NETWORKING 2007, Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet, 6 conf

Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany

4479

Ian F. Akyildiz Raghupathy Sivakumar Eylem Ekici Jaudelice Cavalcante de Oliveira Janise McNair (Eds.)

NETWORKING 2007 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet 6th International IFIP-TC6 Networking Conference Atlanta, GA, USA, May 14-18, 2007 Proceedings

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Volume Editors Ian F. Akyildiz Raghupathy Sivakumar Georgia Institute of Technology, School of Electrical and Computer Engineering Atlanta, GA 30332, USA E-mail: {ian, siva}@ece.gatech.edu Eylem Ekici The Ohio State University, Department of Electrical and Computer Engineering Columbus, OH 43210, USA E-mail: [email protected] Jaudelice Cavalcante de Oliveira Drexel University, ECE Department, Bossone 312 Philadelphia, PA 19104-2875, USA E-mail: [email protected] Janise McNair University of Florida, Department of Electrical and Computer Engineering Gainesville, FL 32611, USA E-mail: [email protected]

Library of Congress Control Number: 2007926314 CR Subject Classification (1998): C.2, C.4, H.4, D.2, J.2, J.1, K.6, K.4 LNCS Sublibrary: SL 5 – Computer Communication Networks and Telecommunications ISSN ISBN-10 ISBN-13

0302-9743 3-540-72605-5 Springer Berlin Heidelberg New York 978-3-540-72605-0 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © 2007 IFIP International Federation for Information Processing, Hofstraße 3, 2361 Laxenburg, Austria Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12066407 06/3180 543210

Preface

General Chairs’ Message It is our great pleasure to welcome you to the Sixth IFIP Networking conference held in Atlanta, May 14–18, 2007. This conference, the sixth of a planned series of annual meetings with a highly selective and a highly competitive technical program, has been established to serve as the premier forum to cover research on all aspects of networking and communication issues. The conference is intended to involve multiple networking paradigms such as wireless and wired networks, ad-hoc networks, sensor networks, mesh networks, and optical networks. An exciting and high-quality technical program was put together by Eylem Ekici, Jaudelice C. de Oliveira and Janise McNair with the help of an exceptional panel of experts who served on the Technical Program Committee. We were very fortunate to have Eylem, Jau and Janice as the Program Chairs to launch the IFIP Networking 2007 conference on a path of academic excellence and practical relevance; we express our sincere thanks to them. Together, they put together a high-quality program that educated attendees and at the same time inspired spirited discussions. A poster session and social events provided other opportunities for discussions, debates and exchange of information amongst conference participants. Chuanyi Ji, the Local Arrangements Chair, performed a good job overseeing all aspects of the meeting planning and organization. Our very special thanks go to Chuanyi for making sure it all happened just right. Faramarz Fekri, our Financial Chair, did an outstanding job to keep the books orderly. Our sincere and special thanks go to Faramarz for his exceptional handling of the finances, and going over and beyond his call of duty to take care of other aspects of the conference organization as well. Benny Bing did an excellent job in collecting the camera-ready papers from the authors and making sure to publish the conference proceedings on time. The co-operation and support of Springer in this matter is also greatly appreciated. We thank Cordai Farrar, who provided essential administrative and logistical assistance to Chuanyi and Faramarz in the planning and implementation of the conference. We also would like to acknowledge the efforts and contributions of Linda Jiang Xie and Ozgur B. Akan, who coordinated and implemented publicity for the conference as well as the management of the Web site. We express appreciation to Ed Knightly, Sherman (Xuemin) Shen and Josep Sole Pareta for raising sponsorship for the conference, to Mehmet Can Vuran and Vehbi Cagri Gungor, the Registration Chairs, to Giacomo Morabito, the Tutorials Chair, and to Giovanni Pau and George Kormentzas, the Workshop Chairs, for all their invaluable contributions. We also thank the members of the Steering Committee

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Preface

for providing much needed support and guidance during the organization of the conference. Finally, we thank all our industry sponsors who provided financial assistance toward the conference organization. We look forward to an exciting week of sharing technical ideas and visions with colleagues from around the world. We hope that you will find the conference to be very engaging and fruitful. We thank you for attending the conference and being a part of this very important event. May 2007

Ian F. Akyildiz Raghupathy Sivakumar IFIP Networking 2007 General Co-chairs

Technical Program Chairs’ Message

Welcome to Networking 2007! Networking 2007 was the sixth event in a series of International Conferences on Networking sponsored by the IFIP Technical Committee on Communication Systems (TC 6). Previous events were held in Paris (France) in 2000, Pisa (Italy) in 2002, Athens (Greece) in 2004, Waterloo (Canada) in 2005, and Coimbra (Portugal) in 2006. Networking 2007 brought together active and proficient members of the networking community, from both academia and industry, thus contributing to scientific, strategic, and practical advances in the broad and fast-evolving field of communications. The conference comprised highly technical sessions organized thematically, keynote talks, tutorials offered by experts, as well as workshops and panel discussions on topical themes. Plenary sessions with keynote speeches opened the daily sessions, covering the three main tracks of the conference. The Networking 2007 call for papers attracted 440 submissions from 40 different countries in Asia, Australia, Europe, North America, and South America. These were subject to thorough review by the Program Committee members and additional reviewers. A one-week discussion phase followed the regular review deadline where TPC leaders summarized comments made by the other reviewers and made a final recommendation. A high-quality selection of 96 full papers and 27 posters, organized into 24 regular sessions and 1 poster session, made up the Networking 2007 main technical program, which covered next-generation networks: content distribution, quality of service, topology design, routing, buffer management, optical networks, TCP, security, network measurement; ad hoc and sensor networks: connectivity and coverage, scheduling and resource allocation, mobility and location, routing, and key management; wireless networks: mesh networks, mobility, TCP, MAC performance, scheduling and resource allocation. The technical program was complemented by three keynote speeches: “Looking into the Future: Grand Challenges for Wireless Networks,” by Ness Shroff (Purdue University); “Key Technologies and Architectures for Next-Generation Networks,” by Krishan Sabnani (Bell Labs); and “Urban Mesh Networks: Coming Soon to a City Near You,” by Ed Knightly (Rice University). In addition to the main technical program, the day preceding the conference was dedicated to three excellent tutorials on “An Introduction to Network Coding,” by Muriel Medard (MIT), “Cognitive Radio Networks,” by Ian F. Akyildiz (Georgia Institute of Technology), and “WiMAX: Technology for Broadband Wireless Internet and QoS Driven Routing: Theoretical and Experimental Considerations,” by Shailender Timiri and Shantidev Mohanty (Intel Corporation).

