposed Fault tolerance services.

B. arasteh (&) Department of Computer, Tabriz Branch, Islamic Azad University, Tabriz, Iran e-mail: [email protected] M. ZadahmadJafarlou Department of Computer, Ilkhchi Branch, Islamic Azad University, lkhchi, Iran e-mail: [email protected] M. J. Hosseini Department of Computer, Sufian Branch, Islamic Azad University, Sufian, Iran e-mail: [email protected]

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Keywords Grid computing Fault tolerance service False alarm Performance overhead



 Dependability 

1 Introduction Grid computing as a large distributed environment integrates diverse and heterogeneous resources and services [1]. It enables the aggregation and sharing of geographically distributed computational, data and other resources as a single, unified resource for solving large-scale computation and data intensive applications in a parallel manner with reasonable costs [2–4]. Grid computing can be exploited as an efficient platform for some critical and computation intensive applications such as molecular sample examining and research concerning nuclear boiling which need many hours, days or even weeks of execution. Safety–critical and real-time distributed applications such as scientific, medical and industrial applications have rigorous objectives for the timing and correct results. Because the grid resources are highly heterogeneous and can leave/join dynamically, fault, error and failure occurrence in each component of grid environment must be considered as common events and consequently the infrastructure of the grid can reconfigure and change dynamically. Therefore, the dependability and its related criteria such as reliability, safety and availability must be considered in the grid resource management and job scheduling processes. In the Globus toolkit the Meta Directory Service (MDS) and HBM (heartbeat monitor) service are used to develop general fault tolerance service in the grid environment [5]. Low coverage, low reliability and low efficiency are the main drawbacks of this monitoring service. Thus, providing a reliable, efficient, scalable, dynamic and economic failure detection service as a basic service must be considered in the grid systems. In this paper, we propose a reliable, efficient and dynamic fault tolerance service by means of component and information replication. The proposed approach can detect and recover high percentage of timing and content failure like Byzantine faults. Using our technique reduces the probability of false alarm (false positive and false negative) and consequently the reliability of fault tolerance service. The cost of the requested services is the other criteria from the user’s and resource provider’s points of view which are considered in this work.

2 System Architecture and Assumption 2.1 System Model The grid infrastructure consists of layered software components deployed in different nodes [6]. A Grid can be defined as a layer of networked services that allow users access to a distributed collection of computing, data, communication and application resources from any location. The term service-oriented architecture refers to a

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architecture of developing reliable distributed systems that deliver functions as services. These services communicate with each other by message passing techniques and are implemented using Web Services, which are built on the same technologies (HTTP, XML, web servers) as the World-Wide Web. Web services technology and the Grid middleware together leads to develop a service-oriented architecture for a grid middleware. The Open Grid Services Architecture (OGSA) represents an evolution towards a Grid system architecture based on Web services concepts and technologies [2, 7]. The OGSA as a new architecture in the Grid middleware provides a more unified and simplified approach to the Grid applications. The Globus Toolkit is an open source software toolkit used for building grids projects. The Globus toolkit includes software services and libraries for resource monitoring, discovery, and management, plus security and file management [3, 8]. The toolkit includes software for resource management, communication, security and reliability. Grid resource allocation management (GRAM), Monitoring and Discovery System (MDS) and Grid Security Infrastructure (GSI) are the main components of Globus toolkit. The HBM (heartbeat monitor) technique is used to handle fault and failures [2, 5, 7]. (Figs. 1, 2). GRAM is responsible for managing local resources and comprises a set of Web services to locate, submit, monitor, and cancel jobs on Grid computing resources. The MDS is the information services component of the Globus toolkit and provides information about the available resources on the Grid and their status. GSI is a set of tools, libraries and protocols used in Globus to allow users and applications to securely access resources. MDS consists of two services: Grid Resource Information Service (GRIS) and the Grid Index Information Service (GIIS). The GRIS is a machine-specific service which contains information regarding the machine on which it is running and provides resources discovery services on a Globus toolkit.

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The resource information providers use a push protocol to update GRIS periodically. GIIS provides a global view of the Grid resources and pulls information from multiple GRIS to combine into a single coherent view of the Grid [3, 7, 9, 10]. Globus is placed into the push resource dissemination category since the resource information is initially periodically pushed from the resource providers. Resource discovery is performed by querying MDS. The GRIS monitors the state of the registered resource and process and the GIIS as the data collector which receives HBM generated by local monitors [5, 7]. When a failure occurs in a local resource, the corresponding GRIS informs its domain GIIS by sending information.

2.2 Fault Model The resources may enter and leave the grid at any time. Hence, the grid is a hazardous environment and resource failure is a common event and not an exception. On the other hand, probability of fault, error and failure in each remote resource and the network framework is not negligible [9, 11]. Failures might happen during many stages of this process as a consequence of many software and hardware faults. The focus of this paper is on the Resource failure and Local environment failure during the job execution. Many transient and permanent hardware faults can lead to resource failure in the grid system. Hardware and omission faults can lead to resource failure [12]. The fault model in this paper is physical faults like Physical fault, Fault in the host machine’s CPU, Fault in the host machine’s memory, Fault in the host machine’s storage, Fault in the software layers of host machine (failure of OS in a host machine), and Fault in the transition channels. And as mentioned, omission faults will also arise when resources become unavailable, leave the grid environment, during a job execution. Early and late results of a request are the other type of faults in the grid. These faults can leads to fail-stop,

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byzantine and timing failure. In the Fail-stop failure, the system does not output any data. It immediately stops sending any events or messages and does not respond to any messages. In the Byzantine failure, the system does not stop and behaves incorrectly and may send out wrong information, or respond late to a message. One of the assumptions in this paper is the correctness and fault freeness of the submitted jobs which are replicated by scheduler. The proposed method based on the replication techniques focuses on fail-stop, byzantine and timing failure handling.

3 Related Works Component Replication [13, 14], Job Replication [15] and Data Replication [16] are Deferent replication methods which can be used in different layers of the grid computing to achieve fault tolerance. In the application-level, the fault tolerance (FT) mechanisms are implemented into the application code by exploiting application-level knowledge [13, 17, 18]. Major Middleware tools that make use of application-level check-pointing are BOINC and XtremWeb [19]. The significant features of this technique are efficiency, low performance overhead, high flexibility and portability. System-level fault tolerance [20] is an automatic and transparent technique which is unaware of applications details. In this technique the application is seen as a black-box and it has no knowledge about any of its characteristics. For example the system-level Fault tolerance (FT) technique is used in Condor and Libckpt. Transparency and simplicity are the advantages of this technique but, it is impractical when the system has a large number of resources because of performance and communication overhead. Multi-level FT tries to combine the advantages of both techniques [21, 22]. The different Ft techniques are embedded in different layers of grid and responsible for handling the corresponding error and failures. But embedding the FT techniques in different layer and components (Mixed-level FT) sometimes are not possible because of diversity and distribution of grid component. In the grid systems the heart beat mechanism as a conventional method is used to implement the failure detection services [23]. In this technique, GRIS periodically sends a heartbeat message to the data collector which shows it is still alive. The heartbeat interval affects on the detection coverage, detection latency, performance and communication overhead. This is an unreliable technique which its reliability depends on the reliability of the GRIS and GIIS. Low detection coverage and low scalability are the other significant drawbacks of this technique. We propose a fault tolerance and resource monitoring service which address the following criteria: Reliability: shows the continuity of correct service to detection and recovery of resource failure. A reliable FT service has low probability of false alarms. Coverage: indicates the percentage of faults which can be handled by the FT services. Latency: the time interval between a resource or process failure and detection. Performance overhead: This criterion refers to the imposed time, resources and communication overhead by the FT service. Scalability, Portability and

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Flexibility: the FT service should be independent of special platform and should be able to adapt to different type of platforms and applications. It must be able to scale to the large number of grid resources and processes. Resource Utilization: Using Dynamic methods in the FT Service improves the resource utilization and consequently reduces the cost of services in the economic grids.

4 Proposed Method After a job is submitted through a host machine and the machine could not schedule it because of needed resources and job deadline, the grid resource management service are invoked to select needed resources and schedule it. We focus on the safety–critical and soft real-time applications execution on grid. The hierarchical architecture arrange processes into some form of hierarchy and the following figure shows the hierarchical architecture of fault tolerance service in the Globus toolkit. Our work focuses on drawbacks and single-point-failures of this model and improves it by utilizing dynamic redundancy techniques. In the hierarchical model, failure detectors monitor processes and resources directly or indirectly through other level of hierarchy. It leads to reduce communication overhead by combining information about several processes in a single message, and by storing information at several levels in the system. In the hierarchical model, a local monitor of a resource monitors the status of running process on the host machine. If a failure, time or content, occurs the corresponding local monitor detects it and inform the site failure detector and resource manager. Local Monitor Coverage refers the probability of detecting an occurred failure in a running resource by a local monitor. Latency of Local Monitor refers to the time between a failure occurrence and detection by the corresponding monitor. Reliability of local Monitor refers to the probability of alarm accuracy which is generated by the monitor. The same points must be considered in the other levels of FT services. For example, the site failure detector may be erroneous and offer faulty information to the requesting client. FT service must detect the occurred process failure or resource failure before it propagates to other informational states and before the deadline of the running application. So we need a FT services to tolerate time, content and byzantine failures in a different layers. The proposed method is comprised of failure detection and recovery services.

4.1 Failure Detection Services After receiving a job from a host, the host scheduler analyzes job information including the needed resources and remaining deadline. If the scheduler could not serve the job in the remaining deadline by means of the ready resources, then the resource discovery service is invoked. GRAM provides an interface for accessing

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system resources. When a job is submitted from a client, MDS which consists of GIIS and GRIS is invoked to show the available resources (in the local domain or the remote domain). The needed degree of dependability and performance and remaining deadline of the job is important to discover the candidate resources. In the resource discovery and resource selection algorithm the following parameters must be considered: Performance of the machine, Dependability of the machine and Locality of the machine (The resources with high degree of locality impose low communication and performance overhead) are the main criteria in the resource selection.  Locality Resource Selection Criteria = Dependability Workload After discovering the needed resources, GRAM generates K replicas of the job. The parameter K is adaptable with respect the needed degree of dependability. In the next step, GRAM selects one ready machine as a candidate from K machines in the site and dispatches the job and starts it. Checkpoints are created concurrently with the running jobs and the local monitor controls the status of corresponding process. The following are considered as the main question in this step: Has the running machine failed? Is the machine running the job correctly? Are the mid results correct? Are the time thresholds considered? For answering these questions FT and monitoring services are needs to detect errors while running the jobs and also detect the failure of the host node. The monitor of a host machine, monitors the status of the corresponding machine and by means of HBM and GRIS informs the fault detector of corresponding domain. The local GRIS of a host machine uses the heartbeat as a notification message to inform the corresponding fault detector. Using a periodic signal as a notification message can detect the resource halt or disconnection and couldn’t detect the correctness of delivered services and information (content and Byzantine failure). In order to detect timing and content failure, this work uses the dynamic redundancy and Periodic diagnosis mechanisms periodically during job execution. Detection of an error during the job execution before it leads to failure is very complex process because many characteristics of real-time application on the remote machine are unknown. When the content failure occurs, the content of information delivered at the service interface deviates from specified results [11]. In the proposed model, detection mechanisms use the combination of function level detection and periodic diagnosis mechanisms. A diagnosis module, comparator and acceptance tests (AT), as a function of detection services, are invoked periodically to verify mid results. The proposed schema failure detection covers both timing failure and content failure. In the timing failure the determined deadline for the computation of the result or delivery of the requested service is not met and the detection service monitors time thresholds or checks time-out and deadline constraints of running. Checking deadline constraints is an important factor in failure detection mechanisms especially in the real-time applications. (Figs. 3, 4). AT [11] as a software module which is executed on the outcome of the relevant independent replica to confirm that the result is reasonable or not. Generating

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perfect acceptance test for real-time application sometimes is not feasible. In this model, the local monitor of the selected host machine starts to execute first replica. During the execution of first replica the AT is executed periodically to check results of execution. If the output of AT is true, then the mid result of computation is considered valid and the local monitors save this mid result as checkpoints into a reliable storage. Otherwise, a fault is detected and a copy of outcome is stored as a back up by local monitor. Also the local monitor informs the corresponding domain failure detector (FD) by sending a notification message which composed of last committed checkpoint. The corresponding FD and site resource manager (domain MDS and GRAM) starts the same job in a different selected machine from checkpoint. This process is repeated until either one of the alternate replicas passes the AT and gives the result or no more replicas from K replicas are left. At last, when the last alternate replica fails to pass the AT, there are two possible

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reasons: All of K replicas on the corresponding host machines failed. May be the AT fails and there are some correct result. In this model the failure of a site FD can lead to failure of the fault tolerance service in the corresponding domain. If the MDS, GRAM and GIIS services in a domain are failed the corresponding monitoring service may be failed. For example the local monitor can be failed because of the corrupted services by the corresponding GRIS and if a GIIS offers faulty information the corresponding monitoring system may failed. Hence, the aprobability of false alarm is not negligible and should be considered to attain the reliable detection service. In order to harden the single-point-of failure, we use information redundancy in this level. In each site, the corresponding FD sends a type of notification message (Heartbeat message) with a specific period to a monitor in other level of Hierarchy. The recently saved information of a FD, which is received as notification message, is used in the recovery services. This mechanism is used in other level of grid environment.

4.2 Reliability and Performance Tradeoff It must be noted that this method focuses on dynamic redundancy, in order to tradeoff between reliability and performance criteria. The work based on dynamic redundancy assumes that, the submitted job is not hard real-time and can tolerate the temporary incorrect outputs. In this method the GRAM just need a host machine to start the job. Hence, the average waiting time to discover the needed resources is low. On the other hand this feature leads to improve the efficiency of the resources and decrease the average resources consumption and consequently the cost of services in the economic grid. Different type of notification message is used in different levels of this model. The time interval between the notification messages has significant impact on the reliability, performance slowdown and communication overhead. Increasing the interval leads to reduce the network traffic but increases the detection latency. Increasing the latency may lead to propagate the erroneous results to other components of system. On other hand, it leads to increase the frequency of checkpoints and consequently the imposed time overhead is increased. (Fig. 5). Hence, an optimal value must be calculated with respect the system status (like job deadline, network delay and needed reliability and performance). The optimal value for interval between notification-message improves the following parameters: • Detection and recovery latency • Time and space overhead which imposed by checkpointing • Number of message passing and network traffic

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5 Evaluation 5.1 Reliability Evaluation In order to evaluate the reliability and safety of proposed scheduling model, we use an analytical approach. Markov model is a conventional analytical method to evaluate dependability and performance of software and hardware systems [12, 24]. We have used the Markov approach to analyze the reliability of proposed service. Resource failure and local environment failure specially computing host failure are intended as the fault model. Both timing and content failure are considered in the fault model. The basic assumptions are listed in the following: Resource failure is independent and has a constant failure rate, k. The Majority voter as a software module is considered perfectly reliable. The AT as a software module is not considered perfectly reliable. The submitted jobs are soft real-time without software development fault. The following figure shows the reliability of proposed monitoring and fault tolerance system and the monitoring system without redundancy technique and the monitoring system by means of triple-modularredundancy (TMR). In order to evaluate the reliability of a system, the mean time to failure (MTTF) is one the significant factor. In the following section, we evaluate the MTTF for the proposed method. MTTF: Mean time to failure, R1 R1 MTTF = RðtÞdt; MTTF TMR-FD = ð3e2kDt  2e3kDt Þdt 0

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Fig. 6 Reliability of proposed monitoring and FT system

Fig. 7 MTTF of proposed FT system with respect of basic FT service

MTTF Mean time to repair: MTTR, Availability = MTTFþMTTR AvailabilityProposed -FD [ AvailabilityTMR-FD(Figs. 6, 7).

5.2 Performance and Resource Consumption Analyses The proposed fault tolerant (FT) Service has a dynamic architecture. This model just needs a single host machines to start the job. Hence, this model does not lead to starvation and reduces the waiting time. After a failure is detected during a job execution the FD and monitor discover (by MDS server) an other candidate machine and does not waste the resources usage before failure occurrence. Therefore, dynamic architecture of proposed model reduces total service time and improves resources efficiency. This model improves the percentage of accepted requested resources and services in the grid systems. On the other hand the NMR based FD (with a seven active host machine) can tolerate three host machine failures by means of seven host machines. Hence, the new model has lower needed resources in average case and this feature improves efficiency and reduces the service cost.

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6 Conclusions In this paper we proposed a dynamic monitoring and fault tolerance service via dynamic redundancy techniques which covers the timing and content failure. The failure detection service was organized in two levels including AT and majority voting which reduce the probability of false alarm and consequently improves the reliability of FT service. The monitors in each level store the status of corresponding monitor able component as checkpoints periodically in a reliable storage. In order to mask the failure of AT, the majority voting module is used to harden the failure detection module. The proposed FT model is less dependent on quality of acceptance test. The analytical evaluation results by means of Markov model shows that the proposed FT service has higher reliability, higher resource utilization, lower resource consumption and lower performance overhead.

References Foster I, Kesselman C (1998) The grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers, Los Altos 2. Foster I, Kesselman C, Tuecke S (2001) The anatomy of the grid: enabling scalable virtual organizations. Int J Supercomput Appl 15(3):200–222 3. Foster I, Kesselman C (1998) The Globus project: a progress report. In Proceeding of the heterogeneous computing workshop 4. Jacob B, Ferreira L, Bieberstein N, Gilzean C, Girard J, Strachowski R, Yu S (2003) Enabling applications for grid computing with globus, IBM 5. Stelling P, Foster I, Kesselman C, Lee C, von Laszewski G (1998) A fault detection service for wide area distributed computations. High performance distributed computing, pp 268–278 6. Baker M, Buyya R, Laforenza D (2002) Grids and grid technologies for wide-area distributed computing. Software—practice and experience, (DOI:10.1002/spe.488) 7. OGSA, http://www.globus.org/ogsa/ 8. Czajkowski K, Foster I, Kesselman C, Karonis N, Martin S, Smith W, Tuecke S. A resource management architecture for metacomputing systems. In: Proceedings of the workshop on job scheduling strategies for parallel processing 9. Bouteiller A, Desprez F (2008) Fault tolerance management for hierarchical GridRPC middleware. Cluster computing and the grid 10. Huedo E, Montero S, Llorente M (2002) An experimental framework for executing applications in dynamic grid environments, ICASE technical report 11. Avizienis A, Laprie J, Randle B, Landwehr C (2004) Basic Concepts and Taxonomy of Dependable and Secure Computing. IEEE Trans Dependable Secur Comput 1:11–33 12. Shooman ML (2002) Reliability of computer systems and networks: fault tolerance, analysis, and design. Wiley, New York, 0-471-29342-3 (Hardback); ISBNs: 0-471-22460-X 13. Nguyen-Tuong A (2000) Integrating fault-tolerance techniques in grid applications. Ph.D. Dissertation, University of Virginia 14. Arshad N (2006) A planning-based approach to failure recovery in distributed systems. A thesis submitted to the University of Colorado in partial fulfilment of the requirements for the degree of Ph.D. 15. Townend P, Xu J (2004) Replication—based fault tolerance in a grid environment. As part of the e-Demand project at University of Leeds, Leeds

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16. Antoniu G, Deverge J, Monnet S (2004) Building fault-tolerant consistency protocols for an adaptive grid data-sharing service. IRISA/INRIA and University of Rennes 1, Rennes 17. Medeiros R, Cirne W, Brasileiro F, Sauve J (2003) Faults in grids: why are they so bad and what can be done about it?’’ Fourth international workshop on grid computing, 00:18 18. Fagg GE, Dongarra JJ (2000) FT-MPI: fault tolerant MPI, supporting dynamic applications in a dynamic world. Lecture Notes in Computer Science, vol 1908. pp 346–354 19. Domingues P, Andrzejak A, Silva LM (2006) Using checkpointing to enhance turnaround time on institutional desktop grids, Amsterdam, p 73 20. Bronevetsky G, Fernandes R, Marques D, Pingali K, Stodghill P (2006) Recent advances in check-point/recovery systems. In: Workshop on NSF next generation software held in conjunction with the 2006 IEEE international parallel and distributed processing symposium 21. Kola G, Kosar T, Livny M (2004) Phoenix: making data-intensive grid applications fault tolerant. In: Proceedings of 5th IEEE/ACM international workshop on grid computing 22. Thain D, Livny M (2002) Error scope on a computational grid: theory and practice. In: 11th IEEE international symposium on high performance distributed computing, 00:199 23. Aguilera MK, Chen W, Toueg S (1997) Heartbeat: a time—outfree failure detector for quiescent reliable communications. In: Proceedings of the 11th international workshop on distributed algorithms, WDAG.97, pp 126–140 24. Lyu M (1996) Handbook of software reliability engineering. McGraw Hill, New York

Vehicle Positioning Technology Using Infra-based Laser Scanner Sensors for Autonomous Driving Service Kyoungwook Min and Jeongdan Choi

Abstract The autonomous driving technology is a vehicle technology which drives automatically to the target destination without human intervention. The traditional autonomous driving technology has been developed using vehicle which has equipped with expensive devices. To control the vehicle movement precisely, the GPS/RTK positioning devices have been used and to recognize the spatial obstacles, the laser scanner and vision sensors have been used. Recently, new technologies have been developed to lower the cost to commercialize the autonomous driving. In this paper, the core technology which is positioning of the unmanned vehicle are developed using infra-based sensors and we have estimated the accuracy of recognized location of controlled vehicle by experimenting in real test-bed area.




Keywords Autonomous driving Distance measurement sensor recognition Vehicle positioning





Spatial object

1 Introduction Autonomous driving and valet parking service enables a vehicle to driving and parking without any human interaction. So far, autonomous driving technology has been developed using vehicle which has equipped with expensive many devices K. Min (&)
J. Choi Vehicle/Infra Fusion Research Team, Vehicle/Ship Fusion Research Department, Electronics and Telecommunications Research Institute, 138 Gajeongno, Yuseong-gu, Daejeon, 305-700, Korea e-mail: [email protected] J. Choi e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_48, Ó Springer Science+Business Media B.V. 2012

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Vehicle Controller · Path Generation · Path Following

x, y, θ : Vehicle Position and heading angle {x, y}obs : Obstacle Positions

control command

δ, α, : Steering angle and Throttle Tb, Ng : Brake Torque and Gear Shifting

Fig. 1 Input data of vehicle controller. The vehicle controller generates the control command a, d value using the current location (x, y) and heading (h) of vehicle

without any environment information. The vehicles of ‘‘Boss’’ [1], ‘‘Junior’’ [2] and ‘‘Odin’’ [3] are winners in Defense Advanced Research Projects Agency (DARPA) urban challenge [4] to drive autonomously about 96 km distance. These all technologies were developed as equipping vehicle with GPS(Global Positioning System)/Real Time Kinematic (RTK), 2D/3D laser scanners and cameras and so on, various sensors to recognize the road and obstacles. To drive vehicle autonomously, the path to the destination has to be generated and vehicle has to be controlled to be followed the path. That is to say, the vehicle controller software has to be able to control the steering, speed and gear shifting as analyzing the several input values. Among the input values, the vehicle position/heading and obstacle position are very important data as in Fig. 1. In general, the expensive GPS/RTK device is used to get the location of vehicle to guarantee the accuracy and the laser scanner device is used to recognize the obstacles [1–3]. We have proposed the new approach recognizing the vehicle position and obstacles using laser scanner sensor located load side. There are a few advantages: the low cost of implementation and high recognition rate in blind spot. So, the goal of this paper is the algorithms of vehicle positioning and recognition of obstacles using infra-based laser scanner sensors as in Fig. 2. The remainder of this paper is organized as follows. Section 2 explains the algorithm of the vehicle positioning as recognizing the vehicle shape using infrabased laser scanner sensors. In Sect. 3, experimentation results and a performance evaluation are given. Finally, Sect. 4 offers some concluding remarks regarding this research.

2 Vehicle Positioning and Obstacle Recognition Algorithm Using Infra-based Laser Scanner Sensors To control the vehicle movement using infra-based sensors, there are systems for infra-based unmanned ground vehicle: the global server system, infra local server system, mobile client system and vehicle controller system. The global server system has the roles generating the route to be followed by vehicle to destination and managing a multiple infra local server systems and local server handover in each local service area to provide service globally [5]. And the global server is able to provide the seamless service provisioning like [6]. The infra local server system recognizes the vehicle location and obstacles using infra-based various

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(a)

Perception Module

Vehicle Controller

Existed Approach

(b) Infra Local Server

Vehicle Controller

V2I

New Approach

Fig. 2 The comparison between (a) existed approach and (b) proposed approach to recognize the obstacle and vehicle position

sensors and transmits the result information to vehicle controller system to control the movement of the vehicle. In the mobile client system, the driver is able to request the driving vehicle autonomously in remote and the mobile map and real time monitoring is provided as advanced driving assistance system [7]. This mobile client system has been implemented on the Android platform in which energy efficient algorithm like [8] is important because of limited mobile resource. And also, mobile client system has to be able to communicate with other server or other mobile device via heterogeneous wireless networks in which security is important [9]. The vehicle controller system controls the actuators to follow the scheduled route generated by the global server. In this research, the vehicle positioning algorithm is executed in the infra local server system using laser scanner sensor located load side. The goal of this research is to recognize the location of the only one vehicle accurately using infrabased laser scanner sensors and we will extend the number of vehicle to be recognized by the infra local server gradually. We have used the Laser Management System (LMS) 291 of SICK cooperation. This sensor is able to detect the object distance maximum 80 m within 0°*180° angular range with 0.5° resolution and 500 k baud-rate–75 times a second [10]. The infra local server system manages the 2 LMS devices in local service area. That is to say, the recognition algorithm is executed as acquiring the raw data from 2 LMS devices at the beginning. The executing flow of the algorithm is as following steps.

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Fig. 3 The extracted object Oi consists of point set of each LMS and a main LMS which detects densely

Step 1. Acquisition raw data and Filtering by spatial map data Step 2. Extract clustered point set (Object set) Step 3. Edge detection and estimate rectangle In Step 1, the raw data from LMS consists of 721 distance value as resolution and angular range. And this data is transmitted to local server high speed: 75 times a second. So the filtering is needed to lessen the computation overhead as discarding the useless value. The raw data from LMS is the scalar value but vehicle position value is the coordinate. So the distance values need to be converted to rectangular coordination system. After converting coordinate value, data not within the road map polygon are discarding. In Step 2, the clustered point set is extracted from filtered raw data in Fig. 3. The basis of the clustering raw data from two LMSs is degree of the spatial approximation. That is to say, near point data are clustered as same object. In the algorithm, although the raw data are acquired from two LMS devices, raw data from just one LMS device which is closer to the object is used to executing the algorithm. Because it is not guaranteed all raw data from two LMSs is able to be gathered by other obstacles. And as long as within some range, the raw data from just one LMS device is enough to executing the recognition algorithm. Finally, vehicle rectangle is estimated using clustered point set data. At first in Step 3, the edge detection type has to be decided whether the detection point set data is SHORT_EDGE or LONG_EDGE as comparing the value of Minimum Bounding Rectangle (MBR) of point set with vehicle size and the # of detection with threshold. The SHORT_EDGE is the front or rear of vehicle and LONG_EDGE is the left or right side. In case short edge, the detection algorithm is in Fig. 4. In case long edge, the detection algorithm is in Fig. 5. We can get two points of the vehicle rectangle as results of the long and short edge detection algorithm. And we can calculate the last two points in easy. Then we get the rectangle of the vehicle and the location of the vehicle is center point of this rectangle.

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(a)

(b)

(c)

Fig. 4 The short edge detection algorithm: In a, outlier was eliminated as analyzing the variation the area of MBR. And next, the linear regression equation [11] is calculated by point set. Finally, the two points of short edge is extracted and adjusted using the vehicle width. a Eliminate Outlier, b Get linear regression equation, c Estimate front/rear two points

Base LRE

(a)

Cores Point P

(b)

Fig. 5 The long edge detection algorithm: In a, each LRE is calculated as eliminating point one by one. The BaseLRE is decided which has minimum difference distance value between each point and LRE. And then the cross point is able to be calculated from division point set and BaseLRE. a BaseLRE, b Estimate side two points

3 Experiments We have experimented with vehicle positioning using infra-based laser scanner sensors in the test-bed area in Fig. 6. The local server system has recognized the location of vehicle over 10 times within 1 s and transmitted the location and heading information to the vehicle controller to control the movement of vehicle to follow the scheduled path from the Start to End. The most important factor of experiment is the accuracy of vehicle position. We have acquired the vehicle position from the local server system recognizing the

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Fig. 6 The experiment area contains the curve road and is 100 m distance. There are two local service area and two LMS devices are in each area

Start LocalService Area 1 (InfraLocalServer 1)

LMS1

50m

LMS2

LocalService Area 2 (Infra Local Server 2) LMS1

50m Handover Area

End

LMS2

Fig. 7 The accuracy of the vehicle positioning is experimented using infra-based laser scanners and the RTK device was used to be compared with accuracy of the recognition of vehicle position. a No-obstacle, b Obstacle

vehicle rectangle using two laser scanners about 25 m long distance. And heading was able to be calculated using current recognized position and before position history. We have tested the recognition of the vehicle as driving the unmanned vehicle autonomously in the environment of the static obstacle on the road or not. The result of the accuracy test is the average 12 cm positioning error and 6 o heading error in Fig. 7.

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4 Conclusion Existed approach to implement the unmanned ground vehicle is used to expensive GPS/RTK to get the current vehicle location accurately to control the vehicle movement. But in this research, we have developed the technology of recognizing the location of the vehicle using infra-based laser scanner sensors as new approach. We have estimated the accuracy 12 cm location error and 6 o heading error in the experiment in the real test-bed area to control unmanned ground vehicle well. As the future work, we will develop the vehicle positioning technology to control multi-vehicles in multi-lanes and to guarantee high accuracy of positioning as fusion the LMSs and other vision algorithm like [12] with vision sensors. Acknowledgments This work was supported by the Industrial Strategic Technology Development Program(10035250, Development of Spatial Awareness and Autonomous Driving Technology for Automatic Valet Parking) funded by the Ministry of Knowledge Economy(MKE, Korea).

References 1. Urmson Chris et al (2008) Autonomous driving in urban environments: boss and the urban challenge. J Field Robotics 25(8):425–466 2. Bacha A et al (2008) Odin: team VictorTango’s entry in the DARPA urban challenge. J Field Robotics 25(8):467–492 3. Montemerlo M et al (2008) Junior: the stanford entry in the urban challenge. J Field Robotics 25(9):569–597 4. DARPAA Urban Challenge, http://www.darpa.mil 5. An K, Choi J, Kwak D, Lim D (2010) Global mission planning algorithm avoiding obstacles for infra-based automatic vehicle guidance system. In: Proceeding of international symposium on remote sensing 6. Kryvinska N, Van Thanh D, Strauss C (2010) Integrated management platform for seamless services provisioning in converged network. IJITCC 1(1):77–91 7. Min K, An K, Jang I, Jin S (2011) A system framework for map air update navigation service. ETRI J 33(4):476–486 8. Liang W-Y, Lai P-T, Chiou CW (2010) An energy conservation DVFS algorithm for the android operating system. JoC 1(1):93–100 9. Xie B, Kumar A, Zhao D, Reddy R, He B (2010) On secure communication in integrated heterogeneous wireless networks. IJITCC 1(1):4–13 10. SICK LMS291, http://www.sick.com 11. Linear regression, http://en.wikipedia.org/wiki/Linear_regression 12. Chong RM, Tanaka T (2010) Motion blur identification using maxima locations for blind colour image restoration. JoC 1(1):49–56

Security Manpower Scheduling for Smart Airports Sangwon Seo, Sanggyun Choi and Chulung Lee

Abstract This paper examines the problem of determining an airport security manpower schedule in the now ubiquitous environments of a smart airport. Passenger service level is the defining factor for the operation of smart airports. Smarts airports need a fast and convenient security check service, as there is a close relation between the time passengers spend waiting and their level of satisfaction. Thus, this research suggests an M/M/s queuing model to determine the waiting time of passengers in the queue and the required numbers of security checkpoints for a given time period. Then, an integer programming model is developed to generate an optimal schedule for airport security personnel. To test an efficiency of the proposed model, a case study is conducted with real data in an airport. The results confirm the effectiveness of our proposed models.





Keywords Smart Airport Ubiquitous Airport Security Manpower scheduling Integer Programming M/M/s Queuing





S. Seo  S. Choi  C. Lee (&) Division of Industrial Management Engineering, Korea University, Seoul, Republic of Korea e-mail: [email protected] S. Seo e-mail: [email protected] S. Choi e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_49, Ó Springer Science+Business Media B.V. 2012

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Fig. 1 Smart airport system (IIAC)

1 Introduction A smart airport is defined as a new generation of smart space with ubiquitous systems that provide more than the basic functions of an airport by serving fast and convenient real-time services to passengers and employees any time and any place. Smart airports comprise passenger oriented facilities and amenities such as entertainment facilities, shopping malls, restaurants, and so on. The role of airports now extended to the center of trade and business. The main goal of intelligent and smart airport implementation is to serve passengers a faster and more reliably. Airports provide different services to fulfill passengers’ preferences and ensure satisfaction. Recent developments like self check-in terminals, ePassports, automated immigration, and automatic boarding gate services have increased the convenience for passengers. These systems are as shown in Fig. 1, integral to smart airports. Nowadays, the roles of airports go beyond the level of simply providing space for airlines so that passengers can fly to different destinations. Instead, modern airports continuously work on improving passenger’s convenience. Passengers now receive real-time information about flight schedules, check-in counters, immigration procedures, and other commercial facilities via smart devices. The system is based on an increased integration of airlines, airport operators and other relevant participants. Although the overall processes have become simpler and more convenient, security procedures still remain as an area that needs to be improved the most. The issue in this area comes from airport security and labor utilization. Those issues are considered important, but difficult to solve, as for instance, the number of passengers increases annually, but the available resources is limited. In terms of costs, the total costs for passenger screenings increased fivefold and costs for passenger screening quadrupled than the pre-9/11 level in the United States [11]. With

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current security screening procedures, passengers need to arrive at the airport early, queue for a long time and in the worst case, experience an airport closure due to a terror warning [5].

2 Problem Description Large hub airports operate 24 h a day, 7 days a week and as a result a minimum number of security personnel have to be assigned at all times, even though there are might only be few passengers at a certain time. During the peak period, enough security personnel are required to screen passengers quickly at the security checkpoints. The number of security teams and passengers has to be balanced in order to screen passengers properly and quickly, as there are time constraints with the scheduled flight departure times of passengers. Optimal airport security manpower planning is hard to achieve compared to similar planning in other industries. Airline schedules are fixed and therefore passengers have to be screened in a given time. In the worst case, a passenger has to reschedule his/her trip to the next possible option. The number of passengers depends on a number of variables: First, the number of passengers depends on the time of day and the day of week. For example, there are usually less passengers in the early morning hours. Then, the number of passengers increases rapidly in the afternoon hours. Thus, a fast and reliable security screening process is necessary to deal with passengers volumes efficiently at given time.

2.1 Security Screening Processes Passengers arrive at the airport around 2–3 h before the departure time to check-in. Afterwards passengers are walking toward the security checkpoints. At the security checkpoints passengers are required to pass the following steps: First, the personal belongings and carry-in bags are screened by an X-ray machine. Then, passengers walk through metal detectors. When any metal is detected, then alarm is set off. When this happens, additional security inspection procedures are required for passengers such as an inspection with a handheld scanner in conjunction with a pat-down. The process continues until security personnel have determined the cause of the alarm.

2.2 Passenger Demand The number of passengers in each time period can be estimated from the passenger forecasting system. It shows the expected number of passengers for each period

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Fig. 2 Average hourly passenger demand

and is calculated based on the airlines’ reservation information systems. The passenger data is collected over a period of month in October, 2010. The average passenger demand over a 24 h period is shown in Fig. 2. From analyzing the data, mainly two peak periods exist, one from 7 to 10 a.m. and one 5 to 8 p.m. After 8 p.m., the passenger demand gradually decreases until midnight. After midnight, only few passengers arrive at the airport until the morning peak time. Airlines choose to schedule their peak hours based on passenger demands and availability, which of course explains that both schedules are similar. Our analysis showed that screening passengers in a given time during peak periods was problematic and that there are variances between the number of passengers at the departure gates.

3 M/M/s Queuing Model This section presents the M/M/s queuing model to determine the optimal number of security checkpoints to be open in a given period. The M/M/s queuing model is suitable for airport security manpower planning problem as security checkpoints are designed as multi queuing lines and multi servers. The M/M/s queuing model assumes arrival process to be in a Poisson distribution. Service rates follow an exponential distribution. Passengers are screened a First-Come, First-Served (FCFS) basis. Each time period represents one hour, so the total number of periods is 24 and represents a full day. We assume that at least one passenger arrives in each time period, because, otherwise, the required number of checkpoints would be less than one, which rarely happens in a real world situation. Arrival rate data can be obtained from the passenger forecast system for 1 h periods. The gathered data is summarized in Table 1. The passenger arrival data show that some variation exists. The main reason for this is that there are relatively more passengers for domestic flights and airlines than for international flights and airlines. Service time is calculated from analyzing data from the case study. On average it takes about 15 s that passenger enters the security screening and out. The total time is about 15 min including the time spent waiting in the queue. On average 240 passengers can be screened per hour under the assumptions mentioned the above. The parameters and formulas used in this section are explained as follows.

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Parameters k The average arrival rate per hour l The average screening time per hour S The number of security checkpoints As the performance measure of security queue, the following formulas are used for determining the required security checkpoints for each time period. "  #1 s1 X ðk=lÞn ðk=lÞs 1 þ ð1Þ P0 ¼ n! s! 1p n¼0 Formula (1) represents the probability that no passengers are in the system. Lq ¼

P0 ðk=lÞs p s!ð1  pÞ2

ð2Þ

With Formula (2), the average number of passengers waiting at security check checkpoints can be calculated. Wq ¼ Lq =k

ð3Þ

With Formula (3), the average time spent waiting at security checkpoints can be calculated. W ¼ Wq þ

1 l

ð4Þ

Formula (4) represents the average time spent in the system, including service. L ¼ kW

ð5Þ

Formula (5) shows the average number of passengers in the service system

4 An Integer Programming Model Using the formulas above, airport security managers can test different policy plans by changing parameters. Here, discussing the matter with airport security managers, the desirable level of waiting time should be lower than 15 min per passenger for the total process system including waiting time on the queue and 15 s per passenger for the screening at the security checkpoints. This corresponds to the numbers the airport authority tries to achieve and we specified for the purpose of our model that 95% of all passengers should achieve this number. The level is the current goal of airport authority tries to achieve. Airport managers can change the parameters based on the situation and the needs of another airport. A resulting integer programming model is given below.

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Table 1 Possible combinations at t = 10 t = 10 j 3 4 5 6 7 8 9 10

8 7 6 5 4 3 2 1

8 7 6 5 4 3 2

8 7 6 5 4 3

8 7 6 5 4

8 7 6 5

8 7 6

8 7

8

Indices i the ith team j the jth working hours t the tth time Sets I the set of team J the set of working hours T the set of time Parameter ci the hourly wage fi the set up costs amax the daily maximum working hours Wq the average time spent waiting in security check points W the average time spent in the system, including service Decision Variable REQt the required number of security checkpoints for each period Xit 1 if team i set up at time t, otherwise 0 Yijt 1 if team i is assigned at time t with j working hours, otherwise 0 Zit 1 if team i is assigned at time t, otherwise 0

4.1 Determining Model of Security Checkpoints Minimize REQt subject to ProbðW  0:25416Þ  0:05

ð6Þ

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ProbðWq  0:25Þ  0:05

ð7Þ

REQt  0

ð8Þ

The objective function of the model is to determine the minimal possible number of security checkpoints. Constraint (6) is that the average time spent in the system including service has to be less than the maximum allowance level (15 min and 15 s in our case) for 95% of all passengers. Constraint (6) is that the average waiting time before entering the system has to be less than the maximum allowance (15 min in our case) for 95% of all passengers. From the model, we calculate the required number of security checkpoints for each time period, as shown in Table 2:

4.2 Allocating Model for Security Checkpoints

Minimize

XX

ðfi  Xit þ ci  Zit Þ

i2I t2T

subject to X

Xit  1 8i 2 I

ð9Þ

Yijt  Xit 8i 2 I; t 2 T

ð10Þ

t2T

X j2I

X

Zit  REQt 8t 2 T

ð11Þ

i2I

X j2J

Yijt þ

t1 X k¼maxf1;t7g

ðYi ;tkþ1;k þ

aX max

Yilk Þ ¼ Zit

l¼tkþ2

ð12Þ

8i 2 I; t 2 T Xit 2 f0; 1g 8i 2 I; t 2 T

ð13Þ

Yijt 2 f0; 1g 8i 2 I; t 2 T

ð14Þ

Zit 2 f0; 1g 8i 2 I; t 2 T

ð15Þ

The objective function is to minimize the total operating costs. The total operating costs include the hourly wages of employee and set up costs for opening a security checkpoint. Constraint (9) is that each team can be assigned only once for a time period. Constraint (10) shows remaining working hours after team is

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Table 2 Numbers of security checkpoints Time period Gate A Gate B Gate C

Gate D

Total required checkpoints

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 1 1 1 1 1 1 5 8 6 3 4 4 4 3 4 4 5 6 5 2 1 1 1

4 4 4 4 4 4 5 19 26 21 13 14 16 12 10 11 12 18 22 14 6 4 4 4

1 1 1 1 1 1 2 6 7 7 5 3 4 3 3 3 4 7 8 4 1 1 1 1

1 1 1 1 1 1 1 4 5 4 3 4 4 2 2 2 2 3 4 2 1 1 1 1

1 1 1 1 1 1 1 4 6 4 2 3 4 3 2 2 2 3 4 3 2 1 1 1

assigned for a time period. Constraint (11) ensures that the number of assigned security teams corresponds to the required number of security checkpoints in each time period. Constraint (12) represents the logic between an assigned team and its possible working hours. For example, if team i is assigned for time period 10, then, all possible combination are given just as shown in Table 1. Team i can be assigned starting 8 h earlier which would be time period 3, with a total of eight working hours. Finally, Constraint (13)–(15) represent binary constraints.

5 Conclusion In this research, we examined the problem of determining the optimal airport security manpower schedule for each given slot, while satisfying operational rules, airport structure, and the checkpoints capacity. We suggest the M/M/s queuing model to determine the required numbers of security checkpoints for each period, while accommodating for the desired level of passenger’ satisfaction. Then, integer programming model is developed to determine an optimal allocation for each security team. The study can be expanded to incorporate future airport

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security systems that automate the process of security screening and classify passenger into various risk levels. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2009-0076365)

References 1. Bamasak O (2011) Exploring consumers acceptance of mobile payments? an empirical study. Int J Info Technol Commun Converg 1:173–185 2. Brusco M, Jacobs LW, Bongiorno JR, Lyons VD, Tang B (1995) Improving personnel scheduling at airline stations. Oper Res 43:741–751 3. Ernst AT, Jiang H, Krishnamoorthy M, Sier D (2004) Staff scheduling and rostering: a review of applications, methods, and models. Eur J Oper Res 153:3–27 4. Gilliam RR (1979) An application of queuing theory to airport passenger security screening. Interfaces 9:117–122 5. Gkritza K, Niermeier D, Mannering F (2006) Airport security screening and changing passenger satisfaction: an exploratory assessment. J Air Transp Manag 12:213–219 6. Holloran T, Byrn J (1986) United Airlines station manpower planning system. Interfaces 16:39–50 7. Jacobson SH, Virta JL, Bowman JM, Kobza JE, Nestor JJ (2003) Modeling aviation baggage screening security systems: a case study. IIE Trans 35:259–269 8. Lee C, Huang H, Liu B, Xu Z (2006) Development of timed Colour Petri net simulation models for air cargo terminal operations. Comput Ind Eng 51:102–110 9. Ling A, Masao M (2011) Selection of model in developing information security criteria for smart grid security system. J Converg 2:39–46 10. Sathappan OL, Chitra P, Venkatesh P, Prabhu M (2011) Modified genetic algorithm for multi objective task scheduling on heterogeneous computing system. Int J Info Technol Commun Converg 1:146–158 11. Seidenstat P (2004) Terrorism, airport security, and the private sector. Rev Policy Res 21:275–290 12. Wright PD, Liberatore MJ, Nydick RL (2006) A survey of operation research models and applications in homeland security. Interfaces 36:514–529 13. Yang T, Yan S, Chen H (2003) An airline maintenance manpower planning model with flexible strategies. J Air Transp Manag 9:233–239 14. Yoo K, Choi Y (2006) Analytic hierarchy process approach for identifying relative importance of factors to improve passenger security checks at airports. J Air Transp Manag 12:135–142

A Study on Static Analysis Model of Mobile Application for Privacy Protection Seil Kim, Jae Ik Cho, Hee Won Myeong and Dong Hoon Lee

Abstract Since mobile application market drastically extended, there have been many problems related to proliferative malicious applications that leak user’s private information stored in own smart device. In Korea, government strives to protect smart device users from these problems and to establish legal basis through ‘Privacy Act’ as a preceding step but studies on analysis models and verification tools for mobile application have not yet much developed. The purpose of this research is to suggest an analysis system to prevent propagation of harmful applications, which compromise the user’s personal information, by extracting signatures from malicious code samples detected in Android. Maliciousness of the applications has determined by learning their parsing information on the components such as DEX, Manifest, SO etc. Keywords Android


Signature
Static analysis
Privacy

S. Kim AhnLab Incorporation, 6th Fl. CCMM Bldg, 12 Teouido-dong, Yeouideungpo-gu, Seoul 150-869, Republic of Korea e-mail: [email protected] J. I. Cho
H. W. Myeong
D. H. Lee (&) Center for Information Security Technologies (CIST), Korea University, 1, 5-Ka, Anam-dong, Sungbuk-ku, Seoul 136-701, Republic of Korea e-mail: [email protected] J. I. Cho e-mail: [email protected] H. W. Myeong e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_50,  Springer Science+Business Media B.V. 2012

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1 Introduction Distribution of Smart phone has dramatically changed our lives: as the performances of such smart devices are almost same with a personal computer, having such a smart mobile device is equal to carrying a computer. It allows mobile office environments, checking e-mail messages and online shopping via internet anywhere and anytime. The main reason for explosive demands for a smart phone is its convenience and portability. Because of the reason, new smart phone subscribers have been greatly increased from 5.0 to 19.5% for only 9 months. As many subscribers have been flowed in smart phone market, the number of applications for smart phone is increased as similar rates to the number of subscribers. (Fig. 1). Now, Google and Apple who are leading the smart phone market and applications for their operating systems are gathered about 260,000 and 420,000 respectively as gathered [1]. However, as the market size becomes greater, the threats on smart phones are grower and these threats are recently developed as the form of privacy infringements and monetary damage on users contrary to earlier hackers who attacked mobile focused on only showing off hacking skills or just getting mobile functions paralyzed. For instance, malware named ‘Cabir’ which was detected on Symbian OS in 2004 took Bluetooth as the path to infect other mobiles around and it consumed batteries a lot to scan others consistently [2]. In particular Android platform of Google is openness-oriented, third markets for application are actively run compare to Apple which runs unified and exclusive application market policy. The matter is, the applications distributed in these third party markets mostly include malwares like spyware to leak user’s private information. As you can see in Figs. 2 and 3, Android platform are mostly more vulnerable to five mobile-applicable attacks. Especially, Android application market does not so verify the origination, content and developer information of application that makes it more vulnerable to attacks through applications [3]. As shown in the Table 1, various malwares related to privacy infringement are more and more increased causing Android mobile users’ alarm. To deal with this problem, Korea government suggested guidelines for private information prevention and enhancement and established ‘Privacy Acts’ that will be put into effect beginning September. Nevertheless, there should be wide discussion on continuously developed malware because of absence of technical security measures for them. In this paper, we suggest advance malware detection model with analyzing API and Permission based on DEX and Manifest parsing on malicious applications that can infringe user’s privacy. This paper consists as follows. In Sect. 2, we search related works on mobile malwares and measures and analyze revealed malicious applications’ APK files with DEX and Manifest to get APIs that infringe user’s private information in Sect. 3. In Sect. 4, we suggest advance malware detection model and in Sect. 5, we conclude and suggest further studies.

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Fig. 1 Current smart phone subscribers [unit: ten thousand]

Fig. 2 Resistance by attack pattern

Fig. 3 Security feature implementation by OS

2 Related Works According to the fourth quarter of 2010 McAfee threats reports, the number of mobile malwares in 2010 has been increased tremendously compare to previous year and its purpose has changed from simple texting SMS or trying international

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Table 1 Malicious codes compromising personal information detected in Android Malicious code name Detailed action Android-Spyware/ •Secretly forwards SMS to a configurable phone number SMSRelpcator Android-Trojan/SmsSend •Runs up a bill by using SMS •Propagation of varieties Android-Spyware/Snake •Sends the user’s GPS to a configurable server •Check the location of the device where TapSnake is installed using GPS Spy Android-Spyware/Ewalls •Sends the personal information including device information to a configurable server •Has other similar programs Android-Spyware/ •Sends a same message to a configurable phone several times Smsbomber Android-Spyware/ •Leaks private information without user agreement Geimini Android-Spyware/ •Leaks SMS/Email/GPS information Mobilefonex •Controls all functions of the device though SMS

call making charge to the form of Botnet that receives commands from C&C and performs malicious action. The threats on mobile has evolved as same level as PCs and that’s why Adobe Flash and PDF vulnerabilities that have been a serious problem are expected to happen on mobile too. For such a reason, Internal and external studies on this matter are actively ongoing. In Korea, Several research institute and universities including government branches like Korea Internet & Security Agency (KISA) and Korea Communication Committee (KCC) have studies on mobile malware. In ‘Analysis Method and Response Guide of Mobile Malwares’, they suggest analysis methodologies using actual execution with available tools like emulator to analyze mobile malwares but it only applies for Windows Mobile Platform and it does not suggests specific measures in technical way. KISA addresses malwares on smart phone based on infection paths to consist malware collection system and arranges existing static and dynamic analysis systems that can be preliminary data for further studies. Moreover, KISA points out that most of anti-virus solutions are based on signature that have difficulty on fast measure and suggests static and dynamic analysis tool for behavior based analysis in ‘Studies about responses to malicious codes in mobile operating systems’. ‘A Study on Protection Model of Propagation through Smart phone Malware Analysis’ analyzes smart phone malware continuously increased and studies on Propagation protection model to prepare to these malware using pattern matching technique that saves detected malware patterns in database then compares the pattern to newly detected pattern to determine whether it is malware or not and send a message about it to user. However this pattern matching technique casts a long shadow when the malware patterns are already collected.

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Symantec report ‘A Window Into Mobile Device Security’ published in July 2011 categorizes five mobile-applicable threats like network based or web based attack/malware attack/social engineering attack/resource abuse attack/data loss or data integrity attack. This report points out that newest malware attack focuses on privacy infringement and social engineering attack and suggests malware lists that threat most in the first half of 2011 as the evidences. And aside from this, it shows iOS and Android platform’s security feature implementation and conclude by saying that iOS provide better security environment from various mobile attacks by slim margin. Especially in the end of this report, it refers that Android is much more vulnerable to malware attack than iOS and mentions the needs of measures in Android platform for malware attack, but no actual measures are suggested.

3 Application Analysis We learn specific features of Application Program Interface (API) relevant to behaviors causing personal information leakage using malicious APK files (malwares). We also discuss details about causes of personal information leakage by analyzing DEX and Manifest of harmful applications.

3.1 Abnormal Behaviors Causing Personal Information Leakage We can summarize the features and processes of malwares, which attempt to compromise personal information including address book, messages, schedule, email, phone numbers, etc., installed applications list, and available services with user account information in the following flow chart. (Fig. 4).

3.2 APIs Accessible to Personal Information (Table 2).

3.3 The Patterns of API Combinations • APIs accessible to personal • APIs for network connection (3G/Wi-Fi or SMS/MMS) (Table 3).

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Fig. 4 Analysis for actions compromising personal information

Table 2 Malicious codes detected during the first half of 2011 Package Class Android platform API android.accounts

AccountManager

Description

getAccounts

Gets All accounts to return getPassword Gets password android.database.sqlite SQLiteDatabase openDatabase Opens database SQLiteOpenHelper getReadableDatabase Gets readable database android.telephony SmsManager getDefault Gets default instance of SmsManager SmsMessage getDisplayMessageBody Gets the message or email body to return TelephonyManager getLine1Number Gets the line 1 number to return

3.4 Analysis on Malicious Codes by Extracting .dex Files The method to analyzing malicious codes is checking whether the Android applications perform malicious activities by extracting .dex files so as to find their methods or parameters to run: you can decompile the .dex file into the source codes using the strings or fields, which is based on reverse engineering.

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Table 3 Combinations of APIs compromising personal information Targeted data/path API Description Phone number/ SMS or MMS

User account/3G or Wi-Fi

TelephonyManager.getLine1Number() API SmsManager.sendTextMessage() API or SmsManager.sendDataMessage() API AccountManager.getAccounts() API

ConnectivityManager.getNetworkInfo() API Contacts list/3G or Context.getContentResolver() API Wi-Fi HttpClient.execute() API

Gets the phone number Sends the phone number via SMS

Gets user accounts and types registered in the device Gets network connectivity such as 3G or Wi-Fi Gets the ContentProvider to get the contacts list Sends the data to the http client

Fig. 5 IMEI in the analyzed DEX file

We analyze the .dex files of the Android applications, which compromise personal information of the smart phone and access to a specific URL for downloads. If you look at the Fig. 5 below about the decompiled .dex file, you can see IMEI, which is personal information, is being transferred.

3.5 Permission Information Based on Manifest Analysis There are several settings relevant to Android applications in AndroidManifest.xml. Most malicious applications often make excessive demand of the permissions for accessing to the personal information, charging for call or message, etc. Those applications demanding excessive permissions are usually suspected as malicious codes since they has more potential to be malicious compared with others, which do not have those permissions. In this chapter, we mainly focus on analyzing permission types assigned to AndroidManifest.xml of the malicious applications. The analyzed application is Monkey Jump, of which normal version is downloadable in the Android market, but infected version is distributed in the third party market. The game seems to run normally, but actually compromises user’s personal information without user’s knowledge or agreement. (Fig. 6).

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Fig. 6 Analysis on malicious APK file

Table 4 Permissions list android.permission.INTERNET android.permission.ACCESS_COARSE_LOCATION android.permission.READ_PHONE_STATE android.permission.VIBRATE com.android.launcher.permission.INSTALL_SHORTCUT android.permission.ACCESS_FINE_LOCATION android.permission.CALL_PHONE android.permission.MOUNT_UNMOUNT_FILESYSTEMS android.permission.READ_CONTACTS android.permission.READ_SMS android.permission.SEND_SMS android.permission.SET_WALLPAPER android.permission.WRITE_CONTACTS android.permission.WRITE_EXTERNAL_STORAGE com.android.browser.permission.READ_HISTORY_BOOKMARKS com.android.browser.permission.WRITE_HISTORY_BOOKMARKS android.permission.ACCESS_GPS android.permission.ACCESS_LOCATION android.permission.RESTART_PACKAGES android.permission.RECEIVE_SMS android.permission.WRITE_SMS

The list indicates that the permissions are assigned for accessing to the personal information including user’s location information, contacts list, and messages, connecting to the Internet to compromise the personal information, and calling or messaging to charge additional service fees. It is not possible to reach the conclusion that this application is found to be a malicious code based on the evidences mentioned, but we can suggest that more detailed analysis for .dex and .elf files is required as the application may perform malicious activities exploiting the excessive permissions. (Table 4).

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Fig. 7 Application analysis process

4 Model for Preventing Mobile Malicious Codes In this chapter, we suggest a system model for preventing mobile malicious codes. The system model for preventing mobile malicious codes is composed of malicious code collecting system, static analysis system, security policy system, and integrated management system. The mainly suggested model for preventing mobile malicious codes, in this chapter, is the statistic analysis system.

4.1 Static Analysis System We analyze the APK files, Manifest files, DEX files, ELF files, Resource files, Web/Meta files, Script files, etc. in static analysis system. The static analysis system is composed with analysis agent system, rule DB, file server, and agent systems, which practically perform the analysis task, are connected to the server. Each agent system analyzes an extracted file and determines whether it is a malicious code. The static analysis system distributes applications collected through a collecting system to each agent system, using distribution modules. The application is distributed to an idle agent system in order. When the agent system requests to distribute an application to the distribution modules, the location will be reserved in the file server. The distribution method is agent pulling, not file server pushing.

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Table 5 Methods for module analysis Module Analysis method APK

Manifest files

DEX files

ELF files

Resource files

Web/Meta files

Script files

Other files

•Analyze based on rules defined by Rule Manager. •Decompress the APK file and analyze its signature and strings. •Determine as a malicious code when a value matching with a policy is found. •Analyze based on rules defined by Rule Manager. •Compares the product name, package name, product version, activity list, service list, AIDL list and permissions list based on policies. •Give a higher penalty of threat level when a value matching with a policy is found. •Parse and save all information about the DEX file in the database. •Analyze based on rules defined by Rule Manager. •Read the DEX file in binary, and compares the method, string and class based on policies. •Give a higher penalty of threat level when a value matching with a policy is found. •Analyze based on rules defined by Rule Manager. •Analyze the ARM ELF file and compare it based on policies. •Give a higher penalty of threat level when a value matching with a policy is found. •Analyze based on rules defined by Rule Manager. •Compare the image and XML files based on policies. •Give a higher penalty of threat level when a value matching with a policy is found. •Analyze based on rules defined by Rule Manager. •Compare the HTML and XML files based on policies. •Give a higher penalty of threat level when a value matching with a policy is found. •Analyze based on rules defined by Rule Manager. •Compare the JSP, PHP and JS files based on policies. •Give a higher penalty of threat level when a value matching with a policy is found. •Analyze based on rules defined by Rule Manager. •Compare the XLS and PDF files based on policies. •Give a higher penalty of threat level when a value matching with a policy is found.

4.2 Static Analysis Process The entire statistic analysis process can be summarized as follow, and agent systems, which practically perform the analysis task, are connected to the server. Each agent system analyzes an extracted file and determines whether it is a malicious code. (Fig. 7). The first process for application analysis is file classification. The agent system downloads an application saved in the file server. After checking the peculiarity in signature, duplication is also checked via database. If the file is found to be a new one, its signature will be saved in the database, or otherwise, there will be no

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Fig. 8 Analysis result of manifest

Fig. 9 Analysis result of Dex

additional analysis process. The .apk file needs to be decompressed, and classified into files. Files are classified by module, and analyzed parallelly. (Table 5). Below Figs. 8 and 9 are the result by static analysis system. When analysis by module is completed, all analysis results will be saved in the database. If a file is found to be malicious in basis of analysis, it will be determined to be a malicious code. If a file is not malicious, but seems potentially harmful as it has high level of penalties, which is to determine the threat level, the file will be reported for more accurate analysis. Malicious signatures are automatically transferred to the scan engine of the mobile security program, and the user can immediately download the engine update.

5 Conclusion In this paper, the system to prevent malicious codes, which illegally attempt to compromise the user’s personal information, and finally threaten user’s privacy, has been suggested by analyzing malicious applications currently distributed in the Android market. As Act on Privacy Protection is due to take effect in September, 2011, in Korea, it is possible to say that a legal standard to prevent applications is prepared, which do not follow the standard guidelines recommended by Article 15: The Collection and Utilization of Personal Information. If the suggested system in this study is applied with the legal restrictions, it will be expected to offer a clean and secure online market for mobile applications to the user by monitoring and scanning malicious applications Acknowledgments This work was supported by the IT R&D program of MKE/KEIT [KI0038421, Develop method and apparatus for diagnosing malicious mobile program and protecting solution].