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Preface

The final day of Networking 2007 was dedicated to two one-day workshops, on the following topics: “Security and Privacy in Mobile and Wireless Networking,” and “Challenges for Next-Generation Networks.” We would like to express our appreciation of the efforts of many people in making Networking 2007 a successful event: to the authors, we are most grateful to the hundreds of authors who spent countless hours preparing their submitted papers; to the Program Committee and to all associated reviewers: we thank you for your hard work, your promptness in submitting reviews, and your willingness and patience to accept extra assignments; to the other Executive Committee members: we thank you for your hard work and dedication to the organizational issues surrounding Networking 2007. Last, but by no means least, we thank our sponsors and supporting institutions, all the people that helped us at the Georgia Institute of Technology, and specially all the volunteers. March 2007

Eylem Ekici Janise McNair Jaudelice C. de Oliveira

Organization

Executive Committee General Co-chairs

Technical Program Co-chairs

Tutorial Chair Workshop Co-chairs

Publicity Co-chairs

Sponsor Co-chairs

Publication Chair Finance Chair Registration Co-chairs

Local Arrangements Chair

Ian F. Akyildiz (Georgia Institute of Technology, USA) Raghupathy Sivakumar (Georgia Institute of Technology, USA) Eylem Ekici (Ohio State University, USA) Janise McNair (University of Florida, USA) Jaudelice C. de Oliveira (Drexel University, USA) Giacomo Morabito (University of Catania, Italy) Giovanni Pau (University of California at Los Angeles, USA) George Kormentzas (University of Aegean, Greece) Ozgur Akan (Middle East Technical University, Turkey) Jiang (Linda) Xie (University of North Carolina-Charlotte, USA) Edward Knightly (Rice University, USA) Sherman Shen (University of Waterloo, Canada) Josep Sole Pareta (Universitat Polit`ecnica de Catalunya, Spain) Benny Bing (Georgia Institute of Technology, USA) Faramarz Fekri (Georgia Institute of Technology, USA) Mehmet Can Vuran (Georgia Institute of Technology, USA) Vehbi Cagri Gungor (Georgia Institute of Technology, USA) Chuanyi Ji (Georgia Institute of Technology, USA)

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Organization

Steering Committee Harry Perros (North Carolina State University, USA) Augusto Casaca (IST/INESC, Portugal) Guy Omidyar (TC6 WG6.8 Chair, USA) Guy Pujolle (University of Paris 6, France) Ioannis Stavrakakis (University of Athens, Greece) Otto Spaniol (RWTH-Aachen University, Germany)

Technical Program Committee Alhussein Abouzeid (Rensselaer Polytechnic Institute, USA) Nael Abu-Ghazaleh (State University of New York at Binghamton, USA) Rui Aguiar (Universidade de Aveiro, Portugal) Ozgur Akan (Middle East Technical University, Turkey) Kemal Akkaya (Southern Illinois University, USA) Fatih Alagoz (Bogazici University, Turkey) Kevin Almeroth (University of California at Santa Barbara, USA) Mostafa Ammar (Georgia Institute of Technology, USA) Tricha Anjali (Illinois Institute of Technology, USA) Regina Araujo (Federal University of So Carlos, Brazil) Lichun Bao (University of California at Irvine, USA) Stefano Basagni (Northeastern University, USA) Christian Bettstetter (University of Klagenfurt, Austria) Raheem Beyah (Georgia State University, USA) Andrea Bianco (Politecnico di Torino, Italy) Semih Bilgen (Middle East Technical University (ODTU), Turkey) Chris Blondia (University of Antwerp, Belgium) Fernando Boavida (Coimbra University, Portugal) Azzedine Boukerche (University of Ottawa, Canada) Torsten Braun (University of Bern, Switzerland) Milind Buddhikot (Bell Labs, Lucent Technologies, USA) Wojciech Burakowski (Warsaw University of Technology, Poland) Tracy Camp (Colorado School of Mines, USA) Guohong Cao (Pennsylvania State University, USA) Antonio Capone (Politecnico di Milano, Italy) Matteo Cesana (Politecnico di Milano, Italy) Chih-Yung Chang (Tamkang University, Taiwan) Han-Chieh Chao (National Ilan University, Taiwan) Edgar Chavez (Universidad Michoacana, Mexico) Ling-Jyh Chen (Academia Sinica, Taiwan) Yuh-Shyan Chen (National Taipei University, Taiwan) Sunghyun Choi (Seoul National University, Korea) Marco Conti (IIT-CNR, Italy) Jun-Hong Cui (University of Connecticut, USA) Jaudelice C. de Oliveira (Drexel University, USA)

Organization

Michel Diaz (LAAS, France) Constantinos Dovrolis (Georgia Institute of Technology, USA) Falko Dressler (University of Erlangen, Germany) Stephan Eidenbenz (Los Alamos National Laboratory, USA) Eylem Ekici (Ohio State University, USA) Hesham El-Rewini (Southern Methodist University, USA) Tamer ElBatt (San Diego Research Center, Inc., USA) Mourad Elhadef (University of Ottawa, Canada) Ehab Elmallah (University of Alberta, Canada) Ozgur Ercetin (Sabanci University, Turkey) Laura Feeney (Swedish Institute of Computer Science, Sweden) Wu-chi Feng (Portland State University, USA) Joe Finney (Lancaster University, UK) Eric Fleury (Insa de Lyon / INRIA, France) Mario Freire (University of Beira Interior, Portugal) Andrea Fumagalli (University of Texas at Dallas, USA) Laura Galluccio (University of Catania, Italy) Sabastia Galmes (Universitat de Ies Illes Balears, Spain) Javier Gomez (National University of Mexico, Mexico) Isabelle Guerin Lassous (INRIA, France) Ozgur Gurbuz (Sabanci University, Turkey) Eren Gurses (Norwegian University of Science and Technology, Norway) Guenter Haring (Universit˝ at Wien, Austria) Paul Havinga (University of Twente, The Netherlands) Ahmed Helmy (University of Southern California, USA) Raquel Hill (Indiana University, USA) Xiaoyan Hong (University of Alabama, USA) Hung-Yun Hsieh (National Taiwan University, Taiwan) Frank Huebner (AT&T Labs, USA) David Hutchison (Lancaster University, UK) Muhammad Jaseemuddin (Ryerson University, Canada) Vana Kalogeraki (University of California at Riverside, USA) Holger Karl (University of Paderborn, Germany) Can Emre Koksal (Ohio State University, USA) Kimon Kontovasilis (NCSR Demokritos, Greece) Turgay Korkmaz (University of Texas at San Antonio, USA) Sastri Kota (Harris Corporation, USA) Yevgeni Koucheryavy (Tampere University of Technology, Finland) Evangelos Kranakis (Carleton University, Canada) Bhaskar Krishnamachari (University of Southern California, USA) Srikanth Krishnamurthy (University of California at Riverside, USA) Santosh Kumar (University of Memphis, USA) Thomas Kunz (Carleton University, Canada) Miguel Labrador (University of South Florida, USA) Bu Sung Lee (Nanyang Technological University, Singapore)

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Organization

Chang-Gun Lee (Seoul National University, Korea) Sung-Ju Lee (HP Labs, USA) Kenji Leibnitz (Osaka University, Japan) Albert Levi (Sabanci University, Turkey) Baochun Li (University of Toronto, Canada) Jie Li (University of Tsukuba, Japan) Wei Li (University of Toledo, USA) Ben Liang (University of Toronto, Canada) Leszek Lilien (Western Michigan University, USA) Yung-Hsiang Lu (Purdue University, USA) Athina Markopoulou (University of California, Irvine, USA) Jose Marzo (Universitat de Girona, Spain) Xavier Masip-Bruin (Technical University of Catalonia (UPC), Spain) Mustafa Matalgah (University of Mississippi, USA) Ibrahim Matta (Boston University, USA) Ketan Mayer-Patel (University of North Carolina, USA) Janise McNair (University of Florida, USA) Sirisha Medidi (Washington State University, USA) Abdelhamid Mellouk (University Paris XII, France) Paulo Mendes (DoCoMo Eurolabs, Germany) Michael Menth (University of W¨ urzburg, Germany) Jelena Misic (University of Manitoba, Canada) Shantidev Mohanty (Intel Corporation, USA) Edmundo Monteiro (University of Coimbra, Portugal) Giacomo Morabito (University of Catania, Italy) Nidal Nasser (University of Guelph, Canada) Srihari Nelakuditi (University of South Carolina, USA) Ioanis Nikolaidis (University of Alberta, Canada) Stephan Olariu (Old Dominion University, USA) Joao Orvalho (IPC, Portugal) Giovanni Pau (University of California at Los Angeles, USA) Harry Perros (North Carolina State University, USA) Chiara Petrioli (University of Rome “La Sapienza,” Italy) Niki Pissinou (Florida International University, USA) Thomas Plagemann (University of Oslo, Norway) Radha Poovendran (University of Washington, USA) Konstantinos Psounis (University of Southern California, USA) Ramon Puigjaner (UIB, Spain) Guy Pujolle (University of Paris 6, France) Hazem Refai (Oklahoma University, USA) Reza Rejaie (University of Oregon, USA) George Rouskas (North Carolina State University, USA) Sergio S´ anchez-L´opez (Technical University of Catalonia, Spain) Paolo Santi (CNR, Italy) Caterina Scoglio (Kansas State University, USA) Subhabrata Sen (AT&T Labs - Research, USA)