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References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Kim GH (2011) Korea communication commission annual report, EKCC Seo SH (2010) Smartphone security threats and response strategies. TTA J 132:44–48 Nachenberg C (2011) A window into mobile device security, Symantec McAfee Labs (2011) McAfee threats report: fourth quarter 2010, McAfee Kim HS (2010) Monthly report of Ahnlab, AhnLab Kim IS (2010) Analysis method and response guide of mobile malwares, KICS 35(4) Kim YM (2010) A study on development of smartphone based malicious code collection and analysis platform, KISA Ryou JC (2010) Study of malware detection based mobile OS, KISA Lim SJ (2010) A study on protection model of propagation through smartphone malware analysis. KIAS 10(1):1–8 Reinbacher T, Horauer M, Schlich B, Brauer Jorg, Scheuer F (2011) Model checking embedded software of an industrial knitting machine. IJITCC 1(2):186–205 Li T, Yu F, Lin Y, Kong X, Yu Y (2011) Trusted computing dynamic attestation using a static analysis based behaviour model. JoC 2(1):61–68 Liang W-Y, Lai P-T, Chiou CW (2011) An energy conservation DVFS algorithm for the Android operating system. JoC 1(1):93–100 Xie B, Kumar A, Zhao D, Reddy R, He B (2011) On secure communication in integrated heterogeneous wireless networks. IJITCC 1(1):4–23

Part III

Digital Convergence Information Technology

China’s Industrial Policy: Focusing on the Won-bong Lee

Abstract After China‘s reform and opening up, the national strategy of China has been to build national prosperity and military power through the economic development. China in the twentyfirst century set two national goals; continuous development and fair society. The industrial development has largely contributed to its economic growth. In 2011, China began to new economic and industrial policies named the \12.5 plan[. It includes qualitative growth, increase in domestic demand through income growth, advance in industrial structure, resource conservation and environmental protection, better livelihoods of people, and promoting strategic industrial policy. Along with the qualitative economic development, the new indsutrial policy implies the China’s national goal that aims to solve political and social problems in line with quantitative economic growth.







Keywords China’s national strategy China’s economic development China’s 12.5 plan Newly developing industry Harmonious industrial policy development










1 Introduction Before 1980, The national goal of China had centered to the communist ideology. However after China’s reform and opening up in 1980s, it was switched from sticking to the ideology into building a strong and wealthy nation [1]. The national

W. Lee (&) Department of Chinese Studies, Kyung Hee Cyber University, 1 Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, South Korea e-mail: [email protected]

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goal of China can be well condensed into the two things: economic development and national reunification.1 Since its reform and opening up, China has achieved rapid economic growth. Chinese economy, which has accomplished average ten percent high-speed growth, passed England in 2009 and Germany in 2009 [2]. Furthermore, it overtook Japan 2010.2 The fast growth of Chinese economy is a result from industrial development of various fields. The Chinese government has promoted economic growth through industrial nurturing and revising policies. Since the twentyfirst century, China’s rapid economic growth has caused new social problems. In other words, the gap between the rich and the poor and among regions, classes, and urban and rural areas is worsening social conflicts [3]. In order to achieve effective economic development and solve social problems simultaneously, the Chinese government has searched for the new industrial policy. This paper intends to analyze features of the transformation process of the Chinese industrial policy. In particular, by focusing on the latest Chinese development plan, named \12.5 plan[, it will analyze the present Chinese industry’s trends, characters, and drawbacks.

2 The National Strategy of China After Deng Xiaoping, the national strategy of China changed from sticking to the communist ideology into seeking for pragmatism. Deng Xiaoping set three goals as the national strategy of China; world peace, `national unification, and ´modernization of four areas. Among these goals, Deng Xiaoping most emphasized modernization of four areas, saying ‘‘the purpose of Chinese foreign policies is to achieve modernization of four areas by creating peaceful environment [4].’’ The national strategy of China is based on the consideration that economic power is the best among other factors determining national power. The target for Chinese national strategy is to accomplish economic development and enhance the national prosperity and defense at the same time [1]. The Chinese leadership assumes Chinese economic development as the key to the legitimacy to Communist Party rule [5]. In addition, in order to improve China’s status in the world and extend its influence to the international society, China recognizes that continuous economic growth is needed. The distinguished character of China in the twentyfirst century is it emphasizes on the domestic stability as well as economic 1

The late Chinese supreme leader Deng Xiaoping directed antihegemony, `unification with Taiwan, and ´economic development and modernization of four areas (i. e. agriculture, industry, national defense, and science technology modernization policy. 2 The China’s GDP ranked the world’s 6th in 2000 passed Japan ($5.4742 trillion, 2010) to become the world’s second largest economy in 2010. China is a nation which has the largest foreign exchange reserve ($2.8473 trillion, 2010). Since 2006, China has have the world’s largest foreign exchange reserve 5 years in a row.

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growth. It is because rapid economic growth has caused grave income gap among regions and classes, so social instability factors emerged. ’’ and The new national strategy of China is ‘‘Scientific Development ‘‘Harmonious Society ’’. Chinese leadership is thinking that after resolving domestic problems, it can pursue continuous economic growth [6]. In the sixteenth Communist Party Congress in 2003, the scientific development plan is arisen. The term, scientific development plan, is the national strategic plan aiming to cope with social and economic problems and seek for sustainable development. As the other national strategy, harmonious society is suggested in the 5th plenary session of the sixteenth Communist Party Congress in 2005. These are national development strategies including efficiency and equality together getting out of the ‘Xienfurun’ (the first to become rich) [7].

3 Industrial Policy of China 3.1 Emergence of Industrial Policy After the reform and opening up, China introduced market economy and sought a new industrial policy. The industrial policy of China independently appeared in 1986 at \The 7th five-year economic development plan (7.5 plan)[. In the \7.5 plan[, industrial policy of China emerged as a official policy measure of the Chinese government. In the plan, objects and principles of industrial restructuring were defined, and integrated circuit, computer, software, program control switchboard were selected as the first development items. The Chinese government established ‘‘863 plan’’ as a high-tech industrial development policy. Based on the plan, the government will input large-scale funds and manpower. In March 1989,\The decision of the State Council for essential points of urgent industrial policies[ is released. This is the first article for the Chinese government that clearly describes the industrial policy structure systematically all the way along the line. In 1994, the State Council formulated \The outline of nation’s industrial policy in 1990s[. In the article, it categorized industry with the pillar industry, the infant industry, and the high-tech industry. Since then, industrial policies’ status has become significant in Chinese nation policies [8].

3.2 Promoting Industrial Structure Revising Policy in the Twentyfirst Century Since 2000s, China has announced new industrial policies commensurate with domestic and foreign environmental changes. After its joining the WTO, enhancing industrial structure come to the fore so as to strengthen national competitiveness.

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In \the 10th five-year economic development plan[ (2001*2005), it released development programs for 13 industry fields; machineries, vehicles, metallurgies, nonferrous metals, petrochemical, chemicals, pharmaceuticals, coal, building materials, light industries, textile manufacturing, electricity and so forth [8]. In addition, development programs are more systemized than before. In December 2005, the State Council established \The provisional rules for promoting industrial structure modification[ and \The ruling lists for industrial structure modification[. It is a measure for enhancing industrial structure powerfully at a governmental level. The provisional rules presented goals, principles and directions for industrial structure modification. \The ruling lists for industrial structure modification[ is the specific list for industrial structure modification. In the article, it classified 26 items that should be promoted and 17 items that need to be limited or died out in manufacturing and service industry especially among production facilities. Through enhancing industrial structure policy, Beijing will induce not only effective distribution of resource inter-industry but also achieve economic efficiency. In 2006, \the 11th five-year economic development plan (11.5 plan)[ is released. In the \11.5 plan[ (2006–2010), industrial policies were deemed important. The \11.5 plan[set economic sustainable development and strengthen national competitiveness. The key means to promote advances in industrial structure are innovation. The \11.5 plan[ adopted the new way of government management method. Furthermore, it aimed to nurture market function and make harmonious development among each industrial field [8].

3.3 Promoting Policy of Ten Industry (2009.9) In September 2007 the Chinese government announced a promotion policy of ten industry which sets technical development and restructuring as key concepts. Direction for promoting industrial policy is promoting industrial structure by expanding domestic demand and maintaining foreign market share, `giving financial support, ´supporting technical innovation and renovation, ˆpursuing business M&A, ˜controlling total amount and dying out old-fashioned facilities. Particularly, the Chinese government started to reshuffle the large car industry and the steel industry (Table 1).

3.4 Characteristics of the Chinese Industrial Policy China changed its industrial policy to adjusted one treating industrial restructuring. Before 2004, China focused on quantitative enlargement under industrial structure imbalances. It led to overinvestment and over-heating. In September 2004, China

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Table 1 Main contents of promoting policy of ten industry [9] Industry Major policies Car Steel Machine equipment Textile Shipbuilding Electronic information Light industry Petrochemistry Nonferrous metals Logistics

Supporting and developing new energy vehicle Restructuring, enhancing technical development ability Pursuing localization, improving technological level Technological development, nurturing Chinese brands Enlarging market consumption, supporting technical development Financial support, applying information technology in traditional industry, expanding domestic demand Expanding domestic consumption, industrializing core technologies, developing Chinese brands Technological remodeling, expanding stockpiling of oil Strengthening technological development, securing resources Enlarging logistics market, standardizing logistics

Table 2 Changes of the basic Chinese industrial policy [10] Period Context Object

Policy stance

1977–1988

Developing old-fashioned industry Developing processing industry Export enhancement

Nurturing

Adjusting over-heating

Modifying

Export enhancement

Nurturing

Business adjustment, improving the livelihoods of people

Modifying

1988–1992 1993–1997 1998–2001 2001–2003 2004*

Industrial policies focusing on light industry Industrial restructuring Promoting heavy industry (car, steel) Industrial restructuring, industrial rationalization Promoting heave industry (car, steel) Industrial restructuring, industrial rationalization

Modifying Nurturing

reinforced qualitative regulation and industrial nurturing policy stood on the basis of scientific development view. In line with these actions, China transformed industrial policy from administrative driven to market driven. In December 2005, the Chinese government took effect the provisional rules and guidelines for industrial restructuring for boosting industrial structure revision. These measures imply that the core of industrial policy lies to industrial restructuring. As stated above, the characteristic of the Chinese industrial policy is it appears nurturing and revising policies in turn (Table 2).

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4 Industrial Policy Related to the 12.5 Plan 4.1 The 12th Five-Year Economic Development Plan (The 12.5 Plan) (1) Outline The 11th National People’s Congress (2011.3) approved \the 12th five-year economic development plan (12.5 plan) [. In the context of \12.5 plan[, China’s development plan transformed from qualitative development model to qualitative development one. Compared with the \11.5 plan[, the \12.5 plan[ aims to switch from focusing export and investment economic structure into focusing domestic demand one. For the basic direction of the new economic structure, the four issues, stable economic growth, restructuring, better livelihoods of people, and unemployment problem are suggested. In order to increase domestic demand, China reduced its economic growth rate by seven percent. Inclusive growth is considered as the keynote of the \12.5 plan[. This strategy is to increase people’s livelihood and consumption by restructuring in industry and economy in line with nurturing high-tech industry [11]. (2) Transforming the way of economic development
economic structure revision The \12.5 plan[ contains the way of economic development and economic structure revision. To do this, China accelerates integration between informationization and industrialization, promoting seven strategic new industry, and urbanization. Furthermore, it pursues constant building of infrastructure, agricultural modernization, harmonious development among urban and rural areas, regions, and industries. In addition, it adopted economic development measures for balanced development between regions [3]. The seven new industry is selected as the main target for future industry development [12]. To realize this goal, China erected tri-level development plan in seven new industries; allocating 8% of GDP in 2015 (2% in 2010), 15% in 2020 and aiming to attain the first world level in 2030. If it increased by 15%, added value amounting to 1.5 trillion dollar have been created, and it have resulted job creation at the same time [11]. It realizes the national strategy that solving social problems by increasing the employment rate. (3) Conservation resource and environmental protection, The \12.5 plan[ aims to conserve resource and protect environment. To be more concrete, it is for saving energy resource and accelerating development in the renewable energy field. At the same time, it includes decreasing the whole amount of pollutant emission. It is realization of national strategy aiming to secure sustainable development and future industrial competitiveness.

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Table 3 The new Chinese seven development industries and its directions [11] Industry Specific area New energy Electric car New material Next generation IT Energy conservation, environmental protection Biotechnology High-tech equipment manufacture

Next generation nuclear technology, solar energy, wind power technology etc. Hybrid car, core technology innovation etc. High quality special steel, alloy material, carbon fiber, nano technology etc. Next generation mobile communication, integrated circuit, new model display etc. Developing high efficient energy, environmental friendly technology etc. Red BT, bio agriculture etc. Aviation industry, satellite development, oceanographic equipment, digitization of equipment industry etc.

(4) Better livelihood for people As a basic direction for the \12.5 plan[, inclusive growth is suggested. It means China will protect the underprivileged, enlarge the economic benefit to the entire nation, and maintain economic development balance. It is largely different from the past development plan which solely put an emphasize on quantitative growth [12]. The\12.5 plan[aims to improve employment rate for the livelihood of people, income, distribution system, and better social security system. Beijing propels income increase policy and minimum wage raising etc. for the better income distribution [3]. The 2011 National People’s Congress passed the National Insurance Act. Owing to the new act, the provision for the old and the scope of policyholder are drastically increased [13]. It is another way of realizing The harmonious society which is one of the Chinese national strategies.

4.2 The New Chinese Seven Development Industries (2009) The seventeenth Communist Party Central Committee (2010.10) issued \The State Council’s decision for nurturing and developing newly developing industries[. The Chinese government choose seven newly development industries. new energy, `Electric car, ´new material, ˆnext generation IT, ˜energy conservation and environmental protection, Þbiotechnology, þhigh-tech equipment manufacture. The Chinese government announced various promoting programs for these industrial fields, such as subsidies, financial support, tax cut and so forth. The characteristics of the strategic new development industry is as follows. First, the range of the strategic industry is enlarged. It enlarged to develop new energy, electric car, new material, environmental protection facilities, and related

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industries. Second, the newly released plan is aiming to lead these technology globally. Third, the industrial policy is more organized (Table 3).

5 Conclusions The national strategy of China is to build national prosperity and military power through the economic development. The industrial development has been driving force for Chinese economy. The economic development of China gives legitimacy to Communist Party rule and elevates the status of China at the same time. The national strategy of China has been adjusted to seek economic development and social equality together. The industrial policy of China switched from quantitative enlargement into qualitative development. It is designed to Chinese national goal to elevating its national status by strengthening national competitiveness. The \12.5 plan[ aims to revise industrial structure and make better life for ordinary people. The policy aims to secure substantiality of Communist Party rule by restraining uneasiness in the Chinese society. The successful industrial policy of China will have an effect on in the international society. China’s better income distribution arises increasing possibility of higher export rate. As a result, it can cause inflation started from China. If economic development focusing on domestic demand successes and the economic structure of China is improved, trade dispute over Chinese yuan can be alleviated and it will have a positive effect on global economic recovery [3]. The \12.5 plan[ aims for resource conservation and environmental protection. Those are the key to the political and economic issues in future global society and integral factors to become a leading country. Currently, through aggressive industrial policies, China is preparing for the second leaping. In the long run, it is being ready for building wealthy country and becoming leading country in the future society.

References 1. Lee W-B (2002) Chinese security strategy and military power in the 21st century, Institute of Global Affairs, Kyung Hee University, Asia Pacific Studies, vol 9 2. Waiming MH (2010) Prospect of Chinese economy in 2011 and the core policy in the 12th five-year economic development plan, KIEP (Korea Institute for International Economic Policy), Korea and China Economic Forum vol 10-07 3. Kwon H-J (2011) The main issues and implication of the 2011 National People’s Congress, SERI Economic Focus, Samsung Economic Research Institute 4. Deng XP (1980) Confronting affairs and tasks. Annals of China, Taipei 5. Ru X, Lu XY (2003) Analyzing and predicting Chinese society. Social Science Literature Publishing House, Beijing 6. Lee W-B (2008) Chinese national strategy and foreign ideology, The Korean Association of Political Science & Communication, Political Science & Communication Research

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7. Jung D-K (2007) Hu Jintao and reconciliation society. East Asia Publishing Corporation, Hong Kong 8. Lee M-H (2006) Advance in Chinese industrial structure and globalization strategy, Korea Institute for Industrial Economics & Trade (KIET), Issue Paper 2006-204 Research for transitional industry in China, p 1 9. Kim Y-M (2010) Outline of the new Chinese seven development industries, Study on Chinese economy and finance, Special focus III 10. KIEP (Korea Institute for International Economic Policy) (2010) Current issues of the Chinese economy. KIEP Briefing of Beijing Office, Beijing 11. National Statistics Office of China (2011) Chinese premier Wen Jiabao’s the full text of government report, March 2011 12. Jang S-W (2009) Chinese business reshuffling to secure industrial hegemony and its implications. Samsung Economic Research Institute, SERI Economic Focus, p 237 13. Jung Y-S (2006) Changes in industrial policy in China, four responding actions, KOTRA, Special research, pp 6–53 14. Hu JT (2007) Boosting great Chinese characteristic socialist banners. Renmin Publishing House, Beijing 15. Cho Y-N (2006) China’s politics in the Hu Jintao Era. Nanam Publishing House, Seoul 16. Lee H-O (2007) Study on Chinese National Strategy. Politeia Publishing House, Bucharest

A Dual CPU Based Fault Tolerance Technique for Manufacturing System HwaYoung Jeong and BongHwa Hong

Abstract To promote productivity is very important work in manufacturing system. For this work, manufacturing system has to operate with high performance without jam or system error. And it has to perform to interface and control with unit component or process in high speed communication and handing the signal and data. In this paper, we propose dual CPU based process for manufacturing system. To perform the process reducing any jam or system error in manufacturing system, we use dual CPU. This framework is able to get more efficient operation performance better than existence method.










Keywords Manufacturing system Fault tolerance Reducing system jam Dual CPU based process

1 Introduction With the globalization of manufacturing, there has been a renewed interest in the competitiveness of the manufacturing sector throughout the world. There is an increasing trend towards higher product variety, smaller lot sizes and shorter lead times in the market place. In this environment, manufacturing companies are forced to implement systems that can provide flexibility and efficiency [1]. H. Jeong Humanitas College of Kyunghee University, Hoegi-dong, Seoul, 130-701, Korea e-mail: [email protected] B. Hong (&) Department of Information and Communication, Kyunghee Cyber University, Hoegidong, Seoul, 130-701, Korea e-mail: [email protected]

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Stabilized performance of the system without any system error or jam is an important factor to increase products from the manufacturing system. However it is very difficult for the manufacturing system to keep in stable operation during the system is running. Because the error or jam is able to easily happen from the collision with system, operation program and communication process which is in unknown situation. Moreover, lots of manufacturing system tends to operate in poor surroundings. The manufacturing system was consisted of various and many actuators such as motors, sensors, cylinders, and robots for special operation. Faults can put at risk the actuators, their task, the working environment, and any humans present there due to their operations which is in very complex and various components or processes. In most environments, the actuators include robot can be repaired after the Fault Detection and Isolation (FDI). There are some environments, however, where human beings cannot be sent to make the necessary repairs immediately, and, thus, fault tolerance must be provided to the actuators [2]. In this paper, we propose the structure to support efficient process using dual CPU to control the actuators for manufacturing system. The structure mainly was divided two parts. One part with one CPU system deals with the operation with control the sensors, cylinders, motors and robots. The other part with another CPU system deals with the process to control and manage the system error, jam, and operational display for user. And this part has mainly functioned to check and repair the manufacturing system when it happen the system error or jam.

2 Fault Tolerance Technique for Manufacturing System 2.1 Manufacturing System Manufacturing system design involves making long-term decisions. As such, it is practical to spend more time analyzing alternatives than would be the case for operational decisions. It was in the general manufacturing system design area that simulation initially gained popularity. Initially, models used for these projects were typically coded using general purpose programming languages. The successful application of simulation in this area led to the development of specialized subroutine libraries and, later, comprehensive simulation languages and software packages. These developments have significantly simplified the use of simulation and have led to its more general use. The general class of manufacturing system design has been further subdivided into facility design, material handling system design, manufacturing cell design, and flexible manufacturing system (FMS) design. Manufacturing system operation applications of simulation generally involve making shorter-term decisions when compared to the system design applications. The manufacturing system operation class has been subdivided into the operations planning and scheduling, real-time control, operating policies, and performance analysis subclasses [3]. Samuel et al. [4] described definition of a unit production process for manufacturing system as shown Fig. 1. In their research, the architectural

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UPP Parts Inut UPP Good Parts Output UPS Parts Input Defective Parts Output

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Fig. 1 Definition of a unit production process (UPP)

combinations of UPPs were based on Burbidge’s classification methodology. A unit factory (UF) can always be decomposed into these basic architectural combinations. Productivity metrics for a UF, including overall throughput effectiveness (OTE) and system cycle time effectiveness (CTE), can thus be derived based on overall equipment effectiveness (OEE) of individual UPPs.

2.2 Fault Tolerance Fault tolerance aims to avoid system failure via error detection and system recovery. Error detection at the architectural level relies on monitoring mechanisms, or probes, for observing the system states to detect those that are erroneous at the components interfaces or in the interactions between these components. On the other hand, the aim of system recovery is twofold. First, eliminate errors that might exist at the architectural state of the system. Second, remove from the system architecture those elements or configurations that might be the cause of erroneous states. From the perspective of fault tolerance, system structuring should ensure that the extra software involved in error detection and system recovery provides effective means for error confinement, does not add to the complexity of the system, and improves the overall system dependability. Since fault tolerance has a global system scope, it should be related to both architectural elements (components and connectors) and architectural configurations. However, the incorporation of fault tolerance into systems normally increases their complexity, making their analysis more difficult. One way of handling the inherent complexity of fault-tolerant systems is to adopt architectural abstractions. These are able to hide system complexity, and provide the means for analyzing how errors are propagated, detected and handled, and how faults in the system are handled [5].

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Fig. 2 Fault tolerance system by Renato et al. [2]. h is the vector of measured joint positions, indicates that the vector is estimated, the subscript d refers to the desired vector, and the matrix PsðxoÞ converts the forces in the end-effectors to squeeze forces

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The fault tolerance system addresses the following categories of faults: freeswinging joint faults (FSJFs), where an actuation loss occurs in a joint of one arm; locked joint faults (LJFs), where a joint of one arm is locked; joint position faults (JPFs), where the measurement of one joint position is not correct; and joint velocity faults (JVFs), where the measurement of one joint velocity is not correct. JPFs and JVFs can occur due to sensor faults, for example. The fault tolerance system proposed here is designed for cooperative manipulators rigidly connected to an undeformable load. When the manipulators are not connected to a load, traditional fault tolerant methods designed for individual manipulators can be used [2]. Renato et al. proposed the fault tolerance system as shown Fig. 2. In their research, the faults was firstly detected and isolated by an FDI system. When a fault is detected, the arms may be locked by brakes and the trajectory planning may be reconfigured starting with zero velocities.

3 Proposed Fault Tolerance System for Manufacturing Environment This research aimed to control and manages manufacturing system with fault tolerance system. For this process, we construct system architecture using dual CPU. The system with first CPU deals with performs the operation of actuators, sensors, motors, cylinders, and robots.

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And the other system with second CPU process to detect system error or jam during the system operates, and repair the system according to repair progress that was saved it to the operation database in the manufacturing system, and display the

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system information to screen to get commend from user or manager. Proposed system structure was shown in Fig. 3. Figure 4 shows the system architecture for fault tolerance system. In this architecture used two CPU systems with dual core and system buffer memory. Hence CPU for Fault Tolerance System (FTS) perform the process to repair the system error or jam when FDI system detect some error during the system running. FDI system is able to get and send the signals or data to the actuators through the controller. When the system occur error or jam, FDI system gets the signals from the actuator, sensor, motor, cylinder or robot. Next step is FTS process perform the repair process to the system after refer repair sequence. If the system repair process is fail, information of this situation was saved to FTS Buffer. All of repair process and operation data was saved to FTS Buffer and send them to CPU for GUI System through Communication process. Figure 5 shows the process Fault Tolerance System and GUI system. Especially, system error detects and FTS process has significant process to repair the system automatically. Therefore system error detects check the actuators status all the time, and FTS process perform to repair the system error or jam according to repair sequence.

4 Conclusion Manufacturing system had performing their process for special purpose in the worst working environment. Therefore, it is very important for developing manufacturing system with overcome the system error or jam to repair the system fault automatically. In this paper, we construct the structure using dual CPU system to control, manage, detect and repair the system error. The reason of this construction is the system with only one CPU is difficult to perform all process for manufacturing system efficiently and to control and manage FTS immediately. The system consists of two main parts, one system with the first CPU is deal with process to detect and repair the system error or jam when it occurs, the other system with second CPU perform GUI process for user and manager. GUI system does work to handling between user and the system, and to calculate and analyze the operational status data include the system Mean Time to Repair (MTTR) and Mean Time to Failure (MTTF). This system using dual CPU is able to perform detects and repairs the system error and manages the data efficiently without any intervention.

References 1. Joseph OA, Sridharan R (2011) Analysis of dynamic due-date assignment models in a flexible manufacturing system. J Manuf Syst 30:93–100 2. Renato T, Terra MH, Bergerman M (2007) A fault tolerance framework for cooperative robotic manipulators. Control Eng Pract 15:615–625

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3. Smith JS (2003) Survey on the use of simulation for manufacturing system design and operation. J Manuf Syst 22(2):157–171 4. Samuel HH, John PD, Shi J,Qi S, Wang G, Rauak MA, Robinson DE (2002) Manufacturing system modeling for productivity improvement. J Manuf Syst 21(4):249–259 5. de Lemos R (2009) On architecting software fault tolerance using abstractions. Electron Notes Theor Comput Sci 236:21–32

A Computational Clustering of Korean Classical Texts: Focusing on the Gogocheonbyeon Passage of Sugungga Woonho Choi and Dong Keon Kim

Abstract In this study, a computational method was used to measure distances between texts, especially to measure variations which took place during the transmission of texts. Gogocheonbyeon is called nundaemog of Sugungga. Nundaemog is one of the most essential and important parts of a Pansori, like arias of an opera. We selected 16 albums of 16 singers, and transcribed narratives from those albums, and also put the musical expressions on the Korean traditional musical score Jeongganbo. From these raw materials, the text-distances of the transcribed narratives were measured using Levenshtein distance, and the distance was normalized by the length of the narrative strings. And then the texts were clustered using hierarchical clustering methods. As a result, we found some differences between the lineage of Pansori transmission and the real narratives that some singers like Park Tongjin and Song Sunseob are somewhat distance from their masters. Keywords Gogocheonbyeon

 Pansori  Levenshtein distance  Text clustering

W. Choi (&) Department of Linguistics, Seoul National Unviersity, Gwanak 1 Gwanak-ro, Gwanak-gu, Seoul, Korea e-mail: [email protected] D. K. Kim Humanitas College, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_53, Ó Springer Science+Business Media B.V. 2012

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1 Introduction This research distinguishes the statistical relationship of manuscripts spread through the process of ‘‘Transmission and Variation’’, and provides a basis for the measurement of similarities and differences. The similarities and differences between various classical manuscripts and the statistical relationship between those manuscripts have been measured by various computational approaches. A prime example of those approaches is The Canterbury Tales Project [1]. The algorithms and techniques applied in various ways in the fields of classical philology, dialectology [2], and historical comparative linguistics [3] were applied to classical Korean materials. Particularly, in this paper, in what way the transmission process of Gogocheonbyeon, a passage from the Pansori Sugungga, has been transformed as it spread is confirmed and accurately measured. Since Pansori is transmitted from generation to generation, not only do many texts exist, there is a great variance between them. Especially because Pansori is not just read, but performed, many difference Pansori schools were formed. However even within the same school, several forms differing in wording, using different expressions exist. Gogocheonbyeon is the passage where Byeoljubu goes on a journey to catch the rabbit, and is known as the Nundaemog in Sugungga. The Nundaemog passage, as one of the most remarkable passages in Pansori, comprises the core of the story, and is sung the most frequently. Because of this, the amount of remaining audio materials is greater for this than other passages. Also because of the importance of this passage, to the story and to the singers’ repertoire it has been transmitted and adjusted many times, and so makes an excellent set of passages for measuring the amount of mutual differences, which is the main goal of this paper. In other words, through ‘transmission’, the similarities between passages, and through the ‘variation’ process, differences can be measured. Put concretely, narrations and musical expressions written on traditional Korean musical scores, Jeongganbo will be compared for each singer. For narration, an edit distance algorithm will be employed. For musical notation grammar, differences in length measurements will be compared. The result will be analyzed through hierarchical clustering methods.

2 Research Data The subjects of the research in this paper are the 16 performers : Song Mankap, Pak Bongsul, Song Sunseop, Im Bang-ul, Kang Dogeun, Kim Yeonsu, Oh Jeongsuk, Pak Dongjin, Jeong Gwangsu, Kim Yeongja, Pak Choweol, Nam Haeseong, Jo Tongdal, Kim Suyeon, Kim Changhwan, and Jeong Hoeseok. The materials used in this paper are listed below in Table 1. The recordings listed in Table 1 contain the recordings of the passage Gogocheonbyeon and are the basis for comparison in this paper. These recordings

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Table 1 Gogocheonbyeon comparative research materials Performer ID Title

Record ID

Recorded year

Song Mankap Pak Bongsul Song Sunseop Im Bang-ul Kang Dogeun Kim Yeonsu Oh Jeongsu Pak Dongjin Jeong Gwangsu Kimg Yeongja Pak Choweol Nam Haeseong Jo Tongdal Kim Suyeon Kim Changhwan Jeong Hoeseok

SRCD-1064 REG.NO.117 SRCD-1490 DYCD-1438 SEL-RO664 NSC-187-1 NSSRCD-047 SKCD-K-0253 8810-G211 KACD-0003 ORC-1448 CMCC-0718 HAEDONG-112 Z-C1-04-0132 SYNCD103 TOPCD-026

about 1935 1982 2003 1959 1990 2007 2001 1988 1988 1999 1994 2006 1994 2004 1920s 1999

SMK PBS SSS IBU KTK KYS OJS PTJ JKS KYJ PCW NHS JTD KSY KCH JHS

Dongpyeonje Pansori Sugungga Sugungga Sugungga 1 Sugungga Sugungga Sugungga Sugungga Sugungga Sorisaem by Kim Yeongja 1 Sugungga 1 Sugungga by Nam Haesung Korean Traditional Music: Pansori Sugungga by Kim Suyeon Pansori 5 Myeongchang Sugungga by Jeong Hoeseok

were transcribed with the Korean traditional musical notation. Because live Pansori performances were used, as live recordings were created, personal and dialectal differences were made to reflect the morphophonemic representation, which was made to be morphophonemic unit syllabification representation.

3 Comparative Method and Results 3.1 Narrative Comparison and Distance Measurement In order to compare the narrative of the Gogocheonbyeon passage of Sugungga, variant texts were aligned and compared pair-wise. To compare the distance between aligned narratives, Levenshtein Distance (LD) [2, 4] is employed. LD takes two strings and counts the number of insertions, deletions, and substitutions it would take to transform one into the other. Figure 1 illustrates the process of applying the LD algorithm to the two strings from variants of Gogocheonbyeon materials. The results for each stage are determined by progressing from top to bottom, and from left to right. The final distance is the number on the bottom right. The results of applying the LD algorithm on all the string pairs from two performers require normalization. When calculating the difference between two strings using the LD algorithm, if one string has a length 12, and the other string has a length of zero, the distance is 12. Also, the difference between two strings is directly related to the length of the strings involved. Normalization will remove the dependence of distance upon string length. The formula for this is as follows.

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Fig. 1 Application of the LD algorithm to measure the distance

NDi ¼ distanceðsi ; ti Þ = lengthðalignedðsi ; ti ÞÞ : n P

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The objects of comparison are the two strings si and ti, and the distance between these two strings is divided by the length of the aligned sequences. The result will always be within the range of 0 B ND B 1. Using the normalized distance of (1), the distance between two performers can be calculated from (2). From this the distances between all pairs of 16 performers can be displayed in a symmetric matrix, as displayed in Fig. 2 below.

3.2 Clustering and its Interpretation After using LD, the result of distance measurements between the narratives of the 16 performers is listed in Fig. 2 above. The results are interpreted through the multivariate analysis methods using R [5, 6].

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Fig. 2 LD symmetric matrix of 16 performers

The narrative of the Gogocheonbyeon passage, in a manner largely similar to the genealogy of Sugungga, is divided into four groups consisting of the Song Mankap subgroup (Eastern), the Yu Seongjun subgroup (Eastern), the Gangsan Group, and the Western Group. Out of these, even though they are in the same Eastern Group, the narrative of the Song Mankap group closer to that of the Western Group and the narrative of the Yu Seongjun Group is closer to that of the Gangsan Group with respect to distance. JHS of the Gangsan Group and KCH of the Western Group each make an individual cluster, however if one interprets the data from Fig. 3, the distance between the Gangsan Group and the Yu Seongjun subgroup of the Eastern Group are similar, and the Western Group and the Song Mankap subgroup of the Eastern Group are similar.

3.3 Musical Notation Comparison While Sects. 3.1 and 3.2 focused on the comparison of narratives, this section primarily concerns itself with the comparison of musical notation. First, the voices of the 16 performers were recorded in Jeongganbo notation and this notation is compared pair-wise. The similarities of narratives was carried out with the use of a string comparison algorithm, however with musical notation every beat was compared directly on a one to one basis for similarities and differences. Figure 4 below displays a fragment of the Jeongganbo data. Every beat on every line were treated the same as a character and compared pair-wise, the results of one verse (known as Jangdan) comparison were gathered, and using their average the distance between performers was measured. When comparing, only if the corresponding pair existed did a comparison take place, so the average measurement depends on the size of the corresponding pair Fig. 5.

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Fig. 3 Narrative distance and pansori Sugungga genealogy

Fig. 4 Fragment of Jeongganbo for Gogocheonbyeon by song mankap

The first thing to point out in the analysis of the musical notation of Gogocheonbyeon is that Song Sunseop (SSS) of the Eastern Song Mankap subgroup is not classed as being a part of the same cluster with Song Mankap (SMK), Pak Bongsul (PBS), and is in fact closer than Pak Dongjin (PTJ) to the Yu Seongjun subgroup. Using the results of the narrative analysis and interpreting

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Fig. 5 Clustering of performers’ distances from Jeongganbo

them together, it can be shown that although Song Sunseop (SSS) inherited the narratives of Song Mankap (SMK) and Pak Bongsul (PBS), there was much influence from the Yu Seongjun subgroup. In the Yu Seongjun subgroup, in the same manner as the narrative analysis, Pak Dongjin (PTJ) and the aforementioned performers each constitute their own cluster, however the distance between the other performers is significantly large. This sort of narrative and musical notation analysis result provides support for the previously uncertain notion that Pak Dongjin had learned from Yu Seongjun.

4 Conclusion This paper is concerned with the measurement of distance in texts where transmission and variation occurs, and experimentation into the methodology of classification and division into clusters. Using the Gogocheonbyeon passage from the Pansori Sugungga, the amount of variance in transmission of Pansori from generation to generation, as well as the measurement of the performers genealogically classified into the same sect was tested. Generally speaking, this research examines the Yu Seongjun and Song Mankap subgroups of the Eastern Group, the Western Group, and the Gangsan Group. The fact that the differences in each group do not manifest in the same way is one of the main points of this research. Also the classification of sects and contrasitive phenomena, in other words, the narratives of Pak Dongjin of the Eastern Yu Seongjun subgroup, and Song Sunseop of the Eastern Song Mankap subgroup, through a composite analysis of

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musical notation, have shown that these performers are relatively distance from their counterparts in the same subgroups. Of course much of these differences were already known, the measurement into the extremity of the differences was newly attempted in this paper.

References 1. Barbrook AC, Howe CJ, Blake N, Robinso P (1998) The phylogeny of the canterbury tales. Nature 394:839 2. Heeringa W (2004) Measuring Dialect Pronunciation Differences using Levenshtein Distance, Ph.D. thesis, University of Groningen 3. Kondrak G (2002) Algorithms on Language Reconstruction, Ph.D. thesis, University of Toronto 4. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Cybern Control Theory 10(8):7070–7710 5. R Development Core Team (2009) R, A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna 6. Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290

The Survey of Quality Model for Software and System Hye-Jeong Jeong and Suck-Joo Hong

Abstract Recently, software quality model is very important factor in software environment and society. Therefore we would like to discuss what the quality model for software is and what quality model’s factor is important. This paper mainly refer from ISO 9126 model.










Keywords Software quality Quality model ISO 9126 Software environment

1 Introduction Most of the staff in enterprises or organizations relies greatly on the software systems to support their routine and non-routine jobs. One of the most important goals of the software industry is to develop high-quality and reliable software for their customers. Software project managers face the major issue of controlling their software quality at every stage of software development. Software quality classification thus aims to predict the software quality and to indicate the defects in software modules early. Identifying the software modules that are likely to be faulty prior to the system testing improves the effectiveness of testing efforts. Such a prediction strategy assists software project managers in the successful release of H.-J. Jeong Department of Digital Information and Statistics, PyeongTaek University, PyongTaek, 450-701, Korea e-mail: [email protected] S.-J. Hong (&) Department of Information and Telecommunication, Kyung Hee Cyber University, 1 Hoegi-Dong, dongdaemun-Gu, Seoul 130-701, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_54,  Springer Science+Business Media B.V. 2012

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high-quality and reliable software systems for their customers [1]. Embedded software systems face additional challenges due to the stringent QoS requirements for these systems. For example, it is crucial to consider real-time, security, reliability, and resource and power constraints in embedded systems. Thus, the integration of embedded systems from components must consider the satisfaction of the functional requirements as well as the QoS requirements. Frequently, multiple components with different QoS tradeoffs can be used to achieve the same functionality. Also, components may be configurable, i.e., some of the program parameters of a component can be configured to achieve different QoS tradeoffs. It can be computationally intensive to determine the most suitable set of components to use with the best parameter settings. For example, consider a small system consisting of 10 program units. Assume that there are two possible components that match the functional requirements of each program unit. Also, assume that each component has a single parameter with 10 potential settings to achieve various QoS tradeoffs. In an exhaustive search, there are (2 9 10)10 choices to be considered in order to find out the choices for a satisfactory QoS property of the system [2]. In this paper, we described quality model for software. The model was referred at ISO 9126, and existence research.

2 Relative Works 2.1 Software Environment Traditional software engineering attempts to find solutions to problems in a variety of areas, such as testing, software design, requirements engineering, etc. A human software engineer must apply his acquired knowledge and resources to solve such complex problems that have to simultaneously meet needs but also be able to handle constraints [3]. During the software development process, accurately identifying the software quality of software systems plays a critical role in targeting quality improvement efforts to the high-risk modules. However, predicting the exact number of faults is too risky, especially in the beginning of a software project when too little information is available. The most commonly adopted approach for software quality classification is to classify software modules into two groups, fp or nfp, in order to identify the fp category easily. Software project managers are able to focus software enhancement efforts on the likely fp software modules based on the suggestions from the software quality classification models. Such software quality classification models yield predictions on a module-bymodule basis, which enables decision makers to target software problems early. Providing an accurate and reliable software quality classification model has become important for effectively assuring quality in software systems [1].

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2.2 Software Change Software change is a long-studied topic. Manny Lehman, a pioneer of the study of software changes, developed the Laws of Software Evolution. These laws describe recurring issues related to the evolution of E-type software. An E-Type system is one that functions in the real world and therefore must continually evolve to maintain user satisfaction.

2.3 Change Classification Change classification schemes have been used to qualitatively assess the impact and risks associated with making certain types of changes. Software change classification schemes also allow engineers to group changes based on different criteria, e.g. the cause of the change, the type of change, the location of the change, the size of the code modification or the potential impact of the change. Another benefit of change classification is that it allows engineers to develop a common approach to address similar changes thereby reducing overall effort compared with addressing each change individually. Lientz and Swanson’s work identified the frequency of the different types of maintenance activities performed by software development organizations [90]. Based on their work and work by Sommerville, four major types of changes were identified. Perfective changes result from new or changed requirements. These changes improve the system to better meet user needs. Corrective changes occur in response to defects. Adaptive changes occur when moving to a new environment or platform or to accommodate new standards [4].

2.4 ISO model for Software Other quality models have been proposed and submitted for use. While studies were useful, they have also caused confusion because of the many quality aspects offered. Thus, the need for one standard model was felt. It is for this reason that the ISO/IEC JTC1 began to develop the required consensus and encourage standardization world-wide. First considerations originated in 1978, and in 1985 the development of ISO/IEC 9126 was started. The ISO 9126 is part of the ISO 9000 standard, which is the most important standard for quality assurance. In this model, the totality of software product quality attributes is classified in a hierarchical tree structure of characteristics and sub characteristics. The highest level of this structure consists of the quality characteristics and the lowest level consists of the software quality criteria. The model specifies six characteristics including Functionality, Reliability, Usability, Efficiency, Maintainability and Portability; which are further divided into 21 sub characteristics. These sub characteristics are

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manifested externally when the software is used as part of a computer system, and are the result of internal software attributes. The characteristics defined are applicable to every kind of software, including computer programs and data contained in firmware and provide consistent terminology for software product quality. They also provide a framework for making trade-offs between software product capabilities. To compare the aforementioned model show the strengths and weaknesses of each model. As discussed before, the ISO model seems to be more complete than the others, and is free of shortcomings of others. The most important characteristics of ISO model are: hierarchical structure, having criteria for evaluation, comprehensive expressions and terms, simple and accurate definitions, and one-to-many relationship between various layers of the model. Therefore, ISO model was chosen as the base model and was customized to make it more suitable for the evaluation of a particular application domain [5].

3 Survey of Software and System Quality Model Quality model is defined as the set of characteristics and the relationships between them; but it only explains the relationship between quality factors and sub-factors without considering their value. However, not all sub characteristics equally are affecting characteristic. Also, these values are not equal for different software products. For example, in ISO quality model, Security and Interoperability are two sub-factors related to Functionality. If the quality of an e-payment system is assessed by the ISO model and the value of Security and Interoperability are respectively computed as S and I, then which of the following results will be the value of Functionality? ‘S ? I’ or ‘2/3 S ? 1/3 I’ or…. To address this problem, for different software products, the relation and impact of each characteristics and sub characteristics should be distinguished. Quality models can be more useful for evaluation of different applications by using weighted quality characteristics and sub characteristics [5]. Moraga [6] proposed the quality model as shown Table 1, 2, 3. Behshid et al. [6] proposed quality model from ISO 9126; characteristics including Functionality, Reliability, Usability, Efficiency, Maintainability and Portability. An overview of the defined characteristics and sub characteristics of this model is shown in Fig. 1.

4 Conclusion In this paper, we discuss the quality model for software and system. It mainly refer from ISO 9126 model: Functionality, Reliability, Usability, Efficiency, Maintainability and Portability. And we could know how the factor is important things in the software environment.

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Table 1 Quality characteristic and sub-characteristic definitions for software Characteristic Definition SubDefinition for portlet context characteristic Functionality Capability of the portlet to Accuracy provide functions which meet stated and implied needs when the software is used under specified conditions Security

Reliability

Capability of the portlet to provide the correct or agreed results or effects with the degree of precision that has been agreed on. Ability of the portlet to prevent unauthorized access, whether accidental or deliberate, to information and data. Self-containment Capability of the portlet to accomplish (by itself) the function that it is expected to perform FunctionalCapability of the portlet to cohesion make use of all its elements in performing its services. Compliance Capability of the portlet to adhere to standards, conventions or regulations in laws and prescriptions relating to functionality. Maturity Capability of the portlet to Capability of the portlet to avoid failure as a result of maintain a specified level faults in the software. of performance when used under specified conditions Fault tolerance Capability of the portlet to maintain a specified level of performance in cases of software faults or of infringement of its specified interface. Recoverability Capability of the portlet to recover from unexpected failures by itself. Degradability Effort needed to re-establish the essential functionality of the portlet after a breakdown. Evaluability Capability of the portlet to enable the portal developers to evaluate its form and/or its content (continued)

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Table 1 (continued) Characteristic Definition

Subcharacteristic Availability

Usability

Efficiency

Reusability

Definition for portlet context

Capability of the portlet to be operational every day of the year 24/7/365 Ability of the portlet to be Understandability Capability of the portlet to used by the portal enable the user to developer when understand what the constructing a portal with portlet is about it. Learnability Capability of the portlet to enable the user to learn how the portlet achieves its aim Customizability This refers to the attributes of portlet that enable it to be customized by the user, to reduce the effort required to use it and also to increase satisfaction with the portlet Compliance Capability of the portlet to adhere to standards, conventions or regulations in laws and similar prescriptions relating to usability. Time behaviour Capability of the portlet to Capability of the portlet to provide appropriate provide appropriate response and processing performance, relative to times when performing its the amount of resources function, under stated used, under stated conditions. conditions. Resource Capability of the portlet to use utilisation appropriate amounts and types of resources when it performs its function under stated conditions. Capability of the portlet to be Understandability Capability of the portlet to enable the user to reused in different portals understand what the by several developers portlet is about Portability Capability of the portlet to be transferred from one environment to another.

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Table 2 Attributes and measures defined for the functionality characteristic Sub-characteristic Attributes Base measures Scale measures Accuracy

Agreement

Correct results

Degree of agreement

List (1-3): low, intermediate, high Natural number Natural number Boolean (0/1)

Number of correct results Total number of results Security Access control The portlet implements the Registration interface The portlet supports the Boolean (0/1) register() operation The portlet cannot be used if Boolean (0/1) the consumer has not been registered. Type of authentication List: wsrp:none, mechanism wsrp:password, wsrp:certificate others The portlet uses authorization Boolean (0/1) mechanisms Security control The portlet stores events Boolean (0/1) related to security Number of mechanisms to Natural number detect attacks Confidentiality The portlet supports secure Boolean (0/1) communication between the client and the consumer. The portlet supports SSL/TLS Boolean (0/1) standard The portlet supports XML Boolean (0/1) Encryption standard Boolean (0/1) The portlet events require a secure communication channel in order to be distributed. Boolean (0/1) The portlet requires secure communication in its entire markup Integrity The portlet identifies different Boolean (0/1) user categories (UC) The portlet uses UC to access Boolean (0/1) other components Self-containment Additional elements Number of additional Natural number elements Functional cohesion Unnecessary Number of unnecessary Natural number elements elements Compliance Security compliance Number of standards that the Natural number portlet adheres to

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Table 3 Measures for the reliability characteristic SubAttributes Base measure characteristic

Scale measures

Maturity

Boolean (0/1)

Management of The portlet captures faults faults The portlet has alternative paths which are used when a fault occurs The portlet provides fault messages Fault Control of faults The portlet has routines which are tolerance used when a fault occurs Number of mechanisms that the portlet possesses for when faults occur. The portlet indicates the type of fault that has occurred The portlet controls all the faults identified in the WSRP standard Supported faults Number of faults that the portlet supports. Recoverability Solutions to a The portlet has a routine to run fault completely if a fault occurs The portlet provides the cachecontrol element Number of fault messages that have defined an aqction as answer Type of state Type of state that the portlet stores (transient, persistent, navigational or none) Degradability

Actions in the portlet

Actions in the additional elements

Evaluability Availability

Preview mode Available time

Boolean (0/1) Boolean (0/1) Boolean (0/1) Natural number

Boolean (0/1) Boolean (0/1) Natural number Boolean (0/1) Boolean (0/1) Natural number List: transient, persistent, navigational or none Boolean (0/1)

The portlet needs to be reinstalled after a fault The portlet needs to be rebooted after Boolean (0/1) a fault The additional elements need to be Boolean (0/1) reinstalled after a fault The additional elements need to be rebooted after a fault The portlet supports the preview mode Real available time Theoretical available time

Boolean (0/1) Boolean (0/1) Decimal number Decimal number

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Fig. 1 The ISO 9126 quality model attribute tree

References 1. Chiu NH (2011) Combining techniques for software quality classification: an integrated decision network approach. Expert Syst Appl 38:4618–4625 2. Ma H, Yen I-L, Zhou J, Cooper K (2006) QoS analysis for component-based embedded software: model and methodology. J Syst Softw 79:859–870 3. Räihä O (2010) A survey on search-based software design. In: Proceedings of computer science review 4. Williams BJ, Carver JC (2010) Characterizing software architecture changes: a systematic review. Inf Softw Technol 52:31–51 5. Behkamal B, Mohsen K, Akbari MK (2009) Customizing ISO 9126 quality model for evaluation of B2B applications. Inf Softw Technol 51:599–609 6. Moraga MÁ, Calero C, Garzás J, Piattini M (2009) Assessment of portlet quality: collecting real experience. Comput Stand Interfaces 31:336–347

Improvement of Retinex Algorithm for Backlight Image Efficiency Seongsoo Cho, Bhanu Shrestha, Hae-Jong Joo and Bonghwa Hong

Abstract An image in a dynamic range widened by limitations in an image sensor, which has limited size of dynamic range, causes mobile phones and digital cameras to produce brightly saturated image or dark image with less exposure unlike observed with human eyes. This study, which explores solutions to improve contrast imbalance triggering higher global contrast but lower local contrast in images acquired in an environment with wide dynamic range, uses exposure information and edge information for weighted value mapping. The map is applied to image composition process to compare high-brightness proposed in the algorithm to improve contrast imbalance. The comparison indicates greater contrast improvement in the test image than in the original algorithm. Specifically, average growth rate in the original algorithm declined by roughly 27% generating a very large contrast loss while it declined by just 9% in the proposed algorithm resulting in hardly any loss. This comparison and numerical analysis point to the original

S. Cho (&)  B. Shrestha Department of Electronic Engineering, Kwangwoon University, 26 Kwangwoon-gil, Nowon-gu, Seoul 139-701, Korea e-mail: [email protected] B. Shrestha e-mail: [email protected] H.-J. Joo Department of HUNIC, Dongguk University, 82-1 Pil-dong 2-ga, Jung-gu, Seoul, 100-272, Korea e-mail: [email protected] B. Hong Department of Information Communication, Kyunghee Cyber University, Dongdaemun-gu, Seoul 130-701, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_55, Ó Springer Science+Business Media B.V. 2012

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contrast improvement performance to be a better choice as proven by the proposed algorithm, which does not incur contrast loss in the HD side and thus, produce a more balanced contrast improvement performance. Keywords Backlight image efficiency



Retinex algorithm



Contrast



MSR



Image

1 Introduction Driven by advances in image sensor technology and digital image processing technology, digital image devices are everywhere these days in user-friendly mobile phones and digital cameras for everybody to use. In particular, explosive demand for mobile phones and portable digital cameras as well as high expectations for high-quality output image are evolving technologies at a speed of light, which in turn motivate research to search for effective solutions to improve color, brightness as part of photo correction [1–4]. Image sensor, which serves as human eyes in mobile phones and digital cameras, accumulates more pixels thanks to advancing semiconductor technologies, but reduced size of area that gets light per pixel is causing more noise, and area researchers are working to improve. Because dynamic range image, which sensor reacts to, is narrower than the range in the actual input image recognized by human eyes, there is inevitable information loss and this creates discrepancy between the image seen with human eyes and that filed with digital camera. To keep information loss to minimum, many studies are underway at this moment to process digital images to compress dynamic range in the input image and expand contrast. Retinex algorithm founded on Retinex Theory, which defines model on humans’ viewpoint, is known to outperform others with better contrast improvement and color reproduction [5–12]. Retinex algorithm uses Gaussian function based on log computation to estimate image illumination and get rid of the estimated illumination component from input image to acquire reflectance, which portrays an object’s characteristics. Final image is produced by restoring color and applying gains and offsets. Images produced through Retinex algorithm are hugely affected by Gaussian Center/ Surround function parameter setting used for illumination component estimation and by weight added when composing the estimated illumination component images. Since uniform or random weighted values are used when composing estimated illumination components for composition, Retinex algorithm depends hugely on parameter setting related to Gaussian center and surround functions, which are used to estimate illumination components. But, it cannot adaptively respond to all different kinds of input images and cannot maximize the unique characteristics of each illumination component images, which cause local contrast loss. Furthermore, backlight images in which the strong illumination component at subject’s rear side enlarges difference of brightness and subsequently darkens subject cause contrast imbalance showing improved contrast in the darker areas

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but declined contrast in the brighter areas. This study proposes Retinex algorithm, which uses weight map, to overcome such foregoing drawbacks and improve balanced contrast. The proposed algorithm clears input image’s illumination components and retrieves edge and exposure components, which affect contrast, from each reflection component image to make weight map that covers characteristics of each image concerned. Mutually complementing weight map is then applied to each reflection component image holding different characteristics to compose images, which go through color restoration process and gains and offsets for conversion into output scale and final image.

2 Original Retinex Algorithm Retinex algorithm is based on Land’s Retinex theory, which claims that brightness recognized by humans is a multiplication of illumination components and reflection components as proposed by Jobson etal. Its concept is to make a more compressive dynamic range and better contrast by taking out illumination components that impact subject recognition and reflection components that highlight input image’s characteristics to get rid of illumination components’ effect and emphasize reflection components only [13]. Iðx; yÞ ¼ Rðx; yÞ  Lðx; yÞ

ð1Þ

In Eq. (1), which defines Retinex algorithm, I (x, y) is input image, R (x, y) is reflection component and L (x, y) is illumination component. The following equation is a log scale conversion of Eq. (1) according to Weber–Fechner’s Law, which establishes log relation between input image’s actual brightness and brightness recognized with human eyes [14]. log Rðx; yÞ ¼ log Iðx; yÞ  log Lðx; yÞ

ð2Þ

Retinex algorithm goes through the process of estimating illumination components using Eq. (2) and removing them from input image to highlight reflection components. There are two types of Retinex depending on image channel. One is single scale retinex (SSR) algorithm applied for individual scale and the other is multi scale retinex (MSR) algorithm applied for RGB’s three scales like color image. The block diagram in Fig. 1, which describes Retinex algorithm processes, shows how image with reflection components are gained after SSR processing in each color component. This process is repeated per size of Gaussian Filter used to estimate illumination components. This is followed by MSR processing in which images with the obtained reflection components are given weighted value for composition for gains, offsets and color restoration before getting MSRCR image in the final stage [15]. Equation (3) defines SSR algorithm equation.

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Fig. 1 Block diagram of Retinex algorithm

Ri ðx; yÞ ¼ log Ii ðx; yÞ  log½Fðx; yÞ Ii ðx; yÞ

ð3Þ

where (x, y) is coordinates of pixels and Ii (x, y) is I th color component. i becomes 1,2,3 in RGB image and Ri (x, y) shows SSR result of ith color. The symbol, * represents convolution computation and F (x, y) is Gaussian center and surround functions to estimate illumination components as defined in Eq. (4). Fðx; yÞ ¼ K exp½ðx2 þ y2 Þ=c2 

ð4Þ

In Eq. (4), C is Gaussian center and surround constant and K is calculated with Eq. (5). ZZ Fðx; yÞdxdy ¼ 1 ð5Þ In SSR, image quality is largely determined by the value of C. Local contrast and sensitivity in low brightness areas are enhanced while global contrast witnesses loss due to lessened difference of brightness in High Brightness (HB) and Low Brightness (LB) areas when C is low. When the value of C is high, the difference of brightness in LB and HB areas is appropriately handled to improve global contrast but reduced difference of brightness in HB areas and overall decline in edge components in the image causes local contrast loss. Jobson et al. proposed 80 to be the proper value of C and Ref. [6] proposes MSR algorithm, which makes up for the foregoing shortcomings. MSR adds weighted value, which is Gaussian center and surround constant value of C, to SSR output images that applied Gaussian filters with different sizes and the following Eq. (6) is used to combine all the values gained to produce an output image [7]. RMSRi ðx; yÞ ¼

N X k¼1

Wk Rki ðx; yÞ;

N X k¼1

Wk ¼ 1

ð6Þ

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Fig. 2 Weighted value map-based Retinex algorithm block diagram

In MSR, random or equal weighted values are used to compose each SSR image but in doing so fails to restore contrasts lost due to Gaussian filter. As a result, quality of output image is over-dependent on the size of Gaussian filter and thus fails to completely clear SSR’s drawbacks. Jobson et al. proposes MSRCR algorithm as shown in Eq. (7), which adds color restoration function in MSR, as an alternative. RMSRCRi ¼ Ci ðx; yÞRMSRi

ð7Þ

Although MSRCR produces more vivid color than MSR by applying color restoration functions covering ratio of each color component, there could be color noise in LB areas, which are more sensitive. Hence, characteristics of output image need to be fully comprehended to set proper value in MSRCR.

3 Weighted Map Retinex Algorithm The original Retinex algorithm is heavily affected by constant values that determine valid area of Gaussian filter used to estimate illuminate components and weighted values added when composing reflection components. This study extracts edge and exposure components that affect contrast in reflection component image for adaptive weighted value mapping in output image in order to remove contrast loss incurred by applying random weighted values and proposes an upgraded algorithm, which is applied to the composition process. Figure. 2 is Retinex algorithm block diagram based on the proposed weighted value map. To get reflection components, RGB color space in input image is converted to YCbCr color space to separate image into brightness component and color difference signal, and apply SSR to brightness component. High-pass filter is used in reflection component image to extract edge components and low-pass filter is applied to extract exposure components to compose both components for weighted value mapping. These weighted value maps organized by size of Gaussian filter

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Fig. 3 Comparison of test images, a Original image, b Original algorithm, c Proposed algorithm

Fig. 4 Comparison of test images, a Original image, b Original algorithm, c Proposed algorithm

used for illumination component estimation go through regularization process before they compose with each SSR image. Final image is produced by taking gain/offset and color restoration process in the image composed with brightness component followed by color restoration and conversion to RGB color space. In YCbCR color space, computation gets smaller if MSR process is applied only to brightness components since output image is separated between brightness components and color difference components. In addition, restoration process gets simple when change rate of brightness components generated by MSR processing is applied to correcting color difference signal since the color is similar to that in the original image. RGB color space in input image should be converted to YCbCr color space since it is easier to extract edge components and exposure components, which make up weighted value map, from brightness signal. The degree of each edge component and exposure component varies in SSR images that removed illumination components estimated with different-sized Gaussian filter. Weighted value map is then organized to highlight characteristics of each SSR image, which is done by extracting the components.

3.1 Extraction of Edge Components In SSR images, noise contained in illumination components amplifies because they execute log computation over the course of removing estimated illumination components. Gaussian Smoothing is used to reduce noise components and Laplacian of Gaussian (LoG) Filter, which emphasizes edge information via

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Table 1 Local standard deviation Input image Original retinex Bright Dark Bright Ratio Figure. 3 9.27 Figure. 4 7.15 Average Ratio (%)

8.24 6.97 7.33 5.01 -27.39

Proposed retinex Dark

Ratio Bright Ratio

-24.80 10.88 31.97 7.68 -29.97 10.28 40.18 7.07 36.08 -9.09

Dark

Ratio

-17.13 10.29 24.77 -1.04 8.91 21.55 23.16

Laplacian masking kernel, is applied to edge component extraction process. LoG Filter is described in the following Eq. (8). LoGðx; yÞ ¼

x2 þ y2  2r2 x2 þy2 2 e 2r r4

ð8Þ

where r is Gaussian standard deviation constant and Log (x, y) refers to LoG Filter, which deleted regularization coefficient to simplify computation. Equation (9) is the process of simplifying LoG Filter into 5 9 5 masking kernel and executing SSR image and convolution computation to extract edge components.

3.2 Extraction of Exposure Components For this, brightness components distributed in the middle band out of SSR image’s total brightness distribution is assumed to be the most optimal exposure components and Gaussian function is used to ensure exposure components are properly reflected in the final output image. Equation (9) is used to get exposure components as shown below. ! 0 ðYSSR ðx; yÞ  0:5Þ2 ð9Þ we ðx; yÞ ¼ exp  2r2 where YSSR (x, y) is image obtained by SSR processing on Y component of regularized input image and r is Gaussian standard deviation constant. This study applied 0.2 as the constant. we (x, y) is exposure components acquired. Y component value is regularized in 0 * 1 range and 0.5, which is the intermediate value, is deduced to extract exposure components that highlight brightness in the middle band.

3.3 Composition of Each Extraction component For Eq. (10) is definition of weighted value mapping process of regularizing edge components and exposure components extracted from SSR image with the equal value range and multiplying them.

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Wk ðx; yÞ ¼ wc;k ðx; yÞ  we;k ðx; yÞ

ð10Þ

where wc,k (x, y) and we.k (x, y) are regularized edge components and exposure components, and Wk (x, y) is weighted value map.

4 Experiment In this study, images were acquired in an environment with wide dynamic range and the extent of improvement in the original Retinex algorithm and contrast was compared in order to evaluate performance of the proposed weighted value mapbased Retinex algorithm. 15, 80, 250, which are the Gaussian center and surround constants applied to text images to compare degree of impact on images resulting from weighted value setting, were equally applied to the original Retinex algorithm and the proposed weighted value map Retinex algorithm. In the original Retinex algorithm, weighted value was uniformly applied by 1/3 each and HB areas and LB areas in input image were divided to calculate standard deviation in each area as suggested by Jobson et al. Larger standard deviation means brightness of each pixel is evenly distributed instead of being concentrated to certain values, indicating large contrast. Figures 3 and 4 show original images and images processed with the original Retinex algorithm and the proposed algorithm. Table 1 shows standard deviation per HB and LB areas in the original Retinex algorithm and the proposed algorithm. As presented in Table 1, average growth rate relevant to the original image is roughly 13% greater in the original algorithm than in the proposed algorithm. However, average growth rate of the proposed algorithm is also over 23%, which indicates that subjects can effortlessly be identified in LB areas, too. In HB areas, contrast improved more in the proposed algorithm than in the original algorithm as proven by test images. Average growth rate in the original algorithm fell by approximately 27%, which caused significant contrast loss while loss was roughly 9% in the proposed algorithm indicating hardly any contrast loss. The test result reflects improvements in the original algorithm’s LB areas and performance improvement of a lost and unbalanced contrast in HB areas. Unlike the original algorithm, there is no contrast loss in the proposed algorithm’s HB area, which means it has a more balanced contrast improvement performance.

5 Conclusion Original Retinex algorithm shows much better image contrast and outstanding picture quality. While contrast improvement is fairly good in backlight image’s dark areas, color components deteriorate in the bright areas. This study analyzes characteristics of backlight image to make up for such shortcoming in Retinex

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algorithm and proposes weighted value map Retinex algorithm to improve the performance. Local standard deviation is calculated to evaluate performance of the proposed algorithm and confirm performance improvement in the global contrast. In HB areas, contrast loss in the original Retinex algorithm is around -27.39% while that in the proposed algorithm is -9.09%, which shows 18% performance improvement compared to the original algorithm. In LB areas, performance of the original algorithm and proposed algorithm improved by approximately 36.08 and 23.16%, respectively. This performance analysis result confirms weighted value map-based Retinex algorithm to have a more balanced contrast improvement performance than the original Retinex algorithm and to be more efficient in improving backlight contrast with wide dynamic range.

References 1. Meylan L, Alleysson D, Sustrunk S (2007) Model of retinal local adaptation for the tone mapping of color filter array images. J Opt Soc Am A 24(9):2807–281 2. Choi DH, Jang IH, Kim NC (2006) Color image enhancement based on an improved image formation model. IEEK SP 4(6):65–84 3. Ke C (2005) Adaptive smoothing via contextual and local discontinuities. IEEE Trans Pattern Anal Mach Intell 27(10):1552–1567 4. Park YK, Kim JK (2007) ‘‘A New methodology of illumination estimation/normalization for robust face recognition,’’ IEEE international Conference image Processing, pp.149–152 5. Land E, McCann J (1971) Lightness and retinex theory. J Optical Soc Am A 61(1):11 6. Jobson DJ, Rahman Z, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6:451–462 7. Jobson DJ, Rahman Z, Woodell GA (1997) A multi-scale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process: Special Issue Color Process 6:965–976 July 8. Rahman Z, Woodell GA, Jobson DJ (1996) A comparison of the multiscale retinex with other image enhancement techniques. Special Issue Color Process 6:451–462 9. Su MC, Guo JH, Lin DT, Wang GC (2002) New compensation algorithm for color backlight images. In: neural networks, Proceedings of the 2002 international joint conference, vol 2, Honolulu, Hawaii, pp 1396–1400 10. Choi DH, Jang IH, Kim MH, Kim NC (2007) Color image enhancement based on singlescale retinex with a JND-based nonlinear filter. In: Proceedings of the IEEE international Symposium Circuits and Systems, New Orleans, USA, pp 3948–3951 11. Jang IS, Park KH, Ha YH (2009) Color correction by estimation of dominant chromaticity in multi-scaled retinex. J Imaging Sci Technol, vol 53(5) 12. Kang BH, Jeon CW, Ko HS (2007) K-Retinex algorithm for fast back-light compensation. J IEEK SP 44(2):126–136 13. Horn BKP (1986) Robot vision. MIT Press, Cambridge 14. Agaian SS, Panetta K, Grigoryan AM (2001) Transform-based image enhancement algorithms with performance measure. IEEE Trans Image Process 10:367–381 15. Sobol R (2004) Improving the Retinex algorithm for rendering wide dynamic range photographs. J Electro Imaging 13(1):65–74

A User Authentication Method for M2M Environments Jin-Mook Kim, Hwa-Young Jeong and Bong-Hwa Hong

Abstract The request about green IT technology is increasing recently. M2M is more request to IT Technology for Cloud computing. And effective use request of computer resource is increasing. So, necessity about Cloud computing is increasing rapidly thereby. However, Cloud computing environment have big security problems because embody using virtualization technology. It is Authentication that must solve in Cloud computing environment urgently. Therefore, we wishes to propose protocol that can solve suitable user and services certification in cloud computing environment. An novel authentication protocol that based on Kerberos. But it is faster than Kerberos and implement is easy. Keywords Authentication


Cloud computing

1 Introduction An interest about the cloud computing techniques is increasing rapidly for efficient use about the Green-IT technology and confined computer resource recently. J.-M. Kim (&) Department of Information Technology Education, Sunmoon University, Cheonan, South Korea e-mail: [email protected] H.-Y. Jeong
B.-H. Hong Department of Information & Telecommunication, Kyunghee Cyber University, Seoul, South Korea e-mail: [email protected] B.-H. Hong e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_56,  Springer Science+Business Media B.V. 2012

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The cloud computing technique refers to computing ability that take advantage of internet technology and offer IT resources (Software, Storage, Server, Network) that is done virtualized service. Because of the cloud computing is based on virtual technology. Those have characteristic that all systems can act as server compare with existent server-client environment. It can lend idleness IT resources through this to necessary user effectively, reduce purchase expense of IT resources, and have usable characteristic effectively. If examine same native place about cloud computing, abroad proposed in Google by first and Amazon, Microsoft, Oracle, IBM, Yahoo, and so on show high interest. But in domestic, government cleared market scale that plan to rear until hereafter 2014 being early state because original-technology is insufficient.

2 Related Work 2.1 Security Threats As before, the cloud computing is embodied through virtualization technique. Therefore, they have much difficulty to apply developed security services that is suitable to existent server-client environment. We show security threat elements in the cloud computing environment on Table 1. Security threats

Explain

Malware Outflow of information Denial of service Authentication

Transmission malignancy code using user request feign Leakage of importance information by inner user Service refusal attack through recursive service requests User certification about virtual machine

It is an authentication service that should be solved as is most urgent among security threat elements appear in Table 1. A user authentication service that when user wishes to use IT-resources, decide permission availability, and use possibility degree for IT-resources.

2.2 Authentication Service The famous authentication service is two kinds. That is a Kerberos and PKI. The Kerberos have a Ticket Server (TS) between user and application server to provide user requested service. TS make the ticket and forward to user. When a application server received ticket from user, using ticket test authentication by TS.

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Table 1 Font sizes of headings. Table captions should always be positioned above the tables Heading level Example Font size and style Title (centered) 1st-level heading 2nd-level heading 3rd-level heading 4th-level heading

Lecture Notes … 1 Introduction 2.1 Printing Area Headings. Text follows … Remark. Text follows …

14 12 10 10 10

point, point, point, point, point,

bold bold bold bold italic

Fig. 1

And application server response to user can be service that is user wanted. This process displayed in Fig. 1. The Public Key Infrastructure (PKI) is to examine authenticate that CA that is the third trust engine issue, and issues certificate to users using public key cipher system and offer certificate about user. It shows to Fig. 2. The Kerberos or PKI have much difficult because it have much problems for applies in cloud computing environment. Because a Kerberos uses asymmetric key cipher system, Ticket server issues ticket each user for authentication particularly. And Ticket server must store this. Therefore, Ticket server has much overhead about huge ticket issued history. And the PKI establishes CA that issue certificate that is very big load to use in cloud computing environment that changed network condition. So, we wishes to propose a more efficient secure authentication protocol that is based public key cryptographic method and modified exist the Kerberos authenticate server. We address our propose protocol more detail in Chap. 3.

3 A User Authentication Method (AUAM) 3.1 Architecture of AUAM A propose protocol in this treatise shows Fig. 3. We compose Authentication Server (AS) and CPS that served various cloud computing services. Table 2 shows terminologies that use in proposal scheme.

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Fig. 2

Fig. 3

3.2 Procedure of AUAM 3.2.1 Initial Procedure Our proposal scheme has two preconditions. (1) Protocol gets two-level acts for user register and service request. (2) For user authentication that use public-key cipher system at initial procedure. Initial procedure that run user register and user authentication explain in Fig. 3. Initial procedure is consisted of two detail sub procedures. (1) First, User sends ID, Password to AS for user registration check. An AS makes Random Number and that encodes by Session Key to CPS. This time, a CPS confirms EAS using store user ID, RND to user list.

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Table 2 Font sizes of headings. Table captions should always be positioned above the tables Identifiers Explain ID, PW # RND M TS OTP AS SAS CPS Enkey[M] Dekey[M] s_key h(M) {a||b} Pu_key Pr_key service# ,(comma) Req_AN

User id, password Number stream Pseudo random number Message (plaintext) Time stamp One time password Authentication server Superior authentication server Cloud-service provide server Encryption to M using key Decryption to M using key Session key Make a message digest using Hash Function Concatenates a data ‘a’ and ‘b’ A public key on the asymmetric key algorithm A private key on the asymmetric key algorithm A service number Separator Request authentication number

(2) If User transmits encrypted Service # and ID to CPS for service request, then a CPS searching ID in stored user list. And confirm service # that is decrypt using fined random number. A CPS make Service number confirm information and send to user. A user and AS, and CPS create initial authentication information and have process that exchange mutually preparing on user register and service request using RND.

3.2.2 Authentication Procedure Figure 4 explain about service authentication request and confirm procedure. A user AS and CPS has three steps service authentication request process. It is process that user transmits information for user authentication via AS for service request to CPS at first step and AS and CPS confirm. Second process that CPS supplies authentication information via AS for user authentication confirms result to user. AS delivers service that user requires to CPS at three steps in right case because follow one step and two step with upside and CPS flows user authentication from AS with user. So we can prevent, and can keep away variation about service request in midway beforehand about security attack that user through above service request and certification procedure disguises social position to gouge CPS. Also, user can detect a man-in-the-middle attack.

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Fig. 4

4 Evaluations We evaluate the Kerberos and PKI with our proposal scheme for compare performance, user accessibility in this chapter. It is display in Table 3.

User authentication Message authentication Usability Efficiency

Kerberos

PKI

Proposal

Symmetric None Normal Fast

Asymmetric Possible Normal Slow

Hybrid Possible More More

A proposed authentication protocols is expected that initial system lag time happens relatively comparing with the Kerberos. However, latency may not affect great total operating time. And propose scheme do not need more troublesome procedure that is cross authentication like a PKI. We described comparative analysis for our proposal against by exist the Kerberos and a PKI. However, this is logical vantage point. So, we will compare characteristics of other various systems with our proposal system on open cloud computing environment hereafter.

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5 Conclusion We are proposed basic idea about effective and more secure user and service authentication protocol in cloud computing environment in this treatise. The cloud computing is high utilization convenience and is receiving ream by next generation internet service because industrial ripple effect is too big. However, it is convenient however and if security service problem is not decided beforehand although industrial expectation effect is high, it may be dead loss. So, we propose an new idea that can solve user and service authentication protocol in cloud computing environment. It can support access control, user authentication, message integrity. Forward we try to solve our proposal scheme have many weakness near future.

References 1. Thomas D (2008) Enabling application agility-software as a service, Cloud computing and dynamic languages. J Object Technol 7(4), May–June 2008 2. Lawton G (2008) Developing software online with platform-as-a-service technology. Comput, June 2008 3. Amazon (2008) Amazon Web Service: Overview of Security Process. http://aws.amazon.com, white paper, Sep. 2008 4. Armbrust M (2009) Above the Clouds: A Berkley View of Cloud Computing. http:// radlab.cs.berkeley.edu 5. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2 6. Amazon Simple Storage Service (Amazon S3). http://aws.amazon.com/s3

Test Driven Development of Model Transformation with Reusable Patterns Jong-won Ko and Young-jae Song

Abstract In order to easily port mobile applications suitable for each platform, that have been developed under diverse development environment for individual wireless communication service providers, or redevelop them on a specific platform, it is required to reuse them at software model level that is a software development paradigm for MDA (Model Driven Architecture). Verification of existing design models and test models for the study, mainly checking (Model Checking) with a code-based software designed to define in the abstract syntax tree or on the models generated using refactoring on design models for refinery operations and define how to perform. The problem with these traditional research methods, but the first model, design model for checking the information with the model by defining a formal representation in the form of an abstract syntax tree, as you’ve shown how to perform verification of the model to perform refactoring. Additional steps need to define more complex due to a software problem that is not the way to the model suitable for optimization refactoring. In this paper, as defined in the MDA-based model transformation studies of a reusable model transformation patterns, abstract factory pattern and the bridge pattern given the pattern information automatically through the software model offers a way to perform refactoring. Keywords Graph model transformation Model comparison algorithms




Model transformation verification




J. Ko (&)
Y. Song Department of Computer Engineering, Kyung Hee University, Yong-in 446701, Korea e-mail: [email protected] Y. Song e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_57, Ó Springer Science+Business Media B.V. 2012

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1 Introduction For model validation and testing of software design research on the current testdriven development (TDD) or software model refactoring has been applied in practice in the same direction. In other words, traditional source-code-level testing and code verification process for the efforts that were already finished at the time of implementation by cost of error correction in the design phase of its software design validation and to test for the model is much reduced if has the advantage that can be used. Verification of existing design models and test models for the study, mainly checking (Model Checking) with a code-based software designed to define in the abstract syntax tree or on the models generated using refactoring on design models for refinery operations and define how to perform [1]. The problem with these traditional research methods, but the first model, design model for checking the information with the model by defining a formal representation in the form of an abstract syntax tree, as you’ve shown how to perform verification of the model to perform refactoring Additional steps need to define more complex due to a software problem that is not the way to the model suitable for optimization refactoring. In this paper, as defined in the MDA-based model transformation studies of a reusable model transformation patterns, mapping pattern and the bridge pattern given the pattern information automatically through the software model offers a way to perform model verification. Existing job opening with a defined pattern applied by Miller and another pattern in the study of annual job Lex Maria and refining the proposed mapping patterns and refactoring patterns by modifying the model according to the elements for the model transformation rules for model verification and test based model transformation, and a source from the model definition is generated by elements of the transformation model.

2 Related Works In this chapter, the existing research on model transformation verification and applied in this paper as an Abstract Factory pattern and the Bridge pattern for model transformation pattern for study.

2.1 Model Transformation Verification With the verification research for the model transformation for MDA, the C-SAW transformation engine which is the AOSD-based model transformation engine for the development of the embedded system has been developed. Also, with the research executed by Jeff [4] who has suggested the testing framework and the

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verification method for the generated model, two aspects for the verification of the converting model have been approached. As the first aspect, the testing tool called M2MUnit has been developed in order to execute and support the text-based test with the application of the testing information as the code test in terms of the source aspect by completing the test case in which the transformation rule will be tested by inserting the file of the model transformation rule. As the second aspect, the comparison of the graph has been executed in the form of the version tree which is used to simply compare nodes and edges between both models by executing the graph-based comparison of the model between the input model and the target model. Regarding the researches which have been executed until now, there are several studies published in regard to the issues required for the sample codes related to the test case, the definition of the algorithm related to the comparison of the model, and the follow-up comparison of the model. In the research executed by Varro [5], the verification mechanism based on the graph transformation and pattern has been suggested by using the VIATRA transformation engine. In regard to the model transformation based on XMI and XSLT, the verification mechanism is executed through the identification of the same patterns by analyzing the model transformation between the input model and the target model and comparing the two models with the patterns of the graph model or pattern based on the graph transformation. Throughout such a research, the specific pattern found on the host graph which executes the model transformation process by applying the pattern based on the graph model is considered to be the sub-graph. By applying the sub-graph on the target model, the model transformation is executed. Also, the verification method for the converting model is executed according to the existence of the pattern in a specific form after comparing the input model and the target model by using the pattern. The research for the model transformation and verification has been recently executed by expanding the graph pattern for VIATRA and applying the design pattern before adding a specific design pattern from the input model. The problem found in the previously-executed model transformation and verification research is related to the great limit related to the verification of the structural property or various information contained in the model when the simple comparison of the graph model is executed through the mutual comparison of nodes and edges in the form of the version tree. It is necessary to supplement the verification mechanism through the test for uniformity in regard to the model transformation in various points of view.

2.2 Model Transformation Patterns GoF design patterns, application of existing in different domains have different purposes for different application pattern study was conducted. The models mentioned in the abstract factory pattern, which converts an alternating pattern and the Bridge to convert a pattern to convert the existing model of the GoF patterns in

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Fig. 1 Model transformation pattern—abstract factory pattern and bridge pattern

the pattern applied to the model transformation is one of the two patterns is the pattern suggested in the study of Miller [1]. First, (Fig. 1) before looking at the abstract factory pattern to convert the abstract factory pattern in GoF traditional purpose of the interface using the interrelated or interdependent objects without specifying concrete classes can be created this code generates the product is used to separate the actual factory. Under this model, by applying transformations (Fig. 1), as shown in the third grade Source Schema, Transformation Schema, Transformation Constraints define the rules as part of the conversion can be divided.

3 Test Based Model Transformation Based on Graph Comparison Algorithms and Reusable Patterns 3.1 Test Based Model Transformation Workflow and Graph Model Redefinition In order to verify a model converted through the model transformation engine, it generates a prediction model by defining test Oracle with another transformation rules on the model transformation process defined by OMG QVT and compares two models with comparison algorithm applied with graph transformation method. When looking into workflow of the whole test based model transformation framework as shown on (Fig. 2), a domain model corresponding to the software application is defined as an source model. In addition, test Oracle is defined with transformation rules and a domain model is also converted into the prediction model. In order to compare and analyze the generated target model and prediction model, more reliable target model can be acquired by defining a model comparison algorithm using graph transformation, comparing difference between two models and performing verification and test for the transformation model. At the moment,

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Fig. 2 Test based model transformation workflow

generation of prediction model using the test Oracle can be diversely used through scalability studies on the test transformation rules in the future. In order to supplement the simple comparison of the graph, various structural property of the software model and the information for the model property in regard to the transformation technology are defined. In the previously-executed study for the model transformation and verification process based on the C-SAW transformation engine, the graph model consisting of nodes and edges is defined. By referring to this graph model, the aspect, behavior and time factors are defined for nodes, while the relationship with nodes and types required for the definition of the meta-factor are added for edges in order to define the information related to the property. Therefore, in regard to the information of the model property for the comparison of the model suggested in the study, the node (N) contains the information about names (n), types (t), attributes (att), aspects (asp), behaviors and times. The property of the aspects show the information for aspects, while the property of the behaviors show the dynamic message called on the note. Also, the node consists of the model node which includes the sub-node and the atom node which is the actual sub-node. The edge (E) contains the information about names (n), source nodes (s), target nodes (d), relations (R) and types (T). By summarizing the contents shown above, it is possible to have the following results, the following Table 1 shows the information for the graph model factors and the model property. 1. 2. 3. 4.

N(node) = {name,type,kind,attribute,aspect,behavior,time} N[{Model node, Atom node} E(edge) = {name,src,dst,relation,type} G(Graph) = {N,E}

602 Table 1 Graph model element and properties

J. Ko and Y. Song Element

Model property

Desciption

Node

Name Type Attribute Aspect Behavior Time Name Source Destination Relation Type

Node’s name Node’s stereo type Node’s attribute (value) Aspect information Message property that call node Node’s time property Edge’s name Edge’s start node Edge’s end node Relationship between node Relation type

Edge

The nodes or edges found in the previous graph model and such property as names and types in addition to the source nodes, target nodes and attributes for the edges have been defined in the previous research. Additionally, the time property for the aspects, behaviors and real-time features are defined with the information for the model property added to the nodes. In order to show the real-time features, the time property can be classified into the property of WCET which is the worst-case execution time of the medium which becomes the node with the sub-property, the priority property for the execution priority order of the medium, and the property of the response time of the medium which becomes the node. The aspect property consist of the subproperty for aspects and the function of the common interest. The joint-point property for a specific dynamic position mainly include the method call or the field-value adjustment as the values of the property. Also, the advice property in regard to the decision for the time to apply the function of the common interest to the core logic are defined before or after calling the method or before starting the transaction. The point-cut property represent the joint points to which the actual advice is applied. The model property related to the node and the sub-property for the additional property of types and relations for the edge are defined in Table 2. As shown above, the comparison between the target models, which are induced through the source model and the model transformation process for the transformation of the model generated based on the graph model to which the information for the model property defined in Clause 3.2 is added, can generate the graph model which has more necessary information for various types of comparative information and the model transformation and verification process than the previous method based on the simple comparison between nodes and edges. Through the generated graph model and the information for the model property, it is possible to improve the model-comparing algorithm for the model verification process.

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Table 2 Model property and its sub-attributes Model Property Sub-Property Discription Node

Time

Aspect

Type

Edge

Relation

WCET Priority Response times Name Joint point Advice Point cut Directional Non-directional Generalization Association Dependency Composition

Worst case execution time Object’s execution priority Object’s response tieme Aspect name Aspect joint point, method call, Set field value Aspect insert logic Adapted joint point by advice Directional edge Non-directional edge Generalization relationship between nodes Association relationship between nodes Dependency relationship between nodes Composition relationship between nodes

3.2 Reusable Pattern Based Model Transformation Rule The Mapping Pattern Establish one-to-one relations between elements from the source model and elements from the target model. This Pattern is the most common and straightforward transformation problem. It occurs when source and target models use different languages or syntax, but otherwise express more or less the same semantics. This pattern is used to a greater or lesser extent in practically every transformation. This is the most basic transformation pattern. Typical examples of transformation rules that are based on this pattern are 1-to-1 model transformation rules. It is in general bidirectional (unless different concepts from the left domain are mapped onto the same concept in the right domain). All other transformation patterns use/include this pattern. top relation UML2RDBMSMapping { nm: String; enforce domain left x: X {context = c1:XContext {}, name = nm}; enforce domain right y: Y {context = c2:YContext {}, name = nm}; when {ContextMapping(c1,c2);}} This rule specifies that some element x of type X is related to some element y of type Y, whenever their respective contexts are related by ContextMapping and their names are equal. When the respective model elements have more properties than a context and a name, these should also be mapped. Consider for example the case where the model elements to be mapped represent associations or relationships between other model elements, their sources and targets. The pattern for this case is specified below:

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Fig. 3 Graph- comparing algorithm with model property

top relation RelationshipMapping { nm: String; enforce domain left a: A {context = c1 : AContext {}, name = nm, source = as : AS {}, target = at : AT {}}; enforce domain right b: B {context = c2 : BContext {}, name = nm, source = bs : BS {}, target = bt : BT {}}; when {ContextMapping(c1,c2); ElementMapping(as,bs); ElementMapping(at,bt);}}

3.3 Graph-Comparing Algorithm for Converted Mode Comparison By referring to the graph-comparing algorithm based on the previous model transformation and verification process, it is possible to include the information related to the mapping process with the information for the model property in the node category. Also, the graph-comparing algorithm which includes the comparison of the graphs between the dynamic graph models is suggested as follows. According to the algorithm, it is possible to compare the nodes and edges as well as the information for each model characteristic after changing the target model and the predicted model generated with the function of the test oracle into the graph models with the information for the model property. Through the previous comparative algorithm, the nodes and edges between the two models are simply defined, while flag values are provided for the sub-property in the information for the property of the nodes and edges and the related values of the property and recognized in the actual codes. In the improved model-comparing algorithm, NodeAttribute_Flag and EdgeAttribute_Flag values are used for definitions. As a result, each sub-characteristic has an ID value. The values of the property (af1, af2, ef1, ef2) are compared in regard to the information for the

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objective graph model and the predicted graph model with the ID values. When the two values are same, the MappingSet information is used for definitions. However, when the two values are different, the DifferenceSet information is used for definitions (Fig. 3).

4 Conclusion and Future works Those areas with embedded system such as mobile applications as domain are difficult for reuse as it is restricted on a specific domain, however, because of it, reuse is also required. This paper apply MDA based model transformation technology for development of the mobile applications that run on the various platform of the wireless communication providers and mobile equipment. In addition, in order to support verification of the target model generated from the source model, the designer to design mobile application architecture by adding test rules and comparing with prediction model can more easily reuse the architecture model. Moreover, if applying test issues at design stage as like that, there is an advantage to reduce modification cost of error comparing to test at the phase when source codes are almost completed. However, as perfect mapping of matching APIs for mobile application model transformation is actually difficult, it requires additional code complement works and it has a restriction that model transformation can be applied to only those domains suitable for a specific domain. In addition, it requires studies on scalability to apply test rules diversely and on improvement of comparison analysis between model, implementation of supporting tools to support it is also in progress.

References 1. Miller SJ (2004) Pattern-based model transformation: a metamodel-based approach to model evolution, Ph.D Thesis 2. Iacob M-E, Steen MWA, Heerink L (2008) Reusable model transformation patterns 3. Kessentini M, Sahraoui H, Boukadoum M (2008) Model transformation as an optimization problem, MoDELS 2008 4. Lin Y, Gray J (2007) A model transformation approach to automated model transformation, Ph.D Thesis 5. Varro D (2003) Automated model transformation for the analysis of IT system, Ph.D Thesis 6. Darabos A, Varro D (2006) Towards testing the implementation of graph transformation, GTVMT’06 7. Csertan G, Varro D (2007) Visual automated transformations for formal verification and validation of UML model, SAC’07 8. Czanecki K, Helsen S (2003) Classification of model transformation approaches. In: OOPSLA’03, workshop on generative techniques in the context of model-driven architecture 9. G Zhao, J Kong, K Zhang (2007) Design pattern evolution and verification using graph transformation. In: Proceedings of the 40th Hawaii international conference on system sciences

An Advanced E-learning System Using Web Services Hae-Gill Choi, Jungsun Kim and JuYeon Jo

Abstract Recently, an application using web services is a new trend. Especially, it has lots of advantage when we develop the web based application. That is, using the web service is that we just load and use the program process from web provider or company without development. In this paper, we proposed E-learning system using the web services. For this process, we make a framework to process e-learning service.










Keywords E-learning system Web service Web application Learning system

1 Introduction E-learning is a fast growth application in the world, it is widely used in universities, enterprises, communities, and over one million users now are learning through it. As a typical technical-driven application, the development of e-learning H.-G. Choi Department of Information and Communication, Kyunghee Cyber University, Hoegi-dong, Seoul 130-701, Korea e-mail: [email protected] J. Kim (&) Department of Nutrition, Hospitality and Retailing, College of Human Sciences, Texas Tech University, MS 12402500 Broadway, Lubbock TX 79409, USA e-mail: [email protected] J. Jo School of Informatics, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas NV 89154-4054, USA e-mail: [email protected]

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has close relationship with IT technologies such as computer network, multimedia technology and etc. The mainstream of e-learning systems now are mostly servercentric based, users access the same e-learning servers to get served (view courseware, have some quiz, discuss etc.), and the user-interfaces are mainly web based for convenience. That kind of e-learning system runs well while the number of user is not so large, and the content user access is mainly web page based [1]. Currently, these systems are mainly based on client–server or peer-to-peer architectures. However, these architectures suffer from drawbacks like poor scalability or low availability. Although there are standards like IEEE LOM [2], ADL SCORM [3], or IMS [4], to name just a few, the interchange of educational content between servers or peers is still a problem which has not been solved satisfactorily. Other approaches address this problem by focusing on architectural design, like design patterns [5], or framework systems [6], which allow the use of plug-in components. Some difficulties still remain, as it is not easy to figure out the interconnections between different systems, often due to bad documentation [7]. In this paper, we make a framework for e-learning system using web services. In next section, we will discuss the environment of e-learning system and web services, the Sect. 3, the proposed framework will discussed and in Sect. 4 we will have a conclusion.

2 E-Learning System Environment with Web Service 2.1 E-Learning System E-learning has attracted a lot of attention in recent years from researchers as well as practitioners. As depicted in Fig. 1, a general agreement exists regarding roles played by people in a learning environment, as well as the functionality of elearning systems required in general. In a typical learning environment, there are several groups of people involved: authors and learners, which are the main players, and administrators and trainers. Authors can be teachers or instructional designers who create e-learning content by using an authoring system. The core of an e-learning system, which is under the control of an administrator, typically consists of a learning management system (LMS) or learning content management system (LCMS). An LMS provides functionality like managing learners and their profiles, tracking their progress, easing collaboration, or scheduling events. An LCMS is aimed at managing learning content which is typically stored in a database. In addition, an LCMS eases content reusability, provides workflow support during content development, or delivers content via predefined interfaces and presentation layers. Although some systems try to focus on one domain, there is a significant overlap of functionality in many systems. We therefore subsume LMS in the class of LCMS in

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Fig. 1 General view on an e-learning system

this paper and assume that a LCMS provides the functionality of both LMS and LCMS [7].

2.2 Web Services The new model of e-learning system is a distributed e-learning system, web service is used to coordinate the main e-learning system and sub e-learning system. In the architecture, main e-learning system contains the full function, service and content, sub e-learning system contains partial function, service and content, sub elearning system can provide service to end users along, also it’s a part of the whole e-learning system. Users access the same portal in the main e-learning system, the main e-learning system in charge of the central service such as authentication, noticing, etc. When a user requires a service that could provide by the sub e-learning system, the request will be redirect to the near sub-server. In broadband environment, the service of on-line courseware viewing will be provided by the sub-server, and the activities of user will pass back to the main server by SOAP of Web Service. The model of web service is as the Fig. 2 [7].

3 E-Learning System Framework Using Web Service By research of Peter [8], Web services enable partners to (re)use easily applications via the Internet. A Web service is essentially a stand-alone software component that has a unique URI (the Uniform Resource Identifier is a unique address)

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Fig. 2 Web service model

and that operates over the Internet and particularly the Web. The basic premise is that Web services have a provider and (hopefully) users or subscribers. Web services can be combined to build new ones with a more comprehensive functionality. Clearly, Web services need to be interoperable. Moreover, they have to be independent of the operating systems; they should work on every Web service engine regardless of their programming language; and they should be able to interact with each other. To achieve these goals, Web services are commonly based on standards; currently, the most common ones are the XML-based specifications SOAP (Simple Object Access Protocol), UDDI (Universal Description, Discovery and Integration), and WSDL (Web Services Description Language). Even for the composition of Web services to build more complex ones, XML based languages are used for the description of this composition as part of a business process definition. The benefits of a Web services architecture is well recognized in the business-to-business (B2B) area, where companies already use it for enterprise application integration, a B2B integration, application construction and a flexible approach to outsourcing, a better access to business functions, a free choice of the best technology platform in each situation, and location and device independence. Even in terms of interoperation of business-to-consumer (B2C) systems, Web services are currently obtaining a growing importance. Jie Wu [1] proposed E-learning system architecture as shown Fig. 3. As each courseware has a UDDI to identify, the courseware should registrate in the main e-learning system, main server also maintain a list of resource that sub server contain, when user ask for some resource(courseware), the main server will find out the best sub server to server. Proposed E-learning system was shown in Fig. 4. In the system Server hosting web service does handing the web service that was made for learning course by e-learning developer or system engineer. And UDDI for e-learning has all information about web service form and interfaces, and it can support information to e-learning server for user. The user can search the web service that he/she wants to process for learning on UDDI.

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Fig. 3 Jie Wu [1]’s E-learning framework using web service

UDDI for E-learning

E-learning Server User

Server Hosting Web Service

E-learning Database E-learning Web Service

E-learning Web Service

E-learning Web Service

Fig. 4 Proposed E-learning system using web services

4 Conclusion In this paper, we make a framework of e-learning system using web services. Server Hosting Web Service can handling the each web service for e-learning, and sends their information to UDDI for registration. UDDI can support all of information about e-learning web service to e-learning server. Through this process,

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user can easily access and use e-learning course and system as just find the service what they want to learn.

References 1. Wu J, Zhang S (2004) Broadband multimedia e-learning system using web service, Fudan University on 2 April 2004 2. LOM—Draft Standard for Learning Object Metadata (2002) IEEE 1484.12.1-2002, 15 July 2002 3. ADL Sharable Content Object Reference Model (SCORM) (2004). http://www.adlnet.org/ 4. IMS Content Packaging Best Practice Guide (2003) IMS Global Learning Consortium, Inc., Version 1.1.3, June 2003 5. Devedzic V (2001) A pattern language for architectures of intelligent tutors. International Conference on Artificial Intelligence in Education (AI-ED’01), pp 542–544 6. Ikeda M, Mizoguchi R (1994) FITS: a Framework for ITS—a computational model of tutoring. IJAIED 5(3):319–348 7. Pankratius V, Sandel O, Stucky W (2004) Retrieving content with agents in web service elearning systems. Symposium on professional practice in AI, IFIP WG12.5. In: Proceedings of the First IFIP conference on artificial intelligence applications and innovations 8. Westerkamp P (2003) E-Learning as a web service, seventh international database engineering and applications symposium (IDEAS’03)

A Study of Mobile Architecture Web Design for Virtual Ecology Learning System Using LED (Light-Emitting Diodes) Lights in Smart-Phone Environment Jang-Mook Kang, Bong-Hwa Hong, You-Jin Song and Jae-sang Cha

Abstract This study is about the architecture of the system utilizing for smart phones and the Augmented Reality (AR) technology. AR is the technology of adding various types of information on the real word to create a third space (real plus internet). Smart phone users can learn the AR information provided by the management server. The proposed system utilizes the location-based information in order to provide customized information optimized to various real world environments of the learner. The architecture proposed in this study roughly consists of a mobile-web management server and application of smart phones. In terms of logical configuration, the system consists of mobile devices of the learning service providers and those of the learners, and is designed to raise the learning effect through interactions between ‘learners and learning service providers’. The proposed mobile AR learning system is characterized by the configuration and performance of the virtual ecology learning based on the J.-M. Kang Electronic Commerce Research Institute, Dongguk University, 707 Seokjang-dong, Gyeongju, Gyeongsangbuk-do 780-714, Korea e-mail: [email protected] B.-H. Hong Department of Information and Communication, Kyunghee Cyber University, Hoegi-dong, Seoul 130-701, Korea e-mail: [email protected] Y.-J. Song Department of Information Management, Dongguk University, 707 Seokjang-dong, Gyeongju, Gyeongsangbuk-do 780-714, Korea e-mail: [email protected] J. Cha (&) Department of Media Engineering, Seoul National University of Science and Technology, Seoul, Korea e-mail: [email protected]

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location information of smart phones. This system is expected to contribute to the mobile virtual ecology learning system and the learning method.







KeyWords LED (light-emitting diodes) Smart-phone SNS (social network service) Education Mobile-Web Location based service










1 Introduction The Internet environment has changed and developed drastically, so few users now access it via modems, which were widely used a few years ago [1]. As a result, the traditional Virtual Terminal (VT) environment has been replaced by the Graphic User Interface (GUI), and text based simple HTML (Hypertext Markup Language) service has become a multimedia based service [1]. Mobile devices are widely used recently. The typical examples are smart phone and tablet PC. Mobile devices have been developed substantially with the development of information and telecommunication technologies. Various functions, such as wired/wireless Internet, various application programs, LocationBased Services (LBS) and Social Network Services (SNS), have been developed, and mobile users can select appropriate interfaces and functions. Mobile devices also help implementing the cloud computing environment. LBS indicate the wireless content services which provide the wireless Internet users with the information according to the changing location of the users. The most significant advantage of LBS is that users can get appropriate information according to their locations without having to input current address or location while on the move. The purpose of this study is to propose and design the architecture of the ecology learning system that can be implemented under the cloud computing environment with the spread of smart phones. This study proposes the learning system which supports originality and voluntary participation of ‘learners and learning service providers. The proposed system may be utilized in various fields, such as medical service, national defense, business and tourism, as well as education.

2 Related Works 2.1 Location-Based Service The GPS location determination technology is the typical technology that enables LBS. In addition to the satellite-based location determination technologies, such as GPS, the mobile communication network provides many non-GPS location-based

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services which are based on the unique location management mechanisms. The typical examples of these services include the cell method utilizing mobile communication stations developed and distributed around year 2000. Recently, new LBS technologies (e.g. Gowalla; www.gowalla.com, Foursquare; www.foursquare.com) have been developed to combine the cell method and the GPS method. They are designed to make up the higher error range of the cell method and the difficulty to use of GPS method in high-story buildings or indoor environment due to high reflection and refraction rate of satellite signals.

2.2 Social Network Service (SNS) Interworking With LBS Recently, LBS interworks with SNS and this is called location-based SNS. Location-based SNS makes a combination of the built-in GPS of a smart phone and SNS, enabling the user to inform friends of his/her location and to share related information with their friends. For example, if a Foursquare user with the application installed in his/her smart phone checks in a specific location, his/her location is displayed on a map. He/she can put up notices with this location information. By deploying a platform on which users can exchange idea and share information, this service enables users to communicate with others based on the ‘location’ information, rather than simply communicate through notices. Because it is possible to provide users with the information on the stores at the location, this service may be utilized in various business models.

2.3 Augmented Reality (AR) Augmented Reality (AR) is a subarea of Virtual Reality (VR). It is a computer graphic technique which synthesizes virtual objects or information to the real environment, making them look as if they actually exist in the real world. The existing VR service requires users to get them absorbed in a virtual world composed of an Internet space and the given objects only, and hence, without the real world. The AR service, however, synthesizes virtual objects onto the real world, providing additional information which is hard to get in a real world. Unlike the VR technology which perfectly assumes virtual world, AR synthesizes virtual objects onto the real world environment, reinforcing the sense of reality or increasing experience. If a physical space is fulfilled with the information artifacts with the computing power, the characteristics of the physical space are changed. Because of these characteristics, unlike the existing Internet service that can be applicable to the limited areas, e.g. games, AR can be used in various reality environments. AR is highlighted as the next generation display technology appropriate for the Ubiquitous network environment, and has been commercialized with the growth of the smart phone market.

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2.4 Other Researches The following technologies are also available for mobile devices: First, there is the microgrid technology. With this diversification of distribution systems, distributed generators used in existing systems can be subdivided into smaller units, called microgrids [2]. The application of smart grid technology has turned the MicroGrid into a system that can digitize in real-time all the processes of power generation, distribution and demand chain [3]. Second, Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multi-functional sensor nodes [4]. These sensor nodes leverage the idea of sensor networks [5]. A Ubiquitous Sensor Network [6] is a wireless network which consists of a large number of lightweight, low-powered sensor nodes. Such sensor nodes consist of sensing, data processing and communicating components. Sensor networks are drawing a lot of attention as a way of realizing a ubiquitous society [4]. These researches can be interpreted as the attempts to apply various sensors to many fields of the society (education, medical service, government sector, commerce, etc.).

3 The Proposed Mobile Learning System 3.1 Overview and Problems of the Proposed System This study is to propose the architecture design and method of mobile virtual ecology learning system that enables configuration and performance of ecology learning based on the location of a mobile device. Therefore, the system has the following problems to be solved. First, it is required to propose the scenario on exchange of education data between learners and learning service providers through the mobile virtual ecology learning system which is synchronized with the management server and the application programs. In other words, it is required to schematize the interaction procedure between smart phones, applications, learners and learning service providers in terms of education. Second, the management server should create the mobile virtual ecology learning DB, and provide LBS through the learner’s application program in the ecology learning performance mode. The management server has to gain the location of the learner, and if the learner approaches within a certain radius, the server should send the ecology learning content to the learner’s mobile device. The learner’s mobile device should receive, play and design the ecology learning content. Third, the ecology learning content should contain at least any one factor out of text information, voice information, sound information and vibration information.

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Fig. 1 Block diagram of the mobile ecology learning system

Therefore, the mobile virtual ecology learning system should be equipped with an effective information processing method through networking.

3.2 System Configuration The proposed system consists of the mobile virtual ecology learning system (Fig. 1), mobile devices of the learning service providers (A), the management server (B), and mobile devices of the learners (C). The above figure (Fig. 1) illustrates the block diagram of the mobile virtual ecology learning system. The system consists of a management server, Internet and the mobile devices supporting the location based services. In the block diagram, the term ‘ecology learning’ indicates the learning of aspects or states of various animals and plants. Learners can acquire knowledge by observing a variety of animals and plants in the nature. For example, the ecology learning content on tree frogs consists of text information, photos, sounds, moving images, and charts on the growth of a tree frog. The distinctive feature of the proposed system is that the learning service provider can create the mobile virtual ecology learning content for the learners. To support the learning of the learner, the learning service provider can create use the map-based location interface running on the smart phone. The learning service provider can select the location of POI (Point Of Interest) on the map displayed on the hand-held smart phone. The service provider can enter the content of ecology learning, use the data in an electronic book, or attach files.

3.3 Augmented Reality (AR) Service The application program of the learner provides the AR user interface in the ecology learning configuration mode. AR is a computer graphic technique that

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synthesizes virtual objects or information to the real environment, showing them as if they exist in the environment. Learning service providers can create the content at the point of ecology learning (marked as POI on the map). If the AR user interface is provided, the learning service provider (A in Fig. 1) can efficiently create the content of ecology learning in consideration of the progress of the learner.

4 Architecture Design of Mobile Vertical Learning Service The mobile device of the learning service provider sends the ecology learning information including location/content of the ecology learning to the management server. The following figure (Fig. 2) illustrates the process flow of the mobile virtual learning service. The service can be designed by each of the following stages: ‘the stage in which the mobile device with the application program in the ecology learning configuration mode sends the mobile virtual ecology learning information including the location and content of the ecology learning service to the management server’, ‘the stage in which the management server receives the mobile virtual ecology learning information, and creates the DB or RDBMS’, ‘the stage in which the management server sends the ecology study contents to the mobile device of the learner in the ecology learning execution mode within a certain radius from the server’, and ‘the stage in which the learner’s mobile device receives and plays the ecology learning content’. The above figure (Fig. 2) illustrates the flow chart of the above mentioned stages.

5 Discussion and Conclusion The proposed system is designed to support creation and performance of ecology learning content based on the location of the learner’s mobile device in compliance with the mobile virtual ecology learning system and method. Therefore, when a learner with the mobile device enters into a specific area, the mobile device receives and plays the dynamic and interesting ecology learning content. This system can be utilized in the following manners: It is difficult for the schools in urban areas to provide the ecology learning course in the nature. This system enables the learning service provider with a mobile device provides the nature learning service at the ecology learning points. And also, it is possible to even provide the ecology learning in the nature, learners can receive additional information on ecology through their mobile devices at the ecology learning points.

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Fig. 2 Process flow of mobile ecology learning service

For the schools located in rural areas, it is possible to implement the downtown areas and tourist resorts on the school ground with the AR technology. The ecology learning service can be also utilized in observing stars during the day or in the urban areas where it is difficult to watch them due to air pollution. The mobile ecology learning service can be utilized as the typical business model showing how the digital information can be utilized in the e-book industry.

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Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0028122).This research is the Republic of Korea Patent Application (Application No. 10-2011-0062910). This has been drawn up based on the completed patent in 06/28/2011. We would like to express my thanks to the co-researchers as the inventor of patents.

References 1. Hong S-R, Na W-S, Kang J-M (2010) A QoS scheme for a congestion core network based on dissimilar QoS structures in smart-phone environments. Sensors 10:1424 2. An J-B, Yoo D-W, Park J-H (2006) KERI: Busan University, Ministry of education science and technology. In: Development of autonomous demand-management type MicroGrid; KERI: Changwon, Korea, pp 20–45 3. Moon H-H, Lee J-J, Choi S-Y, Cha J-S, Kang J-M, Kim J- T, Shin M-C (2011) A study using a Monte Carlo method of the optimal configuration of a distribution network in terms of power loss sensing. Sensors 11:7824 4. Lee W-J, Kim J-I, Kang J-M (2010) Automated construction of node software using attributes in a ubiquitous sensor network environment. Sensors 10:8664 5. Akyildiz IF, Su WL, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40:102–114 6. Chong CY, Kumar SP (2003) Sensor networks: evolution, opportunities and challenges. IEEE 91:1247–1256

A Study on the Tag Performance Test for International Standards Using RFID Emulator Hae-Gill Choi, Sang-Soo Kim, Moon-Taek Cho, Hae-Jong Joo and Euy-Soo Lee

Abstract RFID technology uses communication through the use of radio waves to transfer data between a reader and an electronic tag attached to an object for the purpose of identification and tracking. RFID technology can be applied to the various service areas such as, position determination technology, remote processing management and information exchange between objects by collecting, storing, processing, and tracing their informations from the tag attached to the objects using electronic wave by recognizing the information and environment of those objects. However, to revitalize these various services, it is important to test the RFID tag performance. But there are few instructions which have and hold the RFID emulator technology for organizing the RFID international test environment. Also there are not many manufacturing companies which recognize about the H.-G. Choi (&) Department of Information and Telecommunication, Kyung Hee Cyber University, 1 Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, Korea e-mail: [email protected] S.-S. Kim  M.-T. Cho Department of Electrical and Electronic Engineering, Daewon University, 599 Sinwol-dong, Jecheon, Chungbuk 380-702, Korea e-mail: [email protected] M.-T. Cho e-mail: [email protected] H.-J. Joo Department of HUNIC, Dongguk University, 82-1 Pil-dong 2-ga, Jung-gu, Seoul 100-272, Korea e-mail: [email protected] E.-S. Lee Department of Chemical and Biochemical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_60,  Springer Science+Business Media B.V. 2012

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exact RFID test standard and requirements for the International Standards. In this paper, a construction of Tag Performance test environments and test methods are suggested which are required by EPCglobal or ISO/IEC. Details about RFID Tag performance test items proposed by ISO/IEC FDIS 18046-3 are explained, performed RFID Tag performance test through the performing test against each measured item, and draw a result for the RFID Tag performance of International Standards. On the basis of this research, it is desired to contribute to develop a great performing RFID Tag product through the RFID Tag performance test which comes up to the International Standards and construct its application system and facilitate a revitalization of the RFID services. Keywords RFID emulator EPCglobal



RFID tag



Tag perormance test



ISO/IEC



1 Introduction RFID technology can offer the various services such as, position determination technology, remote processing management and information exchange between objects by collecting, storing, processing, and tracing their informations from the tag attached to the objects using electronic wave by recognizing the information and environment of those objects [1–3]. This technology can be expected to take the lead in an innovation in security, safety, and environmental management as well as distribution and product management by replacing the existing bar code with networked and intelligent product management. In this country, 13.56 MHz bands manual RFID has been widely used in contactless transportation card and pass and it is the time to seek far-reaching business platform for applications through technical importation of 900 MHz bands manual RFID which has long distance of cognition. This increases the necessity of the test certification for conformance, interoperability, and performance, which should be along with, for diffusion of RFID technology. In this paper, test environment for RFID Emulator function test which are necessary for the RFID Tag Protocol conformance test and measuring it’s cognitive distance and test environment using Emulator required by international certificate authority are constructed. Thereby RFID Tag’s cognitive distance is desired to be measured based on the International Standard RFID test methods and test procedures.

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2 The Status of RFID Tag Performance Test Standard 2.1 The Status of International Standard ISO/IEC 18000 Standard Specification for frequency is comprised of 5 frequencies [4]. It is standardized that 18000-2 works at low-frequency bands(135 kHz), 18000-3 at short wave bands(13.56 MHz), and 18000-7 (433 MHz), 18000-6 (860*960 MHz), 18000-4(2.4 GHz) at UHF bands. 18000-6 International Standard using 860*960 MHz bands usually has 3–4 m cognitive distance, it is the type of RFID which uses largest RF output in the 5 International Standards[5]. It uses maximum of 4 W EIRP electric power and its maximum cognitive distance becomes known to 10 m. For a worldwide logistics distribution, frequency bands becomes to 100 MHz, and every countries allows to use to suit their domestic environment in a 860*960 MHz range. Since it should be designed to be worked in a wide frequency range of 100 MHz band being distributed in a worldwide market, the issues of tag design and technology on tag antenna are being magnified. For all this standards, it is on track that the outlook on spread and use of the specification working by EPCglobal, in the worldwide logistics distribution market. EPCglobal [6] was formed in October, 2003 as successor organization to the MIT Auto-ID Center, the original creator of the EPC (Electronic Product Code) technology. It manages EPC network and standards. EPC is a code system for RFID proposed by bar code managing organization existed which is based on the 64bit’s, 96bit’s, 256bit’s product numbering system. EPCglobal is now creating many types of standards, among these, EPCglobal 5, that is, UHF Gen2 specification which has become a major part of 900 MHz bands de-facto standards by which worldwide leading distribution industries such as Wal-mart, Metro and DoD, FDA declare adapting it as standard. As offering a sole 900 MHz bands International Standards, EPCglobal Gen 2 has effected to the technical regulation for each countries.

2.2 The Trend of Domestic RFID Test Certification TTA [7] arranges the Quality Evaluation criteria on the 900 MHz bands RFID reader (fixed type, hand-held type) which is applied for the distribution (logistics, transportation, u-City, home network) and the tag, provides the authentication for the domestic RFID equipments. the scope of authentication for 900 MHz bands manual RFID equipments and test environments are defined on TTA Test Authentication Specification. It authenticates after DUT (Device Under Test) which evaluates the certification for conformance, interoperability, and performance according to the reader or tag models. For the certification for conformance, it evaluates the domestic RFID technical specification and whether its implementation meets ISO 18000-6 Standard or not. For the performance, It evaluates cognoscitive ability using standard reader and tag.

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3 Overview of the Test for RFID Tag Performance To evaluate RFID Tag Performance ISO/IEC and EPCglobal has completed the standardization for UHF bands Tag Performance Test methods. The Standards defined are as follows. • ISO/IEC Standard: ISO/IEC 18046-3 Test method for tag performance [4]. • EPCglobal: Tag Performance Parameters and Test Methods [6].

3.1 Environment for Tag Performance ISO/IEC 18046-3[4] suggests RFID Tag’s cognitive distance test environment which is defined as Fig. 1.

3.1.1 RFID Emulator To work as a virtual reader or tag, it contains reader/tag command, data encoding/ decoding, modulation/demodulation function on the DSP board by Visual DSP program.

3.1.2 Signal Generator It is used for emitting waveform from RFID emulator. In this study, we used Signal generator by Agilent. It is additionally used to meet Interference Tolerance Test required by ECPglobal.

3.1.3 Spectrum Analyzer When RF signals are emitted from RFID emulator, It is used for receiving and analyzing back scrattling input signals. It sends input signals to RFID emulator.

3.1.4 Spectrum Analyzer It measures strength of signal which is transmitted and received through the Signal Generator.

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Fig. 1 Test set function block diagram

3.1.5 Power Meter It is used for the apparatus rotating the Tag on the Azimuth and Elevation axis.

3.2 Test Set Requirements ISO/IEC requires the test environments as Table 1.

3.3 Test Conditions 3.3.1 Number of Tag Tested and Preconditioning We selected 30 samples randomly out of 1000 for testing, reported the best performing 27 out of 30 samples as a final value (90%).

3.3.2 Tag Initialization All EPC codes for Tag should be initialized to ‘‘3034125BB024C34123456789’’, EPC Memory to unlock state, kill and access passwords to 0.

3.3.3 Test Environment All tests are proceeded on the 23C ± 5C, and 60% of humidity.

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Table 1 Test set requirements Requirement Min Type Antenna Frequency Polarization Gain Axial ratio 3 dB Beamwidth Input VSWR Transmitter Frequency Frequency accuracy Output power Power adjust step Signaling

860

960

MHz

RHCP LHCP 5

7 1

50

dBi dB deg

Transmit Receive On-boardsight, measured ailing major axis Two sided, azimuth & elevation

1.4:1 860 -10 7

960 10 27 0.25 PR-

1 dB compression 33 Interfere signaling Interfere output power -3 Receiver Demodulator Frequency Frequency accuracy Sensitivity Noise figure 1 dB compression Output bandwidth Output sampling rate IQ phase imbalance IQ amplitude imbalance IQ offset System Range to tag(r) Mast height(h) Tx to Rx angle

Max Units Comment

860 -10

-10 1 1.5

0.8 1

MHz Static frequency ppm dBm dB ASK dBm

CW 27

dB

Measured at transmitter output Quadrature demodulator with vector output

960 10 -70 35 10 1.5 3 0.1

MHz ppm dBm dB dBm MHz Msps deg dB

0.5

dB

1

m m deg

25

3.3.4 Signaling Table 2 shows the requirements for Transmitter. Transmitter must wait for at least 200 usec until it sends the next query after receiving EPC response from Tag, it must have options of sending CW signal or power cycling while it is waiting. Writing is performed at the secured state. Access password for tag should be set to 0. It writes single word at 20hex address of EPC memory bank.

A Study on the Tag Performance Test for International Standards Table 2 Transmitter requirement

Parameter

Value

Tari PIE DR Miller subcarrier BLF TRext Sel Session Target Q Rise and fall time

25us 2:1 64/3 4 256 kHz 1 All S1 A-[B 0 7.75 ±0.5us

627 Description Data-1=50 ls

Extended preamble

4 Test Methods for RFID Tag performance 4.1 International Standard setting Table 3 shows the test method and items, which they are differ from them suggested by ISO/IEC Standards and EPCglobal.

4.2 Test Environments We perform test on the Kwanyangman u-IT Research Institute’s test environment which satisfying the Standards to meet the requirements by ISO/IEC and EPCglobal [6] as Fig 2a. The test equipments for RFID Performance Test were organized as Fig. 2b, where Signal Generator and Signal Analyzer were provided by the Kwanyangman u-IT Research Institute.

5 Test for RFID Tag Performance and the Results The results of RFID Tag Performance Testing are as follows.

5.1 Test Sample The Standards specify to perform test 30 random samples out of 1000. However, we selected only two samples for the RFID Emulator verification and research.

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Table 3 ISO/IEC Standards and EPCglobal Standards Standard ISO/IEC 18046-3 1 2 3 4 5 6 7 8 9 10

Identification EM-Field threshold and frequency peak Reading EM-Field threshold Writing EM-field threshold Sensitivity degradation Maximum operating electromagnetic field Survival electromagnetic field Delta radar cross section Interference rejection Maximum identification fade rate Maximum write fade rate

EPCglobal Read range Orientation tolerance Frequency tolerance Interference tolerance Backscatter range Write range Write time Tag proximity

Fig. 2 Test Environments a RFID test environments b Construction of performance test system

5.2 Test Results 5.2.1 Identification Electromagnetic Field Threshold Tag Num.

1

UII Num. Frequency peak (MHz) : Test result Frequency (MHz) 860 865 870 875

F234A6DE9012 930 -3 dB BW (MHz) 2.61 2.51 2.98 2.80

ETHR identification (V/m) 111.37 103.17 98.08 86.97 (continued)

A Study on the Tag Performance Test for International Standards (continued) Tag Num.

1

880 885 890 895 900 905 910 915 920 925 930 935 940 945 950 955 960 866

0.86 2.70 1.64 2.50 1.86 2.64 2.78 2.60 2.86 2.54 2.87 2.80 2.80 2.89 2.78 2.69 2.49 2.70

629

77.73 72.58 73.49 65.41 63.00 64.18 64.38 61.22 59.42 55.09 53.97 172.77 55.73 55.06 57.09 55.47 57.69 65.23

5.2.2 Reading Electromagnetic Field Threshold Tag Num.

1

UII Num. Test result Frequency (MHz) 930 930

F234A6DE9012 E read_1st (V/m) 64.31 55.98

E read_2nd (V/m) 65.03 55.18

Ethr Read (V/m) 65.03 55.98

5.2.3 Reading Electromagnetic Field Threshold Tag Num.

1

UII Num. Test result Frequnecy (MHz) 930 930

F234A6DE9012 E Write_1st (V/m) 63.60 53.45

E Write_2nd (V/m) 64.90 53.32

Ethr Write (V/m) 64.90 53.45

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5.2.4 Sensitivity Degradation Tag Num.

1

UII Num. Test result Freqeuncy (MHz) :

F234A6DE9012

Angle () ETHR Identification (V/m)

930 E vs. Azimuth -90 -75 -60 -45 -30 -15 0 15 30 45 60 64.25 68.48 61.23 65.00 75.64 106.01 136.91 77.08 97.48 136.01 0.00

E vs. Elevation Angle () -90 -75 -60 -45 -30 -15 ETHR 66.09 61.32 63.43 63.18 57.61 55.85 Identification (V/m)

0 54.10

15 30 45 64.19 65.43 65.43

75 90 208.31 126.53

60 75 63.89 60.47

90 61.88

5.2.5 Sensitivity Degradation Tag Num.

1

UII Num. Test result Frequnecy (MHz) 930 930

F234A6DE9012 E max (V/m) 312.56 275.64

5.2.6 Delta Radar Cross Section (4RCS) Tag Num.

1

UII Num.: Test result Freqeuncy (MHz) Amplitude (V/ m) 4RCS (cm2)

F234A6DE9012 930 21.4864 22.56072 23.63504 24.70936 25.78368 26.858 27.93232 29.00664 30.08096 31.15528 0.00

1.97

2.11

2.13

2.21

2.19

2.17

2.13

2.06

2.00

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5.2.7 Maximum Identification Fade Rate Tag Num.

1

UII Num.: Test result Frequency (MHz) Interfere frequency offset (kHz) CW interference rejection (V/m) Modulated interference rejection (V/m)

F234A6DE9012 910 0

- 200

+200

- 400

+400

- 600

+600

- 800

+800

- 1000

+1000

0.70

0.46

0.45

0.46

0.46

0.47

0.48

0.52

0.53

0.79

0.81

0.71

0.44

0.44

0.44

0.45

0.50

0.49

0.51

0.53

0.81

0.78

5.2.8 Interference Rejection Tag Num.

1

UII Num. Test result Frequency (MHz)

F234A6DE9012

910 930

Triangle wave modulation frequency (Hz) 120 48

E fade identification (V/m.s) 120 48.864

5.2.9 Maximum Write Fade Rate Tag Num.

1

UII Num. Test result Frequnecy (MHz)

F234A6DE9012

910 930

Triangle wave modulation frequency (Hz) 60 40

E fade write (V/m.s) 60.78 39.68

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6 Conclusions In this study, we suggest the elements which are necessary to composite the RFID Tag performance test environment which is required by ISO/IEC, using RFID emulator. We proceed the performance test according to the test method and procedures which are required by domestic and International Standard. When the RFID is generalized and RFID based industries are revitalized, it is considered that The Tag Performance Test will be the basic requirement by various types and forms. In this paper, we described only ISO/IEC 18046-3 test. But thru the additional studies, the test elements required by EPCglobal can be added and tested by RFID Tag performance test in accord with domestic and international standard. Therefore, it is expected to contribute to develop a great performing RFID Tag product and construct its application system and facilitate a revitalization of the RFID services.