Organization

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Harish Sethu (Drexel University, USA) Ness Shroff (Purdue University, USA) Jorge Silva (University of Coimbra, Portugal) Harry Skianis (National Centre for Scientific Research ‘Demokritos’, Greece) Josep Sole Pareta (UPC, Spain) Otto Spaniol (Aachen University of Technology, Germany) Burkhard Stiller (University of Z¨ urich and ETH Z¨ urich, Switzerland) Ivan Stojmenovic (University of Ottawa, Canada) Aaron Striegel (University of Notre Dame, USA) Tony Sun (University of California at Los Angeles, USA) Karthikeyan Sundaresan (Georgia Institute of Technology, USA) Violet Syrotiuk (Arizona State University, USA) Vassilis Tsaoussidis (Demokritos University, Greece) Tuna Tugcu (Bogazici University, Turkey) Damla Turgut (University of Central Florida, USA) Piet Van Mieghem (Delft University of Technology, The Netherlands) Ramanuja Vedantham (Georgia Institute of Technology, USA) Wenye Wang (NC State University, USA) Xudong Wang (Kiyon, Inc., USA) Steven Weber (Drexel University, USA) Cedric Westphal (Nokia, USA) Lars Wolf (Technical University of Braunschweig, IBR, Germany) Yang Xiao (University of Alabama, USA) Jiang (Linda) Xie (University of North Carolina at Charlotte, USA) Dong Xuan (The Ohio State University, USA) Guoliang Xue (Arizona State University, USA) Boon Sain Yeo (Wavex Technologies, Singapore) Mohamed Younis (University of Maryland Baltimore County, USA) Moustafa Youssef (University of Maryland, USA) Honghai Zhang (Lucent Technologies, USA) Lixia Zhang (University of California at Los Angeles, USA) Yongbing Zhang (University of Tsukuba, Japan) Rong Zheng (University of Houston, USA) Fang Zhu (Verizon, USA) Hao Zhu (Florida International University, USA) Taieb Znati (University of Pittsburgh, USA)

Additional Reviewers Ibrahim Abualhaol Helmut Adam Rui Aguiar Markus Anwander Jesus Arango Baris Atakan

Jeroen Avonts Abdel Aziz Leonardo Badia Seung Baek Mario Barbera Pere Barlet-Ros

Sujoy Basu V´eronique Baudin Osama Bazan Luca Becchetti Alper Bereketli Johan Bergs

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Organization

Antoni Bibiloni Ali Bicak Steven Borbash Bart Braem Tiago Camilo Luca Campelli Davide Careglio Maxweel Carmo Eduardo Cerqueira Coskun Cetinkaya Sriram Chellappan Chao Chen Kuan-Ta Chen Yuh-Shyan Chen Maggie Cheng Roman Chertov Harshal Chhaya Philip Chimento Girish Chiruvolu Youngkyu Choi Cherita Corbett Vedat Coskun Paul Coulton S´ergio Cris´ostomo Sukrit Dasgupta Peter De Cleyn Isabel Dietrich Stylianos Dimitriou Lun Dong Yu Dong Khalil Drira Roman Dunaytsev Christopher Edwards Hesham Elsayed Thierry Ernst Amir Esmailpour Ramon Fabregat Silvia Farraposo Wissam Fawaz Xin Fei Anja Feldmann Pep-Lluis Ferrer Stefano Ferretti Ilario Filippini Rosario Firrincieli

Pedro M. F. M. Ferreira Alexandre Fonte James Gadze Wilfried Gansterer Mar´ıa L. Garc´ıa-Osma Ghayathri Garudapuram Damianos Gavalas Samik Ghosh Paolo Giaccone Silvia Giordano Oscar Gonzalez de Dios Jorge Granjal Vehbi Cagri Gungor Vaibhav Gupta Burak Gurdag Michael Gyarmati Thomas Hacker Omar Hammouri Seon Yeong Han Yuning He Mohamed Hefeeda Helmut Hlavacs Philipp Hofmann Carl Hu Cunqing Hua Pai-Han Huang Mehmet Isik Mikel Izal Mandana Jafarian Gentian Jakllari Jakub Jakubiak Jiwoong Jeong Yusheng Ji Tao Jiang Yingxin Jiang Changhee Joo Teodor Jov’ Carlos Juiz Eleni Kamateri Arzad Kherani Anna Kim Seongkwan Kim Vinay Kolar Jiejun Kong Ibrahim Korpeoglu

Li Lao Anis Laouiti Lap Kong Law Gabriela Leao Mauro Leoncini Nicolas Letor Chengzhi Li Hyuk Lim Ching-Ju Lin Yuan Lin Anders Lindgren Changlei Liu David Liu Jun Liu Jun Liu Ke Liu Yunhuai Liu Mahdi Lotfinezhad Ngok-Wah Ma Dhia Mahjoub Lefteris Mamatas Devu Manikantan Shila Cesar Marcondes Gustavo Marfia Eva Marin/Tordera Ruediger Martin Saverio Mascolo Marco Mellia Michela Meo Lyudmila Mihaylova Fabio Milan Dragan Milic Ingrid Moerman Dmitri Moltchanov Edmundo Monteiro Paolo Monti Xenia Mountrouidou Bala Natarajan Konstantinos Oikonomou Sema Oktug Evgeny Osipov Philippe Owezarski Claudio Palazzi Alessandro Panconesi

Organization

Michael Pascoe Stefan Penz Fabio Picconi Jonathan Pitts Ioannis Psaras Chunming Qiao Sundaram Rajagopalan Thierry Rakotoarivelo Rabie Ramadan Ulrich Reimers Mauricio Resende Jorge Rodriguez Sanchez Utz Roedig Sylwia Romaszko Kevin Ross Dario Rossi Emilia Rosti Nararat Ruangchaijatupon Christos Samaras Kamil Sarac Lambros Sarakis Emre Sayin Matthias Scheidegger Udo Schilcher Francesco Scoto Karim Seddik Alexandro Sentinelli Bartomeu Serra Ghalib Shah Manolis Sifalakis Paulo Simoes

Paul Smith Aaron So Rute Sofia Fernando Solano Donado Christoph Sommer Hanhee Song Hui Song Kathleen Spaey Vladimir Stankovic Thomas Staub Daniel Stutzbach Weilian Su Kyoungwon Suh Oguz Sunay Violet Syrotiuk Lei Tang Saurabh Tewari George Theodorakopoulos Masoomeh Torabzadeh Laurent Toutain Ageliki Tsioliaridou Tuna Tugcu Li-Ping Tung Alexander Tyrrell Suleyman Uludag Anna Urra Nicolas Van Wambeke Giacomo Verticale Michael Voorhaen Serdar Vural Mehmet Vuran

Markus Waelchli Gerald Wagenknecht Jiong Wang Lan Wang Alicia Washington Song Wei Wei Wei Michael Welzl Ralf Wienzek Damon Wischik Rita Wouhaybi Kai Wu Yan Wu Ariton Xhafa Yufeng Xin Kaiqi Xiong Lisong Xu Guang Yang Sichao Yang Yi Yang Marcelo Yannuzzi Sakir Yucel Ahmed Zahran Qian Zhang Wensheng Zhang Jing Zhao Qunwei Zheng Shengli Zhou Yuanyuan Zhou Zhong Zhou Andre Zimmermann Michele Zorzi

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Table of Contents

Ad Hoc and Sensor Networks AHS - Connectivity and Coverage On the Resilient Overlay Topology Formation in Multi-hop Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fei Xing and Wenye Wang

1

Placing and Maintaining a Core Node in Wireless Ad Hoc Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Dvir and Michael Segal

13

Flooding Speed in Wireless Multihop Networks with Randomized Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vasil Mizorov, J¨ org Widmer, Robert Vilzmann, and Petri M¨ ah¨ onen

25

AHS - Scheduling and Resource Allocation Power Amplifier Characteristic-Aware Energy-Efficient Transmission Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kwanghun Han, Youngkyu Choi, Sunghyun Choi, and Youngwoo Kwon

37

Energy Efficient Throughput Optimization in Multi-hop Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Xu and Xin Liu

49

Election Based Hybrid Channel Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Wang and J.J. Garcia-Luna-Aceves

61

Asynchronous Data Aggregation for Real-Time Monitoring in Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jie Feng, Derek L. Eager, and Dwight Makaroff

73

AHS - Mobility and Location Awareness A Novel Agent-Based User-Network Communication Model in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sang-Sik Kim and Ae-Soon Park

85

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Table of Contents

Realistic Mobility and Propagation Framework for MANET Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mesut G¨ une¸s, Martin Wenig, and Alexander Zimmermann

97

Localization for Large-Scale Underwater Sensor Networks . . . . . . . . . . . . . Zhong Zhou, Jun-Hong Cui, and Shengli Zhou

108

Location-Unaware Sensing Range Assignment in Sensor Networks . . . . . . Ossama Younis, Srinivasan Ramasubramanian, and Marwan Krunz

120

AHS - Routing I A Distributed Energy-Efficient Topology Control Routing for Mobile Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Ren, Bo Wang, Sidong Zhang, and Hongke Zhang

132

Integrated Clustering and Routing Strategies for Large Scale Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ataul Bari, Arunita Jaekel, and Subir Bandyopadhyay

143

On-Demand Routing in Disrupted Environments . . . . . . . . . . . . . . . . . . . . . Jay Boice, J.J. Garcia-Luna-Aceves, and Katia Obraczka

155

Delivery Guarantees in Predictable Disruption Tolerant Networks . . . . . . Jean-Marc Fran¸cois and Guy Leduc

167

AHS - Routing II PWave: A Multi-source Multi-sink Anycast Routing Framework for Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haiyang Liu, Zhi-Li Zhang, Jaideep Srivastava, and Victor Firoiu

179

Simple Models for the Performance Evaluation of a Class of Two-Hop Relay Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmad Al Hanbali, Arzad A. Kherani, and Philippe Nain

191

Maximum Energy Welfare Routing in Wireless Sensor Networks . . . . . . . Changsoo Ok, Prasenjit Mitra, Seokcheon Lee, and Soundar Kumara

203

Analysis of Location Privacy/Energy Efficiency Tradeoffs in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergio Armenia, Giacomo Morabito, and Sergio Palazzo

215

Efficient Error Recovery Using Network Coding in Underwater Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Guo, Bing Wang, and Jun-Hong Cui

227

Table of Contents

XIX

AHS - Key Management Key Predistribution Schemes for Sensor Networks for Continuous Deployment Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ u, Onsel ¨ Abd¨ ulhakim Unl¨ Arma˘gan, Albert Levi, Erkay Sava¸s, and ¨ ur Er¸cetin Ozg¨

239

Using Auxiliary Sensors for Pairwise Key Establishment in WSN . . . . . . . Qi Dong and Donggang Liu

251

Privacy-Aware Multi-Context RFID Infrastructure Using Public Key Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ ur Er¸cetin Selim Kaya, Erkay Sava¸s, Albert Levi, and Ozg¨

263

Wireless Networks WiNet - Mesh Networks Minimum Cost Configuration of Relay and Channel Infrastructure in Heterogeneous Wireless Mesh Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aaron So and Ben Liang Optimization Models for the Radio Planning of Wireless Mesh Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edoardo Amaldi, Antonio Capone, Matteo Cesana, and Federico Malucelli

275

287

Interference-Aware Multicasting in Wireless Mesh Networks . . . . . . . . . . . Sudheendra Murthy, Abhishek Goswami, and Arunabha Sen

299

Characterizing the Capacity Gain of Stream Control Scheduling in MIMO Wireless Mesh Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue Wang, Dah Ming Chiu, and John C.S. Lui

311

WiNet - Mobility AP and MN-Centric Mobility Prediction: A Comparative Study Based on Wireless Traces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jean-Marc Fran¸cois and Guy Leduc

322

A Flexible and Distributed Home Agent Architecture for Mobile IPv6-Based Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Albert Cabellos-Aparicio and Jordi Domingo-Pascual

333

Using PANA for Mobile IPv6 Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . Julien Bournelle, Jean-Michel Combes, Maryline Laurent-Maknavicius, and Sondes Larafa

345

XX

Table of Contents

Detecting 802.11 Wireless Hosts from Remote Passive Observations . . . . Valeria Baiamonte, Konstantina Papagiannaki, and Gianluca Iannaccone

356

WiNet - TCP A Scheme for Enhancing TCP Fairness and Throughput in IEEE 802.11 WLANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eun-Jong Lee, Hyung-Taig Lim, Seung-Joon Seok, and Chul-Hee Kang

368

TCP NJ+: Packet Loss Differentiated Transmission Mechanism Robust to High BER Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jungrae Kim, Jahwan Koo, and Hyunseung Choo

380

TCP WestwoodVT: A Novel Technique for Discriminating the Cause of Packet Loss in Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jahwan Koo, Sung-Gon Mun, and Hyunseung Choo

391

Modeling TCP in a Multi-rate Multi-user CDMA System . . . . . . . . . . . . . Majid Ghaderi, Ashwin Sridharan, Hui Zang, Don Towsley, and Rene Cruz

403

WiNet - MAC Performance IEEE 802.11b Cooperative Protocols: A Performance Study . . . . . . . . . . . Niraj Agarwal, Divya ChanneGowda, Lakshmi Narasimhan Kannan, Marco Tacca, and Andrea Fumagalli It Is Better to Give Than to Receive – Implications of Cooperation in a Real Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thanasis Korakis, Zhifeng Tao, Salik Makda, Boris Gitelman, and Shivendra Panwar

415

427

Modeling Approximations for an IEEE 802.11 WLAN Under Poisson MAC-Level Arrivals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ioannis Koukoutsidis and Vasilios A. Siris

439

Performance and Equilibrium Analysis of Heterogeneous IEEE 802.11 Based WLANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hao Zhu

450

Exploring a New Approach to Collision Avoidance in Wireless Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Peng and Liang Cheng

462

Table of Contents

XXI

WiNet - Scheduling and Resource Allocation Video Rate Adaptation and Scheduling in Multi-rate Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sourav Pal, Sumantra R. Kundu, Amin R. Mazloom, and Sajal K. Das

475

On Scheduling and Interference Coordination Policies for Multicell OFDMA Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G´ abor Fodor

488

Distributed Uplink Scheduling in CDMA Networks . . . . . . . . . . . . . . . . . . . Ashwin Sridharan, Ramesh Subbaraman, and Roch Gu´erin

500

Resource Allocation in DVB-RCS Satellite Systems . . . . . . . . . . . . . . . . . . Andr´e-Luc Beylot, Riadh Dhaou, and C´edric Baudoin

511

WiNet - Miscellaneous Enhanced Downlink Capacity in UMTS Supported by Direct Mobile-to-Mobile Data Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Larissa Popova, Thomas Herpel, and Wolfgang Koch