References 1. ETRI Journal vol 22(3), RFID Technology and standardization trends, ETRI, 2007.06 2. Hae-Gill Choi etc., Implementation of Virtual Reader and Tag Emulator System Using DSP Board, KAIS Journal Vol. 11 No. 10, 2010.10 3. Finkenzeller K (2011) RFID handbook, 2nd edn. Wiley, New York 4. ISO/IEC FDIS 18046-3, Information technology—Radio frequency identification device performance test methods—part 3: test methods for tag performance 5. ISO/IEC 18000-6, Parameters for air interface communications at 860 MHz to 960 MHz 6. EPCglobal, Tag Performance Parameters and test Methods Version 1.1.3, 2011 7. TTA Journal No. 108 Testing and certification technology trends, TTA, 2009 8. ISO/IEC 18046-3, Information technology—Radio frequency identification device conformance test methods—part 6 9. Agilent (2011) PSA and ESA series spectrum analyzer measurement guide and programming examples

Part IV

Heterogenous Networks, Computing and Applications

Simulation and Performance Analysis of Ad-hoc Networks Using NS-2 and NS-3 Simulators Makoto Ikeda, Elis Kulla, Masahiro Hiyama and Leonard Barolli

Abstract An Ad-hoc Network is a collection of wireless terminals that are able to dynamically form a temporary network without any aid from fixed infrastructure or centralized administration. In this paper, we evaluate the throughput and received packet rate of wireless ad-hoc networks by simulations. We used network simulator version 2 (ns-2) and network simulator version 3 (ns-3) network simulators considering Optimized Link State Routing (OLSR) protocol. We analyze the performance of ns-2 and ns-3 considering memory usage and runtime metrics. We also compare the average throughput, the average received rate and number of received packets for different area sizes and different number of nodes. Simulation results show that when the network size increases, the ns-3 has better performances than ns-2. Keywords Ad-hoc network


Network simulation
ns-2
ns-3

M. Ikeda (&)
L. Barolli Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811–0295, Japan e-mail: [email protected] L. Barolli e-mail: [email protected] E. Kulla
M. Hiyama Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811–0295, Japan e-mail: [email protected] M. Hiyama e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_61,  Springer Science+Business Media B.V. 2012

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1 Introduction Wireless networks have become increasingly popular and they can provide mobile users with ubiquitous communication capability and information access regardless of locations. A Mobile Ad-hoc Network (MANET) can be used to extend the coverage areas of fixed wireless networks, to enable inter-sensor communications, for vehicular information exchange etc. There are a lot of issues and challenges in designing a MANET system. Most of the work for MANET has been done in simulation, as in general, a simulator can give a quick and inexpensive understanding of protocols and algorithms [1]. So far, we report many simulation results on the performance of MANET, e.g. in terms of end-to-end throughput, round trip time and packet loss. In our previous work [2–4], we carried out experiments and simulations using network simulator version 2 (ns-2). We proved that while some of the Optimized Link State Routing (OLSR)’s problems can be solved, for instance the routing loop, this protocol still have the self-interference problem. There is an intricate inter-dependence between MAC layer and routing layer, which can lead the experimenter to misunderstand the results of the experiments. For example, the horizon is not caused only by IEEE 802.11 Distributed Coordination Function (DCF), but also by the routing protocol. In this work, we investigate by simulations the throughput of wireless ad-hoc networks for small size environment considering OLSR protocol. We compare the memory usage and runtime metrics of ns-2 and ns-3 simulators. We also compare the average throughput, the average received rate and received packets for different area sizes and different number of nodes. The structure of the paper is as follows. In Sect. 2, we make an overview of OLSR protocol. In Sect. 3, we describe the simulation system design and description. In Sect. 4, we show the simulation results. Finally, conclusions and future work are given in Sect. 5.

2 Overview of OLSR Protocol The link state routing protocol that is most popular today in the open source world is OLSR. OLSR with Link Quality (LQ) extension and fisheye-algorithm works quite well. The OLSR protocol is a pro-active routing protocol, which builds up a route for data transmission by maintaining a routing table inside every node of the network. The routing table is computed upon the knowledge of topology information, which is exchanged by means of Topology Control (TC) packets. The TC packets in turn are built after every node has filled its neighbors list. This list contains the identity of neighbor nodes. A node is considered a neighbor if and only if it can be reached via a bi-directional link. OLSR makes use of HELLO messages to find its one hop neighbors and its two hop neighbors through their responses. The sender can then select its Multi Point Relays (MPRs) based on the

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one hop node which offer the best routes to the two hop nodes. By this way, the amount of control traffic can be reduced. Each node has also an MPR selector set which enumerates nodes that have selected it as an MPR node. In short, every node computes the path towards a destination by means of a simple shortest-path algorithm, with hop-count as target metric. In this way, a shortest path can result to be also not good, from the point of view of the packet error rate.

3 Simulation System Design and Description We have implemented a wireless ad-hoc network simulation system which is a platform for analyzing various aspects of these networks, including different topology models. In this work, we use OLSR routing protocol. The OLSR codes used in the simulations can be found in [5, 6], for ns-2 and ns-3, respectively. We constructed two kind of evaluations, such as evaluation of simulation systems and evaluation of network performance using ns-3 simulator. The descriptions of these evaluation systems are shown in the following.

3.1 Evaluation of Simulation To evaluate the simulation systems, we make the following considerations. – – – –

We consider an indoor environment with different number of nodes. We investigate the memory usage and simulation runtime. We constructed a simulation model as grid topology. Simulation time is 10 s.

We consider an indoor environment with different number of nodes. We investigate the memory usage and simulation runtime. We constructed a simulation model as grid topology. Simulation time is 10 s. For the performance evaluation with ns-2 and ns-3 simulators, we set-up the grid topology. We measured the simulation runtime during data transmission. The distance of nodes from each-other is 100 m. For this evaluation, we use the N 9 N topology, where N is the grid-size, and N = {5, 6, …, 15}. Thus, the network size (N2) varies from 25 to 225 nodes. The simulation parameters are shown in Table 1.

3.2 Evaluation of Network Performance For analyzing various aspect of these networks, we consider random waypoint mobility model, propagation loss models and propagation delay models. For this simulations, we make the following considerations.

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Table 1 Simulation parameters

Parameter

Value

Mobility model Min and Max node speed Node pause Constant bit rate traffic Number of packets Packet size Propagation loss model Propagation delay model

Radom waypoint 1.0 m/s and 12.0 m/s 3.0 s 200 pkt/sec 20000 pkts 512 bytes Log-distance path loss model Constant speed model

(b)

70000

16000

ns-2 ns-3

60000

Execution times [sec]

Memory usage [Kbyte/sec]

(a)

50000 40000 30000 20000 10000

12000 10000 8000 6000 4000 2000 0

3

4

5

6

7

8

9

10 11 12 13 14 15

Grid size

ns-2 ns-3

14000

3

4

5

6

7

8

9

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– We consider a small size environment with different number of nodes: 10, 20, 30, and 40 nodes, respectively. – We consider two kind of area sizes: 100 m 9 100 m and 200 m 9 200 m. – We investigate the throughput of wireless ad-hoc network, average throughput, average received rate, and the number of received packets. – We also investigate the throughput of routing packets, and the number of received routing packets. – Nodes on the network move according to random waypoint mobility model. – Simulation time is 150 s.

4 Simulation Results In Figs. 1a and b, are shown the simulation results of memory usage and simulation runtime for ns-2 and ns-3. In this evaluation, we use the GT. In Fig. 1a, we see that when grid-size N is smaller than 5, the ns-2 simulation uses less memory than ns-3. On the other hand, when grid-size is bigger than 5, ns-3 simulator shows better performance. When the network size increases, ns-2 simulator uses more memory than ns-3. From Fig. 1b, we can observe that the time for executing the simulation in ns-2 is bigger than that of ns-3. The simulation runtime of ns-2

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Table 2 Simulation results of CBR for different metrics Nodes Area size: 100 m 9 100 m Area Size: 200 m 9 200 m

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561 477 501 542

117.2 99 104 113

17576 14953 15676 16980

127 292 46 123

27 61 10 26

4000 9202 1484 3854

Table 3 Simulation results of routing packets for different metrics Nodes Area size: 100 m 9 100 m Area size: 200 m 9 200 m

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Recv. Rou. Pkt [pkt]

6 19 34 54

685 1406 1973 2599

4 9 14 25

489 893 1179 1750

increases exponentially, while for ns-3 almost linear. For performance evaluation, we use three metrics: throughput, packet receive rate and received packets. Packet receive rate is the rate of data packets received by destination, while received packets shows the total number of packets received, including control and routing packets. The results are shown in Table 2. In addition, the simulation results of routing packets are shown in Table 3. For OLSR, when area size is 100 m 9 100 m, the average throughput and average received rate values do not change too much for different number of nodes. In this case, the connectivity of nodes is good because the area is small. On the other hand, when area size is 200 m 9 200 m, the difference is noticeable. In this case, nodes can move more freely around the area, and the communicating nodes find themselves far from each other, thus the communication becomes difficult. Also, the values for the 200 m 9 200 m are smaller than in the case of 100 m 9 100 m. In Fig. 2, are shown the simulation results, for throughput of OLSR protocol in an area of 100 m 9 100 m. During the simulation time, there are some disconnections. However, when the number of nodes is 40, the throughput is stable for all the simulation duration. The network is dense, and the connectivity is higher. While for the area size of 200 m 9 200 m (see Fig. 3), the performance decreases in comparison with the small size area, because in the bigger area it is easier to create isolated nodes. Another interesting observation is the fact that, even OLSR has a worse performance regarding connectivity. The maximum throughput is almost theoretical. In the case when routes are changed during a transmission, OLSR protocol, after finding better route, can change to the new route smoothly.

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Fig. 3 Throughput results of OLSR (Area Size = 200 m 9 200 m)

5 Conclusions In this paper, we evaluated by simulations, memory usage, simulation runtime, average throughput, average received packet rate, and received packets metrics of a MANET. For comparison of simulation systems, we made simulations for different number of nodes in the grid network (9, 16, 25,…, 225). While for comparison of network performance, we made simulations for different number of nodes in the network (10, 20, 30, 40) and for different area sizes (100 m 9 100 m and 200 m 9 200 m). From our evaluation, we found the following results. – When the network size increases, the ns-3 simulator uses less memory than ns-2, and the simulation runtime for ns-3 simulator is smaller than ns-2. – For OLSR, when area size is 100 m 9 100 m, the performance do not change too much for different number of nodes. On the other hand, when area size is 200 m 9 200 m, the difference is noticeable. – When the number of nodes is 40, OLSR in the 100 m 9 100 m area shows a stable throughput for all the simulation duration. The network is dense, and the connectivity is higher. While for less nodes there are disconnections.

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– When the area size of 200 m 9 200 m, the performance decreases in comparison with the small size area. – Even though OLSR has a worse performance regarding connectivity, the maximum throughput is almost theoretical. In the future, we would like to consider the case of multiple flows. Moreover, we would like to evaluate other mobility model and compare with testbed results. Also, we would like to extend our simulation system. Acknowledgments This work is supported by a Grant-in-Aid for scientific research of Japan Society for the Promotion of Science (JSPS). The authors would like to thank JSPS for the financial support.

References 1. Font JL, Inigo P, Domínguez M, Sevillano JL, Amaya C (2010) Architecture, design and source code comparison of ns-2 and ns-3 network simulators. In: Proceedings of the 2010 spring simulation multiconference (SpringSim’10). No. 8:109:1–109:8 2. Ikeda M, Barolli L, De Marco G, Yang T, Durresi A (2008) Experimental and simulation evaluation of OLSR protocol for mobile ad-hoc networks. In: Proceedings of NBiS-2008. Italy pp 111–121 3. Ikeda M, Kulla E, Barolli L, Takizawa M (2011) Wireless ad-hoc networks performance evaluation using ns-2 and ns-3 network simulators. In: Proceedings of CISIS-2011. Korea 40–45 4. Ikeda M, Kulla E, Hiyama M, Barolli L, Takizawa M (2010) Analysis of manet routing protocols for indoor environment. In: Proceedings of BWCCA-2010. Japan pp 9–16 5. Ros FJ, Reiz PM (2006) UM-OLSR: Implementation of the olsr routing protocol for the ns2 network simulator. http://masimum.inf.um.es/?Software:UM-OLSR 6. NS-3: The ns3 network simulator. http://www.nsnam.org/ 2011

A Comparison Study for Different Settings of Crossover and Mutation Rates Using WMN-GA Simulation System Evjola Spaho, Tetsuya Oda, Admir Barolli, Fatos Xhafa, Leonard Barolli and Makoto Takizawa

Abstract In this paper, we propose and implement a system based on Genetic Algorithms (GAs) called WMN-GA. We evaluate the performance of WMN-GA for 0.7 crossover rate and 0.3 mutation rate, Exponential Ranking and different distribution of clients considering size of giant component parameter. The simulation results show that for Normal Distribution the system has better performance. We carried out also simulations for Normal Distribution and 0.8 crossover rate and 0.2 mutation rate. The simulation results shows that the setting for 0.7 crossover rate and 0.3 mutation rate offers better connectivity. E. Spaho (&)
T. Oda Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan e-mail: [email protected] T. Oda e-mail: [email protected] A. Barolli
M. Takizawa Department of Computers and Information Science, Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino-Shi, Tokyo 180-8633, Japan e-mail: [email protected] M. Takizawa e-mail: [email protected] F. Xhafa Department of Languages and Informatics Systems, Technical University of Catalonia, C/Jordi Girona 1-3, 08034 Barcelona, Spain e-mail: [email protected] LeonardBarolli Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan e-mail: [email protected]

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Mutation operators
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1 Introduction WMNs distinguish for their low cost nature that makes them attractive for providing wireless Internet connectivity. Moreover, such infrastructure can be used to deploy community networks, metropolitan area networks, municipal and, corporative networks, and to support applications for urban areas, medical, transport and surveillance systems. The main issue of WMNs [1] is to achieve network connectivity and stability as well as QoS in terms of user coverage. This problem is very closely related to the family of node placement problems in WMNs [2–5], among them, the mesh router mesh nodes placement. Here, we consider the version of the mesh router nodes placement problem in which we are given a grid area where to deploy a number of mesh router nodes and a number of mesh client nodes of fixed positions (of an arbitrary distribution) in the grid area. As node placement problems are known to be computationally hard to solve for most of the formulations [6, 7], Genetic Algorithms (GAs) have been recently investigated as effective resolution methods. However, GAs require the user to provide values for a number of parameters and a set of genetic operators to achieve the best GA performance for the problem [8]. In this paper, we deal with connectivity in WMN. Because this problem is known to be NP-Hard, we propose and implement a system based on GAs, called WMN-GA. We evaluate the performance of the proposed system by different scenarios using different metrics such as crossover rate, mutation rate and giant component. We compared the performance of the system for Normal Distribution and different rates of crossover and mutation. The rest of the paper is organized as follows. The mesh router nodes placement problem is defined in Sect. 2. We give a brief introduction of GAs in Sect. 3. The WMN-GA system is presented in Sect. 4. The simulation results are given in Sect. 5. In Sect. 6, we give some conclusions and future work.

2 Mesh Router Node Placement Problem In this problem, we are given a grid area arranged in cells where to distribute a number of mesh router nodes and a number of mesh client nodes of fixed positions (of an arbitrary distribution) in the grid area. The objective is to find a location assignment for the mesh routers to the cells of the grid area that maximizes the network connectivity and client coverage. Network connectivity is measured by the size of the giant component of the resulting WMN graph, while the user

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coverage is simply the number of mesh client nodes that fall within the radio coverage of at least one mesh router node. An instance of the problem consists as follows. • N mesh router nodes, each having its own radio coverage, defining thus a vector of routers. • An area W9H where to distribute N mesh routers. Positions of mesh routers are not pre-determined, and are to be computed. • M client mesh nodes located in arbitrary points of the considered area, defining a matrix of clients. It should be noted that network connectivity and user coverage are among most important metrics in WMNs and directly affect the network performance. Nonetheless, network connectivity is usually considered as more important than user coverage.

3 Genetic Algorithms GAs have shown their usefulness for the resolution of many computationally hard combinatorial optimization problems. They are, of course, a strong candidate for efficiently solving mesh router nodes placement problem in WMNs. For the purpose of this work we have used the template given in Algorithm 1. Algorithm 1 Genetic Algorithm Template Generate the initial population P0 of size l; t= 0. Evaluate P0; while not termination-condition do Select the parental pool Tt of size k; Tt:= Select(Pt); Perform crossover procedure on pairs of individuals in Tt with probability pc; Ptc:=Cross(Tt); Perform mutation procedure on individuals in Ptc with probability Pm; Ptm:= Cross (Tt); Evaluate Ptm; Create a new population Pt+1 of size l from individuals in Pt and/or Ptm; Pt+1:= Replace (Pt; Ptm) T:=t+1; end while return Best found individual as solution As can be seen from the template, several parameters intervene in the GAs: population size, intermediate population size, number of evolution steps, crossover probability, mutation probability and parameters for replacement strategies. On the

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other hand, there are the (families of) genetic operators: crossover operators, mutation operators, selection operators and replacement operators. Selection Operators: In the evolutionary computing literature we can find a variety of selection operators, which are in charge of selecting individuals for the pool mate. Some of these operators are: Random Selection, Best Selection, Linear Ranking Selection, Exponential Ranking Selection, Tournament Selection etc. The operator considered in this work, Exponential Ranking Selection is based on Implicit Fitness technique. It should be noted that selection operators are generic ones and do not depend on the encoding of individuals. Mutation Operators: In the case of mesh routers node placement, the matrix representation is chosen for the individuals of the population, in order to keep the information on mesh router nodes positions, mesh client positions, links among routers and links among routers and clients. The definition of the mutation operators is therefore specific to matrix-based encoding of the individuals of the population. Some move based and swap-based operators are SingleMutate, RectangleMutate, SmallMutate, SmallRectangleMutate etc. Crossover Operators: The crossover operator selects individuals from the parental generation and interchanging their genes, thus new individuals (descendants) are obtained. The aim is to obtain descendants of better quality that will feed the next generation and enable the search to explore new regions of solution space not explored yet. There exist many types of crossover operators explored in the evolutionary computing literature. It is very important to stress that crossover operators depend on the chromosome representation. This observation is especially important for the mesh router nodes problem, since in our case, instead of having strings we have a grid of nodes located in a certain positions. The crossover operator should thus take into account the specifics of mesh router nodes encoding.

4 Proposed and Implemented WMN-GA System In this section, we present WMN-GA system. Our system can generate instances of the problem using different distributions of client and mesh routers. The GUI interface of WMN-GA is shown in Fig. 1. The left side of the interface shows the GA parameters configuration and on the right side are shown the network configuration parameters. For the network configuration, we use: distribution, number of clients, number of mesh routers, grid size, radius of transmission distance and the size of subgrid. For the GA parameter configuration, we use: number of independent runs, GA evolution steps, population size, population intermediate size, crossover probability, mutation probability, initial methods, select method.

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Fig. 1 GUI tool for WMN-GA system

5 Simulation Results In this work, the number of Mesh Routers is considered 16. We take in consideration four distribution methods: Exponential, Normal, Uniform and Weibull. As selection operator we use Exponential Ranking. We carried out many simulations to evaluate the performance of WMNs using WMN-GA system. In Fig. 2 we present simulation results for the grid size (32 9 32).We show the size of giant component vs. the number of generations when using Exponential Ranking selection operator and different client distributions. The crossover and mutation rates are selected 0.7 and 0.3, respectively. As can be seen, the network connectivity is higher when normal distribution is used. Normal distribution is the best because the size of giant component is almost 16 and the number of generations is lower than other distributions. The distribution of mesh routers and clients for different distribution operators are shown in Fig. 3. The Normal Distribution offers a better coverage than other distributions. We carried out other simulations to see the performance of the system for other settings. In Fig. 4 are shown the simulation results for Normal Distribution operator, crossover rate 0.8 and mutation rate 0.2. Comparing Fig. 4a with Fig. 4(b), we can see that for the setting of crossover rate 0.7 and mutation rate 0.3 the system has better performance.

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Fig. 2 Size of giant component versus number of generations a Exponential distribution b Normal distribution c Uniform distribution d Weibull distribution

Fig. 3 Distribution of mesh routers and clients a Exponential distribution b Normal distribution c Uniform distribution d Weibull distribution

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Fig. 4 Simulation results for different rates of crossover and mutation a Crossover=0.8, Mutation =0.2 b Crossover=0.7, Mutation =0.3

6 Conclusions Mesh router node placement is important to achieve network connectivity in WMNs. In this work, we have proposed and implement a system based on Gas called WMN-GA to solve the connectivity problem in WMN. We evaluate the performance of WMN-GA system by different scenarios using size of giant component metric and using different client distributions. The simulation results show that normal distribution offers the best network connectivity. We compared the simulation results using different rates of crossover and mutation. The results show that network connectivity is better for the setting of crossover rate 0.7 and mutation rate 0.3. In the future work, we would like to carry out extensive simulations for different scenarios. Acknowledgments This work is supported by a Grant-in-Aid for Scientific Research from Japanese Society for the Promotion of Science (JSPS). The authors would like to thank JSPS for the financial support.

References 1. Akyildiz FI, Wang X, Wang W (2005) Wireless mesh networks: a survey. Comput Netw 47(4):445–487 2. Franklin A, Murthy C (2007) Node placement algorithm for deployment of two-tier wireless mesh networks. In: IEEE GLOBECOM-2007 pp 4823–4827 3. Muthaiah NS, Rosenberg C (2008) Single gateway placement in wireless mesh networks. In: 8th International IEEE Symposium on Computer Networks, pp 4754–4759 4. Tang M (2009) Gateways placement in backbone wireless mesh networks. Int J Commun Netw Syst Sci 2(1):45–50 5. Vanhatupa T, Hännikäinen M, Hämäläinen DT (2007) Genetic algorithm to optimize node placement and configuration for WLAN planning. In: Fourth international symposium on wireless communication systems, pp 612–616

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6. Lim A, Rodrigues B, Wang F, Zh Xua (2005) k-center problems with minimum coverage. Theor Comput Sci 332(1–3):1–17 7. Wang J, Xie B, Cai K, Agrawal PD (2007) Eficient mesh router placement in wireless mesh networks. In : MASS-2007, Pisa, Italy, pp 9–11 8. Xhafa F, Sanchez C, Barolli L (2009) Ad Hoc and neighborhood search methods for placement of mesh routers in wireless mesh networks. In: ICDCS Workshops of the IEEE 29th international conference on distributed computing systems (ICDCS-2009), pp 400–405

A Survey on MANET Testbeds and Mobility Models Elis Kulla, Makoto Ikeda, Leonard Barolli, Fatos Xhafa and Jiro Iwashige

Abstract In order to evaluate algorithms and protocols in Mobile Adhoc Networks (MANETs), researchers use three major techniques: simulations, emulations and real-world experiments. An important issue for MANETs is mobility. For simulations and emulations, the mobility model is achieved by writing lines of code and making it possible to repeat the same investigation for the same conditions. While, in real-world experiments mobility is a really complicated issue. In this paper, we present a survey of mobility models implemented in testbeds around the world, and discuss some of the issues and findings encountered during implementation. However, by conducting real-world experiments in implemented testbeds, the evaluation is more precise and closer to the real application. The abstract should summarize the contents of the paper and should contain at least 70 and at most 150 words. It should be set in 9-point font size and should be inset

E. Kulla (&) Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 WajiroHigashi, Higashi-Ku, Fukuoka 811-0295, Japan e-mail: [email protected] M. Ikeda
L. Barolli
J. Iwashige Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan e-mail: [email protected] L. Barolli e-mail: [email protected] J. Iwashige e-mail: [email protected] F. Xhafa Department of Languages and Informatics Systems, Technical University of Catalonia, C/Jordi Girona 1-3, 08034 Barcelona, Spain e-mail: [email protected]

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Keywords MANET testbed Mobility model Real-world experiments Survey

1 Introduction A collection of wireless mobile hosts that can dynamically establish a temporary network without any aid from fixed infrastructure is known as a Mobile Ad-hoc Network (MANET). These hosts can move in different directions with different speeds. MANET are found very useful in real applications such as time-lacking implementations and indoor environments. A lot of research for MANETs has been done in simulation, because in general, a simulator can give a quick and inexpensive evaluation of protocols and algorithms. Emulation also is a good tool for research in MANETs. Hardware and simulation software components are mixed together, to create an emulation system. However, experimentation in the real world is very important to verify the simulation or emulation results and to revise the models implemented. One of the most discussed models in literature is the mobility model. There are a lot of mobility models, which can be used in simulations and emulations, for testing MANETs. On the other hand, mobility models for real world experiments are more complicated as they require more cost, more time and/or more people. They can be implemented by people carrying nodes and walking around, or cars driving around, or even robots. In this paper, we will make a survey on MANET’s testbeds and how they implement mobility model. The outline of the paper is as follows. In Sect. 2, we describe shortly the evaluation techniques of algorithms and protocols. In Sect. 3, we present an overview of mobility models. We show some real testbeds with mobility implemented in the world in Sect. 4. A discussion on these experiences is done in Sect. 5. Finally, we give conclusions in Sect. 6.

2 Evaluation Techniques In a research environment, research tools are required to test, verify and identify problems of an algorithm or protocol. These tools are classified in three major techniques: simulators, emulators and real-world testbeds. We will describe them shortly in the following.

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2.1 Simulations A simulation system consists of many assumptions and artificial modeling, in order to reach a certain realistic degree. However, these assumptions and modeling can have errors and in some cases, some realistic effects are not even considered, e.g. gray zones effect [7] are not considered in the well-known simulator ns-2. In the early phases of the development of a MANET algorithm or protocol, usually after the analytical modeling, simulations can give a quick and inexpensive result regarding the theoretical performance. Moreover, we can keep unchanged the simulated conditions and parameters and run the simulations as many times as we want.

2.2 Emulators With a higher degree of realism than simulators, emulators can still control the repeatability of tests and use real hardware combined with simulation software, to conduct experiments in controlled conditions. They use artificial assumptions which are sometimes unrealistic. Emulators can be divided into physical layer emulators and MAC layer emulators. Physical layer emulators, e.g. EWANT [12], use the attenuation of the radio signal to emulate movement or obstacles. MAC layer emulators use MAC filter tools, e.g. Dummynet [11], to decide network topology and emulate mobility. Emulators have higher costs than simulators because they use real hardware.

2.3 Real-World Experiments Real-world testbeds have the higher level of realism because they are not based on assumptions about the experimental conditions. In testbeds, when mobility is present, the node changes its geographical location, which can have different effects on the performance. Testbeds are usually used on the final stages of the development of an algorithm or protocol. Simulation and emulation systems can make assumptions based on experimental results provided by testbeds. However, real-world testbed implementations have higher costs for hardware software and working hours. Also, the repeatability of tests in a testbed is a complicated and costly task.

3 Mobility in MANETs With the growing applications, services and technologies of the Internet, nowadays users apart from using wireless devices, most of them are on the move for most of time. Also in MANET, Wireless Sensor Networks (WSNs) or Vehicular Adhoc

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Networks (VANETs), mobility is a very important feature. When it comes to testing these networks, using the tools explained in Sect. 2, a researcher chooses the pattern of movement of the nodes during the evaluation time. This pattern is defined as a mobility model, and in simulations there are a lot of mobility models proposed and used. In [3], the authors present a survey on mobility models and they classify them in:

Entity mobility models. All nodes move independently from each other. Group mobility models. Nodes movement is dependent from other nodes in the network. Mobility models in reality derive logically from different aspects of life and we can classify them in the following categories. A mobility pattern can be a mixing of all of the following. Biology related mobility models. The movement of nodes is similar to real biological species (insects, birds, fish, animals). Activity related mobility models. Different human activities, as sports, leisure etc., create different mobility patterns. Environment related mobility models. The moving pattern in cities is different from that on an open field and highways. Mobility models driven by environment are used a lot in research recently. Random mobility models. These models are mostly used in simulations, when mobility is not a specific requirement. Nodes choose random directions, random destinations, random speed etc., moving in a specific area. Considering a MANET testbed, the implementation of a mobility model is not a simple task. We will discuss some experimental systems and the mobility they used in the following section.

4 Testbeds with Mobility Implementing mobility in a real-world testbed has encountered a lot of difficulties and tasks. Recently there are a lot of testbeds running in universities or research institutes. Some of them did not even consider mobility [2]. We show the characteristics of the testbeds in Table 1 and will describe some of them in the following, concentrating on the implementation of mobility. In [1], the authors created a testbed for indoor and outdoor experimentations. In indoor environment, they used horizontal and vertical topologies, and implemented mobility by people carrying or pushing the wireless nodes. They used AODV, OLSR and BATMAN routing protocols and measured performance by investigating many metrics. An interesting finding, which is different from what expected, is that TCP transmission has a better performance than UDP transmission.

A Survey on MANET Testbeds and Mobility Models Table 1 Testbeds characteristics Testbed Environment

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Office chairs

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Environment related, entity Both Carried by Environment people related, group Environment Outdoor Cars driving related, entity around the area Both Carried by Random, entity people Indoor Robots Random, entity

Gray Real environment 33 nodes et al. [4] No assumptions TrueMobile Real environment 16 nodes [5] No assumptions ORBIT [10] Real environment 100 nodes Both Emulates mobility

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The authors of [9], introduce their APE testbed consisting of 37 nodes. Mobility is implemented by people carrying laptops and walking around the testing indoor and outdoor area, following the instructions on the screen. They conducted many experiments using group mobility model, which is rarely found in real world experiments. Making experiments with AODV and OLSR, they concluded that using on screen instructions has resulted in a good way to reproduce moving patterns. However, one can use robots, like in [5] instead of people to get a better recreation of moving patterns. The testbed described in Maltz et al. [8], consists of six mobile nodes and two static ones. The experiments are ran outdoors and the authors use DSR protocol. They use a GPS location information system while driving nodes around the area by cars. Five moving nodes move around a given route at different speed and another moving node moves in and out of network at certain times. Exact location information resulted in a good approach to reveal that in unexpected areas the performance became lower. GPS receivers are also used in [4]. In [4], the authors used up to 33 nodes to generate different scenarios and experiments. They conducted outdoor, indoor and simulation testing. They verified that for outdoor scenarios, simulations can be a close approach for predicting performance. However, indoor experiments’ results change a lot from simulation results. Another approach to create mobility is by artificially differing parts of the network condition, making it looks like the topology is changing. In [10], the authors have created ORBIT testbed, which consists of over 100 nodes. They run experiments of different network size, indoor or outdoor, and also different moving scenarios. Mobility is realized using MAC filtering techniques, redirecting traffic to other nodes.

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5 Discussion Implementation of a testbed becomes easier if you make a good planning of how you want things to be done. Observing the experiences of testbed implementers, first of all we would like to mention that, building a testbed with mobility needs hard work and endurance. We did not talk about the beginning of implementation as it is not in the scope of our work. When planning to implement a mobility model in a testbed, the first important thing that should be taken care about is what effects can this mobility pattern have on other research or real applications. It can be an environment-related, activityrelated, entity or group mobility model. After deciding the moving pattern, the next problem is how to make real devices move in your experimental area, in order to be able to repeat the same movement. Driving cars [8], pushing chairs [1] or even carrying devices and walking around following on-screen instructions [9] are preferred ways of completing this task. Using automated movable robots is a more efficient technique. However, this has a higher cost on each robot used. Another way of implementing mobility is emulation [10]. This method is cheaper but makes unrealistic assumptions, by changing topology conditions without physically moving the nodes. When nodes are mobile, we would like to check the position of every node at a given time of the experiment, after some months or years to verify the results. Some used methods consist of GPS receivers [8], security cameras [5], or using the relative Radio Signal Strength (RSS) to compute the location of nodes. The experiences of other testbed builders have good insights on building your own testbed. Another benefit we can get from the experiences of a testbed, is the use of settings in our simulation systems. As proved in [9], a simulation system can have similar results to outdoor experiments, which will help us verify our conclusions.

6 Conclusions In this paper, we described shortly the evaluation techniques of algorithms and protocols. We also present an overview of mobility models and showed some real testbeds with mobility, implemented in the world. We make some conclusions on our work in the following. – When using people to make the movement of the nodes, the on-screen instructions are a good way to recreate the moving patterns. Robots, on the other hand, are more precise, but the cost per node increases. – Monitoring location and time synchronization is a difficult task, but it results in finding unpredictable effects, when nodes are mobile. It can also be used or future reference to the experiments.

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– Simulation systems can give approximate results for experiments, while for indoor environments there are unpredicted results. Thus, testbed experiments give a lot of feedback to make assumptions regarding the simulation systems.

Acknowledgments This work is supported by a Grant-in-Aid for scientific research of Japan Society for the Promotion of Science (JSPS). The authors would like to thank JSPS for the financial support.

References 1. Barolli L, Ikeda M, Xhafa F, Durresi A (2010) A testbed for MANETs: implementation experiences and learned lessons. IEEE Syst J 4(2):243–252 2. Bicket J, Aguayo D, Biswas S, Morris R (2005) Architecture and evaluation of an unplanned 802.11b mesh network. In: MOBICOM-2005, pp 31–42 3. Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. J WCMC 2(5):483–502 4. Gray RS, Kotz D, Newport CC, Dubrovsky N, Fiske A, Liu J, Masone C, McGrath S, Yuan Y (2004) Outdoor experimental comparison of four ad hoc routing algorithms. In: MSWiM2004, pp 220–229 5. Johnson D, Stack T, Fish R, Flickinger D, Ricci R, Lepreau J (2006) truemobile: a mobile robotic wireless and sensor network testbed, Flux Technical Note FTN-2005-02. In: 25th annual joint conference of the IEEE CCS 6. Kiess W, Mauve M (2007) A survey on real-world implementations of mobile ad-hoc networks. J Ad-Hoc Netw 5(3):324–339 7. Lundgren H, Nordström E, Tschudin C (2002) Coping with communication gray zones in IEEE 802.11b based ad-hoc networks. In: WOWMOM-2002/MOBICOM-2002, pp 49–55 8. Maltz DA, Broch J, Johnson DB (2001) Lessons from a full-scale multihop wireless ad-hoc network testbed. Personal Communications 8(1):8–15 9. Nordstrom E (2002) APE—A large scale ad-hoc network testbed for reproducible performance tests. Master’s thesis, Uppsala University 10. Raychaudhuri D, Seskar I, Ott M, Ganu S, Ramachandran K, Kremo H, Siracusa R, Liuand H, Singh M (2005) Overview of ORBIT radio grid testbed evaluation of next-generation wireless network protocols. In: IEEE WCNC-2005, pp 1664–1669 11. Rizzo L (1997) Dummynet: a simple approach to the evaluation of network protocols. J Comput Commun Rev 27(1):31–41 12. Sanghani S, Brown TX, Bhandare S, Doshi S (2003) EWANT: the emulated wireless adhoc testbed. In: IEEE WCNC-2003, pp 1884–1849, March

Evaluation of Wireless Sensor Networks for Multi Mobile Events Using Different Topologies, Protocols and Radio Propagation Models Tao Yang, Leonard Barolli, Jiro Iwashige, Arjan Durresi and Fatos Xhafa

Abstract In this paper, we evaluate the performance of WSNs for single and multi mobile events using different radio model, different topology and different protocols. We consider depletion metric to evaluate the performance of WSN. The simulation results have shown that for Shadowing model, the depletion is better than TwoRayGround model. For both Shadowing and TwoRayGround models, the depletion of DSR is worse than AODV. Also, depletion of DSR using Shadowing is unstable. Keywords Sensor network Topology Radio model





Single mobile event



Multi mobile events



T. Yang (&)  L. Barolli  J. Iwashige Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295 Japan e-mail: [email protected] L. Barolli e-mail: [email protected] J. Iwashige e-mail: [email protected] A. Durresi Department of Computer and Information Science, Indiana University Purdue University at Indianapolis (IUPUI), 723 W. Michigan Street SL 280, Indianapolis, IN 46202 USA e-mail: [email protected] F. Xhafa Department of Languages and Informatics Systems, Technical University of Catalonia, C/Jordi Girona 1-3, 08034 Barcelona, Spain e-mail: [email protected]

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1 Introduction A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. The development of WSNs was motivated by military applications such as battlefield surveillance. They are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control. Recently, there are many research works for WSNs [1]. In this paper, we study a particular application of WSN for event-detection and tracking. The application is based on the assumption that WSN present some degree of spatial redundancy. For instance, whenever an event happens, a certain event data is transmitted to the sink node. Because of the spatial redundancy, we can tolerate some packet loss, as long as the required detection or event-reliability holds. This reliability can be formulated as the minimum number of packets required by the sink node in order to re-construct the event field. We want to investigate the performance of WSN for different topologies considering single and multi mobile events. The remainder of the paper is organized as follows. In Sect. 2, we present the related work. In Sect. 3, we explain the proposed network simulation model. In Sect. 4, we show the simulation results. Conclusions of the paper are given in Sect. 5.

2 Related Work In our previous work [2], we implemented a simulation system for WSNs considering different protocols and different propagation radio models. However, we did not consider the multi mobile events. In [3], the authors consider scenarios where sensors are deployed within a circle. The authors argue that in such cases the mobile sink should follow the periphery of the network in order to optimize the energy consumption of the nodes. In our previous work [4], we obtained the simulation results for consumed energy in case of mobile sink. We found that the consumed energy is better than the stationary sink (about half of stationary in lattice topology). The goodput of random topology is better than lattice topology and the consumed energy of lattice topology is better than random topology. In this work, we assume a network consisting of 100 sensor nodes with single event and multi mobile events, which moves continuously on a random path. The position of the event cannot be determined in advance. Sensor nodes have limited radio range, thus multihop communication is used in the network.

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Fig. 1 Network simulation model

3 Proposed Network Simulation Model In our WSN, every node detects the physical phenomenon and sends back to the sink node the data packets. We suppose that the sink node is more powerful than sensor nodes. In our previous work, the event node was stationary. In this work, we consider mobile event. We analyze the performance of the network in a fixed time interval, which is the available time for the detection of the phenomenon and its value is application dependent. Proposed network simulation model is shown in Fig. 1. For simulation system implementation, we consider modeling and network stack. In this paper, we consider that a single mobile event and multi mobile events are moving randomly in the WSN field. We evaluated the depletion of WSNs using different protocols and topologies.

3.1 Topology For the physical layout of the WSN, two types of topologies have been studied so far: random and lattice topologies. In the former, nodes are supposed to be uniformly distributed, while in the latter one nodes are vertexes of particular geometric shape, e.g. a square grid. For lattice topology, in order to guarantee the connectedness of the network we should set the transmission range of every node to the step size, d, which is the minimum distance between two rows (or columns) of the grid [5]. In fact, by this way the number of links that every node can

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Fig. 2 Lattice topology and random topology. a An example of lattice network. b An example of random network

establish (the node degree D) is 4. Nodes at the borders have D = 2. In the case of random networks, we suppose that the coordinates in the Euclidean plane of every sensor are random variables uniformly distributed in the interval [0,L] 9 [0,L]. Snapshots of lattice and random networks generated in simulations are shown in Figs. 2a and b, respectively.

3.2 Radio Model In order to simulate the detection of a natural event, we used the libraries from Naval Research Laboratory (NRL) [6]. In this framework, a phenomenon is modeled as a wireless mobile node. The phenomenon node broadcasts packets with a tunable synchrony or pulse rate, which represents the period of occurrence of a generic event1. These libraries provide the sensor node with an alarm variable. The alarm variable is a timer variable. It turns off the sensor if no event is sensed within an alarm interval. In addition to the sensing capabilities, every sensor can establish a multi-hop communication towards the monitoring node (MN) by means of a particular routing protocol. This case is the opposite of the polling scheme. We assume that the MAC protocol is the IEEE 802.11 standard. This serves to us as a baseline of comparison for other contention resolution protocols. The receiver of every sensor node is supposed to receive correctly data bits if the received power exceeds the receiver threshold. This threshold depends on the hardware.2 As reference, we select parameters values according to the features of a 1

As a consequence, this model is for discrete events. By setting a suitable value for the pulse rate, it is possible in turn to simulate the continuous signal detection such as temperature or pressure. 2 Other MAC factors affect the reception process, for example the carrier sensing threshold (CST) and capture threshold (CP) of IEEE.802.11 used in NS-2.

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Fig. 3 Transmission ranges of a TwoRayGround model. b Shadowing model

commercial device (MICA2 OEM). In particular, for this device, we found that for a carrier frequency of f = 916 MHz and a data rate of 34KBaud, we have a threshold (or receiver sensitivity) |dB = -118 dBm [7]. The calculation of the phenomenon range is not yet optimized and the phenomenon propagation is assumed to follow the propagation laws of the radio signals. In Fig. 3a and b are shown the transmission range of TwoRayGround and Shadowing models. In particular, the emitted power of the phenomenon is calculated according to a TwoRayGround propagation model [8]. The Shadowing model assumes that the received power at the sensor node is:   d þ SdB ð1Þ Pr ðdÞ jdB ¼ Pt jdB  b0  10a log |{z} d0 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} randompart deterministicpart

where b0 is a constant. The term SdB is a random variable, which accounts for random variations of the path loss. This variable is also known as log-normal shadowing, because it is supposed to be Gaussian distributed with zero mean and variance r2dB ; that is SdB  Nð0; r2dB Þ: Given two nodes, if Pr [ c; where is the hardware-dependent threshold, the link can be established. The case of r ¼ 0; a ¼ 4; d [ d0 is also called the TwoRaysGround model and it is a deterministic model. Where in addition to the direct ray from the transmitter towards the receiver node, a ground reflected signal is supposed to be present. Accordingly, the received power now depends also on the antenna heights and the path-loss is: ! ð4pdÞ4 L ð2Þ b ¼ 10 log G t G r ht hr k2

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where hr and ht are the receiver and transmitter antenna heights, respectively. The formula in Eq. (2) is valid for distances d [ dc ; that is far from the transmitting node.

3.3 Routing Protocols We are aware of many proposals of routing protocols for ad-hoc networks [9]. Here, we consider AODV and DSR protocols. The AODV is an improvement of DSDV to on-demand scheme. It minimizes the broadcast packet by creating route only when needed. Every node in network maintains the route information table and participates in routing table exchange. When source node wants to send data to the destination node, it first initiates route discovery process. In this process, source node broadcasts Route Request (RREQ) packet to its neighbors. Neighbor nodes which receive RREQ forward the packet to its neighbor nodes. This process continues until RREQ reaches to the destination or the node who knows the path to destination. When the intermediate nodes receive RREQ, they record in their tables the address of neighbors, thereby establishing a reverse path. When the node which knows the path to destination or destination node, itself receives RREQ, it sends back Route Reply (RREP) packet to source node. This RREP packet is transmitted by using reverse path. When the source node receives RREP packet, it can know the path to destination node and it stores the discovered path information in its route table. This is the end of route discovery process. Then, AODV performs route maintenance process. In route maintenance process, each node periodically transmits Hello messages to detect link breakage. The dynamic source routing (DSR) protocol [10], uses two mechanisms: route discovery and route maintenance. Route discovery is initiated whenever a node has a packet to send and it doesn’t know the route to the destination. Route maintenance mechanism is used to monitor the link and in case of link failure, propagate route errors messages to source node. Route cache structure is used to avoid frequent route discovery. Route Discovery and Route Maintenance each operate entirely. ‘‘on demand’’. Route maintenance is done only on active routes and network topology changes not affecting active routes are ignored and do not cause reaction from the protocol.

4 Simulation Results We present the simulation results of our proposed WSN. We simulated the network by means of NS-2 simulator, with the support of NRL libraries. In this work, we simulated two patterns considering stationary sink and mobile sink. For AODV

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Fig. 4 Depletion for different radio models. a Depletion for TwoRayGround model. b Depletion for Shadowing model

and DSR routing protocols, the sample averages of Eq. (3) are computed over 20 simulation runs and they are plotted in Fig. 4.

4.1 Performance Metric As long as the WSN is being used, a certain amount of energy will be consumed. The energy consumption rate directly affects the life-time of the network, i.e. the time after which the WSN is unusable. The energy depletion is a function of the reporting rate as well as the density of the sensor network. Recall that the density of the network in the event-driven scenario correlates with the number of nodes that report their data. Accordingly, we define the consumed energy by the network in the detection interval s as: P NEI  Ni¼1 ei ðsÞ ð3Þ DðsÞ ¼ Ns where ei ðtÞ is the node energy at time t and the means are computed over the number of nodes. The number of nodes N is set as power of integers in order to analyse the behaviour of the scaled versions of the network.

4.2 Simulation Results For AODV and DSR routing protocols, the sample averages of depletion are computed over 20 simulation runs and they are plotted in Fig. 4a and b, with respect to the particular radio model used.

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In Fig. 4a, we found that the depletion is an increasing function. For low values of 10Tr ; the network is uncongested. At a particular value of Tr ð  10 ppsÞ; the depletion arise abruptly, because the network has reached the maximum capacity. For Tr [ 10 pps; contention and congestion periods augment, increasing Tr does not ameliorate the depletion and is roughly constant. In Fig. 4a, we used TwoRayGround radio model and lattice topology. In case of multi-events for DSR, the depletion has the highest value. In case of single event for AODV, the depletion has the lowest value. In Fig. 4b, we used Shadowing radio model and lattice topology. In case of Shadowing model, we found that the depletion of Shadowing is better than TwoRayGround. In case of multi-events for DSR, the highest value is 250, however the depletion of DSR is unstable. In the case of single event for AODV, the depletion has the lowest value. The explanation of this effect is not simple, because it is intermingled with the dynamics of MAC and routing protocol. However, intuitively we can say that in the case of Shadowing the on-demand routing protocols are affected by the presence of shadowinginduced unidirectional links [11].

5 Conclusions In this paper, we presented a comparison evaluation for mobile and static event nodes in WSNs considering different radio models and different protocols. We used the consumed energy metric to measure the performance. From the simulation results, we conclude as follows. • The depletion of multi-mobile events for DSR using TwoRayGrund model has the worst performance. • The depletion of multi-events for DSR using Shadowing is lower than TwoRay Ground. However, it is unstable. • Both the depletion of single event or multi events for Shadowing is lower than TwoRayGround.

Acknowledgments The authors would like to thank Japanese Society for the Promotion of Science (JSPS) for supporting this work.

References 1. Giordano S, Rosenberg C (2006) Topics in ad hoc and sensor networks. IEEE Commun Mag 44(4):97–97 2. Yang T, De Marco G, Ikeda M, Barolli L (2006) Impact of radio randomness on performances of lattice wireless sensor networks based on event-reliability concept. Int Mobile Inf Syst MIS 2(4):211–227

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3. Luo J, Hubaux JP (2005) Joint mobility and routing for lifetime elongation in wireless sensor networks. In : Proceedings of the 24th conference on computer communications (Infocom 2005), Miami, FL, USA, pp 1735–1746, March 2005 4. Yang T, Ikeda M, Mino Gj, Barolli L, Durresi A, Xhafa F (2010) Performance evaluation of wireless sensor networks for mobile sink considering consumed energy metric. In: Proceedings of AINA-2010, Perth, Australia, pp 245–250, April 2010 5. Somasundara AA, Kansal A, Jea DD, Estrin D, Srivastava MB (2006) Controllably mobile infrastructure for low energy embedded networks. IEEE Trans Mobile Comput 5(8):958–973 6. Donward I (2004) NRL’s sensor network extension to NS-2. http://pf.itd.nrl.navy.mil/ analysisnrlsensorsim/ 7. Crossbow technology, inc. http://www.xbow.com/ 8. Rappaport TS (2001) Wireless communications. Prentice Hall PTR 9. Perkins C (2001) Ad Hoc networks. Addison-Wesley, New York 10. Johnson D, Maltz D (1996) Dynamic source routing in ad hoc wireless networks. In: Imielinski T, Korth H, (eds) Mobile computing, Kluwer, Dordrecht, Ch 5, pp 153–181 11. Akan¨OB, Akyildiz IF (2005) Event-to-sink reliable transport in wireless sensor networks. IEEE/ACM Trans Netw 13(5):1003–1016

Trustworthiness-Based Broadcast Protocols in Peer-to-Peer Overlay Networks Ailixier Aikebaier, Tomoya Enokido and Makoto Takizawa

Abstract In this paper, we consider the peer-to-peer (P2P) model as a fully distributed, scalable system different from centralized coordinated systems like Cloud computing systems. A P2P system is composed of peer processes (peers). Here, applications are realized in activities and cooperation of a group of multiple peers. Since there is no centralized coordination in P2P systems, each peer has to obtain information about others by itself. In group cooperation activities, each member peer has to be trustworthy so that malicious actions of a peer cannot affect overall outcome of the whole group. It is important to consider the trustworthiness of each peer as a base of cooperation in the distributed environment. The goal of a group and the way to archive the goal are decided by the member peers. During the cooperation, opinions of each group member have to be considered. Malicious and unexpected opinions of member peers can negatively affect the output of a group. Hence, it is significant to discuss how to compose a group only by including more trustworthy peers and making less trustworthy pees not affect other peers. In this paper, by taking advantage of the trustworthiness concept of each peer, we propose a novel approach to composing a trustworthy group in the distributed agreement protocols.

A. Aikebaier (&)  M. Takizawa Department of Computer and Information Science, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino-shi, Tokyo, 180-8633, Japan e-mail: [email protected] M. Takizawa e-mail: [email protected] T. Enokido Faculty of Bussiness Administration, Rissho University, 4-2-16, Osaki, Shinagawa, Tokyo, 141-8602, Japan e-mail: [email protected]

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 Peer-to-peer  Trustworthiness 

1 Introduction Group cooperation activities are one of the most important actions in our human society. Without group cooperation, it is difficult to archive any objectives. It has been proven that group cooperation among individual processes are also really important in computer systems like database transactions [1, 2], robot technologies [3, 4], and sensor-actuator networks [5, 6]. Nowadays information systems are based on distributed architectures. The peer-to-peer (P2P) model is one of the distributed architecture. A huge number and various types of computers are interconnected with P2P applications in large-scale P2P overlay networks laying on the top of underlying physical computer networks like wireless networks. Differently from centralized or hybrid P2P systems, there is no centralized index server which manages the whole system in pure P2P systems. Because of the scalability of the P2P systems, it is difficult for every peer to figure out what kinds of information are distributed to what peers, what kinds of peers exist, and what kinds of relations among peers exist in P2P overlay networks. In addition, malicious peers and faulty peers can join and member peers can leave a P2P system without being authorized. This causes a question on how each peer to trust a target peer in the P2P systems. In Sect. 2, we discuss the trustworthiness of a peer and how to calculate the trustworthiness. In Sect. 3, we present how to compose a trustworthy group based on the trustworthiness of peers.

2 Trustworthiness of Peers In P2P systems, each peer has to obtain information of other peers and propagate the information to other peers through neighbor (acquaintance) peers. A neighbor peer pj of a peer pi means that pi can directly communicate with pj in P2P overlay networks. Here, the peer pi sends a message to a neighbor peer pj and then pj forwards the message to its neighbor peers. Thus, messages are propagated to peers through peer-to-neighbor communication in a group. Here, some neighbor peer pj might be faulty. If so, neighbor peers of the faulty peer pj do not receive the message from pj. Here, let pj and pk be a pair of neighbor peers of a peer pi. If the peer pj had more successfully forwarded messages from pi than pk, the peer pj is more trustworthy than pk for pi. If a peer pi sends a message to a more trustworthy neighbor peer pj, there is more probability that the message is forwarded to other

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Fig. 1 Trustworthiness of peers

peers. We would like to discuss how a peer can trust each of its neighbors peers [7]. N(pr) shows a collection of neighbor peers of the peer pr. Let Tr(pi) indicate the trustworthiness of a neighbor peer pi of a peer pr, which the peer pr holds. The peer pr calculates the trustworthiness Tr(pi) by collecting the trustworthiness values Tk(pi) on a neighbor peer pi from every neighbor peer pk the neighbors set in N(pr): Tr ðpi Þ ¼ averageðfTk ðpi Þjpk 2 Nðpr Þ \ Nðpi Þðpk 6¼ pi ÞgÞ: There is some possibility that the target peer pi is faulty or sends incorrect information. Hence, the peer pr does not consider the trustworthiness Ti(pi) from the target peer pi to calculate the trustworthiness Tr(pi). A peer pk sends a request message to the peer pi and receives a reply from the peer pi. This request-reply interaction is referred to as transaction. If the peer pk receives a successful reply from the peer pi, the transaction is successful. Otherwise, it is unsuccessful. The peer pk considers the neighbor peer pi to be more trustworthy if pk issued more number of successful transactions for pi. Let STk(pi) indicate the subjective trustworthiness [7] on the target peer pi which a peer pk obtains through directly communicating with the peer pi. The subjective trustworthiness STk(pi) is given as the ratio of the number of successful transactions to the total number of transactions issued by the peer pi to the peer pk. Let us consider peers shown in Fig. 1. Here, a source peer pr would like to know the trustworthiness Tr(pi) of a neighbor peer p1. The peer pr has five neighbor peers, p1, p2, p3, p4, and pi. Here, N(pr) = {p1, p2, p3, p4, pi}. The target peer pi is excluded from N(pr), i.e. S = N(pr)-{p1} = {p1, p2, p3, p4}. Here, the source peer pr requests each neighbor peer pk in the neighbors set S to send the subjective trustworthiness STk(pi) of the peer pi (k = 1, 2, 3, 4). After receiving STk(pi) from every peer pk in S, the peer source pr obtains the trustworthiness value Tr(pi) = (ST1(pi) ? ST2(pi) ? ST3(pi) ? ST4(pi))/4.

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3 Trustworthy Groups 3.1 Basic Ideas During distributed agreement procedure, first of all, the initiator peer pi proposes an objective of a group G and invites other peers to the group G to do cooperation together with the peers. The initiator peer pi sends an invitation message to its neighbor peers. Then, a neighbor peer pj forwards the invitation message to its neighbor peers. If a peer pj accepts the invitation, pj is connected with the invitation peer pr and included in the group G. Thus, the group G is constructed and the initiator peer pi is connected with other peers. In this paper, a group is assumed to make decision on the given objectives by exchanging their opinions among group members. In the previous works [8–11], we mainly discuss how to reliably deliver messages to peers in a group after the group is established. In this paper, we would like to discuss how to construct a group. First, an initiator peer invites its neighbor peer to the group. Then, each of neighbor peers invites its neighbor peers. Thus, a group of multiple peers is constructed. We discuss the trustworthiness-based broadcast (TBB) algorithm [12] to choose most trustworthy members to deliver the message to other peers as a relay peer in a group. Here, the trustworthiness of each peer is not considered. The evaluation results [12] show that, if peers in the group do not have enough number of neighbor peers, it is difficult to deliver message to every peer in a group. The basic idea of the TBB algorithm is to choose most trustworthy peers as relay peers to deliver messages to other peers. From the evaluation studies, we found that if some peers which are selected as relay peers do not have enough number of neighbor pees, there is possibility that relay peers are not able to deliver a message from the initiator peer to all the other peers. In this paper, we try to make a trustworthy group which is composed of trustworthy peers. In this paper, we consider how to improve the total trustworthiness of a group of multiple peers. If a group could be composed of more trustworthy peers, messages can be more reliably and efficiently delivered to every trustworthy peer in the group. We would like to discuss how to construct a group G so that every peer can receive messages in presence of untrustworthy peers. The basic idea to make a trustworthy group G is that each peer only invites trustworthy neighbor peers, not all the neighbor peers into the group G. Since an initiator peer pi does not have enough number of neighbor peers to make a group, the initiator peer pi asks a trustworthy neighbor peer pj to introduce its neighbor peers to the initiator peer pi. By choosing trustworthy peers among the neighbor peers introduced and introducing the trustworthy neighbor peers to the initiator peer pi, only trustworthy member peers are included in the group G. Since, only trustworthy peers are included into group there is smaller possibility the member peers might be faulty (Fig. 2).

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Fig. 2 Trustworthy group

3.2 Construction of a Trustworthy Group We assume each peer pi dynamically updates the subjective trustworthiness value STk(pi) of each neighbor peer pi on completion of each transaction. We also assume that each peer pr periodically calculates the trustworthiness value Tr(pi) for each pi of its neighbor peers. Therefore, each peer pi holds an up-to-date subjective trustworthiness value STi(pi) and trustworthiness value Tr(pi) to each pj of the neighbor peers. First, the initiator peer pr specifies the trustworthiness request TreqG and the scalability request SG in order to construct a group G. The initiator peer pr selects trustworthy neighbor peers which satisfy the trustworthiness requirement TreqG from its neighbor peers depending on the trustworthiness record of the neighbor peers. If selected trustworthy peers do not satisfy the scalability requirement SG of the group G, the initiator peer pr requests the selected peers to become a relay peer and to introduce trustworthy peers from its neighbor peers to the initiator peer pr. This procedure is repeated until enough number of trustworthy peers are selected, i.e. the scalability requirement SG is satisfied. A group G is constructed by the following steps: 1. The initiator peer p0 decides on the scalability requirement SG of the group G and the trustworthiness requirement TreqG. 2. The initiator peer p0 selects trustworthy neighbor peers which satisfy the trustworthiness requirement TreqG as group members. 3. If the initiator peer p0 could find enough number of trustworthy peers (CSG) among its neighbor peers, the group is successfully constructed. 4. If the initiator peer p0 could not find enough number of members peers (BG) from its neighbor peers, p0 asks selected peers to introduce trustworthy neighbor peers. 5. If a selected peer introduces its trustworthy neighbor peers to the initiator peer p0, the initiator peer p0 invites every introduced peer pi which satisfies the trustworthiness requirement TreqG in the group G. If the peer pi agrees on member of the group G, the peer pi is included in the group G. This step is repeated until the scalability requirement SG is satisfied.

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6. Unless enough number of trustworthy peers could be found, the procedure terminates and the group construction fails. By applying the trustworthiness concept into the group construction procedure, we can increase the reliability of the group. More trustworthy peers are invited to the group. Even if less trustworthy peers are included, the peers are not relay peers, i.e. do not forward messages. Only more trustworthy peers forward messages. This means that there is smaller possibility that some member peer is faulty to broadcast messages to every member peer and the fault-tolerancy of the group can be increased. On the other hand, groups where the trustworthiness concept of peers is not considered can be vulnerable to the network failure.

4 Concluding Remarks In this paper, we discussed how to construct a trustworthy group of multiple peers in a scalable P2P overlay network. In the decentralized scalable P2P networks, it is difficult to make sure that the information which a peer obtains from its untrustworthy neighbor peers is trustworthy. Only trustworthy neighbor peers of a peer can provide the peer with correct information. In a group, all member peers must be so trustworthy that malicious actions of some faulty peer cannot affect the whole group. Hence, only trustworthy neighbor peers are invited to a group. By using the trustworthiness concept of peers, we newly proposed the trustworthy group concept where only trustworthy peers are included in the group and discuss how to construct a trustworthy group. The reliability of a group and fault-tolerancy of message broadcasting procedure can be increased. Acknowledgments This research is supported by Research Fellowships of Japan Society for the Promotion of Science for Young Scientists (JSPS). This research was also partially supported by the strategy research project of Seikei University and MEXT, Grant in Aid for Building Strategy Research Infrastructure.