522

Impact of Technology Overlap in Next-Generation Wireless Heterogeneous Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmed Zahran, Ben Liang, and Aladdin Saleh

535

An On-Line Measurement-Based Admission Control for VBR Video Traffic in Wireless Multimedia Home Networks . . . . . . . . . . . . . . . . . . . . . . Yi-Hsien Tseng, Eric Hsiao-Kuang Wu, and Gen-Huey Chen

546

On Event Signal Reconstruction in Wireless Sensor Networks . . . . . . . . . . ¨ ur B. Akan Barı¸s Atakan and Ozg¨

558

Next Generation Internet NGI -Content Distribution Peer-Assisted On-Demand Streaming of Stored Media Using BitTorrent-Like Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Niklas Carlsson and Derek L. Eager

570

Multiple Identities in BitTorrent Networks . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Sun, Anirban Banerjee, and Michalis Faloutsos

582

Graph Based Modeling of P2P Streaming Systems . . . . . . . . . . . . . . . . . . . Damiano Carra, Renato Lo Cigno, and Ernst W. Biersack

594

XXII

Table of Contents

Modeling Seed Scheduling Strategies in BitTorrent . . . . . . . . . . . . . . . . . . . Pietro Michiardi, Krishna Ramachandran, and Biplab Sikdar

606

NGI -QoS I Streaming Performance in Multiple-Tree-Based Overlays . . . . . . . . . . . . . . Gy¨ orgy D´ an, Vikt´ oria Fodor, and Ilias Chatzidrossos

617

Path Selection Using Available Bandwidth Estimation in Overlay-Based Video Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manish Jain and Constantine Dovrolis

628

Fundamental Tradeoffs in Distributed Algorithms for Rate Adaptive Multimedia Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vilas Veeraraghavan and Steven Weber

640

Optimal Policies for Playing Buffered Media Streams . . . . . . . . . . . . . . . . . Steven Weber

652

NGI - Qos II Non-parametric and Self-tuning Measurement-Based Admission Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thomas Michael Bohnert, Edmundo Monteiro, Yevgeni Koucheryavy, and Dmitri Moltchanov

664

Optimal Rate Allocation in Overlay Content Distribution . . . . . . . . . . . . . Chuan Wu and Baochun Li

678

SLA Adaptation for Service Overlay Networks . . . . . . . . . . . . . . . . . . . . . . . Con Tran, Zbigniew Dziong, and Michal Pi´ oro

691

NGI - Topology Design Virtual Private Network to Spanning Tree Mapping . . . . . . . . . . . . . . . . . . Yannick Brehon, Daniel Kofman, and Augusto Casaca

703

Optimal Topology Design for Overlay Networks . . . . . . . . . . . . . . . . . . . . . . Mina Kamel, Caterina Scoglio, and Todd Easton

714

Construction of a Proxy-Based Overlay Skeleton Tree for Large-Scale Real-Time Group Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Guo and Sanjay Jha

726

Increasing the Coverage of a Cooperative Internet Topology Discovery Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benoit Donnet, Bradley Huffaker, Timur Friedman, and K.C. Claffy

738

Table of Contents

XXIII

NGI - Routing I Robust IP Link Costs for Multilayer Resilience . . . . . . . . . . . . . . . . . . . . . . Michael Menth, Matthias Hartmann, and R¨ udiger Martin

749

Integer SPM: Intelligent Path Selection for Resilient Networks . . . . . . . . . R¨ udiger Martin, Michael Menth, and Ulrich Sp¨ orlein

762

Beyond Centrality - Classifying Topological Significance Using Backup Efficiency and Alternative Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuval Shavitt and Yaron Singer

774

Incorporating Protection Mechanisms in the Dynamic Multi-layer Routing Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna Urra, Eusebi Calle Ortega, Jose L. Marzo, and Pere Vila

786

NGI - Routing II Accelerated Packet Placement Architecture for Parallel Shared Memory Routers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brad Matthews, Itamar Elhanany, and Vahid Tabatabaee

797

RSVP-TE Extensions to Provide Guarantee of Service to MPLS . . . . . . . Francisco J. Rodr´ıguez-P´erez, Jos´e Luis Gonz´ alez-S´ anchez, and Alfonso Gazo-Cervero

808

An Adaptive Management Approach to Resolving Policy Conflicts . . . . . Selma Yilmaz and Ibrahim Matta

820

Reinforcement Learning-Based Load Shared Sequential Routing . . . . . . . . Fariba Heidari, Shie Mannor, and Lorne G. Mason

832

NGI - Buffer Management An Adaptive Neuron AQM for a Stable Internet . . . . . . . . . . . . . . . . . . . . . Jinsheng Sun and Moshe Zukerman Light-Weight Control of Non-responsive Traffic with Low Buffer Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Venkatesh Ramaswamy, Leticia Cu´ellar, Stephan Eidenbenz, Nicolas Hengartner, Christoph Amb¨ uhl, and Birgitta Weber

844

855

The Effects of Fairness in Buffer Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mei Wang and Yashar Ganjali

867

Time to Buffer Overflow in an MMPP Queue . . . . . . . . . . . . . . . . . . . . . . . . Andrzej Chydzinski

879

XXIV

Table of Contents

NGI - Miscellaneous Fundamental Effects of Clustering on the Euclidean Embedding of Internet Hosts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanghwan Lee, Zhi-Li Zhang, Sambit Sahu, Debanjan Saha, and Mukund Srinivasan

890

A Multihoming Based IPv4/IPv6 Transition Approach . . . . . . . . . . . . . . . Lizhong Xie, Jun Bi, and Jianping Wu

902

Offline and Online Network Traffic Characterization . . . . . . . . . . . . . . . . . . Su Zhang, Mary K. Vernon

912

Catching IP Traffic Burstiness with a Lightweight Generator . . . . . . . . . . Chlo´e Rolland, Julien Ridoux, and Bruno Baynat

924

NGI - Optical Networks Importance of the Maturity of Photonic Component Industry on the Business Prospects of Optical Access Networks: A Techno-Economic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dimitris Varoutas, Thomas Kamalakis, Dimitris Katsianis, Thomas Sphicopoulos, and Thomas Monath

935

The Token Based Switch: Per-Packet Access Authorisation to Optical Shortcuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mihai-Lucian Cristea, Leon Gommans, Li Xu, and Herbert Bos

945

Online Multicasting in WDM Networks with Shared Light Splitter Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuzhen Liu and Weifa Liang

958

Evaluation of Optical Burst-Switching as a Multiservice Environment . . . Pablo Jes´ us Argibay-Losada, Andres Su´ arez-Gonz´ alez, Manuel Fern´ andez-Veiga, Ra´ ul Rodr´ıguez-Rubio, and C´ andido L´ opez-Garc´ıa

970

NGI -TCP The TCP Minimum RTO Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ioannis Psaras and Vassilis Tsaoussidis

981

Improving XCP to Achieve Max-Min Fair Bandwidth Allocation . . . . . . . Lei Zan and Xiaowei Yang

992

TCP Libra: Exploring RTT-Fairness for TCP . . . . . . . . . . . . . . . . . . . . . . . . 1005 Gustavo Marfia, Claudio Palazzi, Giovanni Pau, Mario Gerla, M.Y. Sanadidi, and Marco Roccetti

Table of Contents

XXV

Interactions of Intelligent Route Control with TCP Congestion Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014 Ruomei Gao, Dana Blair, Constantine Dovrolis, Monique Morrow, and Ellen Zegura