References 1. Taniar D, Rahayu JW, Leung CHC, Goel S (2009) Advances in high performance database technology. In: Proceedings of the 11th international conference on information integration and web-based applications and services iiWAS 2. Taniar D, Leung CHC, Rahayu JW, Goel S (2008) High performance parallel database processing and grid databases. Wiley, New York 3. Flowers TR, Gossett KA (2002) Teaching problem solving, computing, and information technology with robots. J Comput Small Coll 17(6):45–55 4. Goodrich MA, Schultz AC (2007) Human-robot interaction: a survey. Found Trends Hum Comput Interact 1(3):203–275. doi:10.1561/1100000005 http://dx.doi.org/10.1561/1100000005

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5. Upadrashta Y, Vassileva J, Grassmann W (2005) Social networks in peer-to-peer systems. In: Proceedings of the 38th Hawaii international conference on system sciences (HICSS-38 2005), CD-ROM 6. Waluyo AB, Taniar D, Srinivasan B, Rahayu JW, Takizawa M (2011) Adaptive and efficient data dissemination in mobile P2P environments. In: The 25th IEEE international conference on advanced information networking and applications workshops (AINA-2011), pp 861–866 7. Watanabe K, Nakajima Y, Enokido T, Takizawa M (2007) Ranking factors in peer-to-peer overlay networks. ACM Trans Auton Adapt Syst (TAAS) 2(3):141, Article No. 11 8. Foster I et al (2008) Cloud computing and grid computing 360-degree compared. In: Proceedings IEEE grid computing environments workshop, IEEE Press, pp 1–10 9. Armburst M et al (2009) Above the clouds: Berkeley view of cloud computing. Technical report UCB/EECS-2009–28, Electrical Engineering and Computer Science Department, University of California, Berkeley 10. Aikebaier A, Enokido T, Takizawa M (2008) Check pointing in a distributed coordination protocol for multiple peer processes. In: Proceedings of the 2nd international conference on complex, intelligent and software intensive systems (CISIS 2008), pp 48–54 11. Aikebaier A, Hayashibara N, Enokido T, Takizawa M (2007) A distributed co-ordination protocol for a heterogeneous group of peer processes. In: Proceedings of the IEEE 21th conference on advanced information networking and applications (AINA 2007), pp 565–572 12. Aikebaier A, Enokido T, Takizawa M, Deen SM (2010) TBB-Scheme for reliably broadcast messages among peer processes. In: Proceedings of the 13th international conference on network-based information systems (NBiS2010), pp 337–344 13. Waluyo AB, Srinivasan B, Taniar D (2005) Global indexing scheme for location-dependent queries in multi channels mobile broadcast environment. AINA 2005, pp 1011–1016

Energy Efficient Distributed Systems for Storage-Based Applications Takuro Inoue, Ailixier Aikebaier, Tomoya Enokido and Makoto Takizawa

Abstract The electric power consumption of servers has to be reduced in information systems. There are computation (CP), communication (CM), and storage (ST) types of applications. In CP and CM applications, CPU and communication resources are mainly consumed, respectively. In this paper, we consider ST applications where storage drives are manipulated on a server. First, we measure the power consumption of a server to perform types of ST application processes; C, R, and W processes which just compute, read, and write, respectively. Then, we discuss a power consumption model of a server by abstracting most factors dominating the power consumption of a server from the experimental results. Here, the power consumption of a server is maximum if at least one process is performed. The maximum power consumption depends on types of concurrent processes.



Keywords Green IT technology Power consumption models Access rate Digital ecosystems



 Storage drives 

T. Inoue (&)  A. Aikebaier  M. Takizawa Department of Computer and Information Science, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino-shi, Tokyo 180-8633, Japan e-mail: [email protected] A. Aikebaier e-mail: [email protected] M. Takizawa e-mail: [email protected] T. Enokido Faculty of Bussiness Administration, Rissho University, 4-2-16, Osaki, Shinagawa, Tokyo 141-8602, Japan e-mail: [email protected]

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1 Introduction In ecosystems [1], we have to reduce the total electric power consumption of servers to perform various types of applications. Here, a client selects one server in a set of possible servers issues a request to the server. There are three types of applications; computation (CP), communication (CM), and storage (ST) types of applications. In CP applications [2], CPU resources are mainly consumed on servers. Here, the simple and multi-level power consumption models are proposed for CM applications. Algorithms to select one server in a set of possible servers are discussed so that not only the deadline constraint holds for each request but also the total power consumption is reduced [3–5]. In the simple power consumption model, a server consumes the maximum power if at least one process is performed. In CM applications, data are transmitted in networks like FTP (file transfer protocol) [6]. The power consumption of an FTP server depends on the total transmission rate at and the number of clients to which files are concurrently transmitted [2]. Algorithms for selecting one sever in a set of possible servers are discussed so that not only the transmission time but also the power consumption can be reduced. The power consumption model of a server to perform a general type of application process is also discussed [5]. Files in storage drives like hard disk are read and written in ST applications. In this paper, we discuss the power consumption model of a server to perform application processes. We first measure the power consumption of a server where files in types of storage drives are manipulated by application processes. We consider three types, C (computation), R (read), and W (write) of processes. In C processes, only computation is performed by consuming CPU resources. In R and W processes, files in storage drives are read and written, respectively. Then, by abstracting essential properties which dominate the power consumption of a server from the experimental results, we obtain a power consumption model of a server. We do not consider the power consumption of each hardware component like CPU and fan of a server at a micro level. We rather discuss how to reduce the total power consumption of a whole server to perform application processes at a macro level. In the model, the maximum electric power of a server is consumed if at least one process is performed on the server. The maximum power consumption depends on types of processes concurrently performed on a server. In Sect. 2, we show the experimental results on the power consumption of a server to perform ST application process. In Sect. 3, we discuss the power consumption model of a server from the experimental results to storage drives.

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2 Experimentation 2.1 Experimental Environments We first measure how much electric power a server consumes to perform storage type (ST) application processes which make an access to secondary storage drives. The power consumption of a server depends on not only hardware components but also software including OS and application processes. We rather measure the power consumption of a whole server at a macro level where processes are performed. We make clear what factors dominate the power consumption of a server based on the experimental results. Processes which read and write files in hard disk drive (HDD) and solid state drive (SSD) are performed in a server with the Linux (Cent OS).We measure the power consumption of a server by using the electric power meter Metaprotocol UWMeter [7] every one hundred milliseconds [J/0.1 s]. We consider a server st with CPU (IntelÒ CoreTM i7) and 500 GB memory. In this paper, a term ‘‘process’’ means an application process. We consider three types of processes, computation (C), read (R), and write (W) processes. R and W processes just read and write files, respectively. In C processes, only CPU resource is consumed [8]. R and W processes read and write data of one [GB] in the storage drive respectively, by using read and write system calls, respectively. If multiple R and W processes are concurrently performed on the server, every pair of processes pi and pj access to different files in a same storage drive. In the paper [9], the total power consumption of a server is shown to be proportional to the size of data to be read and written. In one read/write system call, a data unit of 1024[B] is read and written. In the paper [2], the simple power consumption model is proposed to perform C processes on a server. If at least one C process is performed on a server, the server spends the maximum power consumption. We consider the following types of environments where the total number m (C) of C, R, and W processes are concurrently performed on the server st: 1. 2. 3. 4. 5. 6.

Ct(m): Only C processes are concurrently performed. Rt(m): Only R processes are concurrently performed. Wt(m): Only of W processes are concurrently performed. CRt(m): R processes are concurrently performed with a C process. CWt(m): W processes are concurrently performed with a C process. RWt(m;w): R and W processes are concurrently performed. Here, write ratio w shows the ratio of the number of W processes to m. 7. CRWt(m;w): R and W processes are concurrently performed with a C process where w shows the W process ratio.

Here, RWt(m; 0) = Rt(m) and RWt(m; 1) = Wt(m). CRWt(m; 0) = CRt(m) and CRWt(m; 1) = CWt(m).

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Fig. 1 Average execution time. a R processes. b W processes

2.2 Measurements First, we measure the average execution time AERt(m) and AEWt(m) [sec] of each process in the environments Rt(m) and Wt(m) where the number m (C1) of processes are concurrently performed on a server st as shown in Fig. 1. Here, AERt(m) and AEWt(m) are independent of the number m of concurrent processes, i.e. AERt(m) = AERt(1) and AEWt(m) = AEWt(1). AEWt(m) = AERt(m) and AERt(m) \ AEWt(m). In our experimental results, AEWt(1) = 1.3AERt(1) for HDD. Figure 2a shows the power consumption rates etC.(s), etW(s), etR(s), and etRW(s) [W] of the server st for m = 10 in the environments Ct(m), Wt(m), Rt(m), and RWt(m), respectively. As shown in Fig. 2a, the power consumption rates are independent of the total number m of concurrent processes. The power consumption rate of a server st is maximum if at least one process is performed. In Fig. 2a, the minimum power consumption rate minEt is 101 [W] ([J/0.1 s]). Let maxCt, maxWt, and maxRt indicate the maximum power consumption rates of the server st where only C, W, and R processes are concurrently performed, respectively. Let Nt(s) be the number m of processes concurrently performed on the server st at time s. The power consumption rates [W] are given from experimental results of Fig. 2 as follows: 1. 2. 3. 4.

etC(s) = maxCt if Nt (s) C1, else minEt. etW(s) = maxWt if Nt (s) C1, else minEt. etR(s) = maxRt if Nt (s)C1, else minEt. etRW(s) = maxRWt if Nt (s)C1, else minEt.

Here, maxWt = maxRt = maxRWt B maxCt. In Fig. 2a, maxRt = maxWt = maxRWt = 108 [W] and maxCt = 168 [W]. As discussed in the paper [4], the total power consumption rate etC.(s) is maximum if at least one C process is performed on the server st, m C 1. etC(s) is minimum, i.e. minEt if no process is performed. Hence, we consider the environments CRt(m), CWt(m), and CRWt(m; 0:5) of the server st where the same number of R and W processes are performed on a server st concurrently with a C process, respectively. Figure 2b shows the power consumption rates etCR(s), etCW(s), and

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Fig. 2 Power consumption rates of a server. a C, R, W, and RW. b C, CR, CRW, and CRW

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Fig. 3 Maximum power consumption rates. a etA(s), b maxAt

etCRW(s) of the server st at time s in CRt(m), CWt(m), and CRWt(m; 0:5), respectively. According to Fig. 2b, maxCRWt = maxCRt = maxCWt = 1.1maxCt. In CRWt(m), the more amount of power is consumed than Ct(m). The minimum power consumption rate minEt and the maximum power consumption rates maxRt, maxCt, and maxCRWt of the server st are 101, 105, 168, and 186 [W], respectively.

3 Power Consumption Model We would like to discuss a power consumption model obtained from the experimental results presented in the preceding section. The power consumption rate etA(s) of a server st at time s is given as follows for an environment A [{C, W, R, RW, CR, CW, CRW} as shown in Fig. 3a: etA ðsÞ ¼ maxAt if at least one A process is performed on a server st

ð1Þ

at time s in an environmental A; Nt ðsÞ  1; else minEt :

That is, etA(s) takes either maxAt or minEt (Fig. 3a). The power consumption rate etA(s) is also independent of the number m of concurrent processes. The

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maximum power consumption rate maxAt depends on the type environment A. Here, maxRWt = atmaxCt (0 B at B 1) and maxCRWt = btmaxCt(bt C 1). According to the experimental results, at = 0.65 and bt = 1.1.

4 Conclusions In this paper, we discussed the power consumption model of a server to perform storage (ST) type application processes. We measured the power consumption rate of a server to perform types of application processes. We consider three types C, R, and W processes. Based on the experimental results, we derived the two-state model power consumption model of the server. Here, the power consumption rate of a server is either maximum or minimum. The maximum power consumption rate depends on what types of processes are concurrently performed. By using the power consumption model, we are now discussing algorithms to select a server in a pool of storage servers so that the total power consumption can be reduced. Acknowledgments This research is partially supported by the strategy research of Seikei University and MEXT, Grant-in-Aid for Building Strategy Research Infrastructure.

References 1. Waluyo A, Rahayu W, Taniar D, Srinivasan B (2011) A novel structure and access mechanism for mobile broadcast data in digital ecosystems. IEEE Trans Ind Electron 58(6):2173–2182 2. Enokido T, Aikebaier A, Misbah Deen S, Takizawa M (2010) Power consumption-based server selection algorithms for communication-based systems. In: Proceedings of the 13th international conference on network-based information systems (NBiS2010), pp 201–208 3. Enokido T, Aikebaier A, Takizawa M (2010) A Model for reducing power consumption in peer-to-peer systems. IEEE Syst J 4(2):221–229 4. Enokido T, Suzuki K, Aikebaier A, Takizawa M (2010) Process allocation algorithm for improving the energy efficiency in distributed systems. In: Proceedings of IEEE the 24th international conference on advanced information networking and applications (AINA2010), Perth, Australia, 2010, pp 142–149 5. Enokido T, Aikebaier A, Takizawa M (2011) Process allocation algorithms for saving power consumption in peer-to-peer systems. IEEE Trans Ind Electron 58(6):2097–2105 6. Ghobadi A, Eswaran C, Muthuvelu N, Tan IKT, Kee YL (2009) An adaptive wrapper algorithm for file transfer applications to support optimal large file transfers. In: Proceedings of IEEE the 11th international conference on advanced communication technology (ICACT 2009), p 315–320 7. UWmeter, http://www.metaprotocol.com/UWmeter/Features.html. Accessed 5 July 2011 8. Aikebaier A, Enokido T, Takizawa M (2009) Energy-efficient computation models for distributed systems. In: Proceedings of the 12th international conference on network-based information systems (NBiS-2009), pp 424–431 9. Inoue T, Ikeda M, Enokido T, Aikebaier A, Takizawa M (2011) A power consumption model for storage-based applications. In: Proceedings of the fifth international conference on complex, intelligent, and software intensive systems (CISIS-2011)

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10. Dong H, Hussain FK, Chang E (2010) Semantic service retrieval and QoS measurement in the digital ecosystem environment. In: Proceedings of the intelligent and software intensive systems (CISIS), pp 153–160 11. Zhao G, Xuan K, Rahayu W, Taniar D, Safar M, Gavrilova M, Srinivasan B (2011) Voronoibased continuous k nearest neighbor search in mobile navigation. IEEE Trans Ind Electron 58(6):2247257

Part V

Multimedia and Information Security Technology

Forged Region Detection for Scanned Images Chang-Hee Choi, Min-Jeong Lee, Dai-Kyung Hyun and Heung-Kyu Lee

Abstract As analog media has been replaced with digital media, legal issues in digital media have been in the spot light. In this paper, we propose a detection method of forged regions scanned images. We analyze a specific noise in the scanned images from many scanners. A suspicious image is sliced into small nonoverlapping blocks. We extract the spectral noise from each block and identify the scanner model. In experiments, three synthesis images are examined to prove the performance of our scheme.







keywords Scanner identification Spectral noise Forged region Scanner noise

1 Introduction Recently, interests in digital image forensic have surged. The main reason is rapid increase in digital image acquisition devices such as camcorders, cameras, scanners, and smartphones. Another reason is that digital image editing tools are also C.-H. Choi (&)  M.-J. Lee  D.-K. Hyun  H.-K. Lee Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea e-mail: [email protected] M.-J. Lee e-mail: [email protected] D.-K. Hyun e-mail: [email protected] H.-K. Lee e-mail: hkleeg @mmc.kaist.ac.kr

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developed rapidly. Because of this trend, there have been emerged lots of forged documents such as fake passports and counterfeit notes by using digital devices [1]. Among them, we focus on forged image from scanners. There are many researches for scanner identification. Gloe et al. [2] analyzed scanner devices and used the intrinsic signature for identifying scanner model. They proposed two approaches to specify characteristics of scanner noise. One approach was to make a 2-D reference pattern describing entire scan area and another one was to make a line reference pattern by averaging the 2-D reference pattern. In their experiment, the 2-D reference pattern was better than the line reference pattern to identify scanner models. Choi et al. [3] used spectral noise in scanned images for scanner identification. They made a line reference pattern in the frequency domain and compared it with test images. Khanna et al. [4, 5] presented a method to identify the scanner model based on various features. They extracted many statistical features such as mean, median, mode, maximum, and minimum from noise of scanned images. They fed these feature sets to the support vector machine (SVM) for training. They also proposed a method to determine image source devices such as scanner, camera, and a computergenerated graphic [6]. Gou et al. [7] developed a method to identify scanner models based on many statistical features. The statistical features from neighborhood pixel prediction and noise distribution were added to the basic feature set. The SVM was used for training. McKay et al. [8] propose a method for identifying 4 devices: scanner, camera, computer-generated graphics, and cell phone camera. They used the features related with camera [9] and scanner [7]. Dirik et al. [10] used dust position information on flatbed. They made a 2-D reference pattern based on dust positions and compared it with scanned images. Their technique could discriminate the same scanner models if dust positions were different. In this paper, we propose a new method for detecting forged image region in scanned images. Cycles of DCT basis function of peak values in the reference pattern are calculated and stored for the reference peaks. We slice a suspicious image into small non-overlapping blocks. For each block, the spectral noise is extracted. We investigate the existence of reference peaks in blocks. In experiments, we show our method soundly detect forged image blocks of the suspicious image. The rest of the paper is organized as follows. Section 2 describes the scanner noise. In Sect. 3, we summarize our previous work [3] which extracts the spectral noise from scanned images. We also propose proper technique for small image blocks. Experimental results are shown in Sect. 4, and the conclusion and future works are presented in Sect. 5.

2 Various Types of Noise in Scanners An image sensor in a scanner makes various types of noise due to its imperfect manufacturing process [11, 12]. Figure 1 illustrates subsystems of an image sensor. This noise can be classified into two types. One type is a random noise which varies

Forged Region Detection for Scanned Images Photon Noise Dark Current Noise Fixed Pattern Noise Photo Response Nonuniformity Detector

Sense Node Capacitor

689 1/f Noise Amplifier Noise

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Quantification Noise AnalogDigital Converter

Fig. 1 Subsystems of an image sensor

from image to image. Due to this randomness, it cannot be used to identify scanners. Another type is a fixed pattern noise (FPN) for a specific device. It varies from pixel to pixel. Since frame averaging cannot reduce the FPN, this noise is good information to specify the scanner model. In the FPN, the spectral noise is a good feature for scanner identification [3]. Figure 2 shows an example of the spectral noise which is extracted from 40 scanned images using Canon Lide 200. Three vertical lines marked by red dot boxes in Fig. 2 represent the spectral noise for this scanner. To reduce the FPN, scanner manufacturers sometimes conduct calibration process at first-time installation. However, the spectral noise reappears after the physical or electronic impact to the scanner.

3 Proposed Method In this section, we summarize our previous work [3] and propose a detection method of forged regions in scanned images.

3.1 Extracting Spectral Noise To extract spectral noise from the scanned image, refining processes has to be performed as described in Fig. 3. First, 2-D array noise is extracted from scanned images by using de-noising filter. Unsharp filter in MATLAB is used for de-noising. Since most of the flatbed and handy scanners have linear image sensors, the spectral noise of all vertical lines in the scanned image is same as depicted in Fig. 4. We average the extracted 2-D noise image to reduce random noise and enhance the spectral noise along scanning direction. To detect periodicity of the spectral noise, we apply the discrete cosine transform (DCT) to averaged noise vector. To eliminate this unnecessary low frequency part, high pass filtering is conducted. Finally, we take the absolute operation and normalization to adjust the power of each scanner noise. More detailed description can be found in [3].

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Fig. 2 Example of a spectral noise: 40 scanned images, Canon Lide 200

Extracting Noise

Averaging noise

Appling DCT

High-pass Filtering

Absolute & Normalization

Fig. 3 Refining process

Fig. 4 Scanning noise along scanning direction

Non-noised cell

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1D CCD Cells Scanning direction Scanned image

3.2 Detecting the Synthesized Image Between the Scanned and Photographed Images We make synthesized image between the scanned image and photographed image. Figure 5 show an example of synthesized image. We find local maxima from the refined reference patterns RðkÞ (see [3]) using findpeaks function in MATLAB. Among these local maxima, we select the maximum value as the reference peak for the scanner. The cycle of the DCT basis function at the selected peak is C¼

2  LR ðxp  1Þ

ð1Þ

LR refers to the length of the reference pattern and xp refers to the index of the selected peak. We store this cycle information for the reference of each scanner. In the detection step, we slice the suspicious image into small non-overlapping blocks by size n  n: For each reference cycle, we calculate two peak positions in the DCT domain to consider the round error.     2n 2n ; xceil ðkÞ ¼ CEIL ð2Þ xfloor ðkÞ ¼ FLOOR Cþ1 Cþ1

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Synthesis (Cropping, Scaling, Rotation)

Fig. 5 Synthesis example: scanned image and photographed image. The image at left top is from scanner and the image at left bottom is from digital camera

For each block, spectral noise vector BðkÞ is extracted. Details of the detection algorithm is described in Algorithm 1.

Algorithm 1 Detecting the scanned image block in a suspicious image for all sub-block Blockði; jÞ in a suspicious image I(i,j)do for all t in LRðkÞ do Bm MEDIANðB½LRðtÞ  a; LRðtÞ þ aÞ  PeakðtÞ Bðxfloor ðtÞÞ þ Bðxceil ðtÞÞ =Bm end for if exist the peak value bigger than q in PeakðkÞ then Blockði; jÞ Scanned block end if end for

4 Experimental Results To make reference patterns, we prepared 4 scanners: Canoscan Lide 200, Canoscan N1240U, Epson Perfection 2400, and Epson Perfection V200. We used Epson Perfection 2400 scanner for test scanned images. All images were scaned at 300 DPI. Nikon D90, Canon EOS 500D were used for sample photographed images. In our experiment, we set the threshold q ¼ 6 and block size n ¼ 256: The door image marked by the red box in Fig. 6b was manipulated by cropping, rotation, scaling, hue value change, and intensity histogram modification. Figure 6d show the detection result. Non-scanned image block was considered by forged region, and it is filled with block color. In Fig. 7b, we cropped three ducks

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Fig. 6 Detection result of synthesis image 1. Detected block is filled with black color. a Scanned image: Epson Perfection 2400, b photographed image: Canon EOS 500D, c synthesis image, d detection result

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Fig. 7 Detection result of synthesis image 2. Detected block is filled with black color. a Scanned image: Epson Perfection 2400, b photographed image: Nikon D90, c synthesis image, d detection result

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Fig. 8 Detection result of synthesis image 3. Detected block is filled with black color. a Scanned image: Epson Perfection 2400, b photographed image: Canon EOS 500D, c synthesis image, d detection result

and applied rotation and scaling. As shown in Fig. 7d, the detection result was reliable for small forged region. In Fig. 8, cars which are similar objects were replaced. The detection result was reliable for this synthesis image.

5 Conclusion In this paper, we propose a method to detect the forged image region in scanned image. We use a spectral noise researched in our previous work [3]. The cycles of DCT basis function of peak values in reference pattern are calculated and stored for references. Three experimental results show that our method soundly detects forged region in scanned images. For future works, it is worth to improve our method for smaller block size. Since a power of spectral noise is weak, to develop an algorithm against JPEG compression will be valuable. Acknowledgments This research project was supported by Ministry of Culture, Sports and Tourism(MCST) and from Korea Copyright Commission in 2011.

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References 1. Khanna N, Mikkilineni AK, Martone AF, Ali GN, Chiu GT, Allebach JP, Delp EJ (2006) A survey of forensic characterization methods for physical devices. Digit Investig 3:1728 2. Gloe T, Franz E, Winkler A (2007) Forensics for flatbed scanners. In: Proceedings of the SPIE security, steganography, and watermarking of multimedia contents IX, vol 6505. p 65051I. San Jose, CA 3. Choi CH, Lee MJ, Lee HK (2010) Scanner identification using spectral noise in the frequency domain. In: Proceedings of the international conference on image processing, pp 2121–2124, Hong Kong 4. Khanna N, Mikkilineni AK, Chiu GT, Allebach JP, Delp EJ (2007) Scanner identification using sensor pattern noise. In: Proceedings of the SPIE security, steganography, and watermarking of multimedia contents IX, vol 6505. p 65051K. San Jose, CA 5. Khanna N, Mikkilineni AK, Delp EJ (2009) Scanner identification using feature-based processing and analysis. IEEE Trans Inf Forensics Secur 4(1):123–139 6. Khanna N, Chiu GT, Allebach JP, Delp EJ (2008) Forensic techniques for classifying scanner, computer generated and digital camera images. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 1653–1656. Las Vegas, NV 7. Gou H, Swaminathan A, Wu M (2007) Robust scanner identification based on noise features. In: Proceedings of the SPIE security, steganography, and watermarking of multimedia contents IX, vol 6505. p 65050S. San Jose, CA 8. McKay C, Swaminathan A, Gou H, Wu M (2008) Image acquisition forensics: forensic analysis to identify imaging source. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, pp 1657–1660. Las Vegas, NV 9. Swaminathan A, Wu M, Ray Liu KJ (2007) Nonintrusive component forensics of visual sensors using output images. IEEE Trans Inf Forensics Secur 2(1):91–106 10. Dirik AE, Sencar HT, Memon N (2009) Flatbed scanner identification based on dust and scratches over scanner platen. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 1385–1388. Taipei, Taiwan 11. Holst GC, Lomheim TS (2007) CMOS/CCD Sensors and camera systems. SPIE-international society for optical engine, USA 12. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214

Camcorder Identification for Heavily Compressed Low Resolution Videos Dai-Kyung Hyun, Chang-Hee Choi and Heung-Kyu Lee

Abstract In this paper, we propose a novel camcorder identification method based on photo-response non-uniformity (PRNU) to perform well with heavily compressed low-resolution videos. The existing methods calculate normalizedcross-correlation (NCC) to measure the similarity between two PRNUs. Since the NCC is very sensitive to noises, these methods show low accuracy for heavily compressed low resolution videos. The proposed method calculates the similarity by minimum average correlation energy (MACE) filter. Our method shows that it identifies source device more accurately than existing algorithm. Keywords Sensor pattern noise Camcorder identification

 Minimum average correlation energy filter 

1 Introduction Ever since the replacement of traditional analog imaging devices by their digital counterparts, there has been an increasing demand for a series of reliable digital forensic techniques. Establishing the origin of digital video taken by digital camcorder is one of the most important forensic techniques. These camcorder D.-K. Hyun (&)  C.-H. Choi  H.-K. Lee Department of Computer Science, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea e-mail: [email protected] C.-H. Choi e-mail: [email protected] H.-K. Lee e-mail: [email protected]

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identification algorithms are used to arresting child pornography filmmaker or movie pirates. Among the algorithms that have been developed in this area, the algorithms based on PRNU are most actively being studied [1–4]. The PRNU is first introduced in [5] and generate by different sensitivity of pixels to light. Alles et al. proposed a PRNU extracting method for low resolution heavily compressed images [6]. They removed scene content, stochastic noise and JPEG edge artifacts from residual images. Li et al. [7] proposed a novel approach for attenuating the influence of details from scenes on PRNUs in order to improve the accuracy of the identifier. These algorithms calculate NCC to measure the similarity between reference PRNU (RPRNU) and test PRNU (TPRNU). These algorithms show low accuracy for compressed videos, because NCC is very sensitive to noises. So, Chen et al. [8] removed the noises before calculating NCC. However, in case of heavily compressed low resolution videos, which have the weak energy of PRNU, this method shows low identification accuracy because the de-noising process weakens the energy of the PRNU. In this paper, we propose new camcorder identification method using the MACE filter [9]. Since the MACE filter is robust to noises, the proposed method show high identification accuracy without de-noising process. The rest of this paper is as follows. We present our method in Sect. 2. Experimental results are then exhibited in Sects. 3 and 4 concludes.

2 Proposed Camcorder Identification Method The existing methods measure the similarity by NCC. The correlation plane1 calculated by NCC is easily distorted by noises as seen in Fig. 2a. So, Chen’s method removed noise before calculating the correlation plane. However this de-noising process weakens the energy of the PRNU. Thus, in case of heavily compressed low resolution videos, which have weak PRNU energy, this method shows low identification accuracy. Therefore, to increase the detection accuracy, we propose a new method by MACE filter. The proposed method identifies source camcorder as shown in Fig. 1. We extract the RPRNU and the TPRNU from the reference video and the test video, respectively. The RPRNU is transformed to the form of MACE filter to enhance the detection accuracy. The MACE filter is made for not only minimizing the energy of correlation plane but also ensuring a sharp correlation peak at the origin. Here, we describe the RSPN as a column vector r of dimensionality d equal to the number of the pixels in the RSPN, i.e., r ¼ ½rð1Þ; rð2Þ; . . .; rðdÞT :

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Fig. 1 The proposed camcorder identification procedure

Fig. 2 The correlation plane calculated from two videos extracted from the same camcorder : (a) the correlation plane calculated by NCC (PCE=15.40), and (b) the correlation plane calculated by MACE filter (PCE=284.04)

We denote the correlation function of r(n) with the filter h(n) by g(n): gðnÞ ¼ rðnÞ  hðnÞ:

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where the superscript + denotes the conjugate transpose of a complex vector, and D is a diagonal matrix of size d  d whose diagonal elements are the magnitude square of the associated element of R: Dðk; kÞ ¼ jRðkÞj2 :

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3 Experimental Results We conducted experiments with 7 camcorder as seen in Table 1. With these camcorders, we prepared 91 high quality videos (full resolution, 100 s recoding time) of various indoor and outdoor scenes. All videos were recorded with the fully automatic settings. We extracted RPRNU from a blue-sky. Table 1 Camcorder models used in the experiments Brand Model

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Table 2 Identification rate according to each bit-rate and resolution Resolution Bit-rate Identification rate (%) Proposed

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In order to measure the identification accuracy of the proposed method, we reencoded 91 videos by editing bit-rate and resolution. We carried out the proposed method for every test videos and calculated the identification rates according to each bit-rate and resolution. We also tested it with Chen’s algorithm for comparison. The identification threshold spce have been determined empirically for defined false positive rates (FPR) by applying the methods to all test videos. Table 2 is test results on the identification rate at FPR = 1%. The experimental result shows that proposed method had better performance compared to Chen’s algorithm. Particularly in the case of resolution of 64  64 and bit-rate of 500bps, which have the weakest PRNU energy, it showed 10% better performance than Chen’s algorithm. Figures 3, 4, 5 and 6 depicts ROC curves compiled from all test videos. The ROC curves indicate that the proposed algorithm is indeed more robust to false alarms.

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Fig. 4 ROC curves for test videos with 1000bps : (a) in case of videos with resolution of 64  64; and (b) in case of videos with resolution of 128  128

Fig. 5 ROC curves for test videos with 1500bps : (a) in case of videos with resolution of 64  64; and (b) in case of videos with resolution of 128  128

Fig. 6 ROC curves for test videos with 2000bps : (a) in case of videos with resolution of 64  64; and (b) in case of videos with resolution of 128  128

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4 Conclusion The camcorder identification algorithms are used to arresting child pornography filmmaker or movie pirates. Among the algorithms that have been developed in this area, the algorithms based on PRNU are most actively being studied. The existing methods based on PRNU calculate NCC to calculate similarity between two PRNUs. Since the NCC is very sensitive to noises, these methods show low accuracy for heavily compressed low resolution videos. In this paper, we proposed a novel camcorder identification method by the MACE filter. Since the MACE filter is robust to noises, the proposed method shows high identification accuracy without de-noising process. The experimental results showed up to maximum 10% higher identification rates compared to existing algorithms. Acknowledgments This research was supported by WCU (World Class University) program (Project No: R31-30007) and NRL (National Research Lab) program (No. R0A-2007-000-200230) under the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology of Korea

References 1. Goljan M, Fridrich J (2008) Camera identification from cropped and scaled images. In: The international society for optical engineering (SPIE) 2. Goljan M, Fridrich J, Filler T (2009) Large scale test of sensor fingerprint camera identification. In: The international society for optical engineering (SPIE) 3. Goljan M, Fridrich J, Filler T (2007) Digital imaging sensor identification (further study). In: The international society for optical engineering (SPIE) 4. Houten W, Geradts Z (2009) Using sensor noise to identify low resolution compressed videos from youtube. In: Proceedings of the 3rd international workshop on computational forensics 5. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214 6. Wang EJA, Geradts ZJMH, Veenman CJ (2008) Source camera identification for low resolution heavily compressed images. In: International conference on computational sciences and its applications 7. Li CT (2010) Source camera identification using enhanced sensor pattern noise. IEEE Signal Process Mag 5(2):280–287 8. Chen M, Fridrich J, Miroslav Goljan JL (2007) Source digital camcorder identification using sensor photo response non-uniformity. In: The international society for optical engineering (SPIE) 9. Mahalanobis A, Kumar BVKV, Casasent D (1987) Minimum average correlation energy filters. Opt Soc Am 26(17):3633–3640 10. Kerekes RA, Kumar BV (2008) Selecting a composite correlation filter design: a survey and comparative study. Opt Eng 47(6):67202

Block-based Image Authentication Algorithm Using Reversible Watermarking Dong-Gyu Yeo and Hae-Yeoun Lee

Abstract For the application which need high-confidential images, reversible watermarking method can be regarded as an effective mean to ensure the integrity of received images. Many researches about contents authenticating cannot recover the original image. Reversible watermarking inserts watermark into digital media in such a way that visual transparency is preserved, which enables the restoration of the original media from the watermarked one without any loss of media quality. It has various applications, where high capacity and high visual quality are major requirements. This paper presents a new block-based image authentication algorithm using differential histogram-based reversible watermarking which is able to detect malicious tampering. To judge whether malformed or not, the presented scheme use the DCT-based authentication feature. Through experiments using multiple kinds of test images, we prove that the presented algorithm provides over 97% authenticating rate, complete reversibility, and higher visual quality, while maintaining the induced-distortion low. Keywords Image authentication histogram




Reversible watermarking




Differential

D.-G. Yeo (&)
H.-Y. Lee Kumoh National Institute of Technology, Gumi, Republic of Korea e-mail: [email protected] H.-Y. Lee e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_69, Ó Springer Science+Business Media B.V. 2012

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1 Introduction Highly reliable, and definitely not tampered, images are required in such areas as medical services using patients’ medical images, the management of image data for surveillance camera, military and satellite image, art works, and the remote area telemetry. If a tampered image is used in these areas, it can cause human and physical damages, and lose its effectiveness as a legal evidence. To authenticate the integrity of image data, the received image should be verified as not being tampered, and be processed on a nearly real time basis. In addition, the security condition should be also met, so that the attackers cannot easily speculate or reproduce it. Digital watermarking is a technique that invisibly embeds metadata of the original content or confidential information, such as authentication code and copyright information for the verification of integrity, into digital contents. This technology is used for various purposes, including ownership verification, copyright protection, monitoring of broadcasting, and contents authentication, etc. As the previous studies on the authentication of contents have focused on the robustness of the embedded watermarks, many of the originals could not be restored after removing the watermarks and the accuracy for tampering was not high enough as well. Since the reversible watermarking, which is a fragile watermarking, can be completely restored to the original contents after removing the message in the watermarked contents, it can be valuably used for the verification of tampering and the protection of copyright as well as the authentication of content integrity. When authenticating the integrity of contents, detecting the tampered domain can be more useful in real situation than determining whether the entire contents are tampered. This paper proposes a reversible watermarking-based image content authentication technique to authenticate the integrity of image contents and to detect forged domain. The proposed technique performs the authentication of integrity by dividing image into a small size of blocks and embedding watermarks in each block unit. This paper is organized as follows. Chapter 2 discusses the algorithmic difference that verifies the integrity using the existing major studies in the area of image authentication. It also describes the authentication code for image authentication. Chapter 3 describes a block-based image authentication reversible watermarking algorithm, which is proposed in this paper. Experiment and performance analysis are described in Chap. 4. Chapter 5 presents our conclusion.

2 Related Works 2.1 Image Authentication Algorithm Zhou et al. [1] and Rajendra et al. [2] proposed an LSB-based algorithm to embed information for the verification of image originality. The main drawback of the LSB-based algorithm is that security can be easily collapsed. If a simple technique

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is used, where the LSB of image is permuted with data to be embedded, it is easy to tamper only by copying the LSB data of the authenticated image to the forged images. Yu et al. [3] embedded data using average quantization technique in the wavelet domain, instead of modifying the LSB of image. These methods [1–3] are not highly utilized because they determine the tampering of the whole images only for once. The studies [4–6] localized the detection of tampering by dividing the images into certain domains and embedding the authentication codes. Lin et al. [7] and Lee et al. [8] proposed a hierarchical data hiding technique for the detection and restoration of image tampering, using a method that compares the average brightness value between the adjacent 4 9 4 blocks and checks the parity. However, the methods [1–8] are non-reversible methods in that the original image cannot be completely restored after removing the embedded authentication data, so it is hard to apply them to the areas where the quality of image is an important factor. For the complete restoration of the original image after content authentication, a reversible watermarking algorithm is being used. In order to provide perceptual transparency and complete reversibility, studies on the reversible watermarking embed message using different properties of image. Celik et al. [9] embedded messages in empty space after compressing the bit-plane using lossless compression technique, and Lee et al. [10] embedded messages in the transform coefficient in the frequency domain. In addition, Tian [11] and Thodi et al. [12] used the differential value expansion method which expands and embeds the image features of the original images. Recently, studies on a histogram-based methods [13–17] are actively being underway, which can obtain high embedding capacity without causing any complicated calculation.

2.2 Image Authentication Code The previous studies on the block-based image authentication have used various kinds of authentication codes to differentiate the features of image blocks, including mean value, parity value, checksum, hash value, etc. Since, however, these values are small in size, there exists the possibility of false positive. If, for example, the mean value of 8 bit is used as authentication code, there is a probability of 1/256 for the false positive by accident. If the tampering activities of the attacker are insignificant, the probability of false positive becomes higher. Therefore, the authentication code should be long enough to minimize the probability of false positive. However, because excessively long authentication code can exceed the maximum embedding capacity when embedding messages with watermarking, the size should be appropriate according to watermarking algorithm. In the previous studies, the authentication codes were not only short, but also could be used only for the purpose of authentication. If authentication code can have the additional usages on top of the simple authentication function, its utility will become higher. In this paper, the DCT coefficients for image blocks are used as authentication code. This not only significantly reduces the probability of

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false positive by creating a sole feature with the sufficient length for authentication, but also can be expanded as a technology for the restoration of damaged domain in the future studies.

3 Block-Based Authentication Algorithm Using Reversible Watermarking In this paper, the upper DCT coefficients of 8 bytes are used as authentication code to carry out the block-based authentication of images. As for the message embedding and detection algorithm, a modified reversible watermarking algorithm [17] was used using a progressive differential histogram shifting.

3.1 Block-Based Image Authentication Algorithm To perform the block-based authentication, the authentication code should be embedded in each block with watermarking technique during the embedding process. As presented in Fig. 1, the original image is first separated into RGB channel. Then the separated channel is divided as a block size of 16 9 16, and the authentication code is generated through the processes of DCT and quantization for each block. The pixel value of the block is then renewed by embedding the authentication code using the progressive differential histogram method. If this process is performed by each channel and merged again, the images embedded with the watermark can be obtained. During the detection process, an authentication process is required after the distribution of the images, in which the authentication code is embedded with the watermarking technique, to judge the damage by attackers and external factors. The watermarked images are divided into block units by separating them into the RGB channels. The original image blocks are restored by detecting the authentication code and removing the watermarks for each divided block, using the progressive differential histogram method, and the entire original image is restored by repeating the this process for each channel. Because the original authentication code is the feature extracted from the original image, the feature is extracted through the same processes of DCT and quantization for the restored image, and the secret key and XOR operations are performed. Then, the authentication for the damage is performed by comparing the detected authentication code and extracted authentication code by block unit. If there is any damaged block, the authentication code is not matched, thus can be judged as tampering. The entire process is diagramed in Fig. 2.

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3.2 Authentication Code Generation Algorithm To achieve a high performance authentication rate, the upper DCT coefficients for each block are selected and used as features for the authentication, because the sufficient length of authentication code should be used to minimize the probability of false positive. The optimal length of authentication code obtained from the experiment is 8 bytes. The probability of false positive for this is rare because the probability is 1/(2^64), which is 1/(1.84467E ? 19) = 5.42101E-20. Because the length of authentication code by block that should be embedded is 64 bits, it should be checked whether the embedding capacity of the block is sufficient. An experiment found that dividing the image by 8 9 8 block is not appropriate due to the considerable lack of embedding space. In this paper, therefore, the authentication code is generated, as shown in Fig. 3, by selecting the upper 8 bytes coefficient after dividing the original image into a block with the size of 16 9 16, and sampling each block again in the mean value with 8 9 8 size, and then performing the processes of DCT and quantization. In this case, if the attacker completely cuts the domain bigger than the block, rather than changing the part of color information of the image, an appropriate authentication cannot be performed because the authentication code detected from the block and the calculated authentication code will be the same as zero. To prevent this problem, an XOR operation is performed between the selected DCT authentication code and the secret key, which is shared with the receiver side of the image, and then a final authentication code is generated.

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Table 1 Authentication experimental results Image PSNR Payload Embedding time Authentication time Authentication rate (dB) (bpp) (s) (s) (%) Airplane Baboon House Lena Peppers Sailboat Splash Tiffany Average

(a)

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0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

0.671 0.733 0.686 0.687 0.682 0.686 0.655 0.686 0.69

0.640 0.640 0.639 0.656 0.655 0.640 0.640 0.640 0.64

97.38 98.68 96.04 97.89 98.12 97.60 97.84 98.07 97.70

(b)

Fig. 4 House image authentication result; (a) Attacked image, (b) Authenticated image

4 Experiment and Performance Evaluation In this paper, eight 8 bits color 512 9 512 images were employed for the experiment, and the typical ‘‘Brightness’’, ‘‘Darkness’’, ‘‘Manipulating’’, ‘‘Blurring’’ and ‘‘Copy and Paste’’ attacks were experimented. The result of watermarking authentication code for the experiment images is shown in Table 1. PSNR, a visual quality, is in a good condition with the average of 36 dB. As demonstrated in this result, the performance of the proposed image authentication reversible watermarking algorithm slightly varies depending on the image features. For example, the image having a low frequency component, such as Splash, can have higher embedding capacity while maintaining high quality, whereas the image with more of high frequency, such as Baboon, can have

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relatively low quality. The embedding capacity is the same as 0.75 bpp, because 8 bytes with 16 9 16 block are embedded in each RGB channel. The embedding capacity for a single channel is 0.25 bpp. The time spent for the embedding process is 0.69 s on average, and the time for detection and authentication is 0.64 s. The authentication rate was 97.7%, making most of detection for damaged blocks possible. Figure 4 shows the result of detection for tampering when attacking the House image in real situation.

5 Conclusion This paper proposed a technique as an algorithm to verify the integrity of image contents to authenticate damages by attackers by embedding authentication code using DCT coefficient in each block by dividing the original image into block unit. It enhanced the accuracy by authenticating each RGB color channel of color images. The result from the experiment using the representing images found that the embedding and detection/authentication of authentication code at a high speed is possible while maintaining the visual quality. The feature usage method through 16 9 16 block-based DCT coefficient, which is used as authentication code in this paper, not only significantly reduce the probability of false positive, but also can be used to restore the damaged block, and be expanded as the video authentication technology. As with the various previous studies, this study has a drawback in that tampering cannot be detected when the tampering attack for image is insignificant. For example, when adjusting the brightness of only a single pixel in the block by 1, tampering cannot be detected because there is no change in DCT value. Therefore, this drawback should be addressed through the continued studies in the future. Acknowledgements This research project was supported by Ministry of Culture, Sports and Tourism(MCST) and from Korea Copyright Commission in 2011.

References 1. Zhou XQ, Huang HK, Lou SL (2001) Authenticity and integrity of digital mammography images. IEEE Trans Med Imaging 20:784–791 2. Rajendra AU, Anand D, Subbanna BP, Niranjan UC (2001) Compact storage of medical images with patient information. IEEE Trans Inf Technol Biomed 5:320–323 3. Yu G-J, Lu C-S, Liao H-YM (2001) Mean-quantization-based fragile watermarking for image authentication. Opt Eng 40:1396–1408 4. Li K-F, Chen T-S, Wu S-C (2001) Image tamper detection and recovery system based on discrete wavelet transform. IEEE Pacific Rim Conf Commun, Comput Signal Process 1: 164–167 5. Lin P-L, Huang P-W, Peng A-W (2004) A fragile watermarking scheme for image authentication with localization and recovery. In: IEEE 6th international symposium on multimedia software engineering, pp 146–153

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6. Wang C-L, Hwang R-H, Chen T-S, Lee H-Y (2005) Detecting and restoring system of tampered image based on discrete wavelet transformation and block truncation coding. In: 19th international conference on advanced information networking and applications 7. Lin PL, Hsieh CK, Huang PW (2005) A hierarchical digital watermarking method for image tamper detection and recovery. Pattern Recognit 38:2519–2529 8. Lee T-Y, Lin SD (2008) Dual watermark for image tamper detection and recovery. Pattern Recognit 41:3497–3506 9. Celik MU, Sharma G, Tekalp AM, Saber E (2005) Lossless generalized-LSB data embedding. IEEE Trans Image Process 14:253–266 10. Lee S, Yoo CD, Kalker T (2007) Reversible image watermarking based on integer-to-integer wavelet transform. IEEE Trans Inf Forensics Secur 2:321–330 11. Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans Circuits Syst Video Technol 13:890–896 12. Thodi DM, Rodriguez JJ (2007) Expansion embedding techniques for reversible watermarking. IEEE Trans Image Process 16:721–730 13. Ni Z, Shi Y-Q, Ansari N, Su W (2006) Reversible data hiding. IEEE Trans Circuits Syst Video Technol 16:354–362 14. Kuo W-C, Jiang D-J, Huang Y-C (2007) Reversible data hiding based on histogram. Int Conf Intell Comput, LNAI 4682:1152–161 Springer-Verlag, Qing Dao 15. Tsai P, Hu Y-C, Yeh H-L (2009) Reversible image hiding scheme using predictive coding and histogram shifting. Signal Process 89:1129–1143 16. Kim K-S, Lee M-J, Lee H-Y, Lee H-K (2009) Reversible data hiding exploiting spatial correlation between sub-sampled images. Pattern Recognit 42:3083–3096 17. Yeo D-G, Lee H-Y, Kim BM (2011) High capacity reversible watermarking using differential histogram shifting and predicted error compensation. J Electron Imaging, SPIE 20

Digital Forgery Detection Scheme Incorporating Imaging Device Characteristics Using Wiener Filter Jae Youn Shim and Seong-Whan Kim

Abstract Advent of digital cameras and photo editing software allows digital images easily manipulated and altered. Although accurate forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. As previous research works, we analyzed digital camera image processing schemes, and identified what kind of imaging device characteristics can be unique and how to identify them. To exploit the imaging device characteristics, we perform Wiener filter to extract the unique feature of imaging device. After we identified the device characteristics, we perform EM based forensic scheme for the domain. We design an overlapped Wiener filter based forgery detection scheme for each image blocks to test evidence of forgery in a specific image. Our experimental results show that our forgery detection performance achieved more robustness on JPEG compression (quality factor set to 98). Keywords Digital image forensics

 Image noise  Wiener filter  EM algorithm

1 Introduction Due to the development of digital imaging technology, we can easily obtain a digital image with various devices like smart phone. When creating the digital image using digital camera module, it include signal processing and device features. Digital image can be easily edited with image editing software. Fake image J. Y. Shim (&)  S.-W. Kim School of Computer Science, University of Seoul, Seoul, Korea e-mail: [email protected] S.-W. Kim e-mail: [email protected]

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can make some problem. For example, someone won the Pusan National Tourism Photo Contest in 2009 using forgery photo, which leads to disqualification. Recently, concept of digital image forensics has been proposed to address these issues. The area of digital image forensics can be broadly divided into two branches [1]. The first one is to determine whether a specific digital image has undergone malicious post-processing or tampering. This typed of forensic algorithms are designed to unveil either characteristic traces of image processing operations, or to verify the integrity of particular features introduced in a typical image acquisition process. The second type of digital image forensics is on image source identification, which is obviously based on specific characteristics of the image acquisition device or technology. As forensic algorithms basically rely on particular statistical features, which can be understood as a ‘‘natural’’ and inherent watermark, digital image forensics does not require any prior knowledge of the original image. There have been previous research works for identifying image forgeries such as re-sampling [2], copy and paste [3], double compression [4, 5], etc. Also, there have been previous research works to trace camera models or imaging device sources with the evidence of pixel defects [6], image sensor noise [7], photo-response non-uniformity noise [8], color pattern array interpolated [9]. Popescu and Farid proposed re-sampling detector using EM Algorithm [2]. Luk0 aˇs, Fridrich and Goljan proposed digital camera identification detector using PRNU (photo-response non-uniformity noise) [8]. Thomas Gloe, Matthias Kirchner, Antje Winkler and Rainer Böhme have discussed an approach for image manipulations undetectable to the re-sampling detector. Popescu and Farid have demonstrated how to suppress a reliable image source identification with Luk0 aˇs et al.’s method using Sobel and median attack [10]. Popescu and Farid indicate that it is very hard to detect re-sampling on low quality JPEG compression [2]. In this paper, we address the digital forgery detection scheme which is robust on JPEG compression. In Sect. 2, we review previous digital forensic schemes which are based on EM algorithm. In Sect. 3, we present our digital forensic analysis approach which uses noisy like components by ICA component analysis. We show our experimental results in Sect. 4, and conclude with summarization in Sect. 5.

2 Related Work In this section, we review the digital image processing schemes in imaging device, and review the previous research works to identify forgeries especially in images.

2.1 Digital Camera Image Processing Photo-detectors on CCD (Charge Coupled Device) image sensor save the light amplitude, which leads to gray images. Therefore, we require another changing

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Fig. 1 CFA (color filter array)

method for making color image, which is CFA. CFA is a mosaic of tiny color filters placed over the pixel sensors of an image sensor to capture color information. Most CFA are arranged with Bayer filter style as shown in Fig. 1 Bayer filter mosaic arranges RGB color filters on a square grid of photo sensors. The filter pattern is composed of green (50%), red (25%), and blue (25%) with GRGB or RGGB styles. The CFA consists of an array of color sensors, each of which captures the corresponding color of the real-world scene at an appropriate pixel location. Since only a single color sample is recorded at each pixel location, the other two color samples must be estimated from the neighboring samples in order to obtain a three-channel color image as shown in Fig. 2. Each sensor gets color information with interpolation of neighboring color information. General interpolation scheme includes (1) bilinear based method and (2) gradient based method. Bilinear based method combines neighborhood information with special filters, and gradient based method combines vertical and horizontal neighborhood information. Each of these cell phone camera components, such as color filter array and color interpolation employ a particular set of algorithms with an appropriate set of parameters to modify the input scene. Each of these cell phone camera components, such as color filter array and color interpolation employ a particular set of algorithms with an appropriate set of parameters to modify the input scene.

2.2 Previous Forgery Detection Schemes Previous research works including [2] employs the EM (expectation/maximization) algorithm [11] to simultaneously estimate a set of periodic samples that are correlated to their neighbors, and the specific form of these correlations. They make estimates map from samples model and outlier model. Using this estimates map, we can detect outlier model (image forgeries), which leads to demonstrate image forgeries. The EM algorithm is a two-step iterative algorithm; (1) E-step, the probability that each sample belongs to each model is estimated and (2) Mstep, the specific form of the correlations between samples is estimated. M-step

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Fig. 2 Digital camera image processing

estimates maximum likelihood of data using E-step data. E-step estimate the probability of each sample, obtained using Bayes’ rule. Using EM algorithm, we make estimates map from estimated image’s maximum likelihood. 2D-Fourier transform is applied on estimates map to find pattern peak (autocorrelation of CFA pattern) of image. They use that information for detecting image forgeries. As shown in (a) of Fig. 3, we perform EM and Fourier transforms on original non-forgery images, and we preserve autocorrelation peak of CFA pattern. However, if we perform the same procedure on forgery images, we cannot preserve the pattern layout, which means that the principal axis is skewed. From this observation, we can identify the rotation alterations on original images using EM algorithm.

3 Design of Forgery Detection Scheme In this paper, we use Wiener filter on target images to extract the signal which reflects the imaging device specific characteristics, and we perform EM based digital forensic scheme for the signal. Figure 4 shows the overall architecture of our scheme. For the target images, we perform Wiener filter based de-noising, and perform EM algorithm on the scaled noise images. Finally, we investigate the peak pattern layout to determine forgery for the images.

Fig. 3 Previous forgery detection scheme (Image, EM, Fourier Transform of EM) for nonforgery (a) and forgery (rotation) Images b

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Fig. 4 Overall system architecture

3.1 Identification of Imaging Device Characteristics First we use de-noise filter to cell phone camera image for identifying imaging device characteristics such as image sensor pattern noises. We uses de-noise filter Wiener filter. From the Bayesian viewpoint, Wiener filter is an inference method which computes Maximum Likely (ML) estimates of image signal given the noise variance. Wiener filter assumes Gaussian distribution for both original image and noise. Image f(x, y) has original signals s(x, y) and noises n(x, y). This process can be sped up by suppressing the scene content from the image, which can be achieved using a de-noising filtered image s‘(x, y) and the noise residuals n‘(x, y). fðx; yÞ ¼ sðx; yÞ þ nðx; yÞ

ð1Þ

s‘ðx; yÞ ¼ gðx; yÞ  ½sðx; yÞ þ nðx; yÞ

ð2Þ

n‘ðx; yÞ ¼ fðx; yÞ  s‘ðx; yÞ

ð3Þ

We use this n‘(x, y) for estimates map, that includes noise and edge. We amplified the noise n‘(x, y) with scale factor set to 50.

3.2 Forgery Detection We divide the image into blocks (set block size as 64 9 64), compute estimation map on each block using traditional EM algorithms [2], and detect image peak pattern using Fourier transform of each blocks’ estimation map. To increase the robustness and detection rate under various forgery cases (various forgery image size), we perform overlapped forgery detection over neighboring blocks as shown in Fig. 5. Black blocks mean checking area using EM algorithm. We confirm the each non-forgeries image blocks shows similar peak pattern Fig. 5 down shows characteristic peak from Fig. 5 up. It means any block images form same image have similar peaks pattern. This peak pattern is special feature data of image. Figure 6 shows peaks pattern of block form rotated image. It also blocks shows similar peak pattern.

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Fig. 5 Overlapped (50%) forgery detection scheme: peak pattern in the Fourier transform of original image blocks shows similar layout

4 Experimental Results In this paper, we experimented with uncompressed and compressed images. For uncompressed images, we use TIFF image. For compressed images, we use 98 quality JPEG images. We proposed 3 9 3 size coefficients filter for estimates map. We detect image forgeries on green channel (Y) of images for image modeling and MATLAB 7.9.0 for system processing.

4.1 Identification of Imaging Device Characteristics Figure 7 shows the de-noising process for the original image as shown in Fig. 7a, b and c shows the Wiener filter based de-noised image and the difference image of Fig. 7a, b, respectively. The difference image reflects the imaging device characteristics. To strengthen the characteristics, we amplified the difference using multiplication factor 50 as shown in Fig. 7d.

Fig. 6 Peak pattern in the Fourier transform of forgery (rotation) image blocks shows similar layout

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Fig. 7 a Forgery image (b) de-noised image (c) noise: (a–b), (d) up- scaled noise image c

4.2 Forgery Detection for Uncompressed image In this case previous system and our system have good detection accuracy. Figure 8 shows result of previous and our forgery detection system. Figure 8a is form Fig. 7a, Fig. 8c is from Fig. 7c and Fig. 8d is from Fig. 7d, they process with EM algorithm and Fourier transform. In Fig. 8b we checked de-noising image form Fig. 7b with forgery detection system, but it doesn’t have any special forgery detection information peak. Figure 8b shows forgery detection system using de-noising image result.

4.3 Forgery Detection After JPEG Compressed Image In this case, we use JPEG compression (quality factor set to 98). For the compressed domain, previous schemes does not show good detection rate. However, our experimental results show that our Wiener filter based noise magnification scheme detects forgery with high intelligible peak layout as shown in Fig. 9d. The reason of high intelligible peak layout is that the Wiener filter based noise does

Fig. 8 a c d Peak pattern layout of Fourier transform on Fig. 7a, c and d shows clear forgery evidence, (b) Peak pattern layout of Fourier transform on Fig. 7b shows weak forgery evidence

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Fig. 9 Forgery detection result after JPEG compression (quality factor set to 98): (a, b, c) Peak pattern layout of Fourier transform on Fig. 7a, b and c shows weak forgery evidence, (d) Peak pattern layout of Fourier transform on Fig. 7d shows clear forgery evidence

contain unique imaging device characteristics which cannot be altered with digital image forgeries. Figure 10 and Fig. 11 show the experimental results for the two image sets (source un-tampered image with forgery sub-images). Figure 10 uses bowls image (un-tampered), tampered with a cup image. To forger images, we should rotate the cup images with scaling. Similarly, Fig. 11 uses car image (un-tampered), tampered with a car plate image. We also use sophisticated rotation and scaling to avoid forgery detection.

5 Conclusion In this paper, our system is able to detect a rotated image. We study previous forgery detection system scheme and find previous weak point of previous system and make better system, especially compression. We have shown the efficacy of this approach to detecting traces of digital tampering in lossless and lossy compressed images. Now days, various fields demand digital image forensics technique and forgery technique will be evolve with various system. We need more researches and passion for digital forensics process development to defend.

Fig. 10 Original and forgery (rotation) images peak pattern layout for original image blocks, peak pattern layout for forgery blocks

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Fig. 11 Original and forgery (rotation) images, peak pattern layout for original image blocks, peak pattern layout for forgery blocks

Acknowledgments This research project was supported by Ministry of Culture, Sports and Tourism (MCST) and from Korea Copyright Commission in 2011.

References 1. Ng T-T, Chang S-F, Lin C-Y, Sun Q (2006) Passive-blind image forensics. In: Zeng W, Yu H, Lin C-Y (eds) Multimedia security technologies for digital rights. Academic Press, New York 2. Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans on Signal Process 53(2):758–767 3. Fridrich J, Soukal D, Luk0 aˇs J (2003) Detection of copy-move forgery in digital images. In: Proceedings of the digital forensics research workshop, Columbus, Aug 2003 4. Fan Z, de Queiroz RL (2003) Identification of bitmap compression history: Jpeg detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235 5. Luk0 aˇs J, Fridrich J (2003) Estimation of primary quantization matrix in double compressed JPEG images. In: Proceedings of the Digital forensics research workshop (DFRWS 2003), Cleveland, Aug 2003 6. Geradts ZJ, Bijhold J, Kieft M, Kurosawa K, Kuroki K, Saitoh N (2001) Methods for identification of images acquired with digital cameras. In: Proceedings of SPIE, enabling technologies for law enforcement and security, vol 4232, pp 505–512 Feb. 2001 7. Kurosawa K, Kuroki K, Saitoh N (1999) Ccd fingerprint method–identification of a video camera from videotaped images. Proc IEEE Int Conf Image Process 3:537–540 8. Luk0 aˇs J, Fridrich J, Goljan M (2005) Detecting digital image forgeries using sensor pattern noise. In: Said A, Apostolopoulos JG (eds) Proceedings of the SPIE: image and video communications and processing, 2005, vol 5685. SPIE Press, Bellingham, pp 249–260 9. Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959 10. Gloe T, Kirchner M, Winkler A, Böhme R (2007) Can we trust digital image forensics? MULTIMEDIA ‘07 Proceedings of the 15th international conference on multimedia, pp 78–86 11. Dempster A, Laird N, Rubin D (1977) Maximum lilelihood from incomplete data via the EM algorithm. J Roy Statist Soc 99(1):1–38

Part VI

Computing and Communications

Dynamic Multi-channel Multi-path Routing Protocol for Smart Grid Jih-Ching Chiu, Kai-Ming Yang, Yao-Chin Huang and Ming-Shiou Wu

Abstract When the deployment of Smart Grid networks for the metering, monitoring, and management of utility infrastructures is an interesting topic for most network architects, in the wireless networks, the demand of enhancing the overall end to end network traffic and ensuring a stable connection is growing. In this paper, we proposed a Dynamic Multi-channel Multi-path Routing Protocol (DMMR Protocol) to decompose contending traffics over different channel, different time and different paths to enhance the end to end network traffic. According to the channel usage around node, choosing channel dynamically can avoid inter-flow and intra-flow channel competition in the process of finding paths. We tend to choose paths with less intersection nodes to reduce the effects that multiple paths are broken at same time, when a single node cannot work. For creating multiple connections, multiple interfaces are used at one node. By this way, we can enhance end to end network traffic. We use NS2 to test DMMR Protocol, and analyzing the overall end to end traffic when multiple connections are setting up in the network. If the network can accept a new connection, the increment of end to end traffic is same as the traffic of the new connection. In connection robust test, a single path broken will not affect other flows in same connection and the end to end traffic in the connection will recovery immediately when the broken path is repaired. Keywords Multi-channel


Multi-path
Routing protocol
Smart grid

This study is conducted under the ‘‘Advanced Metering Infrastructure (AMI) Enhancement Project’’ of the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the Republic of China. J.-C. Chiu (&)
K.-M. Yang
Y.-C. Huang
M.-S. Wu Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_71, Ó Springer Science+Business Media B.V. 2012

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1 Introduction Saving energy problem has become the main problem for human in recent year, and smart grid which integrate generation, transmission and distribution into electricity network is brought up. For demand of smart grid in future, the increase in the overall end to end traffic congestion caused by the Internet has become an inevitable problem [1]; therefore we maintain the information flow and connectivity to improve the stability of daily demand. Due to higher wireless network signal transmission power, smart grid will need more power [2]. Under the green power, concern, we use ZigBee [3], as our based wireless network protocol to issue our design. In order to alleviate network congestion can be expected in the future, and to provide a larger bandwidth such as voice transmission applications, an agreement in the existing network to provide high-end routing protocol traffic will be a worthy research and issues of concern. To make data transmission in a vulnerable wireless environment, still retain some quality, to establish a more robust connection to the maintenance of stability of the transmission is also our goal.