NGI - Security Fast and Scalable Classification of Structured Data in the Network . . . . 1026 Sumantra R. Kundu, Sourav Pal, Christoph L. Schuba, and Sajal K. Das An Efficient and Secure Event Signature (EASES) Protocol for Peer-to-PeerMassively Multiplayer Online Games . . . . . . . . . . . . . . . . . . . . 1037 Mo-Che Chan, Shun-Yun Hu, and Jehn-Ruey Jiang Unified Defense Against DDoS Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 M. Muthuprasanna, G. Manimaran, and Z. Wang Integrity-Aware Bandwidth Guarding Approach in P2P Networks . . . . . . 1060 Wen-Hui Chiang, Ling-Jyh Chen, and Cheng-Fu Chou

NGI - Network Measurement Measuring Bandwidth Signatures of Network Paths . . . . . . . . . . . . . . . . . . . 1072 Mradula Neginhal, Khaled Harfoush, and Harry Perros A Non-cooperative Active Measurement Technique for Estimating the Average and Variance of the One-Way Delay . . . . . . . . . . . . . . . . . . . . . . . . 1084 Antonio A. de A. Rocha, Rosa M.M. Le˜ ao, and Edmundo de Souza e Silva The P2P War: Someone Is Monitoring Your Activities! . . . . . . . . . . . . . . . 1096 Anirban Banerjee, Michalis Faloutsos, and Laxmi Bhuyan On-Line Predictive Load Shedding for Network Monitoring . . . . . . . . . . . . 1108 Pere Barlet-Ros, Diego Amores-L´ opez, Gianluca Iannaccone, Josep Sanju` as-Cuxart, and Josep Sol´e-Pareta On the Schedulability of Measurement Conflict in Overlay Networks . . . . 1120 Mohammad Fraiwan and G. Manimaran

Poster Session SEA-LABS: A Wireless Sensor Network for Sustained Monitoring of Coral Reefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1132 Matt Bromage, Katia Obraczka, and Donald Potts

XXVI

Table of Contents

Capacity-Fairness Performance of an Ad Hoc IEEE 802.11 WLAN with Noncooperative Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136 Jerzy Konorski Multi-rate Support for Network-Wide Broadcasting in MANETs . . . . . . . 1140 Tolga Numanoglu, Wendi Heinzelman, and Bulent Tavli BRD: Bilateral Route Discovery in Mobile Ad Hoc Networks . . . . . . . . . . 1145 Rendong Bai and Mukesh Singhal Correction, Generalisation and Validation of the “Max-Min d-Cluster Formation Heuristic” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149 Alexandre Delye de Clauzade de Mazieux, Michel Marot, and Monique Becker Analytical Performance Evaluation of Distributed Multicast Algorithms for Directional Communications in WANETs . . . . . . . . . . . . . . . . . . . . . . . . 1153 Song Guo, Oliver Yang, and Victor Leung Beyond Proportional Fair: Designing Robust Wireless Schedulers . . . . . . . 1157 Soshant Bali, Sridhar Machiraju, and Hui Zang A Voluntary Relaying MAC Protocol for Multi-rate Wireless Local Area Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1161 Jaeeun Na, Yeonkwon Jeong, and Joongsoo Ma Throughput Analysis Considering Capture Effect in IEEE 802.11 Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165 Ge Xiaohu, Yan Dong, Zhu Yaoting Performance Improvement of IEEE 802.15.4 Beacon-Enabled WPAN with Superframe Adaptation Via Traffic Indication . . . . . . . . . . . . . . . . . . . 1169 Zeeshan Hameed Mir, Changsu Suh, and Young-Bae Ko Analysis of WLAN Traffic in the Wild . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173 Caleb Phillips and Suresh Singh Enhanced Rate Adaptation Schemes with Collision Awareness . . . . . . . . . 1179 Seongkwan Kim, Sunghyun Choi, Daji Qiao, and Jongseok Kim A Study of Performance Improvement in EAP . . . . . . . . . . . . . . . . . . . . . . . 1183 Eun-Chul Cha and Hyoung-Kee Choi Characterization of Ultra Wideband Channel in Data Centers . . . . . . . . . 1187 N. Udar, K. Kant, R. Viswanathan, and D. Cheung Evaluating Internal BGP Networks from the Data Plane . . . . . . . . . . . . . . 1192 Feng Zhao, Xicheng Lu, Baosheng Wang, and Peidong Zhu

Table of Contents XXVII

Performance of a Partially Shared Buffer with Correlated Arrivals . . . . . . 1196 Dieter Fiems, Bart Steyaert, and Herwig Bruneel Filter-Based RFD: Can We Stabilize Network Without Sacrificing Reachability Too Much? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1200 Ke Zhang and S. Felix Wu Network Access in a Diversified Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1204 Michael Wilson, Fred Kuhns, and Jonathan Turner Outburst: Efficient Overlay Content Distribution with Rateless Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1208 Chuan Wu and Baochun Li Adaptive Window-Tuning Algorithm for Efficient Bandwidth Allocation on EPON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217 Sangho Lee, Tae-Jin Lee, Min Young Chung, and Hyunseung Choo Optical Burst Control Algorithm for Reducing the Effect of Congestion Reaction Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1221 Myungsik Yoo and Junho Hwang Incremental Provision of QoS Discarding Non-feasible End-to-End Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1225 Alfonso Gazo-Cervero, Jos´e Luis Gonz´ alez-S´ anchez, and Francisco J. Rodr´ıguez-P´erez Enhancing Guaranteed Delays with Network Coding . . . . . . . . . . . . . . . . . 1229 Ali Mahmino, J´erˆ ome Lacan, and Christian Fraboul LPD Based Route Optimization in Nested Mobile Network . . . . . . . . . . . . 1233 Jungwook Song, Heemin Kim, Sunyoung Han, and Bokgyu Joo PIBUS: A Network Memory-Based Peer-to-Peer IO Buffering Service . . . 1237 Yiming Zhang, Dongsheng Li, Rui Chu, Nong Xiao, and Xicheng Lu A Subgradient Optimization Approach to Inter-domain Routing in IP/MPLS Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1241 Artur Tomaszewski, Michal Pi´ oro, Mateusz Dzida, Mariusz Mycek, and Michal Zago˙zd˙zon Cost-Based Approach to Access Selection and Vertical Handover Decision in Multi-access Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 Fanchun Jin, Hyeong-Ah Choi, Jae-Hoon Kim, Se-Hyun Oh, Jong-Tae Ihm, JungKyo Sohn, and Hyeong In Choi Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1249

On the Resilient Overlay Topology Formation in Multi-hop Wireless Networks Fei Xing and Wenye Wang Department of Electrical and Computer Engineering North Carolina State University, Raleigh, NC 27695, USA [email protected], [email protected]

Abstract. In this paper, we study the problem of how to design overlay topologies in multi-hop wireless networks such that the overlays achieve perfect resilience, in terms of all cooperative nodes included but misbehaving nodes excluded, and preserve the k-connectivity with high probability. To address this problem, we propose a new distributed topology control protocol called PROACtive. By using PROACtive, every node pro-actively selects its cooperative adjacent nodes as neighbors by mutually exchanging neighbor request and reply messages. As a result, the union of all neighbor sets forms a resilient overlay for a given network. Our analysis finds that the PROACtive protocol is light-weighted with the message complexity of only O(m), where m is the number of links in the original network. Our simulation results validate the effectiveness of PROACtive and show that the overlays generated by our protocol preserve the k-connectivity with high probability (> 90%) and low false positive ratio (< 5%).

1

Introduction

Multi-hop wireless networks, especially mobile ad hoc networks, are more vulnerable to failures compared with wired networks due to nodal mobility and errorprone wireless channels. In addition, node misbehaviors, such as selfishness by refusing to forward packets of other nodes and maliciousness by launching Denial of Service (DoS) attacks, can also cause failures. For example, two DoS attacks, Jellyfish and Blackhole, were shown in [1] to have the network partitioning effect which degrades the network performance severely. In [2], a stochastic analysis on node isolation problem also shown that misbehaving nodes may damage the connectivity of mobile ad hoc networks substantially. Since misbehaving nodes may not provide connectivity to other adjacent nodes, existing routing protocols cannot cope with the failures caused by misbehaving nodes, which leaves the design of resilient multi-hop wireless networks an open and challenging problem in the presence of misbehaving nodes. To enhance the resilience to misbehaving nodes, some efforts were made by using different approaches. Two techniques called watchdog and pathrater were proposed in [3] to identify misbehaving nodes and avoid them in routes. A creditbased system called Sprite was proposed in [4] to stimulate cooperation among I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 1–12, 2007. c IFIP International Federation for Information Processing 2007 

2

F. Xing and W. Wang

selfish nodes. In [5], a secure ad hoc routing protocol called Ariadne was presented to prevent attacks from tampering routing control messages by using symmetric cryptographic primitives. Multi-path routing scheme in [6] introduced redundancy to avoid single path failure caused by node failures or node misbehaviors. Nevertheless, the previous schemes are “passive” to misbehaving nodes since even a misbehaving node can be detected, it is very difficult to prevent it from being selected as intermediate relays for all paths. In this paper, we study the problem of how to design overlay topologies in multi-hop wireless networks such that the overlays achieve perfect resilience, in terms of all cooperative nodes included but misbehaving nodes excluded, and preserve the k-connectivity with high probability (w.h.p.). Through the formation of resilient overlays, routing and data transferring can be performed upon cooperative platforms. Our contributions are mainly on two aspects. 1. A new distributed and localized protocol called PROACtive is proposed to generate a resilient overlay for a given network. By using PROACtive, every node pro-actively selects only cooperative adjacent nodes as its neighbors, which results in the exclusion of misbehaving nodes from the overlay. 2. The PROACtive protocol is shown to be light-weighted with the message complexity of only O(m), where m is the number of links in the original network, and the overlays preserve k-connectivity w.h.p. (> 90%) and low false positive ratio (< 5%). Note that our objective distinguishes itself from the existing topology control works [7,8,9], which usually focused on minimizing the energy consumption as well as keeping networks connected. For example, in [7], the K-Neigh protocol, based on distance estimation, was proposed to preserve the connectivity of static multihop networks, with efficient power consumption, by selecting K closest neighbors for each node. Nevertheless, our approach differs from the existing resilience-enhancing works in that we employ the topology control technique in the PROACtive protocol to connect cooperative nodes dynamically by mutual neighbor selections. The remainder of this paper is organized as follows. In Section 2, we formulate the problem. In Section 3, we describe the details of the PROACtive protocol. In Section 4, we validate our approach by simulations, followed by conclusions in Section 5.

2

Problem Statement

In this section, we describe the system model and formulate the perfect resilient overlay generation (PROG) problem. 2.1

System and Threat Model

In this paper, we denote multi-hop wireless networks by M(N ), where N is the set of nodes. All nodes are assumed to be distributed independently and

On the Resilient Overlay Topology Formation

3

uniformly on a two-dimensional plane, and they use omni-directional antennas with the same transmission radius r. For a pair of node u and v, they are called adjacent if the distance between them, denoted by d(u, v), is no greater than r. When d(u, v) > r, u and v may communicate via multiple intermediate hops. It is known that the geometric random graph (GRG) [10], denoted by G(N, r), is a graph in which N vertices are independently and uniformly distributed in a metric space with edges existing between any pair of vertices u and v if and only if d(u, v) ≤ r. Thus, we use the GRG to model the underlying topologies of multi-hop wireless networks. In order to identify the type of misbehaviors our work has targeted, we loosely classify the different types of misbehaviors in a multi-hop wireless network below, though the classification is not intended to be comprehensive. (I) Nodes participate in routing but not in data forwarding, like Jellyfish and Blackhole; (II) Nodes do not cooperate in forwarding control or data packets for others, like selfish nodes; (III) Compromised nodes, though appearing to be legitimate, malfunction maliciously; (IV) Malicious attacker nodes generate DoS traffic or signals, impersonate legitimate nodes, or tamper with routing messages. Our research focuses on misbehavior (I); while our approach can also be applicable to address misbehavior (II) and (III). Our approach, however, does not address misbehavior (IV), which requires suitable authentication and privacy mechanisms. Further, colluding attacks are out of the scope of this work. 2.2

Problem Formulation

The objective of this work is to enhance the resilience of multi-hop wireless networks against node misbehaviors. As mentioned in Section 1, misbehaving nodes may undermine network connectivity and network performance. Here we take an example to look at the effect of misbehaving nodes on path reliability. For a path with h relay hops, let the probability of any relay node being failed (due to node mobility or energy depletion) be Pf , then the path reliability, denoted by RP , can be presented by RP = (1 − Pf )h . While, if any relay node may also misbehave to disrupt communications, the representation of RP becomes RP = (1 − Pf − Pm )h , where Pm is the probability of any node misbehaving. Then we can easily show that RP can be significantly decreased by the route disruption effect resulting from misbehaving relays, and the negative impact is more exaggerative when the number of hops h increases. Therefore, it has been an important issue in the design of resilient networks to “exclude” misbehaving nodes. For a multi-hop wireless network in the presence of misbehaving nodes, we call a connected subnet consisting its all and only cooperative nodes as a perfect resilient overlay (PRO). If the routing and data transfer operations are conducted only on the induced PRO, then the communication between cooperative nodes is guaranteed to be resilient to misbehaving nodes. Here we formulate our problem as the perfect resilient overlay generation (PROG) problem, as follows:

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Definition 1. PROG Problem: Given a connected multi-hop wireless network M and a connectivity requirement k, generate a perfect resilient overlay M− such that M− is k-connected with high probability. To solve the PROG problem, we propose a distributed and localized protocol called PROACtive, by which every node can pro-actively select cooperative adjacent nodes as its neighbors, which will be described in detail right next.

3

PROACtive Protocol Design

In this section, we propose the PROACtive protocol as the solution to the PROG problem. 3.1

Basic Idea

In the PROACtive protocol, each node is assumed to be able to know whether its adjacent nodes forward packets for other nodes. For example, if wireless cards operate in promiscuous mode, a node can use the Watchdog method [3] to tell if its next-hop node drops packets instead of forwarding. By this way, a node u should quantitatively measure the cooperativity (borrowed from Biochemistry) of its adjacent nodes, which indicates the likelihood that a node performs normal network operations. Based on the obtained cooperativities, a node u can select neighbors by sending the soliciting messages called Neighbor Request (Ngbr-Rqst) to its adjacent nodes with high cooperativities. Once receiving an acknowledge message called Neighbor Reply (Ngbr-Rqst) from one of its adjacent nodes, say node v, then u knows that v has agreed to accept u as v’s neighbor, so u can add v into its neighbor set. By this mutual neighbor selection process, a cooperative node can have a cooperative neighborhood easily; while a misbehaving node can hardly have any neighbors. As a result, the union of cooperative neighbor sets generates a perfect resilient overlay which excludes misbehaving nodes. To satisfy the constraint of the k-connectivity, we refer to some results shown in a few recent literatures, which reveal the probabilistic relations between the connectivity κ(G) and the minimum degree δ(G) of the graph G. It was proved in [10] (Theorem 1.1) that if N is sufficiently large, the GRG G(N, r), obtained by adding links between nodes in the order of increasing length, becomes k-connected at the instant when it achieves a minimum degree of k, w.h.p.. In [11] (Theorem 3 ), it was shown that for a GRG G(N, r) P r(κ(G) = k) ≈ P r(δ(G) ≥ k)