2 Related Works In the multi-channel, multi-path routing protocol to understand, Tam and Tseng [4] aimed to improve end to end flow, by the time divided into time slots, it adjusting the slot of the channel utilization and transmission scheduling into different channels, time, path, to avoid collision. JMM Protocol can be divided into multichannel link layer, as well as multi-path routing of two parts. Xiaofei et al. [5] proposed another routing protocol, it solved that the channel cannot be dynamically allocated problems in the JMM Protocol. In this routing protocol, it used multiple radios and orthogonal channels to further enhance the end to end traffic, but because he assigned a radio for each channel, rather than the dynamic use of the channel to switch the radio, when near nodes of a node use a channel, so that it cannot use this channel to transfer data, in this situation he has to let the radio idle, resulting in waste of resources.

3 Design of the Dynamic Multi-channel Multi-path Routing Protocol (DMMR) Protocol We propose a dynamic multi-channel multi-path routing protocol for wireless mesh networks [6, 7] which let each node, have multiple interfaces allowed switching between orthogonal channels. By making use of multiple interfaces, so that we can transfer packets between the nodes with more flexible use of channels.

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3.1 Route Discovery We divided time into time slots and then composed them to superframe, and first slot of superframe is designated for information exchange between nodes and the route discovery, and each node must have an interface switch to a predefine channel in broadcast slot. To avoid synchronization error between nodes, we added a gap at the end of each time slot, and the gap length is equal to the length of the longest data packet transmission time. Because in the middle node, incoming and outgoing traffic is very close, so we plan a superframe contains 2n+1 time slots, where n is a positive integer greater than 0, superframe structure shown in Fig. 1. In order to avoid channel contend in process of channel assignment, we must first look out the limit of channel assignment. Because we know that competition will only occur at the receiving node, the first constraints that sender selected pair of slot and channel to transfer is that cannot have been selected to receive by neighbors. We try not to use pairs that neighbor’s neighbor has been used to send, in order to find more routes. In addition to the channel contend is occurred to other nodes, but also must be considered with the interfaces at the same node are used the same pair, causing channel contend situations. Compared to select the channel transmission, to decide whether the receive can use pair to receive is simple, just check whether there are other interfaces in its own node to send packets using the same pair, and the neighbors are using the same pair to send packet. In order to select the channel to avoid channel contend, nodes must know the pairs used by 2-hop nodes to select the appropriate pair, so we have established Neighbor Table (NT) within the nodes, used to record pair usage within 2-hop nodes. In order to obtain pair usage within the 2-hop nodes, we broadcast HELLO packet in broadcast time slot, by adding neighboring tables of neighbor nodes and itself. When each node received the HELLO packets, they can get pair usage within 2-hop nodes by neighbor table. In order to ensure the table used to determine the channel assignment is the latest in route discovery process, all the routing request must begin after HELLO packet transmission. When switching in the time slot, the node will be based on the contents of the table, and then switch the channel on each interface. Our routing table field is shown in Table 1. Src column must exist because we have to precise that this route is established from which source node, or we cannot clear the route in that repair process. Because a single source node will establish multiple route to the destination node in a connection, it must be pointed out that the next hop on the routing path by the path number. Nexthop, send_slot, and send_ifs point out the next hop, and the time slot and interface for transmission of a routing path, here we do not record the channel, because channel switching control has been complete by the neighbor table. Prevhop, recv_slot, recv_chan are used for providing information for unicast in route repair process, and to safeguard the information needed by neighbor table. When a data packet generated at the source node and transmitted to destination node, the source node will first look the routing table for finding the route to the

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Fig. 1 Superframe structure of DMMR Protocol

Table 1 Route table field Field

Field size

Meaning

src dst path_num seqno nexthop send_slot send_ifs prevhop recv_slot recv_chan expire

2 2 1 4 2 1 1 2 1 1 4

Source node Destination node Path number Sequence number Next hop Slot used for sending Interface used for sending Previous hop Slot used for receiving Channel used for receiving Expire time

bytes bytes byte bytes bytes byte byte bytes byte byte bytes

destination node, without routing the first packet buffer up and start routing discovery. Since only in the broadcast slot, all nodes will use the same channel, the Route Request (RREQ) packet must be sent in the broadcast slot. This means that if the route search is not initiated at the broadcast time slot, you must wait until the next broadcast slot and wait for the transmission of HELLO packet, and then broadcast the RREQ packet. If there is a node in the network due to damage or depletion of energy resources, and cannot continue to provide the other nodes for transmitting, the route through the node will be broken, in order to avoid this situation, we hope to find the multiple routes that have less intersection node between routes [8]. In order to let destination node determine the number of intersection nodes between the routes, the destination node must know all the nodes through the route, so route request packet must carry information of passed nodes on the route. When the source node broadcasts a route request packet, it must select a pair for the route by the neighbor table. Pair selected restriction has been describing the previous section, it includes no other interfaces use the same pair in its own node,

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cannot select the pairs neighbors used to receive, and do not choose pairs neighbors’ neighbors used to send as much as possible. The most important thing is the node itself must have at least one interface for the new route. After decided the pair to use, node must update this information to its neighbor table, the interface could be any idle interface in the time slot, and the expired field is populated with the time is equal to current time period plus the length of broadcast slot. The reason is that we cannot ensure this path whether is really established in the broadcast slot, but we still need to keep this pair until the end of this broadcast slot, to prevent him being repeated use. After updating the neighbor table, we add the pair with its own node number to the routing request packet, and starting the routing request. With or without a request to be transferred to the routing packet, the any node receives a route request packet, the node must first view the route request packet in the nodes, slots, chans field, if the packet through the nodes exists in its 2-hop neighbor table, pairs information will be recorded in the corresponding adjacent table. Because we cannot update the neighbor table of the nodes closer to the source by route request packet, the contents of the neighbor table error will be occur, and affect the choice of the correct pair. To solve this issue, we use the Neighbor Table Maintaining (NTM) packet to inform the nodes near the source about the information about selected pairs. Any node receives a route request packet, it must use the src and the bid field to look up broadcast ID cache. If the hop_count field is greater than ever received route request packets, node has to send a 1-hop NTM packet which include of the node number of the route request packet and the information of selected pair. The destination node of the route must also send a neighbor table maintaining packet, because the nodes around destination node will encounter a similar problem. After updating the neighbor table, node have to check node itself whether the destination of this route. If node itself is not the destination, looks for broadcast ID cache by the src and bid field in the route request packet. If the hop_count in this packet is equal to or less than received route request packet, node can do next judge. Transfer hop_count equal to the received route request packet with the smallest hop_count is to be able to build as many as possible for the purpose of route. Next, to confirm whether node can receive this route request, starting check the node’s neighbor table whether has free interface in the selected time slot to receive, to be sure, we check whether there is a neighbor use the same pair, if the request is not allowed to receive and then drop the packet. The pair selection process at intermediate nodes is similar to the source selection process, in different places are listed here only. The first is the choice in the time slot, the intermediate nodes will select the next slot of preferred time slot. Next is to ensure this pair, at least one neighbor node can receive the route request. When the destination node of this route receive a route requests packet which it can be accepted, node must reply a route reply (RREP) packet to the source node, and to establish route, but because of a routing request may have multiple route

730 Table 2 Route cache

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Field size

Meaning

src dst path_num seqno nodes

2 2 1 4 2

Source node Destination node Path number Sequence number The nodes in the route

bytes bytes byte bytes bytes/hop

from the source to the destination, so the destination node must select one of these routes to respond. As the route request packets will not reach the destination node at the same time, so the destination node must buffer this packet, and waits for a period of time to receive the other route request packets to obtain other route, the length of time waiting by the size of the network topology. After the destination node receiving the multiple routes, node begin the routing decision. I have chosen is: If this is the first path of this connection, select the smallest hop count as a priority choice, if there are multiple routes have equal hop count, chosen route received earlier. If this is the connection of the second or latter path, the first priority of choice is still the least number of hop count, if multiple route have equal hop count, select the route have less intersection nodes between previous routes. This is because it can reduce the damage caused by a single node fault situations, when there are multiple routes have the same number of intersection nodes, chosen the earlier received. After deciding route to use, the destination node must record the receiving pair to its neighboring table, and the expire field written for the current time plus a period of not less than a superframe time, which represents the pair is used really, and then records the nodes on the route to route cache, this is due to the destination needs the information for judging the intersection nodes of the second or latter routes. The route cache is shown as Table 2. In order to be able to find the reverse route to source node, we record the node in the route at route reply packet. After next hop node receives the route reply packet, node must update the routing table according to the information in this route reply packet. After updating routing table and neighbor tables, node can find the information about next hop, so over and over again until the reply packet back to the source node of this route to complete the route discovery.

3.2 Data Transmission When the source node generates a data packet, and it has route to the destination node, the source node will first add path number to the packet, and then looks for the route table that it should be sent at which slot and interface, and then buffers up this packet until the slot it should be send, then the packet will put into the queue of the interface which it should be used to transfer.

Dynamic Multi-channel Multi-path Routing Protocol Table 3 Parameters in our simulation Parameter Wireless network protocol Topology Node spacing Receiver and transmission radius Radio-propagation model Application Data load Data rate Interface queue length Routing protocol parameter Simulation time

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Setting IEEE 802.15.4 4 9 4 grid mesh 14 m 15 m Two ray ground CBR over UDP 100 bytes 0.05 s/packet 100 13 slots per superframe, 0.5 s per slot, 2 interfaces per node, 2 paths per connection 100 s

3.3 Route Maintenance When the route is broken, broken node must generate a route error (RERR) packet to the source node, and the information of previous hop can be found at routing table. When source node receives this packet, it has to restart the route discovery process to find a new route to the destination node. In our routing protocols, even if a route is not available, he will not affect other routes of the same connection, they still can continue the work of the transmission break before the route repair, which gives us a more robust routing protocol.

4 Simulation Result We use ns2 [9, 10] to test our routing protocol, and the parameters used in our simulation are show as Table 3. In the test if increasing the overall average end to end traffic, we gradually increase the number of network connection, and observe the affect by the newly added connection, and the result is compared with the AODV routing protocol [11]. In AODV routing protocol the packet loss is concentrated in dropping packets, caused by the interface queue is full, this is because data packets must wait the RTS-CTS handshake to send packets. In DMMR protocol, as long as the network nodes have the ability to create a new connection, the increment of overall end to end traffic is equal to the production traffic of each new connection. The result is shown as Fig. 2. In the robust test, when one route of the same connection was broken, it will not affect the transmission by other routes. When the broken route is repaired, the end to end traffic will be recovered. The result is shown as Fig. 3.

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Fig. 2 Average end to end traffic versus the number of connection

Fig. 3 Average end to end traffic versus the number of connection

5 Conclusion and Future Work By adding QoS to DMMR protocol [12], we can give any applications in smart grid different priority and bandwidth according to demand, and letting applications have greater flexibility. Since we put DMMR protocol at the top of MAC layer, as long as a wireless network protocol has multiple orthogonal channels, we can port DMMR protocol [13] on it.

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References 1. Pipattanasomporn M, Feroze H, Rahman S (2009) Multi-agent systems in a distributed smart grid: design and implementation. Power Systems Conference and Exposition, 2009. PSCE ‘09. IEEE/PES 2. Farhangi H (2010) The path of the smart grid. Power and Energy Magazine, IEEE January– February 2010 TM 3. IEEE Std 802.15.4 -2006 4. Wai-Hong T, Yu-Chee T (2007) Joint multi-channel link layer and multi-path routing design for wireless mesh networks. In: 26th IEEE international conference on computer communications, 2081–2089, May 2007 5. Xiaofei W, Wei C, Ye Y, Taekyoung K, Yanghee Ci, Wenhua Z (2009) A framework of distributed dynamic multi-radio multi-channel multi-path routing protocol in wireless mesh networks. In: International conference on information networking, 1–5 Jan 2009 6. Akyildiz Ian F, Xudong W (2005) A survey on wireless mesh networks. Commun Mag, IEEE 43(9):S23–S30 7. Marina MK, Das SR (2001) On-demand multipath distance vector routing in ad hoc networks. In: Ninth international conference on network protocols, 14–23, Nov 2001 8. Kevin F, Kannan V (2009) The ns Manual, Jan 2009, http://www.isi.edu/nsnam/ns/doc/ ns_doc.pdf 9. Teerawat I, Ekram H (2008) Introduction to network simulator NS2, Springer, USA, pp 38– 39 July 2008 10. Calvo RA, Campo JP (2007) Adding multiple interface support in NS-2, Jan 2007, http:// personales.unican.es/aguerocr/ 11. Siva Ram MC, Manoj BS (2004) Ad hoc wireless networks: architectures and protocols, Prentice Hall, USA 71–77 12. Hamid MA, Mahbub MA, Hong CS (2008) Design of a QoS-aware routing mechanism for wireless multimedia sensor networks. In: IEEE global telecommunications conference, 1–6, Nov 2008 13. Anguswamy R, Zawodniok M, Jagannathan S (2009) A multi-interface multi-channel routing (MMCR) protocol for wireless ad hoc networks. In: IEEE wireless communications and networking conference, 1–6, Apr 2009 14. Perkins CE, Royer EM (1999) Ad hoc on-demand distance vector routing. In: Second IEEE workshop on mobile computing systems and applications, pp 90–100 Feb. 1999 15. Yan B, Gharavi H (2006) Multi-path multi-channel routing protocol. In: Fifth IEEE international symposium on network computing and applications, 27–31, July 2006 16. Network simulator 2, http://nsnam.isi.edu/nsnam/index.php/Main_Page 17. Zainaldin A, Lambadaris I, Nandy B (2008) Video over wireless zigbee networks: multichannel multi-radio approach. In: International wireless communications and mobile computing conference, 882–887, Aug 2008

Speed and Surge Control for an Unmanned Aircraft Vehicle with Turbojet Engine Min-Seok Jie and Beyong-In Jung

Abstract This paper proposed fuzzy PID controller which prevents compressor surge and reduces the acceleration time of the fuel flow control system for turbojet engine. The fuzzy PID controller is to stabilize the unmanned aircraft vehicle upon occurring unexpected engine surge. This controller is designed by applying fuzzy PID control algorithm, inferred by applying Mamdani’s inference method and defuzzified by using the center of gravity method. Fuzzy inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine. The controller is designed to converge to the desired speed quickly and safely. The performance of the proposed controller is verified by performing computer simulations with MATLAB. Keywords Turbojet engine Surge control

 Fuzzy PID control  UAV  Fuel flow control 

1 Introduction Turbo-jet engine used for commercial aircraft and unmanned aircraft requires proper control to ensure operational reliability within predictable operating range [1]. The fuel flow and the exhaust nozzle of area is the most important among the control measures of jet engine used in aircraft. Besides, control methods are used in air scoop, air bleed valve, turbo charger, lubrication system and control system. In general, take-off and landing distances should be short. Especially military aircraft M.-S. Jie (&)  B.-I. Jung Department of Avionics, Hanseo University, TaeAn-Gun, ChungNam, South Korea e-mail: [email protected] B.-I. Jung e-mail: [email protected]

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requires high mobility. To satisfy the above requirements, turbo-jet engine acceleration time has to be short. But due to sharp increase in fuel flow could be occurred the state such as surge or over-temperature rise. Instabilities that arise in the unsteady fluid and structural dynamics are among the main challenges in the design and operation of jet engines. These instability phenomena are compression system rotating stall and surge. The pressure of the air flow increases and the air flow into engine decreases. Therefore, surge causes a stall. Surge is an unstable operation mode of the compressor and the stability boundary in the compressor map is called the surge line. Surge is characterized by oscillations in pressure rise and mass flow. These oscillations can cause severe damage to the machine’ due to vibrations and high thermal loading resulting from lowered efficiency. Surge has been avoided using surge avoidance schemes. Typically, a surge control line is drawn at a distance from the surge line, and the surge avoidance scheme ensures that the operating point does not cross this line. This method restricts the operating range of the machine, and efficiency is limited. It is important to control the fuel flow effectively that is the nub of jet engine controller design. Likewise each system should be installed with electronic system in order to control the aircraft engine automatically under optimum conditions. Small engine as unmanned aerial vehicle limit the dimension of exhaust nozzle to simplify system. So the engine control system suited to SISO (Single Input Single Output) system should be designed to achieve desired engine performance by controlling fuel flow effectively. PID control method should be applied to control the turbo-jet engine. The characteristics of jet engine system were established and tracking control for baseline based PI control method has been generalized. The application of different control approaches has been studied to improve efficiency of the turbo-jet engine such as ECU algorithm of turbo-jet engine for calculating the fuel flow by applying fuzzy logic [2] and the fuel flow controller using neural networks [3, 4]. This paper designs approach applying fuzzy inference method to small turbo-jet engine for unmanned aircraft in which fuel flow is used as a single input. The efficiency of the fuzzy rule will depend on variables set such as fuzzification/defuzzification, fuzzy inference. We establish the fuzzy control rule from the results of existing PI controller and will design the fuzzy PID controller. The proposed control method is proved by simulation using liner model engine. Also PID controller are compared with the controller.

2 Engine Control System Design 2.1 Turbojet Engine System Turbo-jet engine for aircraft is the target model. The purpose of controller design for the input fuel flow control is to achieve a high thrust performance.

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Fig. 1 Schematic of a turbojet engine

Simplified turbo-jet engine consists of the four main stages (Compressor, Combustor, Turbine, Nozzle) as shown in Fig. 1. The air is compressed by compressors. The compressed air then enters a combustion chamber where, in combination with fuel, it undergoes combustion in almost static pressure. High pressure gas is transferred in the turbine and rotated the turbine. And then the gas quickly erupts through the nozzle. Since the engine thrust is in proportion to the rotor speed of compressor, the engine thrust can be estimated by measuring the rotor speed of compressor and the estimated thrust value can be applied to engine control system. The engine acceleration and decrease controller is designed to control fuel flow so that the desired thrust can be controlled under the command of speed which is transferred into the rotor speed of compressor. Acceleration control is achieved by setting a surge margin line in which sufficient surge margin is secured, and having engine control line to be positioned near the surge control line. In this case, if the reference command is set up as a compressor rotating speed, then in the initial stage the engine may run over the surge margin by sudden acceleration, however through the surge control, the fuel flow is controlled.

2.2 Fuzzy Controller Design After Mamdani and Assilian make fuzzy controller which is based on fuzzy logic in 1974, fuzzy controller was realized easily and had good performance by applying to the steam engine control. The fuzzy controller was applied to the object which wants to control accumulated experience and knowledge of human. The control rule was designed mostly by professional’s experience and knowledge. Particularly, the fuzzy controller has good performance in the system which has a lot of the control variables of process even if it is non-linear model and the linearization model [5].

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PID controller which was well known has high performance at variety of environments and is used widely because it is control method which is a familiar to engineers. However, classical PID controller doesn’t have good performance in the non-linear system. Fuzzy controller has robustness for non-linear system because it can express verbalism. The fuzzy controller has been studied as PI type and PD type. The PI type is used universally because it can fix error of normal state to ‘0’. But, although the fuzzy PI type performs well in good condition in steady-state, it has limits that it can’t improve performance in transient state and the PD type can’t reduce steady-state error. Fuzzy PID controller was suggested by the SIIM (Simplified Indirect Inference Method). Since Z.Y. Zhao and the rest suggested the PID gain regulator based of the fuzzy logic [6], also the PID controller study has progressed rapidly. Malki and the rest suggested the non-linear system fuzzy PID controller which was divided into 20ea of control input by applying the original 2ea of fuzzy input variable and 4ea of fuzzy rules, non-linear defuzzification which is based on the linear model of the digital PID controller. Malki and the rest’s fuzzy PID controller which is based on the linear digital PID structure is designed so that control performance of the linear structure should be the non-linear and time variant parameter gain. Therefore, it can establish the good performance in the linear plant and non-linear plant [7]. However, because the control input is divided to 20ea of domain by combination of fuzzy input and fuzzy input variables increase, it would be hard to apply, So controller of the fuzzy PI ? D type or the fuzzy PD ? I type is realized. Figure 2 shows the structure of fuel flow control system which is setting reference speed in the light of surge control and is tracking for reference command [8]. We design the controller using fuzzy inference and define error between the compressor reference speed and actual speed. eN ¼ NR  N

ð1Þ

Where, NR is compressor reference speed for control as the desired thrust and N is actual speed. Z t eN ðsÞds UP ðtÞ ¼ KP ðeN ; eP ÞeN þ KI ðeN ; eP Þ 0 ð2Þ deN þ KD ðeN ; eP Þ dt Where, ep is an error of compression ratio at inlet compressor. The control gain KP, KI and KD are chosen as fuzzy inference method using eN, eP. Input variables of fuzzy inference models are defined eN, eP, the output variables are defined PID control gain weight value Q. Table 1 shows all fuzzy rule and fuzzy rule is written in the same way as above. The Table 1 defines the fuzzy rule of the input fuzzy variables and output fuzzy variables, and divides language value of input variable into 5 steps (NB, NS, ZE,

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Fig. 2 Structure of the engine control system Table 1 Fuzzy rule eN NB eP

NS

ZE

PS

PB

NB NS ZE PS PB

VB M S M VB

VB S VS S VB

VB M S M VB

VB VB B VB VB

VB VB B VB VB

PS, PB) and the output fuzzy variables into 5 steps (VS, S, M, VB, B) and the membership function is defined as Fig. 3. Response of the input fuzzy variables in closed-loop system is estimated based on expert knowledge, and fuzzy control rule was added in order to achieve control objectives. Weight value Q is obtained through defuzzification using the center of gravity method. If the language values of fuzzy input variables are eN ¼ x01 ; eP ¼ x02 and the goodness of fit (Wi) of the ith rule (Ri) is calculated as follows. Wi ¼ Ai1 ðx01 Þ  Ai2 ðx02 Þ;

i ¼ 1; 2. . .n

ð3Þ

The result of inference of fuzzy rule ðCi0 Þ is generated by using Mamdani inference method, lCi0 ðQÞ ¼ Wi  lCi0 ðQÞ

ð4Þ

The result of inference of defuzzification using the center of gravity method ðQ ¼ COGðCi0 ÞÞ is calculated as follows. n P

Q¼

Wi Q i

i¼1 n P

ð5Þ Wi

i¼1

We define the PID control gain inference result Q as follows.

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Fig. 3 Membership function of input and output variables

KP ¼ Q  k1 ; KI ¼ Q  k2 ; KD ¼ Q  k3 ðk1 ; k2 ; k3 : constantÞ

ð6Þ

Control gains decide the fuel flow for the control of engine speed.

3 Simulation The performance of proposed fuzzy inference engine control method presented in this paper is shown by simulation using MATLAB. We use a linear model expressed with state space equation. x_ P ðtÞ ¼ AP xP ðtÞ þ BP uP ðtÞ

ð7Þ

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Fig. 4 Simulation results. a compressor rotation speed, b compressor outlet pressure ratio, c turbine inlet temperature, d fuel flow rate

Where, xP ¼ ½ xP1 xP2 xP3 uP T : state vectors xP1 : compressor rotation speed xP2 : turbine inlet temperature xP3 : compressor outlet pressure uP : fuel flow As shown in Fig. 4, in the condition that the engine rotates at 21000 rpm, the engine is accelerated to 7000 rpm for 3 s, and then maintained at 28000 rpm for 3 s, and then decreased to 21000 rpm until 9 s. Figure 4 shows engine speed according to reference speed, compressor pressure ratio, turbine inlet temperature, and fuel flow rate. Fuzzy PID controller does not exceed the limits of a surge control line and we can observe that the engine follows the reference speed command. On the other hand, PID controller shows obvious error between reference command and current state because it could not be tracked to reference value. The control gains of the proposed fuzzy inference PID controller are chosen as KP ¼ Q  0:0000027; KI ¼ Q  0:00003; KD ¼ Q  0:000002: The weigh value Q is decided depending on the control state by fuzzy inference.

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Fig. 5 Simulation results. a compressor rotation speed, b compressor outlet pressure ratio, c turbine inlet temperature, d fuel flow rate

The transient response of the feedback control system was improved using the cascade compensation. It has high response time and effective decreasing to steady-state error. The result of engine thrust is shown high efficiency in close proximity to surge control line. Figure 5 shows results of the ability to protect the engine. The controller reduces compulsorily the fuel flow by 25% for 2 s when the compressor pressure ratio over the limits of the surge control line. This method makes it possible to engine operate under the surge control line with optimum surge protection feature which enable to eliminate unnecessary fuel flow drop and increase operation efficiency.

4 Conclusions In this paper, we propose a turbo-jet engine controller of unmanned aircraft based on Fuzzy Logic and PID algorithm. Engine speed value is used as reference input and fuzzy inference rule is applied to PID control. By simulations, the proposed

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controller is compared with the existing fuzzy controller. The proposed method effectively controls the fuel flow input of the control system and it has good tracking performance for the reference acceleration and deceleration commands. To prevent any surge or a flame out event during the engine acceleration or deceleration, the fuzzy controller effectively controls the fuel flow input of the control system.

References 1. J-H Boo, M-S Pang, K-W Lee, S-S Yoo, C-D Kong (1993) Characteristics of a turbojet engine linear model using DYGABCD code. The Korea Navigation Institute, 81–90 2. Montazeri-Gh M, Yousefpour H, Jafari S (2010) Fuzzy logic computing for design of gas turbine engine fuel control system. 2nd Int Conf Comput Autom Eng 5:723–727 3. Jing M (2006) Adaptive control of the aircraft turbojet engine based on the neural network. Int Conf Comput Intell Secur 1:937–940 4. Wu C-H, Fan D, Jin-Ven Y (1992) Stand test research of fuzzy control theory for speed digital control system in a turbojet engine. Int Conf Power Electron Motion Control 3:1207–1211 5. Li-Xin W (1996) A course in fuzzy systems and control. Prentice Hall PTR, New Jersey 6. Zhao Z, Tomizuka M, Isaka S (1993) Fuzzy gain scheduling of PID controller. IEEE Trans Syst Man Cybemetics 23(5):1392–1398 7. Misir D, Malki HA, Chen G (1996) Design and analysis of a fuzzy proportional integaral derivative controller. Fuzzy Sets Syst 79:297–314 8. Li-Ling W, Hong-Rui W (2009) Fuzzy PI ? D tuning for permanent magnet linear synchronous motor. Int Conf Mach Learn Cybernetics 2:663–667

Influence of G729 Speech Coding on Automatic Speaker Recognition in VoIP Applications Dalila Yessad, Abderrahmane Amrouche, Mohamed Debyeche and Nabil Ramou

Abstract In this work, the ITU-T G.729 speech coder is used to encode and decode the speech input for text independent speaker recognition over IP networks. Speaker recognition system was designed to use three kinds of features coefficients extracted in processing phase: (i) the first one is the LFCC coefficients extracted from clean database, (ii) the LFCC features vector extracted from the G729 transcoded database (resynthesized speech), and (iii) the LPCC coefficients calculated directly from the coded parameters embedded in the ITU-T G.729 bitstream. Experiments were performed using the TIMIT database, and the effect of the G729 on speaker recognition performance is investigated. In order to improve recognition accuracy of GMM-UBM used with the G729, speaker recognition using T-norm score normalization was also examined. The speaker recognition was performed using a Gaussian mixture model-universal background model (GMM-UBM) with the LIA SpkDet system based on the ALIZE platform 3. In the emerging method that use the LPCC derived directly from the G729 encoded bit stream, the recognition performances reach to 64% but are still poor compared to the conventional speaker recognition methods or that achieved using G729 resynthesized speech.

D. Yessad (&)  A. Amrouche  M. Debyeche Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, P.O. Box 32El Alia, Bab Ezzouar, 16111 Algiers, Algeria e-mail: [email protected] A. Amrouche e-mail: [email protected] M. Debyeche e-mail: [email protected] Communications Systems Laboratory, National Polytechnic School, El Harrach, Algiers, Algeria

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_73, Ó Springer Science+Business Media B.V. 2012

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Keywords Automatic speech recognition (ASR) Universal background model (GMM-UBM) G729 VoIP Resynthesized speech T-norm scoring









1 Introduction In the last decade, emerging speech communication technologies based on Voice over the Internet Protocol (VoIP) have been developed and implemented in wired networks. The motivation of VoIP is the low cost to transmit voice over Internet, especially for long-distance calls. The commonly used VoIP codecs are G.711, G.729 and G.723.1, which are standardized by the ITU-T in its G-series recommendations. The use of speech recognition technology in digital speech communication systems, especially in VoIP applications, is one of the major goals over the last 10 years. There has been increasing interest in the performance of the automatic recognition of resynthesized coded speech [1, 2, 3]. For instance, speaker verification based on GSM, G.729, and G.723.1 resynthesized speech was studied in [2]. It was shown that recognition performance generally degrades with coder’s bit rate. In [2] and [3], techniques that require knowledge of the coder parameters and coder internal structure were proposed to improve the recognition performance of G.729 coded speech. However, the performance is still poorer than that achieved by using resynthesized speech. The main goal of this work is to investigate the efficiency of the automatic speaker verification using VoIP-based speech communications systems. We are particularly focused on the performance recognition obtained with the encoded bit stream, which is the key of the development of VoIP application in speech technologies. In this work, the ITU-T G.729 [4] speech coder is used to encode and decode the speech. Experiments were performed over the TIMIT corpus [5] and the influence of the G729 in the transcoded database is investigated on speaker recognition performance. The rest of this paper is organized as follows. The G729 speech coder is explained in Sect. 2. The speaker recognition system used in all the experiments is presented in Sect. 3. Experiments using original, transcoded databases and features extracted directly from the G729 encoded parameters are given in Sect. 4. Finally, conclusions and future work are drawn in Sect. 5.

2 G729 Speech Coder G.729, also known as CS-ACELP (Conjugate Structure Algebraic Code Excited Linear Prediction), is specified by the ITU (International Telecommunications Union) [4]. It compresses speech from 16 bit, 8 kHz samples (128 kbps) to 8 kbps,

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Fig. 1 GMM-UBM speaker recognition system

and was designed for cellular and networking applications. It provides toll quality speech (that is, as good as the telephone network), works well with background noise, and has been designed to perform well under error conditions. In G.729, forward adaptation is used to determine the synthesis filter parameters every 10 ms. These filter coefficients are then converted to line spectral frequencies (LSFs) and quantized using predictive two stage vector quantization. Each of the 10 ms frames is split into two 5 ms subframes and the excitation for the synthesis filter is determined for each subframe. The long-term correlations in the speech signal are modeled using an adaptive codebook with fractional delay. An algebraic codebook with an efficient search procedure is used as the fixed codebook. The adaptive and fixed-codebook gains are vector quantized using a two stage conjugate structure codebook. The entries from the fixed, adaptive, and gain codebooks are chosen every subframe using an analysis-by-synthesis search.

3 Speaker Recognition System The speaker recognition system is a Gaussian mixture model-universal background. The GMM-UBM approach (see Fig. 1) is the state of the art system in text- independent speaker recognition [6]. This approach is based on a statistical modelling paradigm, where a hypothesis is modelled by a GMM model: pðx=kÞ ¼

im X i¼1

 X  ai Nðxli ; iÞ

ð1Þ

P where, ai ; li and i are, respectively, the weights, the mean vectors, and the covariance matrices (generally diagonal) of the mixture components. During a test, the system has to determine whether the recording Y was pronounced by a given speaker S: This question is modelled by the likelihood ratio

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 pðykhyp Þ  s  pðykhyp Þ

ð2Þ

where Y is the test speech recording, khyp is the model of the hypothesis where S pronounced Y; khyp corresponds to the model of the negated hypothesis (Sdid not pronounce Y;) pðyjmÞ is the GMM likelihood function, and s is the decision threshold. The model khyp is a generic background model, the so-called UBM, and is usually trained during the development phase using a large set of recordings coming from a large set of speakers. The model khyp is trained using a speech record obtained from the speaker S. It is generally derived from the UBM by moving only the mean parameters of the UBM, using a Bayesian adaptation function. In this study The GMM-UBM system is the LIA SpkDet system [7] based on the ALIZE platform3 and distributed under an open source license. This system produces speaker models using MAP adaptation by adapting only the means from a UBM with a relevance factor of 14. The UBM component was trained on a selection of 60 corpus. For all the experiments, the model size is 128 and the performances are assessed using DET plots and measured in terms of equal error rate (EER) and minimum of detection cost (minDCF). In T-norm (Test Normalization) [5], the idea is to compare each test utterance against possible impostor models. This results in a set of scores used for normalizing the score’s mean and variance: log KM;T ¼

log KM lI oI

ð3Þ

where KM;T is the T-normed score for model M (for implied utterance x). Here, the lI and rI are the statistical properties of the scores from the test utterance evaluated on impostor models. Of course, this has to be done on-line for each test utterance and is thus considered a costly approach. In this work, the use of score normalization has significantly improved the performance of speaker recognition systems when the speech has been decoded.

4 Results and Discussion 4.1 Speech Database and Features Extraction In this work we used the ‘‘long training/short test’’ protocol [8] for speaker recognition on TIMIT. The features corresponding to the three SI sentences are concatenated for training each speaker model. 430 speakers of the database (147 women and 283 men) are used in the speaker identification system for testing. Three SX sentences of every speaker is tested separately (430 9 3=1290 test

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Fig. 2 Speech data; LFCC extracted from clean speech, LPCC extracted from coded speech and LFCC extracted from decoded speech

patterns of seconds each, in average). The experiments are totally text independent. The remaining 60 speakers of the database are used to train the world model needed for the speaker verification experiments. 1290 client accesses and 1290 impostor accesses are made (for each client access, an impostor speaker is randomly chosen among the 429 remaining speakers). Speaker utterances were represented by 19 Linear Fequency Cepstral Coefficients (LFCC or LPCC) determined through filterbank analysis, with their first derivatives and the delta energy. Altogether, a 40 coefficients vector is extracted from each signal frame. Mean subtraction and variance normalization were applied to all features. In this work we adopted three different features representations in the experiments: Feature A: LFCCs obtained from the front clean TIMIT corpus. Feature B: LFCCs computed from the G729 resynthesized speech. Feature C: LPCCs derived from the G729 encoded speech. A plot of the three kinds of features extractors is shown in Fig. 2.

4.2 Experimental Results Three different experiments are presented. In the first experiment, we obtained a best performance with clean TIMIT corpus, 95% in average. In the second experiment, the recognition performance degradation due to the utilization of the G729 decoded speech following the LFCC feature extraction is found at 87%. In the third experiment, the features are calculated directly from the G729 encoded bit stream by LPCC extraction method. With this later the recognition performances are bad and found at 64% in average. The recognition performances are shown in Fig. 3. In order to improve recognition performance of GMM-UBM with the G729 transcoded databases result,

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Fig. 3 The performances of GMM-UBM system with three kind of features (LFCC clean, LFCC from resynthesised speech and LFCC from encoded speech)

Speaker Detection Performance 40

Feature A Feature B Feature C

Miss probability (in %)

20

10

5

2 1 1

Fig. 4 The performances of GMM-UBM system with LFCCs computed from the G729 resynthesized speech and T-norm normalization

2

10 20 5 False Alarm probability (in %)

40

Speaker Detection Performance

Miss probability (in %)

40

Feature B Feature B with Z-norm

20

10

5

2 1 1

2

5 10 20 False Alarm probability (in %)

40

T-norm score normalization has been also contributed. Recognition rate improves to 87% for GMM-UBM with T-norm scoring (see Fig. 4). In the emerging method that use the LPCC derived directly from the G729 encoded bit stream, the recognition performances reach to 64% but are still poor compared to the conventional speaker recognition methods or that achieved using G729 resynthesized speech.

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5 Conclusions In this paper we have investigated the influence of G729 speech coding on a textindependent speaker recognition system based on GMM-UBM classifier. The recognition performance when extracting LFCC features from G729 transcoded speech was measured, and it was compared with LFCC extracted from original speech. Thus, experiments were carried out, using the LPCC features extracted from G729 coded speech, to measure the degradation in performance introduced by different aspects of the coder, and to explore the possibility of performing recognition by extracting features directly from coder parameters. It was found that the performance can be improved by using features extraction from transcoded speech rather than from encoded speech, and it was shown also that the performance derived from transcoded speech is still poorer than that achieved by features extraction derived from clean dataset. In order to perform recognition performance of GMM-UBM with the G729 transcoded databases, T-norm score normalization has been contributed, recognition rate improves in average to 87% for G729 transcoded databases with GMM-UBM after T-norm scoring.

References 1. Quatieri TF, Singer E, Dunn RB, Reynolds DA, Huerta JM, Stern RM (1998) Speech recognition from GSM coder parameters. In: Proceedings of the 5th international conference on spoken language processing, vol 4, pp 1463–1466 2. Campbell JP (1999) Speaker and language recognition using speech codec parameters. In: Proceedings of Eurospeech’99, vol 2, pp 787–790 3. Quatieri TF, Dunn RB, Reynolds DA, Campbell JP, Singer E (2000) Speaker recognition using G.729 codec parameters. In: Proceedings of ICASSP’2000, pp 89–92 4. ITU-T Recommendation G.729—Coding of speech at 8 kbit/s using conjugate-structure algebraic-code-excited linear-prediction (CS-ACELP), March 1996 5. Fisher W et al (1987) An acoustic-phonetic database. J Acoust Soc Am 81(Suppl 1) 6. Bimbot F, Bonastre J-F, Fredouille C, Gravier G, Magrin-Chagnolleau I, Meignier S, Merlin T, Ortega-García J, Petrovksa-Delacrétaz D, Reynolds DA (2004) A tutorial on textindependent speaker verification. EURASIP J Appl Signal Process 4:430–451 7. http://www.lia.univ-avignon.fr/heberges/ALIZE/LIARAL 8. Bimbot F, Magrin-Chagnolleau I, Mathan L (1995) Second order statistical methods for textindependent speaker identification. Speech Commun 17(1–2):177–192 9. Reynolds DA, Quatieri TF, Dunn RB (2000) Speaker verification using adapted gaussian mixture models. Digit Signal Process 10(1–3):19–41 10. Martin A, Przybocki M (2000) The NIST 1999 speaker recognition evaluation—an overview. Digit Signal Process 10(1–3):1–18 11. Linguistic Data Consortium. http://www.ldc.upenn.edu; 1996–1999 NIST speaker recognition benchmarks 12. Auckenthaler R, Carey M, Lloyd-Thomas H (2000) Score normalization for textindependent speaker verification systems. Digital Signal Process 10:42–54

Scheduling DAGs with Parallel Tasks in Multi-Clusters Based on Parallel Efficiency Silvio Luiz Stanzani and Líria Matsumoto Sato

Abstract The Multi-cluster Grid environment is an effective infrastructure for the execution of DAGs composed by parallel tasks. The scheduling of DAGs in these environments is a challenging task. This paper details two scheduling strategies: one which maps parallel tasks to different clusters and another to a single cluster. Both approaches were evaluated with five workloads in a multi-cluster environment. Keywords DAG scheduling

 Parallel task scheduling  Parallel efficiency

1 Introduction Multi-cluster Grid environments have emerged as an effective infrastructure for supporting large scientific collaborations by the means of sharing resources, such as: computational resources, high performance networks, disk space and software components [1]. Such environments are capable of provide for massively parallel and computationally intensive applications an amount of resources that would rarely be available in a single cluster. Applications in multi-cluster environments are executed with the support of Grid middleware, which provides a low-level interface for the development of grid

S. L. Stanzani (&)  L. M. Sato Avenida Prof. Luciano Gualberto, Escola Politécnica da Universidade de São Paulo (USP), travessa 3 n8 380 CEP, São Paulo-SP 05508-970 Brazil e-mail: [email protected] L. M. Sato e-mail: [email protected]

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applications. Complex applications consisting of a set of interdependent tasks are executed by Scientific Workflow Management Systems (ScWFMS) [2], which provide an abstraction layer above grid middleware for the composition of complex applications. The scheduling of scientific workflows involves the mapping of tasks to resources [3], considering two conditions. The first condition is the execution precedence constraint between two dependent tasks. The second condition is that only one task can be executed on a cluster node at one time. Finding a schedule to minimize the execution time of a workflow is an NP-complete problem [4]. The approach to scheduling scientific workflows presented in this paper is based on a two-phase algorithm, namely priorization and task scheduling. Parallel efficiency is used to estimate the execution time in the priorization phase. The remainder of this paper is organized as follows: Sect. 2 presents DAG scheduling and related work. Section 3 presents the computational model. Section 4 presents the simulation setup. Section 5 presents the simulation results. And, finally, Sect. 6 presents the conclusion and future works.

2 DAG Scheduling and Related Work Scientific workflows can be defined as a DAG (Directed Acyclic Graph), in which the vertices represent tasks and the directed edges represent the dependencies between them. DAGs are executed in Grid Environments by submitting each DAG task to one grid resource. In a multi-cluster grid, a resource is a cluster comprising of a set of nodes managed by a LRM (Local Resource Manager). A number of DAG scheduling strategies have been developed and implemented in ScWFMS which uses file dependency between tasks as a criterion for scheduling the tasks. Pegasus [5] executes a scheduling algorithm which clusters a number of tasks to the same resources in order to minimize file transfer between tasks. Gridbus [6] implements an algorithm for scheduling DAG tasks to resources which are close to the data source. The task parallelism is also used as a criterion for scheduling DAG tasks on the grid. In [7], a static list scheduling algorithm is presented in order to schedule parallel tasks in heterogeneous clusters. Mixed parallel tasks represents applications which presents parallelism in the task level, and in the data level, in [8] a number of algorithm for scheduling DAGs composed by mixed parallel tasks is evaluated. In [9] bi-criteria algorithm is proposed for scheduling DAGs composed by mixed parallel tasks, aiming at find a schedule which minimize execution time and minimize the quantity of resources allocated for each task. The scheduling strategy described in this paper aims at demonstrating the viability of executing parallel tasks in multi-cluster grids. In this sense, the strategy considers tasks which the file transfers between tasks has low costs, and also has low communication costs. The approach used is the immediate mode [10], which performs task scheduling during DAG execution for tasks without dependencies or tasks with dependent tasks that have already been completed. The objective is to

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minimize the execution time of parallel tasks being executed simultaneously, using parallel efficiency for an execution time estimation. The next section details the support of parallel task execution and scheduling in multi-cluster grids.

2.1 Executing and Scheduling Parallel Tasks in Multi-Clusters The MPI Paradigm is a de-facto standard for the development of parallel applications on parallel machines, such as clusters and supercomputers. Such parallel machines provide fast and reliable communication links among nodes optimized for MPI communication libraries, which enable efficient use of a number of resources by the same application. A multi-cluster grid can support the execution of MPI tasks enabling the use of resources from diverse clusters. Accordingly, the grid MPI frameworks such as PACX-MPI [11], MPICH-G2 [12], GridMPI [13] and the MPI Gateway [14], implement mechanisms for transparent inter-cluster communication, enabling the porting of a parallel application to the grid without the need for software code modifications [15]. The heterogeneity of clusters can lead to poor performance without an adequate scheduling strategy. In this sense, the scheduling of parallel tasks is essential. The scheduling of parallel task is the process of map a set of parallel tasks to a set of resources. A parallel task can be rigid, moldable or malleable [16]. Rigid tasks require a fixed quantity of resources; moldable tasks can be executed with any quantity of resources, which will be reserved to the task until its completion; and malleable tasks can also be executed with any quantity of resources, although the quantity of resources can vary throughout the execution of the task. In this sense, the parallel task scheduling algorithm has to assign an quantity of resources to tasks, and also map the task to resources. In the context of multi-clusters grid such resources can be deployed in a single cluster, or can be deployed in diverse clusters.

3 Computational Model The computational environment considered for this study was a multi-cluster grid environment, which is a set of clusters interconnected by each cluster head node using internet infrastructure. Each cluster consists of a number of P processors with a processing capacity and connected by a switched Ethernet. The connection speed among nodes in the same cluster is represented by the cluster switch bandwidth and latency, and the connection speed among nodes from different clusters is the sum of connections to the local cluster switch, the connection between local switches and the router and the router to other cluster switches.

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The application is represented by its precedence constraints in a DAG format. The tasks are defined with the following characteristics: Computation size, communication size (the amount of data that will be transferred along with the execution), minimum number of cores (mincores), maximum number of cores (maxcores), and parallel efficiency, which is defined as a function of speedup [17]. The tasks can be sequential, rigid or malleable. The sequential tasks have mincores = 1 and the rigid tasks have mincores equal to maxcores. The malleable tasks have maxcores greater than mincores. The efficiency is represented by the amount of code that can be parallelized Teff : The task computation size is defined in flops/s and represents the sequential task size. Tcomp: The task communication size is defined as Tcomm: The task computation size is a function of efficiency and the quantity of resources used: Tcomp ¼ ð

Tcomp  Teff Þ þ Tcomp ð1  Teff Þ cores

3.1 Scheduling Strategy The objective of the developed scheduling strategy is to minimize the execution time of DAGs, consisting of sequential and parallel tasks in a multi-cluster environment. The execution time of a parallel task is a function of the resources used according to parallel efficiency. In this context, the objective of the strategy is to find a schedule that minimizes execution time for all the tasks to be executed simultaneously, since the execution time of a set of ready tasks will be limited by the execution time of the slowest task. The algorithm has two phases, namely priorization and scheduling: In the priorization phase the resources of available resources in each cluster is shared among the tasks in order to minimize the execution time of tasks with greater computational sizes. In the scheduling phase the tasks are mapped to resources according to the quantity of resources allotted to each task, following the max– max heuristic in which the highest computational size tasks are mapped to the highest capacity cluster. For sequential and rigid tasks, the quantity of resources allotted will be fixed according to task requirements, and for the malleable tasks the quantity of resources will be defined in priorization phase. The scheduling phase maps the tasks to resources according to the quantity of resources defined in priorization phase. Such quantity of resources can be available on a single cluster, or can be available as a subset of resources from a number of clusters. In this sense, the scheduling phase was developed following two approaches. In the first approach parallel tasks can be executed in one cluster or in diverse clusters. In the second approach parallel tasks is executed always in a single cluster.

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The priorization phase of algorithms works in the following way: Priorization (task_list) 1) for each ready tasks 2) sort ready tasks by Tcomp 3) sort resources by power capacity 4) for each malleable task i 5) i.cores = i.mincores 6) for each available_resources 7) for each malleable task i 8) if execution_time(i) [ task_execution_time 9) slowest_task_ind = i 10) slowest_task_ind.cores ++ The first approach for the scheduling phase of algorithm schedule the parallel task to the first cluster which has the available resources required to execute the task, if none of cluster has enough resources, the task will be mapped to more than one cluster. Scheduling_multi_cluster (task_list) 1) for each ready tasks i 2) for each resource r 3) if available_resources(r) [ i.cores 4) taskmap[i][0] = r 5) if taskmap[i] == null 6) for each resource r 7) if available_resources(r) [ 0 8) i.cores = i.cores - available_resources(r) 9) taskmap[i][c] = r 10) c++ The following algorithm performs the scheduling phase following the second approach. This algorithm schedule the parallel task to the first cluster which has the available resources required to execute the task, if none of cluster has enough resources, the task will be mapped to the cluster with highest amount of available resources. Scheduling_one_cluster (task_list) 1) for each ready tasks i 2) for each resource r 3) if available_resources (r) [ i.cores 4) taskmap[i][0] = r 5) if taskmap[i] == null 6) for each resource r 7) if available_resources(r) [ max_avail_resources

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Table 1 Resources Cluster 1

Cluster 2

Cluster 3

10 nodes Flops: 63840160000 Intel 2.00 GHz Bandwidth = 100 Mb/s Latency = 100 ms

4 nodes Flops: 25534816000 Intel 1.60 GHz Bandwidth = 100 Mb/s Latency = 100 ms

2 nodes Flops: 11200384000 Intel 2.80 GHz Bandwidth = 100 Mb/s Latency = 100 ms

Table 2 Workloads Tasks Workload 1

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Malleable Rigid Sequential

2 0 0

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8) 9)

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max_avail_resources = r i.cores = available_resources(r)

The next section will present an evaluation comparing the algorithm using the scheduling strategy that maps the task to more than one cluster with the one mapping the task to a single cluster.

4 Simulation Setup The proposed strategy evaluation was carried out with the support of gridsim [18], using the MSG Framework. The multi-cluster environment was deployed with 3 homogeneous clusters. Each cluster node consisted of one core machine. Intra-cluster communication was modeled as a 100 MB/s Ethernet switch network, and inter-cluster communication was modeled as a 1 GB/s Ethernet switch network connected by a router. Table 1 presents the clusters configuration. The workloads were created with a varying number of sequential, rigid and malleable tasks, and different communication and computation costs. Table 2 presents the workload details.

5 Simulation Results The results shows the execution time of the scheduling strategy which maps parallel tasks to one cluster (scheduling_one_cluster), and the execution time of scheduling strategy which can maps tasks to diverse clusters (scheduling_multi_cluster). The

Scheduling DAGs with Parallel Tasks Table 3 Workload execution time

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1 2 3 4 5

Execution time (s) multi-cluster

Execution time (s) one-cluster

5.00588 25.0059 21.0533 52.6358 40

6.49161 31.2292 21.0533 52.6358 40

utilization of parallel efficiency as a criterion to estimate execution time in the priorization phase was also evaluated. The Table 3 shows the simulation results. The utilization of scheduling_multi_cluster performed better for workload 1 and workload 2 than the scheduling_one_cluster. For the workload 3, 4 and 5 both algorithms performed in the same time. Considering the workload 1 and workload 2 composed by two malleable tasks which are very efficient, the scheduling_multi_cluster performed better because the tasks could use resources from diverse clusters without significant network overhead. The workloads 3, 4 and 5 are composed by tasks which require more resources than is provided by the multi-cluster environment. In this case, the priorization phase associated an amount of resources for each task which is lower than the maxcores of task, the consequence is that the scheduling_multi_cluster and scheduling_one_cluster executed the same task mapping.

6 Conclusion and Future Works A two-phase scheduling algorithm was designed to schedule DAGs consisting of sequential and parallel tasks in a multi-cluster grid made up of homogeneous clusters. Such algorithms were developed by means of two approaches. The first approach was to schedule parallel tasks to more than one cluster (scheduling_ multi_cluster), while the second approach was to schedule parallel tasks to only one cluster (scheduling_one_cluster). Both approaches were tested using five types of workloads. The scheduling_multi_cluster performed better than scheduling_one_cluster approach for workloads with few tasks, and it presented the same execution time for workloads with more tasks than available resources. In future work, two further aspects could be investigated. The first aspect is an analysis of the network overhead, considering a scenario with heterogeneous bandwidth and latency. The second aspect is an evaluation within a production environment.

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References 1. Foster IT (2001) The anatomy of the grid: enabling scalable virtual organizations. In: First IEEE international symposium on cluster computing and the grid (CCGrid’01), pp 1–4 2. Yu J, Buyya R (2005) A taxonomy of scientific workflow systems for grid computing. SIGMOD Rec 34(3):44–49 3. Yu J, Buyya R, Ramamohanarao K (2008) Workflow scheduling algorithms for grid computing. In: Metaheuristics for scheduling in distributed computing environments 4. Jansen K, Zhang H (2006) An approximation algorithm for scheduling malleable tasks under general precedence constraints. ACM Trans Algorithms 2(3):416–434 5. Deelman E et al (2005) Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci Program 13(3):219–237 6. Venugopal S, Buyya R, Winton L (2006) A grid service broker for scheduling eScience applications on global data grids. Concurr Comput: Pract Experience 18(6):685–699 7. Barbosa J, Morais C, Nobrega R, Monteiro AP (2005) Static scheduling of dependent parallel tasks on heterogeneous clusters. In: 2005 IEEE international conference on cluster computing, pp 1–8 8. Casanova H, Desprez F, Suter F (2010) On cluster resource allocation for multiple parallel task graphs. J Parallel Distributed Comput 70(12):1193–1203 9. Desprez F, Suter F (2010) A bi-criteria algorithm for scheduling parallel task graphs on clusters. In: 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (CCGrid), pp 243–252 10. Couvares P, Kosar T, Roy A, Weber J, Wenger K (2007) Workflow management in condor. In: Workflows for e-Science. Taylor IJ, Deelman E, Gannon DB, Shields M (eds). Springer, London, pp 357–375 11. Graham RL, Woodall TS, Squyres JM (2005) Open MPI: a flexible high performance MPI. In: 6th annual international conference on parallel processing and applied mathematics 12. Karonis NT, Toonen B, Foster I (2003) MPICH-G2: a grid-enabled implementation of the message passing interface. J Parallel Distrib Comput 63(5):551–563 13. Takano R et al (2008) High performance relay mechanism for MPI communication libraries run on multiple private IP address clusters. In: 8th IEEE international symposium on cluster computing and the grid. CCGRID’08, pp 401–408 14. Massetto F et al (2011) A message forward tool for integration of clusters of clusters based on MPI architecture, In: Methods and tools of parallel programming multicomputers, vol 6083, Hsu C-H, Malyshkin V (eds). Springer, Berlin/Heidelberg, pp 105–114 15. Coti C, Herault T, Cappello F (2009) MPI Applications on grids: a topology aware approach. In: Euro-Par 2009 parallel processing, vol 5704. Sips H, Epema D, Lin H-X, (eds). Springer, Berlin/Heidelberg, pp 466–477 16. Feitelson DG, Rudolph L, Schwiegelshohn U, Sevcik KC, Wong P (1997) Theory and Practice in Parallel Job Scheduling. In: Proceedings of the job scheduling strategies for parallel processing, pp 1–34 17. Trystram D (2001) Scheduling parallel applications using malleable tasks on clusters. In: Proceedings of the 15th international parallel and distributed processing symposium, pp 2128–2135 18. Casanova H, Legrand A, Quinson M (2008) SimGrid: a generic framework for large-scale distributed experiments. In: 10th international conference on computer modeling and simulation, UKSIM 2008, pp 126–131

Tomographic Reconstruction of a Multi-Attenuation Phantom by Means of Ultrasonic Method Mohd Hafiz Fazalul Rahiman, Ruzairi Abdul Rahim and Herlina Abdul Rahim

Abstract This paper presents a work on tomography reconstruction of a multiattenuation phantom by means of ultrasonic method. A transmission-mode approach has been used for the sensing modality. The reconstruction has been conducted on an array of 32-ultrasonic transceiver with 333 kHz resonance using fan-shaped beam scanning geometry. In this work, the tomographic images are derived from Back-Projection algorithm. Some of the results based on the Linear Back-Projection algorithm (LBP) were presented and discussed. Keywords Tomography Inverse problem



Multi-attenuation phantoms



Forward problem



1 Introduction Process tomography involves obtaining measurement from sensors around the circumference of an industrial column and reconstructs the cross-section images of the corresponding column. The cross-section images provide useful information M. H. F. Rahiman (&) School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] R. A. Rahim  H. A. Rahim Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia e-mail: [email protected] H. A. Rahim e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_75, Ó Springer Science+Business Media B.V. 2012

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Fig. 1 The ultrasonic transducer arrangement on the experimental column

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such as shape, location and size of the multi-phase media and this information can be used to obtain both qualitative and quantitative data needed in modeling a multi-fluid flow system [1]. The modeling is carried out to predict the spatial and temporal behavior of a process and it becomes more significant as the inherent complexity of a process increases [2]. In this paper, we present a tomography work carried out by means of ultrasonic method and examine on several multi-attenuation phantoms. The aim of the research is to evaluate the ability of such technique to distinguish several phantoms in liquid media with five different attenuation coefficient, a namely 1.0, 0.8, 0.6, 0.4, and 0.2. Figure 1 shows the configuration used in this study. This system employs 32-ultrasonic transducer of 333 kHz resonance with 9.3 mm in diameter each. The transducer works in transceiver-mode thus, allowing both transmission and reception of ultrasonic signals possible on every transducer. The transducers were arranged around the circumference of a 110 mm diameter experimental column. The ultrasound projection path is denoted by the red line in Fig. 1.

2 The Attenuation Model The attenuation process may be modelled by Lambert’s exponential law of absorption, where the ultrasonic energy intensity of transmitter and receiver are related as in Fig. 2 and Eq. (1) where L represents the total path length.  Z  ð1Þ P ¼ P0 exp  f ðx; yÞdP L

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Fig. 2 The ultrasonic attenuation model

f (x,y) P

Po L

Object field

where P = the measured sound pressure (dB), P0 = the initial sound pressure (dB), L = path length in the object field (m) and f (x,y) = the attenuation function of the object field (dB/m). Because pressure is proportional to the voltage measured by the transducer, Eq. (1) can be written as Eq. (2). vRx ¼ vTx eaL

ð2Þ

where vRx = the ultrasonic receiver voltage (V), vTx = the ultrasonic transmitted voltage (V) and a = the attenuation coefficient of the object field (Np/m). As introduced above, the attenuation will critically depend upon the material through which the ultrasonic wave travels.

3 Image Reconstruction: The Forward Problem The forward problem determines the theoretical output of each of the sensors when the sensing area is considered to be two-dimensional [3]. The cross-section of the pipe was mapped onto a 64 by 64 rectangular array consisting of 4096 pixels. The forward problem can be solved by using the analytical solution of sensitivity maps which produces the sensitivity matrices [4]. Each transmitting sensors is virtually excited and the affected pixels are taken into account. The calculation of the sensitivity maps are outlined in the following section.

3.1 The Sensitivity Maps A plot of sensitivity distribution is called sensitivity map. The sensitivity distribution can be determined by calculating the ultrasonic energy attenuation at position of each receiver due to obstruction in the object space. To create sensitivity maps, a model of measurement section has been developed. The measurement section model is divided into 256 nodes to create the round image plane model with 256 pixels in radius, r. Each node is separated by an angle, h of 1.4063 degrees. The transducer diameter or transducer arc (Sd) is represented by the seven black nodes on the image plane model. This is shown in Fig. 3.

764 Fig. 3 Nodes representing transducer arc on the image plane model

Fig. 4 The virtual projection for TR25 to TR14

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Px+3 Px+2 Px+1 Px Px-1 Px-2 Px-3

ls 6 pixe r = 25

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The image plane model in Fig. 1 was represented by 512 by 512 pixels image plane. This size then was reduced to 64 by 64 pixels by grouping the 512 by 512 pixels into 8 by 8 pixels each. This is shown in Fig. 4. To generate a series of sensitivity maps, custom created software on Visual Basic 6.0 was developed. The projection of each transmitter to the receiver was represented by the virtual projection. The illustration of virtual projection for projection of TR25 to TR14 is shown in Fig. 4. The virtual projection that lay on the projection path was colored black. The computer graphic memory was used to retrieve the small pixels (512 by 512 pixels) color occupied by the projection using the function provided by the Windows API function library. Any small pixels occupied by projection (blacked) is counted and summed into the corresponding major pixels (64 by 64 pixels). The result for projection in Fig. 4 was then formed into a matrix. During the image reconstruction process, normalized sensitivity map had been used to ease the coordination of the color level on the tomogram.

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4 Image Reconstruction: The Inverse Problem The inverse problem is to determine from the system response matrix (sensitivity matrices), a complex transformation matrix for converting the measured sensor values into pixel values. It is known as the tomogram [4]. The details for tomogram reconstruction are presented in the following section.

4.1 Image Reconstruction Algorithm To reconstruct the cross-section of image plane from the projection data, backprojection algorithm has been employed. Most of the work in process tomography has focused on the back-projection technique. It is originally developed for the X-ray tomography and it also has the advantages of low computation cost [5]. The measurements obtained at each projected data are the attenuated sensor values due to object space in the image plane. These sensor values are then back projected by multiply with the corresponding normalized sensitivity maps. The back projected data values are smeared back across the unknown density function (image) and overlapped to each other to increase the projection data density. The process of back-projection is shown in Fig. 5. The density of each point in the reconstructed image is obtained by summing up the densities of all rays which pass through that point. This process may be described by Eq. 3 [6]. Equation 3 is the back-projection algorithm where the spoke pattern represents blurring of the object in space. fb ðx; yÞ ¼

m X

gj ðx cos hj þ y sin hj ÞDh

ð3Þ

j¼1

where f b(x, y) = the function of reconstructed image from back-projection algorithm, hj = the jth projection angle and Dh = the angular distance between projection and the summation extends over all the m projection. The algorithm is further extended to Linear Back Projection to include the linear function of ultrasonic transducer as depicted in Eq. 2. The detail of Linear Back Projection algorithm is described in the next section.

4.2 Linear Back-Projection Algorithm (LBP) In Linear Back-Projection algorithm (LBP), the concentration profile is generated by combining the projection data from each sensor with its computed sensitivity maps. The modeled sensitivity matrices are used to represent the image plane for each view.

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Fig. 5 The back-projection method

Fig. 6 Reconstruction results for several attenuation coefficients

To reconstruct the image, each sensitivity matrix is multiplied by its corresponding sensor loss value; this is same as back project each sensor loss value to the image plane individually [6]. Then, the same elements in these matrices are

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summed to provide the back projected voltage distributions (concentration profile) and finally these voltage distributions will be represented by the color level (colored pixels). This process can be expressed mathematically as below [6]: VLBP ðx; yÞ ¼

32 X 32 X

STx; Rx  MTx; Rxðx; yÞ

ð4Þ

Tx¼1 Rx¼1

where VLBP(x, y) = voltage distribution obtained using LBP in the concentration profile matrix, STx,Rx = sensor loss voltage for the corresponding transmission (Tx) and reception (Rx) and MTx; Rxðx; yÞ = the normalized sensitivity map for the view of Tx to Rx.

5 Conclusions Five similar phantoms with different a were tested. The results are shown in Fig. 6. The density of the tomogram is clearly visible when a is 1.0, 0.8 and 0.6. However when a is 0.4 and 0.2, the phantoms are slightly blurred but the shape and the region can still be justified. Overall, the ultrasonic tomography according to our configuration can be used for imaging multi-attenuation phantoms. Although the reconstructed phantoms are hazy at low attenuation coefficient, however, using suitable image processing, the shape and region can be further improved.

References 1. Rahim RA, Rahiman MHF, Zain RM, Rahim HA (2011) Image fusion of dual-modal Tomography (Electrical Capacitance and Optical) for solid/gas flow. Int J Innov Comput Inf Control 7(9):5119–5132 2. Rahiman MHF, Rahim RA, Zakaria Z (2008) Design and modelling of ultrasonic tomography for two component high acoustic impedance mixture. Sens Actuators A Phys 147(2):409–414 3. Rahmat MF, Sabit HA, Rahim RA (2010) Application of neural network and electrodynamic sensor as flow pattern identifier. Sens Rev 30(2):137–141 4. Rahmat MF, Isa MD, Rahim RA, Hussin TAR (2009) Electrodynamics sensor for the image reconstruction process in an electrical charge tomography system. Sensors 9:10291–10308 5. Garcia-Stewart CA, Polydorides N, Ozanyan KB, McCann H (2003) Image reconstruction algorithms for high-speed chemical species tomography. In: Proceedings 3rd world congress on industrial process tomography. Banff, Canada, pp 80–85 6. Rahiman MHF, Zakaria Z, Rahim RA, Ng WN (2009) Ultrasonic tomography imaging simulation of two-phase homogeneous flow. Sens Rev 29(3):266–276

A Healthcare Cloud Computing Strategic Planning Model Kuo Mu-Hsing

Abstract Healthcare requires continuous and systematic innovation in order to provide high quality services. Cloud computing is a new model of computing that promises to provide more flexibility, less expense and more efficient IT services to end-users. The aim of this paper is to propose a healthcare cloud computing strategic planning model that could be used by a healthcare organization to determine its direction, strategy, and resource allocation for a cloud project. Keywords Cloud computing Strategic planning


Electronic health record (EHR)
Healthcare


1 Introduction According to the US National Institute of Standards and Technology (NIST) definition, ‘‘cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or serviceprovider interaction’’ [1]. Its applications have been reported in business, industry, research, education, transportation and even national security [2–5]. In healthcare, managers and experts also believe that it can increase Electronic Health Record (EHR) adoption, reduce in-house IT maintenance burdens and therefore improve healthcare services. For example, Kudtarkar et al. [6] used

K. Mu-Hsing (&) School of Health Information Science, University of Victoria, 3050 STN CSCVictoria, B.C V8W 3P5, Canada e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_76,  Springer Science+Business Media B.V. 2012

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Amazon Web Services (AWS) elastic compute cloud (EC2) to compute orthologous relationships for 245,323 genome-to-genome comparisons. The computation took just over 200 h and cost $8,000 USD, approximately 40% less than expected. Dudley et al. [7] also used EC2 to perform a statistical analysis to uncover expression quantitative trait loci (eQTL). The study demonstrated that cloud computing is a feasible and economical technology that enables large scale data integration and analysis for studies in genomic medicine. In viewing the existing processes for patients’ vital data collection require a great deal of labour work to collect, input and analyze the information, Rolim et al. [8] proposed a cloud based system to automate the process. The American Occupational Network (AON) is improving patient care by digitizing health records and updating its clinical processes using cloud-based software. The company has reduced medical transcription costs by 80 percent and provided faster and more accurate billing to individuals and insurance companies, reducing the average time to create a bill from 7 days to less than 24 h [9]. Despite the many reported successful applications of cloud computing in healthcare, there are several obstacles that restrict its full adoption [10]. In this paper, the author discusses the challenges of adopting cloud computing and proposes a healthcare cloud computing strategic planning model that could be used by an organization to determine its direction, strategy, and resource allocation for a cloud project.

2 The Challenges of Using Cloud Computing Cloud computing provides potential opportunities for improving EHR adoption, healthcare services and research. However, there are a number of issues that will need to be addressed before its widespread use in healthcare. Some important challenges include [10–13]: • Lack of trust by healthcare professionals—In some cases, a service level agreement (SLA) may not offer a commitment to allow cloud user to audit its data. The loss of data governance causes concerns when user’s sensitive data and mission-critical applications move to a cloud computing paradigm where providers cannot guarantee the effectiveness of their security and privacy controls. • Data jurisdiction issues—Cloud computing is a shared resource and multi-tenancy environment. Physical storages could be widely distributed across multiple jurisdictions. Different jurisdiction may have different laws regarding data security, privacy, usage, and intellectual property. For example, the U.S. Patriot Act gives the government a right to demand data if it defines conditions as being an emergency or necessary to homeland security. The problem is that many main cloud providers such as Microsoft, Google and Amazon are US based. The data jurisdiction concern certainly obstructs cloud computing adoption.

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• Unpredictable performance—The cloud computing market is becoming more crowded with large providers entering the playing field, each of which is trying to attract customer by providing lower-priced services. In order to maintain the profit they usually cut corners in the value delivery system. For example, they limit access to the cloud resources, use out-of-date hardware/software, deploy older CPU technology, etc. The result is variable and unpredictable performance in the service. Furthermore, because of high competition, many cloud providers over commit computing resources to customers. This difference between the customer’s expectation and what the provider can really deliver represents a major technical challenge for the customer to provide high quality service to its own users. • Data interoperability issues—Currently, most cloud infrastructures provide very little capability on health data, application and service interoperability. This makes it difficult for the health organization to migrate from one provider to another, or move data and services back to an in-house IT environment (i.e. data lock in). • Resources separation failure—Cloud computing is usually accessible to many different customers. Therefore, it poses increased data protection risks for customers For example, a customer requests to delete data stored in the virtual infrastructure; as with most operating systems, this may not result in true erasing of the data immediately. The data are still stored in the disk but are just not available. In the multiple tenancies environment, hardware resources are reused by other customers. In this case, a third party could have access to another customer’s ‘‘deleted data’’. This represents a higher risk to the cloud customers than with dedicated hardware. • Malicious internal users—The other new security challenge for cloud computing is the damage which may be caused by malicious insiders. As cloud use increases, employees may increasingly become targets for criminal organizations. If the malicious insider is a system administrator, then he could use his privileges to steal critical data.

3 The Healthcare Cloud Computing Strategic Planning Model When an organization has decided to move to the cloud service, it needs a strategic planning model to ensure a clear objective defined for a cloud project, concrete procedures to achieve the objective, and all factors related to the effort, both positive and negative, need to be identified and addressed. For the purpose, this paper proposes a strategic planning model that could be used by a health organization to determine its direction, strategy, and resource allocation for moving traditional IT Infrastructure towards clouds. The model includes four iteration stages: Identification, Evaluation, Action, and Follow-up (see Fig. 1).

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2. Evaluation Stage

Identify the QI goal by hearing the VOC

Study and analyze the benefits & risks of Cloud Computing Apply SWOT method to evaluate the feasibility of the cloud based approach Deal with Cloud challenges

Analyze the current status of service process Define the QI performance indicators Select the cloud project team members Accomplish the project charter and job assignments

Healthcare Cloud Computing Strategic Planning

4. Follow-up Stage Develop a follow-up plan Determine when to measure and how to measure the healthcare service improvements assess the magnitude of the improvement Knowledge diffusion and application

3. Action Stage Draw up an implementation plan Determine the cloud service and deploy model Compare different cloud providers Obtain assurance from selected cloud provider Consider future data migration

Fig. 1 The healthcare cloud computing strategic planning model

Stage 1—Identification In the model, the first stage is to analyze the current status of the organization’s service process and identify the fundamental objective of quality improvement (QI) by hearing the Voice of Customer (VOC) or the patients. The root causes analysis (RCA) method can be applied to analyze the problems of current service process [14]. The objective identification and its scope must be clarified so as to serve the patients more efficiently and effectively. In addition, the strategic planning team has to define healthcare service quality indicators and explain their purpose as well as the use of each indicator. More importantly, the performance indicators’ definition and their evaluation methods must be agreed upon and confirmed by the people involved. This stage of the model provides the strategic planning team with a well-defined scope for the problem being faced. Stage 2—Evaluation The second stage of the model is to evaluate the opportunities and challenges of adopting cloud computing. The SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis could be applied to evaluate the feasibility of the cloud based approach [15]. In SWOT, strengths and weaknesses are internal factors; opportunities and threats are external factors. Stage 3—Action After evaluation of the opportunities and challenges of the cloud computing model, the organization will be able to determine whether it will move a service

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into the cloud. If the answer is yes, then the third stage is to deal with the cloud issues and draw up an implementation plan. Many references are available for the purposes [11–13, 16–18]. In this paper, the author recommend the plan should include following. Determine the cloud service and deploy model As discussed in [1], cloud computing can refer to several different service types, including Application/Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and different deployment models, including private cloud, public cloud, community cloud and hybrid cloud. Each service type or deploy model has its own benefits and risks. Therefore, the key considerations in contracting for different type of service or deploy model should be different. Compare different cloud providers Choosing a proper cloud provider is the most important part of the execute plan. Different providers may offer different service models, pricing schemes, audit procedures and privacy/security policies. The organization has to compare different provider offerings. Also, it needs to evaluate the provider’s reputation and performance before it signs a contract. Obtain assurance from selected cloud provider The organization needs assurances that the selected provider will provide quality of service (QoS) and follow sound privacy, security and legal practices/ regulations. The QoS assurances include among others on-demand access, payper-use, rapid elasticity, on time trouble shooting support and operational transparency. The privacy and security assurances cover data confidentiality, integrity, availability, authenticity, authorization and non-repudiation. Also, the provider must assure that the data, including all of its backups, are stored only in geographic locations permitted by contract, service level agreement (SLA) and regulation. Consider future data migration The organization may have to move data and services to another provider or back to an in-house IT environment because the provider ceases business operations, has an unacceptable decrease in service quality or there is a contract dispute. Data portability and interoperability must be considered up front as part of the executive plan. Stage 4—Follow-up The last stage is to deploy the cloud computing infrastructure and develop a follow-up plan. The plan indicates when to measure and how to measure the healthcare service improvements. Reasonable targets are established beforehand and the results of the new services are measured against the specified targets or performance indicators to assess the magnitude of the improvement. Finally, the gained knowledge and experience must be diffused and deployed throughout the organization. The application of the strategic planning model to improve healthcare service is an iteration process. The follow-up information reflects the new current service condition. If it is not satisfied, the health organization needs to review what facts

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influence the objective achievement. If the main cause of unsatisfied service condition is from the cloud provider, the organization will consult and discuss with the provider to improve service or may consider migrating data and services to another provider. In this care, the new condition is the start of next service improvement iteration.

4 Conclusion When a health organization considers moving its service into the cloud it needs strategic planning to examine environmental factors that may affect it, assess its capabilities to achieve the goal, and identify strategies designed to move forward. In this paper, we proposed a cloud computing strategic planning model that could be applied by a health organization to determine its direction, strategy, and resource allocation to migrate from traditional health services to the cloud based services. The model includes four iteration stages (Identification, Evaluation, Action and Follow-up). At the first stage, the organization analyzes the current status of the service process and identifies the fundamental service objective. Stage 2 is to evaluate the opportunities and challenges of adopting cloud computing. By utilizing the SWOT analysis, the organization could determine the internal strength and weakness factors as well as the external opportunity and threat factors of adopting the new model. Then, in stage 3, the organization draws up a cloud computing executive plan. We suggest that this should include at least following items: determine the cloud service and deploy model, compare different cloud providers, obtain assurance from selected cloud provider, and consider future data migration. The last stage is to deploy the cloud computing infrastructure and develop a follow-up plan to measure the healthcare service improvements. If the new service condition is not satisfied, the condition is the start of next service improvement iteration.

References 1. Mell P, Grance T (2010) The NIST definition of cloud computing. Commun ACM 53(6):50 2. Koufi V, Malamateniou F, Vassilacopoulos G (2010) Ubiquitous access to cloud emergency medical services. In: 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), IEEE Press, New York, pp 1–4 3. Behrend TS, Wiebe EN, London JE, Johnson EC (2011) Cloud computing adoption and usage in community colleges. Behav Inf Technol 30(2):231–240 4. Li Z, Cheng C, Wang K (2011) Cloud computing for agent-based urban transportation systems. IEEE Intell Syst 26(1):73–79 5. NSA embraces cloud computing, http://www.darkgovernment.com/news/nsa-embracescloud-computing/ 6. Kudtarkar P, DeLuca TF, Fusaro VA, Tonellato PJ, Wall DP (2010) Cost-effective cloud computing: a case study using the comparative genomics tool. Roundup Evol Bioinform 6:197–203

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7. Dudley JT, Pouliot Y, Chen R, Morgan AA, Butte AJ (2010) Translational bioinformatics in the cloud: an affordable alternative. Genome Med 2(51):1–6 8. Rolim CO, et al (2010) A cloud computing solution for patient’s data collection in health care institutions. In: Second IEEE international conference on eHealth, telemedicine, and social medicine, IEEE Press, New York, pp 95–99 9. Strukhoff R, O’Gara M, Moon N, Romanski P, White E Healthcare clients adopt electronic health records with cloud-based services. Cloud Comput Expo, http://cloudcomputing.syscon.com/node/886530 10. Kuo MH (2011) Opportunities and challenges of cloud computing to improve healthcare services. J Med Internet Res (JMIR) (in press) 11. Armbrust M., et al (2009) Above the clouds: a Berkeley view of cloud computing. Technical report no. UCB/EECS-2009-28, EECS Department, UC Berkeley 12. The European Network and Information Security Agency (ENISA) (2009) Cloud computing—benefits, risks and recommendations for information security 13. Cloud security alliance. Security guidance for critical areas of focus in cloud computing (V2.1), http://www.cloudsecurityalliance.org/csaguide.pdf 14. Lee TS, Kuo MH (2009) Toyota A3 report: a tool for process improvement in healthcare. In: International conference on information technology and communications in health (ITCH2009), Victoria 15. Kuo MH, Kushniruk AW, Borycki EM (2011) Can cloud computing benefit health services?—A SWOT analysis. In: Medical informatics Europe (MIE 2011), Oslo, Norway (in press) 28–31 August 2011 16. Ward BT, Sipior JC (2010) The internet jurisdiction risk of cloud computing. Inf Syst Manag 27(4):334–339 17. Jansen W, Grance T (2011) NIST guidelines on security and privacy in public cloud computing. National Institute of Standards and Technology, USA 18. Kuo MH, Kushniruk AW, Borycki EM (2010) Design and implementation of a health data interoperability mediator. In: International conference on challenges of interoperability and patient safety in healthcare (STC2010), pp 101–107

Part VII

Mobile Platform, Consumer Electronics and Multimedia Services

Multimedia Data Placement and Retrieval for Mobile Platform Seung-Ho Lim

Abstract Multimedia applications are one of the most frequently used applications for mobile devices. For each mobile device, lots of multimedia data are frequently stored, read, edited, and deleted, and these work are repeated indefinitely. At the aspect of multimedia data operations, storage system management is most important for efficient multimedia operations. In this paper, we propose an efficient data placement and retrieval scheme mobile embedded systems. In the mobile devices, the main storage media is NAND flash memory. We note that in the NAND flash memory, video data should be stored and retrieved with the consideration of flash memory page and block boundaries. In the view of this, we reorganize multimedia frames taking into account of NAND flash memory location. Keywords Multimedia storage


Retrieval
NAND flash
Mobile device

1 Introduction In recent years, high performance computing and networking technology have made possible the generation of large amounts of multimedia content, and there has been explosive growth in the market for mobile multimedia devices such as Personal Video Recoder (PVR), Portable Multimedia Player (PMP), and smart phones. In these devices, flash memory, especially NAND flash memory, is

S.-H. Lim (&) Department of Digital Information Engineering, Hankuk University of Foreign Studies, Seoul, Republic of Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_77, Ó Springer Science+Business Media B.V. 2012

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typically used as the main storage device due to its outstanding characteristics, including increased capacity, low power consumption, small size, and light weight [1]. With NAND flash storage, multimedia data should be managed with the support of an efficient multimedia storage subsystem. In multimedia storage subsystem, the characteristics of multimedia files should be considered to support real-time recording/playback, as well as efficient management of large-capacity storage. NAND flash memory is used for mobile device to download the video streaming data in their own systems, which might be permanently or temporarily. Since NAND flash memory has many inherent restrictions, we should consider efficient data placement and retrieval schemes with the characteristics of flash memory, such as page and block boundary operations of NAND flash memory. In NAND flash memory, read and write commands are performed in units of page. The crucial feature for NAND flash memory is that erase operation should be done before writing to the pages. The erase operation is performed in units of block, whose size is much larger than that of pages. Typically, the size of one page is 4 KB and doubles as manufacturing process advances, and one block is composed of 64 or 128 pages. For read operations, the IO bandwidth is relatively proportional to the IO size since there is little additional flash operation. The consideration factor with read operation is how many pages are read, since specific command level’s NAND flash address is assigned for each page, whether whole the page are read or not. On the other hand, write operations should consider efficient erase operations. When the number of free pages is insufficient for write operations, free pages should be made by garbage collection (GC), where GC is the process that makes available free region by selecting one block, moving data of valid pages to other region, and erasing the block. Thus, for more efficient garbage collection, the selected block for erase has minimum valid pages. In multimedia embedded systems, multimedia Video stream consists of m GOP (Group of Picture)s, each GOP has n frames. For example, MPEG stream is represented as a sequence of I-, P-, and B-frames. With the series of GOP picture, video player should extract the partial video frames that correspond to the requested resolution from the full resolution video stream for each service round time. The service round can be defined as the time period in which the video player should retrieve video data to guarantee real-time playback capability. In this situation, the video play capability is closely related with the IO and storage subsystem management since proper IO should be guaranteed to extract proper video sub streams. In this paper, we propose efficient multimedia data arrangement and retrieval scheme for mobile multimedia device, whose storage is NAND flash memory. In the mobile multimedia devices, main storage media is NAND flash memory. We note that in the NAND flash memory, video data should be stored and retrieved with the consideration of flash memory page and block boundaries. In the view of this, we reorganize and place the multimedia frames taking into account of NAND flash memory location such as page and block boundaries. In addition, we retrieve the reorganized multimedia data taking in account of NAND flash memory read

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Table 1 Characteristics of NAND flash memory Parameters

Specification

Capacity Page size Block size Interface Page read to register Page program from register Bus transfer time Block erase Program/Erase cycle

1 GB * 32 GB 2 KB, 4 KB, 8 KB 128 KB * 1 MB Page based I/O 25 ls/page 200 ls/page 100 ls/page 1.5 * 2 ms/block 10 K * 100 K

operations. With the multimedia data placement and retrieval scheme, it is possible to efficiently record and play the multimedia data for mobile multimedia platform.

2 Background In this section, we describe the characteristics of NAND flash memory in terms of the storage media, and we introduce multimedia data features to be applied to NAND flash memory. NAND flash memory is array of memory cells that consist of floating-gate transistors. There are three commands used in NAND flash memory; read, program, erase. The program command makes electron charges are trapped in the floatinggate, while erase command makes electron charges be escaped from floating-gate and reside in transistor substrate. The read command reads the status of the transistor. The read and program command is related with data transfer between host and SSD devices, and the data unit is page. The erase command has no data transfer between host and SSD, and the erase is operated at the block-based (Table 1). Each command and data transfer operation for read and write operations is accomplished by three steps; cmd, data and program/read. Among three, cmd and data occupy physical channel between flash controller and flash memory chip, while program/read operation is flash memory’s internal operation. Table*\ref{tab:flash} summaries typical flash memory parameters for each operations. As shown in the table, one page read operation consumes about 125 ls, including page read to internal chip register and bus transfer from chip register to host side. As a same manner, one page write operation consumes about 300 ls. Multimedia Video stream consists of m GOP (Group of Picture)s, each GOP has n frames [2]. For example, MPEG stream is represented as a sequence of I-, P-, and B-frames. Among these frames, the average size of I frame is largest, and the average size of B frame is smallest. The ratio of frame size between frames is roughly 4:2:1 or similar like that. Let the structure of one GOP in encoded MPEG video be {IBBPBBPBB}. The next possible frame sequence of fast-forward scan or degraded

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Fig. 1 Data arrangement and placement scheme for multimedia data to NAND flash memory

quality of video play could be {IPPIPP..} which does not include any B-frames, and the next one is {II..} without any P-frames, and so on. During each real-time playback round, multimedia application should retrieve the fixed number of video frames for each playback level to ensure the continuous playback. In other words, each subsequence accessed during a round is required to retrieve together from storage device to enhance the IO utilization and guarantee continuous playback.

3 Data Placement and Retrieval Scheme In this section, we describe the multimedia data reorganization and retrieval scheme for mobile multimedia devices. The overall placement scheme is described in Fig. 1. For simplicity, we assume that video data are downloaded from multimedia data server. The other case of storing data to mobile device can be easily expanded from the following description. As shown in the figure, the frames having same frame level are clustered and stored together in same block repository. When the sub-streams of (K ? 1)th GOP are received from the server, each frame is scattered and written to its repository block. Since write operations are performed with page, we preserve buffer in memory for tail of frames, so the remaining tailored part of frame is buffered if the amount of data are not fit into flash page, and later it is written with front part of next frame. The required buffer size is maximum number of frames repository. In the figure, four buffers are required. For each block repository, if the available space for current allocated block repository is not sufficient to store the next incoming frame,

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Fig. 2 Data retrieval and buffering scheme for multimedia data from NAND flash memory

a new block is allocated and the remaining part is written the new allocated block. Then, the block is included in the corresponding frame’s block repository. This operation is repeated for every video service round, that is, each GOP that is transferred by server is temporarily buffered in memory buffer, and each frame is placed into each block repository as a real-time playback manner. This data reorganization and placement makes it possible to treat frames with each frame level, i.e., I-frame, P-frame, and I-frame is treated independently, to edit video streams very efficiently with litter operation overhead. Let us consider if there is little storage space available so we should remove some files to make available space but all the data are important. In this case, instead of remove the whole the video data file, we can decrease the quality of video file by removing sequence of frames of less important part, that is B frames or P frames. Since, the sequence of frames that have same frame features are clustered in same block repository, we erase the block repository of corresponding frame sequences without any data move operations. Thus, we can downgrade the quality of video files and make available space very easily. In addition, the multimedia data editing is also very easy with this placement. If we would like to make 29 fast forward video file from the original video data, we just links block repositories of only

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Fig. 3 It plots the experimental results for video retrieval schemes according to corresponding video placements; conventional method and proposed method

I-frame and P-frame, without copying any part of video data. Since we know the position of each frame level, it is very simple to link for each frame type. When video is played by the mobile user as a offline manner, the video streams are retrieved from mobile storage device. To analysis retrieval operation for NAND flash memory-based storage system, we should know about the detail of NAND flash memory read operation. When data is requested to NAND flash memory, the data is first moved from NAND cell to internal page buffer that resides in the chip, and it is transferred from the internal page buffer to main memory via DMA operation. Two factors should be considered in the read operations. The first one is NAND read unit, i.e., page. Since read command is performed in unit of page, the whole page data is moved from NAND cells to internal page buffer for each page command. The command is generated for each page, although the pages are serialized. Read performance of NAND flash memory-based storage system is proportional to the number of pages to be read. The second one is the amount of really requested data in the page. Among the page data in the internal page buffer, the only request data are transferred to main memory via DMA operation. Since DMA operation means transfer time, we should reduce the DAM time as short as possible. Thus, if we consider the two factors, read operations should be generated like that the number of requested page should be as less as possible, at the same time, the amount of useful data for each requested page should be as large as possible. The data retrieval operation of Kth service round time is described in Fig. 2. For example, we consider that user tries to retrieval with B-frames. In our retrieval operations, only required frames are retrieved for each GOP service round time. As shown in Fig. 2, I-frame and P-frame are retrieved for Kth service round time when user plays video with 29 fast forward or with lower quality video playing.

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Thus, the minimum pages for the current service round are read from storage, since the unnecessary parts are not retrieved at all. If the last page of the unit sub-stream of Kth GOP is also has data for (K ? 1)th GOP, it is buffered in main memory buffer and will be used next service round time. Thus, all the page data is useful to play video streams.

4 Performance Evaluation We evaluated the proposed placement and retrieval schemes with emulated NAND flash memory-based embedded system simulation environment. The evaluation board employs a 266 MHz ARM processor and 128 MB of SDRAM. The NAND flash chip is emulated in the RAM regions. We modeled delay factors in our emulated flash chips based on the real chip’s datasheet, so that the emulated chip sleeps during the requested operations, enabling us to measure the I/O performance. We evaluate video play times required for multimedia retrieval operations. For this, multimedia video files are generated and stored in storage device. Multimedia files used for play operations correspond to a 3 min 1Mbps mpeg video file. 12 video files were stored in NAND flash memory. There are two placements policy. The first is conventional method, that is, video files are stored as encoding orders, which does not consider any NAND flash memories page and block boundary condition. The second is proposed placement scheme, which allocates different flash block repositories for each frame types, and places them into each flash block repository. The average response times for each GOP retrieval operation are evaluated as the number of concurrent video play increases. Among the video retrieval operations, one-third is full stream retrieval, one-third is video retrieval for I and P frames, and one-third is video retrieval for only I frames. Figure 3 plots and compares average response times for two different multimedia data placement and retrieval schemes. As shown in the figure, our proposed scheme outperform conventional scheme owing to the efficient retrieval method. The performance gap is becoming enlarging as the number of concurrent played video increases. Since our method only retrieves the required video sequence on time, there is no waste of IO bandwidth as well as NAND flash memory operation. By using our video data treatment scheme, the multimedia mobile device can have high utilization for video data storing and playing, also it gives system efficiency such as power efficiency and NAND flash memory lifetime.

5 Conclusion In mobile multimedia devices such as Personal Video Recoder (PVR), Portable Multimedia Player (PMP), and smart phones, NAND flash memory is typically used as the main storage device due to its outstanding characteristics, including

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increased capacity, low power consumption, small size, and light weight. With NAND flash storage, multimedia data should be managed with the support of an efficient multimedia storage subsystem. In multimedia storage subsystem, the characteristics of multimedia files should be considered to support real-time recording/playback, as well as efficient management of large-capacity storage. We study the efficient multimedia data placement and retrieval schemes for mobile multimedia platform, whose storage is NAND flash memory. We note that in the NAND flash memory, video data should be stored and retrieved with the consideration of flash memory page and block boundaries. In the view of this, we reorganize and place the multimedia frames taking into account of NAND flash memory location such as page and block boundaries. In addition, we retrieve the reorganized multimedia data taking in account of NAND flash memory read operations. With the multimedia data placement and retrieval scheme, it is possible to efficiently record and play the multimedia data for mobile multimedia platform.

References 1. Lawton G (2006) Improved flash memory grows in popularity. IEEE Comput 39(1):16–18 2. Mpeg Software Simulation Group (1996) encoder/decoder. Version 1.1a 3. Lim S, Jeong Y, Park KH (2008) Data placement and prefetching with accurate bit rate control for interactive media server. ACM Trans Multimedia Comput Commun Appl 4(3): 1–25 4. Berson S, Ghandeharizadeh S, Muntz R, Ju X (1994) Staggered striping in multimedia information systems. In: Proceedings of the 1994 ACM SIGMOD international conference on Management of data, pp 79–90, 24–27 May 1994 5. Gopalan K, Chiueh T-C (2002) Multi-resource allocation and scheduling for periodic soft real-time applications. In: Proceedings of ACM/SPIE multimedia computing and networking, pp 34–45 6. Lee J (2002) On a unified architecture for video-on-demand services. IEEE Trans Multimedia 4(1):38–47 7. Wu M, Zwaenepoel W (1994) eNVY: a non-volatile, main memory storage system. In: Proceedings of the 6th international conference on architectural support for programming language and operating systems, pp 86–97, 1994 8. Samsung Electronics Co., K9XXG08XXM flash memory specification, http://www.samsung. com/global/business/semiconductor/, 2009 9. Intel Corporation Understanding the flash translation layer(FTL) specification, http:// developer.intel.com/ 10. Lawton G (2006) Improved flash memory grows in popularity. IEEE Computer 39(1):16–18 11. Quinlan S Dorwards S (2002) Venti: a new approach to archival storage. In: Proceedings of USENIX conference on file and storage technologies, 2002

A Platform-Independent Smartphone Application Development Framework Min Choi

Abstract Smartphones are generally less powerful than other mobile computing devices. Therefore, it is necessary to offload the computation-intensive part by careful partitioning of application functions across the cloud computing. In this paper, we propose a platform-independent framework for smartphone application development. The core technology of our framework is based on web service and SOAP protocol. Because we are targeting on OS independent platform, web service is the best fit for the framework that is not depending on a certain smartphone OS platform. To this end, we propose the framework architecture and evaluate the system using the application of PI value computation. The result shows that our mobile cloud computing platform performs better as increasing number of cloud nodes. Keywords Smartphone applications

 Platform-independent  Web service

1 Introduction The convergence of mobile computing and cloud computing is a paradigm shift in field of mobile computing. Within a few years, we can expect a major shift from traditional mobile application technology to mobile cloud computing. It improves application performance and efficiency by offloading complex and time consuming tasks onto powerful computing platforms. By running only simple tasks on mobile

M. Choi (&) Chungbuk National University, 52 Naesudong-ro, Heungdeok-gu, Cheongju Chungbuk, 361-763, Republic of Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_78, Ó Springer Science+Business Media B.V. 2012

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devices, we can achieve a longer battery lifetime and a greater processing efficiency. This offloading with the use of parallelism is not only faster, but it can also be used to solve problems related to large datasets of non-local resources. With a set of computers connected on a network, there is a vast pool of CPUs and resources, and you have the ability to access files on a cloud. In this paper, we propose a novel approach that realizes the mobile cloud convergence in transparent and platform-independent way. Users need not know how their jobs are actually executed in distributed environment and users need not take into account their mobile platforms are IPhone or Android. This is because the core technology of our framework is based on web service and SOAP protocol through HTTP 80 port. We are targeting on OS independent smartphone application development platform. The OS independent platform means that the structure not depending on smart-phone platform. It consists of web service and mobile web (HTML5 like) device API standard. By running complex task on cloud as a web service, it is possible to reduce computation time and battery power. Likewise, complex business logics and computations will be offloaded by cloud computing platforms. With this mobile cloud computing framework you will be able to enjoy all such application only if you can access web through your cell phone.

2 Related Work Despite the market’s heterogeneity, the nexus of smartphones, wireless broadband, and network-based cloud computing constitutes a perfect storm of opportunity for application developers, luring their attention toward the new platforms. COMSOL [1] reduces the upfront investment in equipment and technical expertise so dramatically that high-performance computing is now ready for the main stream. Exploratory speedup factors of 69 and 119 in the context of embarrassingly parallel COMSOL Multiphysics computations provide a powerful business justification for Windows HPC. The ability to divide and conquer by distributing the memory required of any problem size allows us to draw conclusions to problems we can’t even fathom today. Dandelion [3] provides a system implementation on the Maemo Linux smartphone platform and the Rice Orbit body sensor platform. They evaluate Dandelion by implementing real-world applications, and show that Dandelion effectively eliminates the programming gap and significantly reduces the development efforts. We further show that Dandelion incurs a very small overhead; in total less than 5% of the memory capacity and less than 3% of the processor time of a typical ultra low power sensor. It allows developers to easily write data processing code to be executed on sensors, in a programming style similar to traditional smartphone development. With the minimalist design of the runtime system, Dandelion incurs very small overhead and therefore can be easily ported to various re-sourceconstrained sensor platforms.

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While successful for complicated mobile applications, such programming styles significantly differ from those used in smartphone application development, making it is difficult for smartphone developers to adopt them. In contrast, Dandelion leverages the simplicity of smartphone-centered body sensor networks and focuses on supporting in-sensor data processing tasks. With this trade-off, Dandelion is able to provide transparency in programming style. Let’s take a look at heterogeneous distributed systems and support different levels of programming transparency. In most cases, the programmer has to program each platform in the system directly without any transparency. Some systems support programming transparency with an unified OS abstraction or distributed runtime system, mostly based on a virtual-machine approach to hide ISA variances. This approach, however, proves to be inefficient on resourceconstrained sensors. In contrast, Dandelion achieves transparency by limiting the senselet functions to data processing and by introducing an extra compilation phase to produce.

3 Platform-Independent Smartphone Application Development Framework This framework provides a commonly accessible layer which is platform independent, for example W3C mobile web standard. Our proposed approach makes use of web service architecture through the W3C mobile web standard layer. However, only providing facilities such as vast pool of servers are not enough for mobile cloud convergence. For offloading mobile computation by cloud service, framework support is necessary. Especially, the support has to be a type of serviceoriented. A simple remote procedure call (RPC) or an inter-process communication (IPC) in cloud side is dependent on a certain platform or target, such as Android and IPhone. So, this is not the proper solution of general purpose offloading framework. In terms of platform independence, android IPC is better than the RPC or IPC since it utilizes a platform independent interface definition language (IDL). Actually, android IPC runs as an android service which runs at background while other activities (applications) are running at foreground. The service can be utilized between multiple activities. Once an android activity is bound to currently executing service, it will communicate with the service using a predefined interface by a stub/proxy pair. The service interfaces are defined in an interface definition language, known as AIDL. But, the Android IPC still has a drawback in that it is only able to communicate within a local node. The android IPC is still has a limitation because the communication technique is localized within the local platform. The android IPC is not applicable when we are trying to communicate with other computers or devices. Thus, if we want to communicate with other sides, it is better to use network socket instead of android IPC. Therefore, our

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Fig. 1 Platform-independent smartphone application development framework

smartphone application development environment does not depend on any native platform. This is to provide environment of platform independent smartphone application development without concern of the OS. In addition to the platform independence, one of the important things in area of smartphone application development is the fast implementation and fast delivery (rapid application development: RAD). If an idea is realized as a smartphone application by a developer, the first development is being the standard in the area. For this fast development and quick deployment, development method by building up with commodity components is more recommendable than implementing from scratch. Through reusable and composable elementary components, we can easily make new services and applications very quickly. In this paper, our mobile cloud architecture is based on web service and we provide common service and composable elements as a sort of web service. Figure 1 shows the job distribution strategy of PI value computation on our platform-independent smartphone application development framework with web service. By this offloading framework, computation oriented parts run on a remote cloud node as a web service and the rest of the parts run on mobile devices. As a result, this framework leads to performance improvement and longer battery lifetime of mobile devices. Figure 2 depicts the concept of the service oriented smartphone application development framework. The web service oriented infrastructure is realized by SOAP with Attachments API for JAVA (SAAJ). This framework provides 6 key administration policies and services; resource management, load balancing, certification, access control, security, and charging. As shown in the stage 2 of Fig. 2, we can establish a service repository consisting of elementary/commodity services. All these commodity/elementary services are implemented and deployed by a type of web service. Thus, this is totally platform independent and fast deliverable for smartphone application development. For the convenience of smartphone

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Fig. 2 System architecture of web service oriented smartphone application development framework

application development, we expect that the following common/elementary service are necessary; augmented reality, HPC, voice/image processing, ubiquitous computing, E-learning, and so on. However, due to the limit of time and budget, we currently provide only PI value computation web service as a prototype. In Fig. 2, applications on mobile devices need to locate their wanted service. For this purpose, web service architecture comes with Universal Description, Discovery and Integration (UDDI). UDDI is a platform-independent XML based registry for worldwide to list themselves on the Internet and a mechanism to register and locate web service application. Our framework also makes use of UDDI to locate the proper user wanted web service.

3.1 Case Study: Task parallelization of PI (p) Calculation In this section, we show a development procedure of the cloud-based applications on a mobile platform, especially p calculation. The first step in building the mobile cloud converged application is to identify sets of tasks that can run concurrently and/or partitions of data that can be processed concurrently. The second step is to eliminate dependency, if any exists, between every computational phases in the algorithm. The dependency limit of the degree of parallelism results in performance degradation. p is a mathematical constant whose value is the ratio of any Euclidean plane circle’s circumference to its diameter; this is the same value as the ratio of a circle’s area to the square of its radius. Many formulas from mathematics, science, and engineering involve p, which makes it one of the most important mathematical

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constants. The simplest method to calculate p is circumference divided by diameter. However, it is difficult to get the exact circumference using this simple method. As a result, there are other formulas to calculate p. These include series, products, geometric constructions, limits, special values, and pi iterations. To calculate p through mobile-could convergence, we first need to convert the algorithm into a parallelized version. We present a p calculation with infinite series that puts forth a parallelization method for ease of application on the mobile cloud convergence. To calculate p, we compute the Maclaurin series generated by f ðxÞ ¼ tan1 ðxÞ as shown above. tan1 ðxÞ ¼ x 

x3 x5 x7 x2nþ1 þ  þ    þ ð1Þn þ  3 5 7 2n þ 1

A function to compute this based on the above form, cannot be parallelized because each computed value is dependent on previously computed values. To offload the computation-bound part to the cloud, a parallelized form of this equation should be provided. To this end, we convert the equation into an integral form that is suitable for mobile cloud convergence. Take the derivative of the above equation with respect to x, and change the variable x to t, for the sake of convenience. d 1 tan1 ðtÞ ¼ dt 1 þ t2 Integrating this equation for the interval a to b yields the integral form of tan1 ðtÞ, by substituting p4 ¼ tan1 ðtÞ into this formula we get parallelized form that is executable on cloud computing platform. p ¼ 4tan1 ðtÞ ¼

4 1 þ t2 a

Zb

ð8a; b 2 RÞ

We approximately get the p value by integrating this equation for the interval to 12. Unlike an infinite series representation, the integral form is fully parallelizable and it is easy to divide the problem into chunks/parts of work. We distribute and map these tasks onto multiple clouding nodes. However, this equation cannot be executed on cloud computing which is highly parallelized and distributed in a computing environment. This is an example of task parallelization and partitioning, and it can be run on a mobile cloud convergence platform.  12

4 Performance Evaluation We constructed our system with 8 nodes from cloud service, of Core2 Duo 2 GHz machines each with 2 GB RAM. The machines are connected by 1 Gbps Ethernet. Figure 2 shows an overview of our mobile cloud convergence framework,

A Platform-Independent Smartphone

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Table 1 PI value calculation result (execution time) for each cloud node Node 1 Node 2 Node 3 Node 4 Node 5 Node 6

Node 7

Node 8

1 2 3 4 5 6 7 8

2.094 1.828

1.828

14.703 7.343 4.906 3.688 2.953 2.453 2.11 1.843

7.344 4.89 3.672 2.937 2.453 2.11 1.844

3.688 2.938 2.453 2.109 1.829

2.937 2.454 2.109 1.828

2.453 2.094 1.844

20

execution time(sec)

Fig. 3 System performance as # of cloud nodes

4.907 3.672 2.954 2.453 2.093 1.843

15 10 5 0 1

2

3

4

5

6

7

8

designed in 3 stages: the first one is, and web service oriented infrastructure, the second one is commodity/elementary web services, and the third one is operating environment. As the number of nodes increases, the total execution time for computing PI value is decreases. The following Table 1 shows the execution time for each cloud node. Since our PI value calculation algorithm distributes the same amounts of data to all participants, the execution times in a row are almost the same. And the final execution time contains more time such as communication overhead, forkjoin overhead, processing overhead on mobile devices. A row in Table 1 represents the computation participants; for example, line 4 denotes that our PI value computation algorithm distributes the same amount of data to all participants. The plotted graph value in Fig. 3 becomes the sum of maximum value in Table 1 PI value calculation result (execution time) for each cloud node with TCP communication overhead.

5 Concluding Remarks In this paper, we propose a platform-independent framework for smartphone application development. Smartphone are generally less powerful than other mobile computing devices. Therefore, it is necessary to offload the computation-

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intensive part by careful partitioning of application functions across the cloud computing. For realizing the OS independent platform, web service is the best fit technology for the framework that is not depending on a certain smart-phone OS platform. To this end, we proposed a novel framework architecture and evaluated the system using the application of PI value computation. The result showed that our mobile cloud computing platform performs better as increasing number of cloud nodes.

References 1. Cross-Platform Smartphone Apps Still Difficult, IEEE Spectrum, June 2010 2. JueThner K (2011) Dramatically improve compute-intense applications in the supercomputing cloud. COMSOL HPC white paper, June 2011 3. Lin FX, Rahmati A, Zhong L (2010) Dandelion: a framework for transparently programming phone-centered wireless body sensor applications for health. ACM Wireless Health, San Diego, Oct 2010 4. Jeremy (2010) Cross-platform development options for smartphone apps, White Glove Apps, May 2010 5. Patel S, Kientz J, Hayes G, Bhat S, Abowd G (2006) Farther than you may think: an empirical investigation of the proxim-ity of users to their mobile phones. In: Proceedings of Ubicomp, pp 123–140, 2006

Energy-Efficient Scheduling for Mobile Sensor Networks with Renewable Energy Source Kae Won Choi

Abstract In this paper, we propose a time-scheduling algorithm for mobile sensor networks with a rechargeable battery which can be replenished by a renewable energy source, e.g., solar and wind power. Due to the mobility of sensor nodes, the channel gains between sensor nodes are time-varying as well as the battery replenishment rates are. To design a time-scheduling method for such a highly dynamic sensor network, we make use of the dual decomposition method that decomposes the resource allocation problem into multiple subproblems of manageable size. The simulation results show that the proposed algorithm optimally allocates transmission time to sensor nodes, minimizing overall energy consumption of the sensor network.



Rechargeable battery Keywords Sensor network Scheduling algorithm Dual decomposition





Renewable energy



1 Introduction The wireless sensor network is constituted by hundreds of thousands of low-cost and low-power sensor nodes which gather data from surrounding environment for serving various purposes such as military surveillance, traffic monitoring, and factory instrumentation, etc. [1]. Since the sensor network is typically deployed over a vast geographical area and can even be mobile, it is not feasible to make sensor nodes tethered to perpetual energy sources. Therefore, energy efficiency, K. W. Choi (&) Department of Computer Science and Engineering, Seoul National University of Science and Technology, 172 Gongreung 2-dong Nowon-gu, Seoul, 139-743, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_79, Ó Springer Science+Business Media B.V. 2012

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which prolongs the network lifetime, has been a key issue in designing the sensor network [2]. One way to tackle this problem is to equip sensor nodes with rechargeable batteries that are able to harvest renewable energy in the form of solar, wind, water flow, and vibration (e.g. [3, 4]). For the sensor networks with rechargeable batteries, managing energy consumption is of great importance since the power supply from a renewable energy source generally varies over time depending on the environmental condition and the time of the day. There have been some literatures on the design of an energy management mechanism for the sensor network with rechargeable batteries. The Markov decision process (MDP)-based node activation scheme for power management is proposed in [5]. In [6], the authors explore a scheduling algorithm designed in consideration of a time-varying battery recharging rate. The performance of various sleep and wake-up strategies for a solar-powered sensor network is analyzed by means of a queuing analytical model in [7]. In this chapter, we propose a radio resource allocation scheme for a mobile sensor network with a time-division multiple access (TDMA) scheme. Since we consider mobile sensor nodes, the channel gains between sensor nodes are time-varying as well as the battery replenishment rates are. In this highly time-varying network model, the proposed scheme aims to allocate a fraction of time to each sensor nodes in such a way to minimize total energy consumption. To this end, we make use of the dual decomposition method [8] to decompose a large-scale optimization problem into small subproblems that can be solved independently at each time instance. Although there have been several literatures investigating the application of the dual optimization technique to the sensor network (e.g. [6, 9]), the problem solved in this chapter, which is the time allocation problem in the TDMA-based sensor network equipped with a rechargeable battery, has yet to be addressed. The remainder of the chapter is organized as follows. Section 2 describes the system model and assumptions. In Sect. 3, the optimization problem for minimizing energy consumption is formulated and the optimal time scheduler is designed by using the dual decomposition method. We show representative performance evaluation results in Sect. 4. Section 5 concludes the chapter.

2 System Model 2.1 Network and Protocol Model We consider a sensor network that consists of one master node (MN) and N slave nodes (SNs). The MN is at the center of the star topology network, and controls the operation of all the SNs. Note that this network can be a standalone small-scale sensor network or a cluster within a large-scale multi-hop sensor network. Nodes are indexed by n (¼ 0; . . .; N), and the index of the MN is zero. The time is divided into frames, each of which is indexed by i. In Fig. 1, we show the frame structure. A frame consists of the control subframe and the data

Energy-Efficient Scheduling for Mobile Sensor Networks

Fig. 1

797

Frame structure

subframe. In the control subframe, control information is exchanged between the MN and the SNs. On the other hand, during the data subframe, sensing data are transmitted in both the uplink and downlink directions. Nodes share a data subframe in the time-division multiple access (TDMA) and time-division duplex (TDD) manner. Let tnD ðiÞ and tnU ðiÞ be the amount of the time allocated to the SN n at the frame i for the downlink and uplink transmissions, respectively. The time scheduler residing in the MN takes charge of deciding the time schedule, which is the amounts of the times allocated to all SNs, i.e., tD n (i)’s and tnU ðiÞ‘s for all n. If the duration of a data subframe is denoted by T, the time P scheduler in the MN allocates the time so that Nn¼1 ðtnD ðiÞ þ tnU ðiÞÞ  T is satisfied. The MN notifies the time schedule to the SNs by the downlink control packet in the control subframe. To decide the time schedule, the MN receives some information from the SNs by the uplink control packet in the control subframe. This information includes the battery status, the channel quality, and the amount of data packets arrived at the previous frame. The reported information at the frame i is used to decide the time schedule in the frame ði þ 1Þ.

2.2 Recharging, Channel Quality and Data Traffic Model We assume the nodes are equipped with rechargeable batteries such as solar, wind, water flow, thermal and vibration. Each node estimates the amount of battery recharge at each frame. Let hn ðiÞ be the amount of battery recharge of the node n, which is estimated at frame ði  2Þ, reported at frame ði  1Þ, and used for timescheduling decision at frame i. We assume quasi-static or slow fading channel between the MN and SNs. For channel estimation, the MN transmits a preamble sequence during the first part of the control subframe, right before transmitting the downlink control packet (see Fig. 1). SNs estimate the channel gains from this preamble sequence and report them to the MN by using the uplink control packet. Let gn ðiÞ be the channel gain estimated and reported at the frame ði  1Þ, and used for scheduling at frame i.

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Data traffic is generated at each frame. We assume a delay-sensitive application where generated data packets should be delivered in a limited time. Let dnD ðiÞ and dnU ðiÞ be the amount of data that should be transmitted within the frame i respectively to the node n (i.e., downlink transmission) and from the node n (i.e., uplink transmission). The MN can be aware of dnD ðiÞ’s for all n without delivering any control information, while the SNs should report dnU ðiÞ‘s to the MN for timescheduling. The amount of batter recharge, the channel gain, and the amount of data constitute the network state, which is an input to the time scheduling algorithm. Let K be the set of all possible network states. When the network state is k, the amount of batter recharge of SN n is hk;n , the channel gain of SN n is gk;n , and the D U amount of data to and from SN n is dk;n and dk;n . Let sðiÞ denote the network state at the frame i. If the network state of the frame i is k (i.e., sðiÞ ¼ k), we have D U hn ðiÞ ¼ hk;n , gn ðiÞ ¼ gk;n , dnD ðiÞ ¼ dk;n , and dnU ðiÞ ¼ dk;n . We assume that the network state is a stationary random process, and the probability of the network state k is pk , i.e., Pr½sðiÞ ¼ k ¼ pk .

3 Optimal Time Scheduler 3.1 Problem Formulation and Dual Decomposition In this section, we design the time scheduler that aims at minimizing the total energy consumption of all nodes under the constraint that, for each node, the rate of energy consumption cannot exceed the rate of battery recharge. The time scheduler decides the time schedule at the frame i (i.e., tnD ðiÞ and tnU ðiÞ for all n) D U and tk;n as the only based on the network state sðiÞ. Therefore, we can define tk;n amount of time allocated to the downlink and the uplink transmissions of the SN n, respectively, when the network state is k. Then, if the network state of the frame i D U is k (i.e., sðiÞ ¼ k), we have tnD ðiÞ ¼ tk;n and tnU ðiÞ ¼ tk;n . The following is the energy consumed to transmit the data of d bits through the channel of which the gain is g during the time t. fðd; g; tÞ ¼

d No W  t  ð2tW  1Þ; g

ð1Þ

where W is the bandwidth and No is the noise spectral density. From (1), we calculate the energy consumption of the MN when the network state is k as follows. ck;0 ¼

N X n¼1

D D fðdk;n ; gk;n ; tk;n Þ:

ð2Þ

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Also, we calculate the energy consumption of the SN n when the network state is k as follows. U U ck;n ¼ 1ðdk;n ; gk;n ; tk;n Þ:

ð3Þ

By using the formula for the energy consumption, we formulate the optimization problem follows: minimize

N X X

ð4Þ

pk ck;n

n¼0 k2K

subject to

X

pk ck;n 

k2K

X

pk hk;n ;

for all n ¼ 0; . . .; N

ð5Þ

k2K

N   X D U tk;n þ tk;n  T;

for all k 2 K:

ð6Þ

n¼1

The objective of this optimization problem (4) is to minimize total energy consumed by all nodes including the MN and all SUs.PThe first constraint (5) keeps pk ck;n ) under the average the average energy consumption of node n (i.e. k2K P battery recharge of that node (i.e., k2K pk hk;n ). The second constraint (6) makes N P D U þ tk;n Þ) sure that the sum of the amounts of times allocated to all nodes (i.e. ðtk;n n¼1

does not exceed the length of a data subframe (i.e. T). By relaxing the constraint (5), we derive the Lagrangian as follows. ! N X N X X X X pk ck;n þ kn pk ck;n  pk hk;n ; Lðt; kÞ ¼ n¼0 k2K

n¼0

k2K

ð7Þ

k2K

D D U U ; . . .; tk;N ; tk;1 ; . . .; tk;N Þ, t ¼ ðtk Þk2K , kn denotes the Lagrange mulwhere tk ¼ ðtk;1 tiplier corresponding to the constraint (5) for node n and k ¼ ðk0 ; . . .; kN Þ is a vector of the Lagrange multipliers. The dual function minimizes the Lagrangian with respect to t as follows. ( ) N N X X X X pk ð1 þ kn Þck;n  pk hk;n : ð8Þ f ðkÞ ¼ min Lðt; kÞ ¼ min t

t

k2K

n¼0

n¼0 k2K

The dual problem is to maximize the dual function (8) under the condition that the Lagrange multipliers are no less than zero. That is, f ðkÞ

ð9Þ

subject to k  0;

ð10Þ

maximize

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where 0 denotes a zero vector and the notation  denotes a component-wise inequality. The solution to the dual problem (9) is called the optimal Lagrange multiplier. We can find the solution to the optimization problem (4) by solving the dual problem (9).

3.2 Frame Subproblem To solve the dual problem, we first have to derive the dual function by finding the U minimizer of the Lagrangian. Let sD k;n ðkÞ and sk;n ðkÞ denote the minimizer of the D U ðkÞ and tk;n ðkÞ. From (8), we can see Lagrangian, respectively corresponding to tk;n that the minimizer is the solution of the following problem: minimize N N P P P PN P D U ð1 þ kn Þck;n g  n¼1 ðtk;n þ tk;n Þ  T for k2K pk f k2K pk hk;n subject to n¼0

n¼0

all k 2 K. Thanks to the structure of this optimization problem, this problem can be decomposed into smaller subproblems which can independently be solved for each network state. For the network state k, the subproblem is minimize

N X

ð1 þ kn Þck;n

ð11Þ

D U ðtk;n þ tk;n Þ  T:

ð12Þ

n¼0 N X

subject to

n¼1

From (2) and (3), the subproblem for the network state k is rewritten as minimize ð1 þ k0 Þ

N   X D D f dk;n ; gk;n ; tk;n n¼1

þ

N X U U ð1 þ kn Þfðdk;n ; gk;n ; tk;n Þ

ð13Þ

n¼1

subject to

N X D U ðtk;n þ tk;n Þ  T:

ð14Þ

n¼1

This subproblem is solved by a greedy algorithm. Since the function fðd; g; tÞ is a concave and decreasing function of t, the algorithm can allocate a small fraction of time one by one to the node whose energy consumption decrease the most after allocating that fraction of time. The length of the data subframe is divided into M time fractions each of which has an equal length of T=M. The greedy algorithm takes M steps and, at each step, allocates one time fraction to one uplink or downlink transD U and tk;n;m denote the sum of all time fractions allocated to uplink mission. Let tk;n;m

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and downlink transmissions of SN n until the mth step of the algorithm. Initially, the D U algorithm sets tk;n;0 0 and tk;n;0 0. At the mth step, the algorithm calculates the reduction in weighted energy consumption for each transmission by allocating one U time fraction, which is denoted by cD k;n;m and ck;n;m for downlink and uplink transmissions of SN n, respectively. The algorithm calculates cD k;n;m

D D D D ð1 þ k0 Þffðdk;n ; gk;n ; tk;n;m1 Þ  fðdk;n ; gk;n ; tk;n;m1 þ T=MÞg

ð15Þ

cU k;n;m

U U U U ð1 þ kn Þffðdk;n ; gk;n ; tk;n;m1 Þ  fðdk;n ; gk;n ; tk;n;m1 þ T=MÞg

ð16Þ

for all n ¼ 1; . . .; N. The algorithm selects downlink and uplink transmissions whose energy consumptions reduce the most by allocating one time fraction, D U U which are gD k;m ¼ argmaxn¼1;...;N ck;n;m and gk;m ¼ argmaxn¼1;...;N ck;n;m , respectively. D U D D tk;n;m1 þ If maxn¼1;...;N ck;n;m  maxn¼1;...;N ck;n;m , the algorithm updates tk;n;m D D D D U U tk;n;m1 for n 6¼ gk;m , and tk;n;m tk;n;m1 for all T=M for n ¼ gk;m , tk;n;m U U tk;n;m1 þ T=M for n ¼ 1; . . .; N. Otherwise, the algorithm updates tk;n;m U U U D D D n ¼ gk;m , tk;n;m tk;n;m1 for n 6¼ gk;m , and tk;n;m tk;n;m1 for all n ¼ 1; . . .; N.

3.3 Subgradient Method to Solve Original Problem U At each frame, the minimizer of the Lagrangian, sD k;n ðkÞ and sk;n ðkÞ, is calculated U by using the greedy algorithm. From sD k;n ðkÞ and sk;n ðkÞ, the Lagrange multiplier can be updated on a frame-by-frame basis until it converges to the optimal Lagrange multiplier (i.e., the solution to the dual problem (9)). For doing this, we make use of the projection subgradient method [8]. Let us explain the operation of the projection subgradient method. Let kðiÞ ¼ ðiÞ

ðiÞ

ðk0 ; . . .; kN Þ be the estimate of the optimal Lagrange multiplier in the frame i. In the frame i, if the network state is k, the scheduler solves the subproblem to ðiÞ ðiÞ ðiÞ ðiÞ U D U calculate sD k;n ðk Þ and sk;n ðk Þ. By using sk;n ðk Þ and sk;n ðk Þ, the Lagrange multiplier kðiÞ is updated as ( ðiþ1Þ k0

¼

ðiÞ ½k0

þd

ðiÞ

N X

) ðiÞ D fðdk;n ; gk;n ; sD k;n ðk ÞÞ

 hk;0 þ

ð17Þ

n¼1

h n    oiþ ðiÞ U ; gk;n ; sU for all n ¼ 1; . . .; N; ð18Þ  hk;n knðiþ1Þ ¼ kðniÞ þ dðiÞ f dk;n k;n k where dðiÞ is the step size for the frame i and ½xþ ¼ maxð0; xÞ. The Lagrange multiplier kðiÞ converges to the optimal Lagrange multiplier as t ! 1. As kðiÞ U converges to the optimal one, the minimizer of the Lagrangian, sD k;n ðkÞ and sk;n ðkÞ, also converges to the optimal solution to the original optimization problem (4).

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Fig. 2

Variation of Lagrange multiplier over time

Fig. 3

Variation of remaining energy over time

4 Numerical Results In this section, we present numerical results of the proposed scheme. Let us explain the simulation parameters. We consider a small representative sensor network with two SNs. Both SNs are 1 km away from the MN. The bandwidth, W, is 1 MHz and the length of a data frame, T, is 10 ms. We assume data traffic with a constant bit rate of 2.5 Mbps for each direction of each SN. The channel gain varies according to the exponential distribution. The battery recharging rates for the MN, SN 1, and SN 2 are 2, 3, and 1.5 W, respectively.

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In Figs. 2 and 3, we show the variations of the Lagrange multipliers and the remaining energy in the battery over time. In Fig. 3, we can see that the energy in the batteries of the MN and SN2 discharges over time until around the 100th frame. As the Lagrange multipliers for the MN and SN 2 increase as in Fig. 2, the energy level in the MN and SN 2 becomes stabilized after the 100th frame.

5 Conclusion In this chapter, we proposed a scheduling algorithm for sensor networks with a renewable energy source. We applied the dual decomposition method to the timescheduling problem and designed an optimal scheduling algorithm. The simulation results show that the proposed algorithm well distributes the radio resource (i.e., time) to nodes in such a way that the battery consumptions of all nodes are stabilized.

References 1. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52:2292–2330 2. Chen Y, Zhao Q (2005) On the lifetime of wireless sensor networks. IEEE Commun Lett 9:976–978 3. Corke P, Valencia P, Sikka P, Wark T, Overs L (2007) Long-duration solar-powered wireless sensor networks. In: 4th workshop on embedded networked sensors, pp 33–37 4. Kansal A, Potter D, Srivastava MB (2004) Performance aware tasking for environmentally powered sensor networks. In: Joint international conference on measurement and modeling of computer systems—SIGMETRICS/PERFORMANCE, pp 223–234 5. Kar K, Krishnamurthy A, Jaggi N (2006) Dynamic node activation in networks of rechargeable sensors. IEEE/ACM Trans Netw 14:15–26 6. Liu RS, Sinha P, Koksal CE (2010) Joint energy management and resource allocation in rechargeable sensor networks. In: INFOCOM, pp 1–9 7. Niyato D, Hossain E, Fallahi A (2007) Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks: performance analysis and optimization. IEEE Trans Mobile Comput 6:221–236 8. Palomar DP, Chiang M (2006) A tutorial on decomposition methods for network utility maximization. IEEE J Selected Areas Comm 24:1439–1451 9. Wang H, Yang Y, Ma M, Wang X (2007) Network lifetime optimization by duality approach for multi-source and single-sink topology in wireless sensor networks. In: IEEE International Conference on Communications, pp. 3201–3206

Issues and Challenges in Determining Quality of Service Requirements in Wired, Wireless and Mobile Networks P. Calduwel Newton and L. Arockiam

Abstract The Quality of Service (QoS) is a set of service requirements to be met by the network. The set of service requirements are determined by many levels of QoS building blocks such as the user, application, infrastructure, standard, regulator, etc. These levels increase the complexity in providing QoS. In other words, QoS requirements must be carefully mapped from user-level to application-level, application-level to infrastructural-level, infrastructure-level to standard-level, etc. Apart from intra-network level mapping, there is a need for inter-network level mapping as data may travel via heterogeneous networks such as wired, wireless and mobile networks. Here, intra-network denotes within single network and internetwork denotes between networks. The challenges involved in mapping QoS requirements from wired network to wireless network to mobile network and vice versa are inevitable. The outcome of this chapter throws light on the issues and challenges involved in intra-network and inter-network levels. Keywords Quality of Service Application


Heterogeneous Networks
Regulator
User


P. Calduwel Newton (&) Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, Tamil Nadu, India e-mail: [email protected] L. Arockiam Department of Computer Science, St.Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_80,  Springer Science+Business Media B.V. 2012

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1 Introduction The internet consists of many kinds of networks such as wired networks, wireless networks, mobile networks, etc. It becomes essential part of our life. There are various services offered by the internet. But providing quality of service is a challenge as the internet is a complex system. It needs more efforts to improve the Quality of Service (QoS). Comparatively, providing QoS in wired networks have low complexity, wireless networks have medium complexity and mobile networks have high complexity. The subscribers of mobile networks are rapidly increasing. The ever increasing high-speed mobile networks are supporting wide variety applications. These applications integrate several media such as text, graphics, audio, video, image, etc. These heterogeneous data and recent advances in mobile networks present complex traffic patterns and QoS requirements. Multimedia services (video call, VoIP, video streaming, instant messaging, web access, etc.) may require high restrictions in network parameters. Hence the support for QoS is necessary [1]. The services offered by the internet can have qualitative and quantitative QoS parameters [Korea]. Qualitative parameters are security, availability, reliability, etc. Quantitative parameters are bandwidth, jitter, delay, etc. These parameters together determine the Quality of Service (QoS). As the need for internet in general and mobile network in particular is growing exponentially, there is a need to analyze the challenges and issues involved in offering QoSs. There are issues and challenges that cannot be avoided in providing QoS and become an obstacle in ensuring QoS. The proper mechanisms to solve the QoS issues and challenges are the need of the hour. This chapter is organized as follows. Section 2 highlights the issues that are the bottom line of the QoS problem. Section 3 presents the challenges that are the bottleneck of the QoS problem. Section 4 devoted to the conclusion.

2 Issues: A Bottom Line of the QoS Problem The issues involved to enhance Quality of Service are significant. The careful mapping of QoS requirements specification from one level to another level may be an issue. There are many levels of QoS building blocks. In other words, QoS depends on many levels such as user, application, infrastructure, standard, regulator, etc. This section presents the QoS requirements specified by these levels. User level The user may determine the QoS requirements in relative terms such as low, medium, high. For example, the user may like high quality video. The word ‘‘high’’ must be carefully mapped. The word ‘‘high’’ is qualitative term and it must be quantified. It can be quantified as follows. In order to ensure high quality video,

Determining Quality of Service Requirements

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Table 1 Quality of service requirements [2] Application Reliability

Delay

Jitter

Bandwidth

E-mail Videoconferencing Audio File Transfer

Low High Low Low

Low High High Low

Low High Medium Medium

Table 2 QoS requirements specified by the infrastructure [2]

High Low Low High

Parameters Available Bandwidth Queue rate Queue size Peak data rate Min. packet size Max. packet size

Unit Bits/sec Bytes/sec Bytes Bytes/sec Bytes Bytes

the parameters such as delay, jitter, etc., may be carefully mapped. That is, delay must be in 120 ms and jitter must be from 40 to 60 ms, etc. Application level Table 1 illustrates how applications determine the QoS requirements based parameters such as reliability, delay, jitter and bandwidth. Here, reliability is a qualitative QoS parameter and other parameters are quantitative parameters. The QoS requirements of e-mail application are opposite of videoconferencing application. The QoS requirements are given in qualitative term. So, qualitative parameter (reliability) and qualitative QoS requirements (high, medium, low) must be carefully quantified. For example, reliability can be quantified as number of errors per 100 packets and high, medium and low can be quantified as high delay = 120 ms, low delay = 150 ms, high bandwidth = 8 mbps, low bandwidth = 2 mbps, etc. Here, high, medium, low represents the sensitiveness. For example, high delay means highly sensitive to delay (i.e. delay must be very less). Infrastructure level Table 2 exemplifies the QoS requirements of specified by the infrastructure or network. In this table, all the parameters are quantifiable. There should be a proper mechanism to map the QoS requirements from user level to application level to infrastructure level. Otherwise, there will be a mismatch among different levels and it may lead to QoS deterioration. Standards level Tables 3, 4 presents the QoS requirements determined by European Telecommunications Standards Institute (ETSI) based on reliability and delay. Table 3 shows that the reliability class 1 could be used for very error-sensitive applications that cannot perform error correction. Then, class 2 can be used if the application exhibit greater error tolerance. Finally, class 3 is the choice for error-insensitive applications or applications that can handle error correction [3].

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Table 3 Reliability classes in GPRS [4] Reliability Lost Duplicate Class SDU Prob. SDU Prob. 10-9 10-4 10-2

1 2 3

10-9 10-5 10-5

Out of Seq. SDU Prob.

Corrupt SDU Prob.

10-9 10-5 10-5

10-9 10-6 10-2

Prob.: Probability, SDU: Service Data Unit

Table 4 Delay classes in GPRS [4] Delay Class SDU size 128 byte 1 2 3 4

SDU size 1024 byte

Mean

95 percentile

Mean

95 percentile

\0.5 s \5 s \ 50 s Unspecified

\1.5 s \25 s \250 s

\2 s \15 s \75 s

\7 s \75 s \375 s

Table 4 shows that all delays in GPRS are higher than fixed network delays. Typical round trip times (RTT) in fixed networks are in the order of 10–100 ms. But in GPRS the delays are in seconds. The delay class 1 could be used for very delay-sensitive applications that cannot tolerate delays. The delay class 2 can be used if the application exhibit greater delay tolerance. The class 3 is the choice for delay-insensitive applications and class 4 is unspecified and can be used in future. Regulators level The regulators are responsible for ensuring QoS provided by the service providers. The following points indicate the QoS requirements specified by the regulators. Network Latency: • The network latency within the local network i.e. from the Broadband customer to the Gateway should be \ 120 ms for 90% of the time during busy hours. • The network latency for the International segment should be \ 350 ms for 90% of the time and \ 800 ms for satellite based communication during busy hours. Packet Loss: • The packet loss should not be more than 1% with a quarterly reporting period Since there are many levels in QoS building blocks, mistakes in mapping is possible. If there is a mistake in any one level of mapping then it will forwarded to another level. Finally, it will lead to pay the price for QoS deterioration. Based on the above observation, there should be a mechanism to do the proper mapping of QoS requirements from one level to another level. Some mechanisms to improve QoS can be found in [5, 6].

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Wireless Network

Mobile Network

A

1

X Y

2

3

4 B

C

Z

Fig 1 Example heterogeneous network

3 Challenges: A Bottleneck of the QoS Problem Apart from the issues discussed above, there are many challenges in determining QoS in heterogeneous networks. That is, mapping of QoS requirements from one level to another level is an issue. Otherwise, many levels of mapping have to be done to ensure QoS. In heterogeneous networks, there will be many kinds of networks such as wired networks, wireless networks, mobile networks, etc. The data has to travel from one network to another network. The end-to-end path with have combination of these networks. The data has to travel from wired network to wireless network to mobile network and vice versa. The example heterogeneous network is depicted in Fig. 1. Assume that, a node in wired network wants to send data to a node in mobile network. So, the data has to travel via wireless network. The QoS requirements of wired networks will differ from wireless networks and of wireless networks will differ from mobile networks and vice versa. So, providing QoS is a great challenge when data has to travel via various networks. For example, node ‘‘2’’ in wired network wants to send data to node ‘‘Y’’ in mobile network. The data has to travel all the levels in wired network, wireless network and mobile network. The characteristics of all levels in wired network will differ from all levels in wireless network and all levels in mobile network and vice versa. At the end of each and every network, there is a need for QoS mechanism to map QoS requirements properly. When the data traffic exits the wired network and enters the wireless network, there is a need to translate QoS requirements of wired networks into QoS requirements of wireless networks into QoS requirements of mobile networks and vice versa. If there is a QoS mapping problem when the data traffic exits the wired network then that problem will be carried forward to wireless network which in turn forwards to mobile network. In this scenario, the QoS requirements for wired network, wireless network and mobile network must be considered and there should be QoS mechanism to map QoS requirements of wired network to wireless network to mobile network at the appropriate levels. There are various kinds of wireless network and mobile network determined by the factors such as infrastructure, infrastructure-less, single-hop, multi-hop,

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ad hoc [7], host mobility [8, 9] and network mobility [10–12]. The data may transfer via all or combinations of these network. When the data travel via various kinds of networks, there is a possibility for QoS deterioration. In other words, ensuring QoS becomes complex. This necessitates the dynamic QoS provisioning [12] mechanism at each kind of networks. There is a great challenge for wireless and mobile networks [13–15] to improve QoS than wired networks, it has to be dealt properly.

4 Conclusion This chapter has vividly presented the various issues and challenges in providing QoS in the heterogeneous network environment. There are many levels involved in QoS building blocks such as the user, application, infrastructure, standard, regulator, etc. In each level, there should be a proper mechanism to ensure the correctness of QoS requirements mapping. If there is a mapping mistake in one level then it will be carried forward to another level. This will lead to pay the price for the QoS deterioration. The need of the hour is to evolve a mechanism to solve these bottom lines of the QoS problem. Comparatively, providing QoS requirements for wired network has low complexity, wireless network has medium complexity and mobile network has high complexity. At the end of each and every network, there is a need for QoS mechanism to map QoS requirements properly. As the wireless and mobile networks have many kinds, greater care must be given for QoS mapping. This will avoid the bottleneck QoS problems in each every network. From user point of view, QoS requirements are same in all these networks. But network point of view, QoS requirements are different from one another. This necessitates the need for a QoS mechanism that should perform tradeoff among these heterogeneous networks.

References 1. Calduwel Newton P, Arockiam L (2009) Reliability analysis for general packet radio service with quality of service support. CiiT Int J Wirel Comm (ISSN 0974–9756) 79–83 2. Tanenbaum AS (2005) Computer networks, 4th edn. pp 397 3. Calduwel Newton P, Arockiam L (2009) A quality of service strategy to select coding schemes in general packet radio service system. Int J Adv Sci Technol 7:1–9 (ISSN: 20054238) June 4. Schiller J (2003) Mobile communications, 2nd edn. Pearson, London 5. Arockiam L, Calduwel Newton P et al (2010) A quality of service strategy to optimize bandwidth utilization in mobile networks. In: Proceedings of international conference on advances in computer science, ACEEE, India, December, pp 186–189 6. Calduwel Newton P, Arockiam L (2009) A novel prediction technique to improve quality of service (QoS) for heterogeneous data traffi. J Intell Manufact [ISSN: 0956-5515 (Print)/15728145 (Online)], Springer Netherlands. doi: 10.1007/s10845-009-0361-z

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7. Aggelou G (2009) Mobile ad hoc networks: from wireless LANs to 4G networks. Tata McGraw-Hill, India 8. Perkins C (2002) IP mobility support for IPv4, RFC 3220 9. Johnson DB, Parkins CE, Arkko J (2004) Mobility support in IPv6, RFC 3775 10. Devarapalli V, Wakikawa R, Petrescu A, Thubert P (2005) NEtwork MObility (NEMO) basic support protocol, RFC 3963 11. Hu X, Li L, Mao ZM, Yang R (2008) Wide-area IP network mobility. IEEE INFOCOM, USA, April 13–19 12. Rafidah Md N, Edwards C (2006) A dynamic QoS provisioning model for network mobility. In: The 7th annual post-graduate symposium on the convergence of telecommunications, networking and broadcasting, June 2006 13. Soldani D (2006) Means and methods for collecting and analyzing QoE measurements in wireless network. In: Proceedings of the international symposium on a world of wireless, mobile and multimedia networks, IEEE CS 14. Kajackas A et al (2005) Estimation of QoS dynamics in the wireless networks. In: Proceedings of the 4th management committee meeting, Germany, 13–14 Oct 2005 15. Hu F et al (2004) Priority-determined multiclass handoff scheme with guaranteed mobile QoS in wireless multimedia networks. IEEE Transs Veh Technol 53(1):118–135

Implementation of the River Monitoring System Using Embedded RTU Server Dae-Sik Ko

Abstract In this study, the main board, power board, sensor board, and communication module were designed separately by the separate module in order to improve the performance of RTU, so the failure diagnose function was added, and the Web Server function was designed by being embedded in the main board for monitoring the rivers state by the real-time video monitoring. The proposed RTU provides the video information of the river through the Web browser such as PDA, Smart Phone, Laptop and Computer etc. in real-time. The proposed RTU system was implemented by using ARM920T processor as ARM processor line and H.264 codec chip as compression codec and it was confirmed that the function of transferring data to the remote area by using the RTSP server and H.264 compression codec after inputting the function, video image that transfers the failure status by detecting the equipment status in real-time in the results of the comparative analysis of the performance. Keywords Water level measurement system


Remote surveillance
RTU
Embedded

1 Introduction Every year, river and coastal flooding in the world results in major loss life and property. The monitoring water level in a river or in a reservoir is important in the applications related to agriculture, flood prevention, and fishing industry, etc. [1].

D.-S. Ko (&) Department of IT Engineering, Mokwon University, Daejeon, Korea e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_81, Ó Springer Science+Business Media B.V. 2012

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In order to monitor and control the rivers state such as the water quality of river, water level, rainfall, the administrator should visit the facilities directly in the past, so the rapidity was insufficient. Lately, in order to overcome this, the various information transfer systems about river facilities were constructed by using USN, CDMA, WCDMA or LAN net. But the existing RTU system was dependent on one-dimensional data such as water level, contamination status, so the reliability and visibility of information were insufficient. In addition, existing river management type RTU was designed in the main board including the communication module, so it has disadvantages that even if a failure occurs on the main board or the communication module, then the information about the system failure status cannot be transferred to the administrator [2–4]. In this study, the main board, power board, sensor board, and communication module were separately designed by the separate module in order to improve the performance of RTU so the failure diagnose function was added, and the Web Server function was designed after embedded in the main board for monitoring the rivers state by the real-time video. The proposed RTU provides the video information of the river through the Web browser such as PDA, Smart Phone, Laptop and Computer etc. in real-time. The proposed RTU system was implemented by using ARM920T processor as ARM processor line and H.264 codec chip as compression codec and the function of transferring data to the remote area by using the RTSP server and H.264 compression codec after inputting the function, and the video image that transfers the failure status by detecting the equipment status in real-time were analyzed.

2 Design of RTU for River Management Monitoring System 2.1 Design of Embedded RTU System The proposed system separates major components and designed it as four separate modules differently with original system, so it check the failure types of the certain modules and transfers that information to the management center. Figure 1 shows the composition of the module RTU system. As shown in Fig. 1, the development system is basically composed with the power board, main board, communication board, sensor IO board and back board which connect each board. The power board supplies necessary 5 and 12 V for whole system. The main board has function that transfers data to remote area by using TCP/IP function, and transfers the communication board and each collected data of IO board to the remote area and transfers the rivers state into Web after monitoring by the Web server function. It has video camera input device for inputting the video image signal in RTU, H.264 codec for compressing the input video signal and the video server function to transfer the compressed video. The video transfer technology has RTSP function as the transfer protocol in the video server so the current rivers state can be checked at the remote area through video

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Fig. 1 Modular design of the RTU

by using the media player (ex: VLC Media Player) with H.264 codec, so the system was composed for more efficient management [5]. The communication board has function to check the failure status of RTU. In other words, the current connected board can be checked by detecting the connection status after mounting the main board and each sensor board in RTU, and if there is no communication when even the board was connected, the failure status can be measured by communication with the board which was detected by connection, so it can be transferred to the main board. In addition, it has separate battery in the communication board, so if the system was not operated caused by the power supply of whole system is blocked, then the failure status of built-in battery can be transferred to the remote area through SMS service. Finally, the sensor and IO board has function that transfers data into the communication board by checking the sensor data measure and digital input status, and digital output control function by receiving the transferred control signal through the function, communication board t was designed to be extended when the number of sensor is increased.

2.2 RTU Server for Monitoring Real-Time Video The proposed river management monitoring program in this study was designed as Web server form in order to check the remote state in real-time from the remote area, and it has inside serial communication line for communication with Web server program, external communication board and sensor IO board in the program, so it can communicate with communication board in real-time, so it received the sensor IO board status for renewing Web server. Figure 2 is the block diagram of RTU server software, it composes the Ethernet socket and Serial thread programs for expressing treated data on the Web in real-time, so it was designed for marking the current status on the Web server. In Fig. 2, the RTU servers are divided with the thread charged in the serial and the thread responsible for socket, and performed the function to deliver each input/ output mutually. The information received by the serial was stored by RTU server,

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Fig. 2 Software diagram of the RTU server

Fig. 3 Design of the main board

Table 1 RTU built-in monitoring system development environment Development environment Windows 7, VMWare Workstation 7, Ubuntu 9.10 SDK

STR8100 Linux SDK 6.7.1 Linux 2.6.16 Kernel with Star’s patch U-Boot 1.1.4 GNU GCC Cross Compiler 3.4.6 with uClib Lib. 0.9.28 for ARM uClibc lib 0.9.28

Implementation of the River Monitoring System Table 2 Item

Existing RTU

CPU

32 bit microprocessor (Speed: 180 MHz) System memory 16 Mbps External 1 GB memory I/O Digital and analog Communication Ethernet and serial mode mode Function - Water level and velocity monitoring - Remote control - SMS using CDMA and data communication using Ethernet

817

Proposed RTU High speed RISC ARM processor (Speed: 250 MHz above) 32 Mbps above 2 GB above Digital and analog Ethernet, Wi-Fi, serial mode - Water level and velocity monitoring - Remote control - SMS using CDMA and data communication using Ethernet - Detect and transmit RTU’s fault - Real time video communication using H.264 codec and RTSP server

and gives the value back for the Web browser’ request, but on the contrary, if there is inputting Web browser, it stores it and transfers to the serial.

3 Implementation and Performance Analysis 3.1 Implementation of Main Board Five modules were designed separately for analysis of performance of proposed RTU, and the RTU system was implemented through the system. For the main board, ARM920T processor as the ARM processor line was used, and H.264 codec chip as compression codec was used for transferring the video decoder chip and input video signal for inputting video and Fig. 3 is the main board design drawing. H.264 codec based systems can save the contents of more than two or three times quantity and it support the full D1 resolving power of maximum 30 frs/s and encoder/decoder of maximum 720 9 480 pixel. It can save large-capacity by supporting the power consumption of 200 mW scope and mobile DDR, so it is suitable for system environment that should work for 24 h a day and 365 days in a year. Multiple decoders and multiple accesses are available, so it can observe and monitor the remote areas at the various places at the same time.

3.2 Implementation of RTU Server The Web server are composed with the system status bar showing the current main board status, monitoring for controlling rivers state, monitoring bar for controlling, the network address bar showing or renewing the network address of network

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address, the date input bar showing or inputting current date, video Properties bar controlling inputting video image size and video, compression and the number of frames, the destination definition for transferring video which treats input video image, encoding bar for definition of transfer protocol, the live video bar for receiving encoded signal on Web, and the Firmware upgrade bar for upgrading Firmware and boot loader at the remote area. Web pages charged in UI of Web monitoring in real-time was developed as AJAX, and it requests date to RTU server periodically after page is loaded, and the reply should be shown in the monitor. The user can be inputted by transferring inputted value to RTU server. As protocol to transfer the video in real-time, there is method to drive the Multicast method, UDP Unicast method, TCP Unicast method and RTSP Serve. The multicast transfer’s video by definition of Group IP, the TCP/UDP Unicast method transfers the video by setting Destination IP. The current commercial general method drives the RTSP server, so receives the video by using Live Video function of Web server at remote area. Meanwhile, RTU built-in monitoring system was implemented in the following environment (Table 1). In addition, a certain amount of video can be saved in its memory. When the setting is completed and encoding is started, and then the transfer can be started.

4 Conclusion This study proposed RTU which can monitor the rivers state in real-time video by designing the Web Server function embedded in the main board with the failure diagnose function. As results of test of implementation of RTU, it was confirmed that CPU, memory were operated by the high-performance, especially, the failure detect function and real-time video monitoring are excellence. Table 2 summarized the results of comparative analysis of existing RTU and proposed RTU.

References 1. Van Blaricum VL, Hock VF, Water distribution system modeling and remote monitoring. U. S. Army Engineer Research & Development Center 2. Arampatzis TH, Lygeros J, Manesis S (2005) A survey of applications of wireless sensors and wireless sensor networks. In: IEEE proceedings of the 13th mediterranean conference on control and automation limassol, Cyprus, 27–29 June 2005 3. Hentea M (2008) Improving security for SCADA control systems. Interdiscip J Inf Knowl Manag 3:73–86 4. Yu J, Hahn H (2010) Remote detection and monitoring of a water level using narrow band channel. J Inf Sci Eng 26:71–82 5. www.altera.com

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6. Flynn B (2008) Case studies regarding the integration of monitoring & diagnostic equipment on aging transformers with communications for SCADA and maintenance, DistribuTECH 2008 Conference and Exhibition, 22–24 Jan 2008, Tampa Convention Center, Tampa, FL 7. Quinn NWT, Ortega R, Rahilly PJA, Royer CW (2010) Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal wetland real-time water quality management. Environ Model Softw 25:1045–1058

A Study on Message Batch Verification Scheme Using Bloom Filter in VANET Su-Hyun Kim and Im-Yeong Lee

Abstract The group signature and verification using the existing group signature scheme in VANET environment have a shortcoming that there is a possibility of overhead problem as more vehicles use the communication method. Accordingly, In this paper, We proposed a message batch verification scheme using Bloom Filter that can verify multiple messages efficiently even for multiple communications with many vehicles. In addition, We propose a group organization method to prevent frequent sign-in and sing-out of group members.





Keywords VANET V2V Group signature Batch verification Bloom filter



 Authentication 

1 Introduction VANET (Vehicular Ad-hoc Network) is a type of MANET (Mobile Ad-hoc Network) that is the next-generation networking technology to provide communication between vehicles or between a vehicle and RSU (Road Side Unit) using wireless communication. This VANET is usually divided into V2V (Vehicle to Vehicle) communication or V2I (Vehicle to Infrastructure) communication. V2V communication can be

S.-H. Kim (&)  I.-Y. Lee Division of Computer Science, Soonchunhyang University, Asan City, Republic of Korea e-mail: [email protected] I.-Y. Lee e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_82,  Springer Science+Business Media B.V. 2012

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done by forming the vehicle’s own network without assistance of the infrastructure that can provide information. It is generally used to provide safety services, including emergency information, as well as messages for anti-collision and alerts. Various requirements for security should be satisfied, as all messaging information can cause a fatal accident, because V2V communication depends on information broadcast by the internal network participants. Various studies on security technology using a group signature scheme, which could provide functions such as authentication, conditional privacy, and non-repudiation to provide this, have been undertaken [1]. Because of VANET’s own distinctions, the message signature and verification in the situation where there are many vehicles can cause overhead issue. Therefore, I suggests in this paper a message batch verification scheme using Bloom Filter that can verify multiple messages efficiently even for multiple communications with many vehicles. In addition, I propose a group organization method to prevent frequent sign-in and sing-out of group members. This paper is organized as follows. Section 2 introduces relevant techniques needed to understand the techniques suggested in this study. Section 3 proposes suggestions. Section 4 analyzes the safety of these suggestions. Section 5 gives conclusions and outlines the future research directions.

2 Related Work 2.1 Batch Verification Batch verification scheme is a method that many messages signed can be verified at a single signature and verification cost. The concept was first introduced by Fiat in 1997 [3]. Currently many studies have been conducting to apply existing batch verification schemes to VANET. Zhang and his fellows employed a batch verification technique to verify multisigned messages in VANET efficiently [4]. Their scheme is to make RSU process batch verification on behalf of a vehicle. It turned out efficient in the situation dense with cars (per RSU). However, it is inefficient in that there aren’t many vehicles per RUS. Furthermore it has a disadvantage of high overhead in processing ID-based signature verification. RAISE is another batch verification scheme developed to tackle the overhead problem that is expected to bring out in car-concentrated areas [5]. In the system, RSU aggregates messages sent from vehicles in hash and transmit it to receiving vehicles. The receiving vehicles only checks if the received messages are included in the data sent from RSU. It makes authentication more efficient than existing verification schemes using operation by node. However, it should run unnecessarily frequent comparative processing, even more than the number of vehicles, so that it is unavoidable for it to compare as many times as the number of cars (n number of cars = n times of comparison).

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3 Proposed Scheme 3.1 System Models and Assumptions All the vehicles in the suggested system are pre-registered with a trusted authority (TA) before they are distributed on a network. This is a necessary process for vehicle tracing, when the trace is requested by a group manager in the process of tracking a user in the future. It is assumed that all the vehicles performed all of calculations on communication using tamper resistant hardware (TRH) of an on-board unit (OBU) loaded in a vehicle and all vehicles and TA synchronize time through an OBU. It is assumed that RSU is always a trustful authority and has a superior operability to OBS. The overall system model is shown in Fig. 1.

3.2 System Parameters The protocol was planned using the following system coefficients in the suggested method. • • • • • • • • •

ID* : Identifier of vehicle * P : A point on an elliptic curve s : A group secret key YGA : A group signature key d* : A private key of vehicle* Zq* : Multiplication group of modular q H() : One-way Hash function (q, G1, GT, e, P, H, Y) : Public parameter e : Bilinear map e:G19G1?GT

3.3 Group-Joining and Private Key-Distribution Phase A group joining was controlled by a vehicle group manager. The following process was passed to register a group to have a private signature key. A vehicle which wanted to join a group came to send the joining request message at a certain time interval and a vehicle group manager who received this message sent the message of ap approving a group request by judging that the speed and direction of a vehicle which wanted to join were similar. Step 1: The vehicle group manager organizes the information described below to safely send a private and group signature key of a group member. – n=pq, ed=1(mod [(n))

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Fig. 1 Message verification using Bloom Filter

– – – –

Public information : n, e Secret information : p, q, d Group secret key : s2 Zq* Group signature key : YGA=sP

Step 2: The vehicle that v wants to register in a group sends its own identification information and a group-joining request message. – IDv, Tfirst – IDv, Tsecond Step 3: A group manager transmits the joining approval message after identifying identification information of a user and the joining request message. – IDGA||IDV||P Step 4: The vehicle v selects a random value r that a vehicle group manager could not identify to calculate the d value to generate a private signature key. The vehicle transmits a public information value of a vehicle group manager to a group manager after the exponentiation calculation. Random r2 Zq* d=H(IDV||P)+rP de Step 5: The secret key s is multiplied to be transmitted with a group public key after de-scrambling the d value received from vehicle v. – sd – (sd||YGA||Texp)

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Step 6: Vehicle v calculates a private signature key to store it with a group signature key, YGA, using the value received from a vehicle group manager and the value generated by it. – dv=sd-rd – YGA

3.4 Communication Phase Between a Vehicle and RSU RSU receives and sends the messages sent by the vehicles in the same group. The received messages by RSU are verified through the group signature keys and checked if they are messages sent from the eligible members. Using the received messages, RSU creates Bloom Filter and broadcasts them again. Step 1: Vehicle v signs the message using its own private signature key. – U=(M||IDv)H(e(dv,YGA) – r=rP – UserSign M = (U,r) Step 2: The signature value is broadcast to transmit after the group signature process has passed using a group signature key, so the personally signed message could be verified between the same group members – – – –

Random K2 Zq* L=KP W=(M||U||r)H(e(P,YGA)K GroupSign (M||U||r) = (L,W)

Step 3: Group members who receive broadcast messages verify the messages via a group signature key to check it was transmitted by a rightful group member – GroupSign Verify(L,W) – WH(e(YGA,L) = (M||U||r) Step 4:

RSU operates (processes) the received messages by Hash function algorithm and uses Bloom Filter to create BFM

H1(M1), H2(M1),….,Hi(M1)=BFM1 H1(M2), H2(M2),….,Hi(M2)=BFM2 …..,

H1(Mi), H2(Mi),….,Hi(Mi)=BFMi

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3.5 Messages Verification Phase The vehicles receiving the same messages as the messages that RSU received will receive BoolmFilter created and transmitted by RSU. At this point, the Bloom Filter makes the vehicles be bale to confirm in a single comparative operation (batch) if the messages are valid ones, even for multiple messages.

3.6 Tracking Phase of a User The identity of a user could be requested as a group member transmits the decoded signature value(U,r) to a vehicle group manager through a group signature key when the message that caused the dispute is discovered. A vehicle group manager identifies the identifier of a vehicle to request a trace of a user to TA. Step 1: A group member transmits the decoded signature value(U,r) to a vehicle group manager via a group signature key. - GroupSign Verify(L,W) Step 2: A vehicle group manager who receives (U,r) took out an identifier of the vehicle v using his own secret key. – UserSign Verify(U,r) – UH(e((YGA-r)d,s) = (M||IDv)

4 Analysis of Security and Efficiency 4.1 Safety Analysis • Privacy The privacy of a signer was guaranteed, even if U was obtained via a group signature key, as U and r were generated by rP that (M||IDv)H(e(dv,YGA)value, including a private key of a signer and the random value generated by a signer are included. • Traceability

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Table 1 Comparison of the security functions of vehicles with group signature scheme [4] [5] Proposed scheme Message authentication Conditional privacy User tracing Key escrow problem solution Operation by node Batch verification Comparative scheme searching

s 4 s 9 3P+M+3A –

s 9 9 9 – In-order searching

s s s s – Hashing consequence searching

(P Paring, M Multiplication, A Addition)

In this suggested method, the process that an identifier of a vehicle requested by a vehicle group manager is identified when a dispute occurs after the process in which the vehicle was registered in TA before the vehicle was distributed on the network. A key escrow problem could be prevented, because the authentication for a group manager is made via RSU, even if an identifier of a vehicle is exposed by a group manager. • Key Escrow Problem H(IDV||P)+rP is calculated to obtain the d value using the random value, r, which only vehicle v knew to solve this problem in the proposed method. This d value is coded using public information of a vehicle group manager to transmit it to a group manager. As a group manager re-transmitted the value by which his or her own secret key is calculated, vehicle v could generate its own private signature key without exposing the r value known only by itself to a group manager.

4.2 Comparison of the Functions and Efficiency Table 1 summarizes the comparison of batch verification schemes applied to vehicle communication and proposed protocols. The scheme proposes a batch verification method by RSU to reduce the overhead of operation by node. Because RSU creates Bloom Filter in advance and transmits it, each node doesn’t operate unnecessary process but only a simple comparison operation, which is enough for the messages to be authenticated.

4.3 Efficiency Analysis A vehicle group manager prevented unnecessary group joining by inferring the movement direction and speed of surrounding vehicles considering characteristics of the node with a fast speed. In general, this study compared and analyzed the

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Fig. 2 The Number of Group Members’s Vehicles according to the time interval of requesting message

method which accepted a group joining request of all the vehicles and the suggestion method. It was seen that the number of group members’ cars was decreased up to 50% compared with the method that group joining was accepted to all of existing vehicles if the time interval of request message was controlled in three seconds as shown in (Fig. 2). Results were composed under the following assumptions. • The interval of message for requesting joining(mt) : 3, 4, 5 s • The number of vehicles moving on the lanes of the opposite side(n) : 200 vehicles • The movement distance of vehicles requested for group joining per second(Vs) • The movement distance of group management vehicles per second(Gs) • The range of vehicle communication(R) : 250 m • Vehicle speed of a group managers : 80 km •

n

  n  ðmt ðvs þ Gs ÞÞ R

5 Conclusion This study proposed a batch verification scheme using RSU to reduce overhead in VANET linked with many vehicles. It also made an additional proposal for the group signature-based preventive measure from unnecessary group sign-into apply the existing group signature to VANET properly. This group signature base satisfied various security requirements for VANET and increased the operational efficiency by node better than the existing ways. However, the batch verification scheme has a shortcoming. If the scheme fails in verifying (in a batch) n- number of messages, re-verification should be run to identify the wrong message(s). Therefore, this study only paves the grounds on the proposed batch verification scheme for the next studies, leaving behind how to extract abnormal messages and create flexible Bloom Filter to the size of group.

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References 1. Guo J, Baugh JP, Wang S (2007) A group signature based secure and privacy—preserving vehicular communication framework, Proceedings of 2007 mobile networking for vehicular environments, pp. 103–108 2. Chaum D, van Heyst E (1992) Group signatures, advances in Cryptology-EUROCRYPT’91, LNCS 547, Springer, pp. 257–265 3. Fiat A (1997) Batch RSA, J Cryptol, 10(2):75–85 4. Zhang C, Lu R, Lin X, Ho P, Shen X (2008) An efficient identity-based batch verification scheme for vehicular sensor networks, Proceeding of the IEEE INFOCOM 2008, pp. 246–350 5. Zhang C, Ling X, Ho P-H (2008) RAISE: An efficient RSU-aided message authentication scheme in vehicular communication networks, Proceeding IEEE ICC 2008, Beijing, pp. 1451–1457 6. Guo J, Baugh JP (2006) Security and privacy in vehicle safety communication applications, SAE transactions: J Passeng Cars—Electron Electr Syst, pp. 721–727 7. Raya M, Hubaux J (2005) A security of vehicular ad hoc networks, Proceedings of the 3rd ACM workshop on security of ad hoc and sensor networks SASN ‘05, pp. 11–21

Computing Text-to-Text Semantic Relatedness Based on Building and Analyzing Enriched Concept Graph Zoleikha Jahanbakhsh Nagadeh, Fariborz Mahmoudi and Amir Hossein Jadidinejad

Abstract This paper discusses about effective usage of key concepts in computing texts semantic relatedness. Thus, we present a novel method for computing texts semantic relatedness by using key concepts. Problem of appropriate semantic resource selection is very important in Semantic Relatedness algorithms. For this purpose, we proposed to use a collection of two semantic resource namely, WordNet, Wikipedia, so that provide more complete data source and accuracy for calculate the semantic relatedness. Result of this proposal is compute semantic relatedness between almost any concepts pair. In purposed method, text is modeled as a graph of semantic relatedness between concepts of text that are exploited from WordNet and Wikipedia. This graph is named Enriched Concepts Graph. Then key concepts are extracted by analyzing ECG. Finally, texts semantic relatedness is obtained semantically by comparing key concepts of texts together. We evaluated our approach and obtained a high correlation coefficient of 0.782 which outperformed all other existing state of art approaches.



WordNet Keywords Wikipedia extraction Semantic relatedness





Enriched concept graph



Key concept

Z. Jahanbakhsh Nagadeh (&)  F. Mahmoudi  A. H. Jadidinejad Islamic Azad University, Qazvin Branch, Qazvin, Iran e-mail: [email protected] F. Mahmoudi e-mail: [email protected] A. H. Jadidinejad e-mail: [email protected]

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2_83,  Springer Science+Business Media B.V. 2012

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1 Introduction The last years have seen a large amount of works in Natural Language Processing (NLP) by using measures of semantic similarity and relatedness. Power of Semantic Relatedness algorithm depends primarily on appropriate semantic resource selection. For example, methods only use the lexical semantic resource for computing semantic relatedness; suffer from low coverage of domain-specific concepts. In this paper, we attempt to resolve these problems. Hence, by considering benefits and weaknesses of various semantic knowledge sources, we proposed to use collection of two strong resources (i.e. WordNet and Wikipedia), as lexical relations between words in WordNet is well specified and also Wikipedia covers appropriately the specific names, so the weaknesses of one source is improved by benefits of other, so that it is provided more complete data source [1]. Various methods have been purposed for comparing texts semantically. We use key concepts within texts for comparing texts. Key concepts are as set of n-grams that provide a compact representation of a document’s content. Key concepts extraction is as a field of information retrieval and aims at representing the essence of the intended message carried by the document according to the concepts exploited. For demonstrating important of keywords, we utilized those in an application; therefore use these keywords for computing texts semantic relatedness. To extract the keywords, we make use of Enriched Concept Graph. ECG is a weighted graph where vertices are concepts in text; the weighted edge between a pair of concepts is obtained value from semantic relatedness of the two concepts that is computed by WordNet and Wikipedia. We utilized purposed method in [2] for extracting key concept, with this difference that we use two semantic resource WordNet and Wikipedia for computing concepts semantic relatedness but in [2] only has been used Wikipedia. Therefore, built graph in our method is richer than proposed method in [2], because of more relations that is found by WordNet and Wikipedia. In order to, we named it namely Enriched Concept Graph. Then ECG is analyzed for extracting key concepts. We use the key concepts of texts for computing text-to-text semantic relatedness. The rest of the paper is organized as follows: Sect. 2 discusses about related works that has done in semantic relatedness measure. Section 3 describes utilized methodology for extracting key concepts from texts. In Sect. 4 shown application of key concept in computing texts semantic relatedness. In Sect. 5 is shown the results of our experiments and evaluations and conclusions of paper and some future directions for research are provided in Sect. 6.

2 Related Work The problem of determining the semantic relatedness between two texts has been an area of interest to researchers from several areas for a long time. Some preliminary approaches that calculated the semantic relatedness are simple, but perform bad

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when the texts share few words. This technique is also inappropriate for comparing individual words. The latter two techniques attempt to circumvent of this limitation. Existing lexical databases such as Roger’s Thesaurus [3] and WordNet [4] and [5] encode relations between words such as synonymy, hypernymy and not dependence to number of share word and use relations between words. Other researchers have proposed to use the description of words presented in dictionaries [6] and techniques such as LSA [7] to compute semantic relatedness. However, due to very limited size of WordNet as a knowledge base, Strube and Ponzetto [8] have shown that Wikipedia can easily outperform WordNet in calculating the semantic similarity, when a variety of approaches to semantic relatedness, including paths in graph and the text content in the articles, are employed. Syed et al. writers [9] have used the articles from Wikipedia along with the category and link graphs to identify concepts which are common to a set of documents. Gabrilovich and Markovitch [10] have proposed an approach called ‘‘Explicit Semantic Analysis (ESA)’’ in which concepts derived from Wikipedia are used to represent the meaning of any text and compute the semantic relatedness between parts of natural language text. According to [10], the ESA results are better than the results of other existing state of art approaches.

3 Methodology Key concepts extraction is a basic step for various applications of Natural Language Processing, such as categorizing text, clustering text, summarizing text and deriving more prevalent content of document text. In this section, we intend to extract appropriate concepts related to the basic issue of text (i.e. key concepts) and purpose to use from these key concepts in one of the usages of NLP, such as computing texts semantic relatedness. Therefore, obtain texts semantic relatedness by comparing key concepts of any text to other text semantically. We utilized purposed method by Grineva et al. [2] for extracting key concepts. Their method is based on terms graph that semantic relatedness of the pair of terms as edges of graph is computed by Wikipedia. Built graph in our method is richer than their method, whereas we use collection of two strong resources WordNet and Wikipedia for computing concepts semantic relatedness, hence relation is found between more concepts pair and built graph will be larger and richer. So we named it ‘‘Enriched Concept Graph’’.

3.1 Text Pre-Processor and Extraction of Candidate Terms We processed the input text by removing stop-words (words to don’t give important information about content document, such as ‘‘the’’, ‘‘a’’, ‘‘is’’, etc.) and special characters such as ‘‘@’’ and rare words, and then extract all possible N-grams. An N-gram is a fixed-length string without linguistic meaning.

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The N-grams are extracted by sliding a window of length n by one character in the text and recording a sequence of characters in the window at each time.

3.2 Computing Semantic Relatedness Between Terms Similarity between those words represents the association strength between terms. Our method is based on ECG analysis. So the conversion of the analyzed text into a concept graph requires the addition of the relationships between terms. We compute the terms relatedness measure by two semantic resources WordNet and Wikipedia.

3.2.1 Computing Semantic Relatedness by WordNet WordNet is a lexical database for the English language and excels older lexical resources, based on meanings of words (not according to the words form). The main core of WordNet is set of synonym terms which are called synset. For each synset, there is a semantic description that called gloss. WordNet specifies number of lexical relationship between the words and synset [1]. A word can have more than one meaning that can lead to ambiguity. For example: the word ‘‘apple’’ has two different ‘‘apple computer’’ and ‘‘Pine apple’’. Disambiguation is the process of finding out the most appropriate sense of a word that is used in a given sentence. We attempt to relatively resolve ambiguous words problem in our method. For this purpose, we apply adapted Lesk algorithm for computing semantic relatedness that is combined with WordNet. This algorithm is extended by Banerjee and Pedersen [11] which uses the rich network of relationships between concepts and presents WordNet. The major objective of its idea is to count the number of words that are shared between two glosses that is relatedness of two words is proportional to the extent of overlaps of their dictionary definitions. A word is assigned to the sense whose gloss shares the largest number of words in common with the glosses of the other words existent in text. If a word is found on WordNet, it is much easier and faster to determine its synonyms, hypernyms and hyponyms as compared to Wikipedia. Hence, we first extracted candidate terms send to the WordNet database for computing semantic relatedness between candidate terms-pairs. If a lexical relationship could not be determined, we move to a larger data source (i.e. Wikipedia) to determine the relationship.

3.2.2 Computing Semantic Relatedness by Wikipedia Wikipedia is a source of comprehensive and online encyclopedia, multilingual, Web-based and free. Researched on the Wikipedia summarily includes semantic relatedness between words, improved efficiency of information retrieval systems,

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Fig. 1 Semantic graph built from 12th news text of Lee dataset

semantic search engine and text categorization. So we also apply Wikipedia for semantic relatedness measure in our method. Wikipedia-based semantic relatedness measure for two terms can be computed using either the links found within their corresponding Wikipedia articles [12] and [13], or Wikipedia categories structure [14], or the article’s textual content [10]. All links between Wikipedia articles are an important source of lexical semantic. We use only the links between articles [12] rather than their textual content (such as ESA), that have less volume and also do not require processing article content.

3.3 Building a Enriched Concept Graph (ECG) At this step analyses text is modeled as a weighted graph that where vertices are concepts in text (except stop-words); the weighted edge between a pair of concepts is obtained value from semantic relatedness of the two concepts that is computed by WordNet and Wikipedia in previous sections. This graph is an enriched graph, because determined semantic relatedness between almost any concepts-pair by WordNet and Wikipedia. So, we named it namely Enriched Concept Graph (ECG). Figure 1 shows built ECG from text sample of Lee dataset (text 12th) this article tells that ‘‘U.S. intelligence cannot say conclusively that Saddam Hussein

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has weapons of mass destruction, an information gap that is complicating White House efforts….’’

3.4 Detection of Communities in the ECG A document converted to a semantic graph may contain many concepts added during the building graph phase that are unrelated or weakly related to the document topic. Additionally, it is possible that different sections of the document concern different topics. To solve this problem and grouping graph into some dense sub graph, we apply Girvan–Newman algorithm [15]. By this algorithm, the concepts related to the main topics of the document tends to bunch up into densely interconnected sub graphs or communities, while non-important concepts fall into weakly interconnected communities, or even become isolated vertices [2]. Girvan–Newman algorithm [15] iteratively remove edges from the network which have more value betweenness to split it into communities. (The edges removed don’t base on less weight edge but is assigned a number to each edge that is large if the edge lies ‘‘between’’ many pairs of nodes.) Again computes rest betweenness of edges to selects next edge in most betweenness for removing.

3.5 Selecting Valuable Communities At this step detected communities are ranked and then select the communities that have high rank, because the highest ranked communities would contain important concepts related to the main topics of the text (key concepts), and possible disambiguation mistakes (concepts which meaning was chosen wrong on the second step). We show the communities by dashed lines and are numerated according to the marks in Fig. 1. Therefore we select communities with highest rank value that in compared to other communities have difference of upper rank and extract its nodes. Ranking is based on the density and informativeness of communities: (1) Density: Density of a community is a sum of weights of all inner-community edges divided by the number of vertices in this community. (2) Informativeness: Informativeness of a community is a sum of keyphraseness measure of all concepts in a community divided by the number of concepts. Keyphraseness measure gives higher values to the named entities than to general concepts. We compute keyphraseness measure of concepts using Wikipedia corpus: the number of Wikipedia articles in which the concept appears and is marked up as a link divided by the total number of Wikipedia articles where the concept appears [16].

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Text 8th Abu Nidal Baghdad Iraq President Intelligence Al-Qaida Saddam Hussein

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Text 12th Saddam Hussein Iraq Weapons President Bush White House

Fig. 2 Illustrating the functionality of comparing key concepts of two texts

Eventually, rank value each community is obtained from product of Density in Informativeness.

4 Application We extracted key concepts of any text by analyzing ECG. Extracted key concepts of ECG have high precision and quality. For demonstrate usage of key concepts, we apply these key concepts for computing texts semantic relatedness. Figure 2 represents how computing texts semantic relatedness by key concepts.

4.1 Comparing the Extracted Key Concepts of Text Pairs At this step we perform a statistical comparison of the extracted key concepts from valuable communities concern to two texts. For this purpose, we use link structures of Wikipedia. To achieve that, we compute a statistical similarity measure between all pairs of concepts \A,B[ , where A and B belong to the extracted key concepts of first text and second text, and calculates the average of all similarity scores to determine the relatedness between the texts. We assume that we want to find the semantic relatedness between following two texts 8th and 12th of Lee dataset. To compute the similarity between a key concept (i) from the first text A, denoted ternA(i), and the second text B, we compute the semantic relatedness between termA(i) and all key concepts termB(j) in the text 2 (B) and then take the average of all scores obtained. Pn j¼1 wikisimðtermAðiÞ; termBðjÞÞ ; ð1Þ simðtermAðiÞ; groupðBÞÞ ¼ n

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Table 1 Results of computing text semantic relatedness Semantic resource

Correlation

WordNet Wikipedia WordNet and Wikipedia

0.427 0.503 0.782

Table 2 Compare our method to other existent method for computing text relatedness Technique Correlation with humans Bag of words [3] LSA [7] ESA [10] Our approach

0.1–0.5 0.60 0.72 0.78

where n is the total number of key concepts in group (A). We calculate this score for each concept in the first set ‘‘A’’ and then pass on the array of scores and compute the average of the scores to obtain a final score for the two texts. That is, Pm simðtermAðiÞ; groupðBÞÞ : ð2Þ scoreðA; BÞ ¼ i¼1 m

5 Experimental Results and Evaluation We used a collection of 50 documents from the Australian Broadcasting Corporation’s news mail service [17] for our evaluation. As it is explained in previous sections, we apply the WordNet and Wikipedia for computing concepts semantic relatedness in our method. Therefore we installed WordNet 2.1 locally and used the available API (WordNet::Similarity)1 that is in Perl language to access the WordNet database efficiently. Also, for using Wikipedia database, we applied the Wikipedia Miner Toolkit2 that is an API in Java language so that we easier make use of the structure and content of Wikipedia. After computing concepts semantic relatedness and building ECG, we use the Girvan-Newman algorithm3 for analyzing ECG. Finally, key concepts of texts are compared together. To evaluate our method, we conducted three experiments. In the first experiment, we only used WordNet as lexical resource for computing concepts semantic relatedness. In the second experiment, we only used Wikipedia as semantic resource. In the third experimentf, we tested the system by providing a collection of two resources WordNet and Wikipedia. The goal of these three experiments was 1 2 3

http://www.d.umn.edu/*tpederse/similarity.html http://wikipedia-miner.sourceforge.net http://cs.unm.edu/*aaron/research/fastmodularity.htm

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that to test the ability of the system for computing text-to-text semantic relatedness by using different semantic resource. The results of our experiments on the Lee dataset are shown in Table 1. We evaluated our method by comparing with other existing methods. Table 2 summarizes the results of the comparison. As specified, our method achieves much better results than the other existent method for computing texts semantic relatedness. Our method is both cheaper and more accurate than ESA: cheaper, because it use only links structure of Wikipedia instead textual content of Wikipedia articles, and more accurate, because correlation coefficient of our method is 0.782 that in compare to ESA method, it computes texts semantic relatedness with 6% more precision.

6 Conclusion and Future Works In this paper, we presented usage of extracted key concepts from text for computing text-to-text semantic similarity. For computing concepts semantic relatedness, we purposed using two semantic resources (i.e. WordNet and Wikipedia), so that by improving the weaknesses of one source by benefits of other, it provides much more of complete data source and it has the capability to determine semantic relatedness between almost any word-pair. In order to, built graph is an enriched semantic graph of concepts. Our other reason about using from WordNet with Wikipedia is that if a word is found on WordNet, it is much easier and faster for determine its synonyms, hypernyms and hyponyms as compared to Wikipedia, and this helps to enhancing speed of operations. Another of advantage of our method is in compared to famous method ESA; it is that our method unlike ESA doesn’t need machine learning techniques. Also ESA uses the article’s textual content but we only use links structure of Wikipedia articles that have less volume. In order to evaluate our approach, we obtained a high correlation coefficient of 0.78 which outperformed all other existing state of art approaches for texts semantic relatedness measure. Future work will involve applying utilized methodology for extracting key concept in various tasks, such as document classification, document clustering and WSD. Also we can insert the web as semantic resources to collection of semantic resources for building graph, so that is built more enrich graph, whereas Web has good performance related to the general concepts. So it is improved the weaknesses of one source by benefits of other, so that it improved the semantic knowledge coverage and is increased the range of vocabulary and use of strengths of each source provide the more complete data source, and certainly affect the methodology of key concept extraction and comparing texts semantic relatedness.

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References 1. Jahanbakhsh Z, Mahmoudi F, Malekzadeh R, Jadidinejad AH (2011) Using different knowledge resources for measuring semantic relatedness of words. In: Proceedings of the 2nd international conference on contemporary issues in computer and information sciences (CICIS’11), IASBS, Zanjan, pp 218–222 2. Grineva M, Grinev M, Lizorkin D (2009): Extracting key terms from noisy and multi-theme documents: International World Wide Web Conference Committee (IW3C2), ACM 978-1-60558-487-4/09/04, Madrid, Spain 3. Jarmasz M, Szpakowicz S (2003) Rogets thesaurus and semantic similarity. In: Proceedings of RANLP-03, pp 212–219 4. Banerjee S, Pedersen T (2003) Extended gloss overlap as a measure of semantic relatedness. In: Proceedings of the international joint conference on artificial intelligence (IJCAI-03),pp 805-810 5. Finkelstein L, Gabrilovich E, Matias Y, Rivlin E, Solan S, Wolfman G, Ruppin E (2002) Placing search in context: the concept revisited. ACM Trans Info Syst 20(1):116–131 6. Lesk M (1986) Automatic sense disambiguation using dictionaries. In: Proceedings of the 5th international conference on systems documentation 7. Deerwester S, Dumais S, Furnas G, Landauer T, Hashman R (1990) Indexing by latent semantic indexing. In J Am Soc Info Sci 8. Strube M, Ponzetto SP. (2005) Exploiting semantic role labeling, wordnet and wikipedia for coreference resolution. In: Proceedings of the main conference on human language technology conference of the North American chapter of the association of computational linguistics, NJ 9. Syed Z, Finin T, Joshi A (2008) Wikipedia as an ontology for describing documents. In: Proceedings of 2nd international conference on weblogs and social media 10. Gabrilovich E, Markovitch S (2007) Computing semantic relatedness using wikipedia-based explicit semantic analysis In: Proceedings of the international joint conference on artificial intelligence (IJCAI-07), pp 1606–1611 11. Banerjee S, Pedersen T (2002) Adapting the lesk algorithm for word sense disambiguation to wordnet. Master’s Thesis. Department of Computer Science, University of Minnesota, USA 12. Milne D, Witten IH (2008) An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: Processing of AAAI08 Workshop on wikipedia and artificial intelligence, Chicago 13. Turdakov D, Velikhov P (2008) Semantic relatedness metric for wikipedia concepts based on link analysis and its application to word sense disambiguation. In colloquium on databases and information systems (SYRCoDIS) 14. Strube M, Ponzetto S. (2006) WikiRelate! Computing semantic relatedness using Wikipedia. In: Proceedings of the 21st national conference on artificial intelligence (AAAI-06), Boston, pp 1419–1424 15. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113 16. Medelyan O, Witten IH, Milne D (2008) Topic indexing with Wikipedia: In Wikipedia and AI workshop at the AAAI-08 conference (WikiAI08), Chicago 17. Lee D, Pincombe B, Welsh M (2005) An empirical evaluation of models of text document similarity, In CogSci2005, pp 1254–1259

Author Index

A Horri A., 269 Herlina Abdul Rahim, 761 Ruzairi Abdul Rahim, 761 Dino Ablakovic, 11 Ailixier Aikebaier, 669, 677 Mehdi Zand Akbari, 349 Abderrahmane Amrouche, 745 Nuzli Mohamad Anas, 305 Bahman Arasteh, 487, 497 Parham Arjomand, 415 Sarah Atkinson, 31 Fatemeh Azam, 221 Naziha Ahmad Azli, 315

Hyeonjoong Cho, 121, 335 Jae Ik Cho, 529 Jung-Sik Cho, 113 Moon-Taek Cho, 621 Seongsoo Cho, 579 Young In Cho, 105 Chang-Hee Choi, 687, 695 Euna Choi, 463 Hae-Gill Choi, 607, 621 Jeongdan Choi, 511 Kae Won Choi, 795 Min Choi, 787 Sanggyun Choi, 519 Woonho Choi, 561

B Changgyu Bak, 463 Jewan Bang, 43 Ahmad Baraani-Dastjerdi, 221 Admir Barolli, 643 Leonard Barolli, 643, 651, 659

D Wenwen Dai, 193 Mohamed Debyeche, 745 Alireza Dehghan, 415 Chuan Deng, 427 Peter Dolog, 355 Arjan Durresi, 659 Izudin Dzafic, 11, 67, 167, 211, 659

C Yuan Cao, 427 Jae-sang Cha, 613 Jirawat Chaiareerat, 251 Hangbae Chang, 203 Yue-Shan Chang, 295 Su ChangNam, 105 Shao-Jui Chen, 295 Xiao-feng Chen, 437 Jih-Ching Chiu, 725

E Tomoya Enokido, 669, 677

G Zihou Ge, 3 Dastghaibyfard Gh., 269, 281 Fatemeh Ghadamyari, 21

James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Convergence, Lecture Notes in Electrical Engineering 114, DOI: 10.1007/978-94-007-2792-2, Ó Springer Science+Business Media B.V. 2012

841

842

G (cont.) Saeedeh Gholamrezazadeh, 21 Sumit Ghosh, 81 Dawu Gu, 453

H Tobias Haag, 211 Wan Mohd Hafeez, 305 Ehsan Ul Haq, 135, 145 Tormod Håvaldsrud, 93 Masahiro Hiyama, 635 Sabine Hoesch, 405 Bonghwa Hong, 553, 579, 589, 613 Seokhie Hong, 105 Suck-Joo Hong, 569 Mohammad Javad Hosseini, 497 Yao-Chin Huang, 725 Dai-Kyung Hyun, 687, 695

I Dzafic I, 155 Suhaimi Ibrahim, 395 Makoto Ikeda, 635, 651 Takuro Inoue, 677 Jiro Iwashige, 651, 659

J Amir Hossein Jadidinejad, 553, 589, 831 Hye-Jeong Jeong, 569 Young-Sik Jeong, 55 Min-Seok Jie, 735 JuYeon Jo, 607 Hae-Jong Joo, 579, 621 Beyong-In Jung, 735

K Kamal K. Bharadwaj, 325 Alaeddin Kalantari, 395 Javad Kamyabi, 241 Jang-Mook Kang, 613 Jonggu Kang, 203 Zahra Khoshmanes, 231 Chang Han Kim, 105 Dohyun Kim, 43 Dong Keon Kim, 561 Hye-Young Kim, 259 Jin-Mook Kim, 589 Jungsun Kim, 607 Sang-Soo Kim, 621

Author Index Seil Kim, 529 Seong-Whan Kim, 713 Su-Hyun Kim, 821 Yanghoon Kim, 203 Dae-Sik Ko, 813 Jong-won Ko, 597 Weihao Kong, 453 Yeonhwa Kong, 335 Elis Kulla, 635, 651 Mu-Hsing Kuo, 769

L Arockiam L., 805 Nenad Lecek, 67, 167 Chulung Lee, 519 Dong Hoon Lee, 529 Euy-Soo Lee, 621 Hae-Yeoun Lee, 703 Heung-Kyu Lee, 687 Im-Yeong Lee, 821 Jung-Won Lee, 463, 475 Meeyeon Lee, 475 Min-Jeong Lee, 687 Sangjin Lee, 43 sHeung-Kyu Lee, 695 Won-bong Lee, 543 Youngkon Lee, 365, 375, 385 Wei Li, 453 Wenhui Li, 3 Jaehwan Lim, 203 Seung-Ho Lim, 779 Ya Liu, 453 Yanping Liu, 325 Yuhua Liu, 193 Zhiqiang Liu, 453 Yo-Ming Lo, 295 Chidchanok Lursinsap, 251

M Linru Ma, 427 Fariborz Mahmoudi, 831 Farzaneh Maleki, 241 Maryam Marzban, 231 Shahrin Md.Ayob, 315 Thomas Meier, 211 Kyoungwook Min, 511 Min Zhang, 31 Mahmood Vahedi Moghaddam, 349 Roslina Mohamad, 305 Priyanka Mohapatra, 67 Birger Møller-Pedersen, 93

Author Index Nammee Moon, 463, 475 Hee Won Myeong, 529

N Lecek N., 155 Mansouri N., 269, 281 Youngsub Na, 203, 831 Norkharziana Mohd Nayan, 315 Amirahmad Nayyeri, 415 Hans-Theo Neisius, 11

O Tetsuya Oda, 643

P Newton P. Calduwel, 805 Rong Pan, 355 James J. (Jong Hyuk) Park, 113, 193 Seung Soo Park, 475 Syed Jahanzeb Hussain Pirzada, 135, 145

R Mohd Hafiz Fazalul Rahiman, 761 Nabil Ramou, 745 Brendan Ryan, 31

S Mohsen Amini Salehi, 21 Wan Mohd Salleh, 305 Ashkan Sami, 231, 241 Líria Matsumoto Sato, 753 Jaydip Sen, 81 Sangwon Seo, 519 Hassan Shakeri, 21 Jae Youn Shim, 713 Hyunchu Shin, 135, 145 Bhanu Shrestha, 579 Bjørnar Solhaug, 93 Eun-Ha Song, 55 You-Jin Song, 613 Young-jae Song, 597 Peraphon Sophatsathit, 251 Evjola Spaho, 643

843 Silvio Luiz Stanzani, 753 Ketil Stølen, 93 Joohyung Sun, 121

T Donlagic T., 155 Masoud Tabatabaey, 145 Hamed Taherdoos, 395 Makoto Takizawa, 643, 669, 677

U Arijit Ukil, 81

V Siegfried Voessner, 405

W Jing-jing Wang, 437 Wei-Jen Wang, 295 Ming-Shiou Wu, 725

X Fatos Xhafa, 643 Naixue Xiong, 193 Guandong Xu, 355 Kaihua Xu, 193

Y Kai-Ming Yang, 725 Lin Yang, 427 Su-Hyun Yang, 55 Tao Yang, 659 Xiaodong Yang, 183 Xiao-yuan Yang, 437 Dong-Gyu Yeo, 703 Dalila Yessad, 745 Parisa YousefzadehFard, 487

Z Manouchehr ZadahmadJafarlou, 487, 497