(1)

holds if N  1 and P r(δ(G) ≥ k) ≈ 1. The result was further verified by extensive simulations in [11], [12], and [13]. Moreover, it was shown in [2] that to achieve the k-connectivity in a multi-hop wireless network where misbehaving nodes present, a necessary condition is that each node should have at least k cooperative neighbors. This result implies that for a network M, if let θ(M) denote the minimum number of cooperative neighbors of M, then P r(κ(M) = k) ≈ P r(θ(M) ≥ k)

(2)

On the Resilient Overlay Topology Formation

5

holds for the sufficiently large system size N . Therefore, in our protocol, each (cooperative) node should maintain at least k cooperative neighbors such that the k-connectivity is achievable in the overlay w.h.p.. In the next section, we describe the details of our approach. 3.2

PROACtive Protocol Details

As described briefly in Section 3.1, the essential idea of the PROACtive protocol is to build up cooperative neighbor sets, which is done by the mutual neighbor selections via Ngbr-Rqst and Ngbr-Rply message exchanges. In this section, we provide the detailed procedures of building up cooperative neighbor sets. For clarity of the description, we denote the adjacent nodes and cooperative neighbors of a node u by Adj(u) and N gbr(u), respectively, and denote the cooperativity of a node u by c(u). We will first discuss the procedure of querying potential neighbors as follows. Once a node u knows the cooperativities of its adjacent nodes, u selects a node v of the highest cooperativity from set Adj(u) as a potential neighbor, if v is not in set N gbr(u). Then u sends a Ngbr-Rqst message to v, indicating that u intends to add v to its neighbor set. If u receives a Ngbr-Rply message from v within a timeout, then u can add v into set N gbr(u); otherwise, u queries another adjacent node of the next highest cooperativity. Node u will continue the inquiries until k Ngbr-Rply messages from different adjacent nodes are received, which guarantees u with at least k neighbors. This procedure is summarized by Algorithm 1 as follows. Algorithm 1. Procedure of querying potential neighbors Input: k, node u, and Adj(u) 1: Initiate N gbr(u) := ∅, create a temporary set T emp(u) := ∅, create a counter numRplyRcvd := 0 2: ∀v ∈ Adj(u), Measure c(v) 3: while (numRplyRcvd < k AND T emp(u) = Adj(u)) do 4: Select v if c(v) = max{c(w) : ∀w ∈ Adj(u) − T emp(u)} 5: Send Ngbr-Rqst to v 6: T emp(u) := T emp(u) + v 7: if (Receive Ngbr-Rply from v) then N gbr(u) := N gbr(u) + v 8: 9: numRplyRcvd := numRplyRcvd + 1 10: end if 11: end while

Next we discuss how a node processes the incoming neighbor requests. In our approach, each node, say u, can calculate its own threshold, based on the information available from its local environment, to decide if it should accept a querying node as its neighbor. We call this threshold as the neighbor cooperativity threshold and denote a node u’s threshold by c∗ (u). For a node u, since u can

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measure the behaviors of its adjacent nodes quantitatively, u’s threshold can be defined as the average cooperativity of its adjacent nodes, i.e., c∗ (u) =

1 d



c(v), for d = |Adj(u)|.

(3)

∀v∈Adj(u)

Hence, when u receives a Ngbr-Rqst message from one of its adjacent nodes v, it compares c(v) to its threshold c∗ (u). If c(v) ≥ c∗ (u), u replies v with a Ngbr-Rply message and adds v into u’s neighbor set; Otherwise, u discards this neighbor request and replies nothing. Algorithm 2 summaries the procedure of processing neighbor requests. Algorithm 2. Procedure of processing neighbor requests Input: node u, and Adj(u) 1: ∀v ∈ Adj(u), Measure c(v) 2: Calculate c∗ (u) by Equation (3) 3: if (Receive Ngbr-Rqst from v ∈ Adj(u)) then / N gbr(u)) then 4: if (c(v) ≥ c∗ (u) AND v ∈ 5: Send Ngbr-Rply to v 6: N gbr(u) := N gbr(u) + v 7: else 8: Discard Ngbr-Rqst 9: end if 10: end if

By this mutual neighbor selection, a node with high cooperativity, in contrast to the nodes with low cooperativities, may receive multiple requests and immediate replies from adjacent nodes. Consequently, a resilient overlay topology can be constructed from the neighbor sets. We use Algorithm 3 to summarize the perfect resilient overlay generation. Algorithm 3. Generate a perfect resilient overlay M− Input: k, and a multi-hop wireless network M 1: Let N − be the node set of M− , N − := ∅ 2: for each u ∈ M do 3: Build up N gbr(u) by using Algorithms (1) and (2) 4: N − := N − ∩ N gbr(u) 5: end for 6: return M− induced from N −

3.3

Cooperativity Measurement Scheme

To measure the cooperativity of a generic node, we investigate the characteristics of misbehaving nodes on the network layer. A selfish node, for selfish reasons such as saving energy, usually refuses to forward data packets for other nodes.

On the Resilient Overlay Topology Formation

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A malicious node can do anything such as dropping partial data packets at a random or periodic manner, or pretending to be adjacent to a node actually far-away from it, thus trapping all packets destinated to that node afterwards. Thus, dropping “transient” packets is one of the most common characteristic of most misbehaviors, especially the Type-I misbehavior mentioned in Section 2.1. This observation implies that for a node u, we can use u’s packet drop ratio, denoted by qdrp , to measure u’s cooperativity c(u). Let nf wd (u) and ndrp (u) denote the numbers of packets should be forwarded and dropped, then we have, c(v) = 1 − qdrp (v) = 1 −

ndrp . nf wd

(4)

We use an example in Fig. 1(a) to illustrate this method. In Fig. 1(a), every time node u asks node w to forward a packet to v, u increases a counter nf wd (w) by 1. If u cannot overhear w’s forwarding after a timeout (e.g., round-trip delay), u increases another counter ndrp (w) by 1. Moreover, when one of u’s adjacent nodes, x, requires w to forward packets to v, u can record w’s behavior as well. Based on the measurements from both “own experience” and ”indirect observation”, u can calculate w’s cooperativity c(w) by (4). 00 11

00 x11 00 11

u

w w

v

u

(a)

v ACK

1 0 0 1 0 1

x

(b)

Fig. 1. Measuring cooperativity (a) by promiscuous mode (b) by ACKs

Notice that in our PROACtive protocol, the cooperativity measurement scheme is not limited to the technique that employs promiscuous mode only; it can also use other techniques such as close-loop feedbacks. For example, in Fig. 1(b), when u sends a data packet to w, it can piggyback an ACK request. Based on whether u can receive an ACK from one of w’s downstream nodes, say x, u may tell if w has forwarded the packet successfully. 3.4

Features of PROACtive Protocol

In this section, we discuss some unique features of our approach. First, the “individual” threshold defined in (3) allows each node to reach a trade-off between system resilience and individual connectivity, compared to a global threshold. This is due to the fact that a node surrounded by nodes with relatively low cooperativities can hardly find enough neighbors although a relatively high global threshold can achieve a resilient overlay of only cooperative nodes. On the contrary, by using the individual threshold, for a node u with adjacent nodes of relatively low cooperativities, its neighbor cooperativity threshold can decrease accordingly. Thus u may still have enough “neighbors”. Nevertheless, a global threshold can be also applicable for our protocol and more flexible decision policies in neighbor can be designed by combining the global threshold and individual thresholds.

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Second, regarding the issue of neighbor set updating, our PROACtive protocol is able to deal with the dynamic topology changes due to node mobility. For instance, a mobile node can refresh its neighbor set when it detects a disconnection with its neighbor(s). If the topology is highly dynamic (e.g., mobility is high), then mobile nodes can keep the records of its neighbors to avoid frequent neighbor set updates. Further, the PROACtive protocol is able to deal with the dynamic changes of node behaviors as well due to the flexibility provided by our threshold design. For instance, the updating overhead can be also reduced by deleting a neighbor only if its cooperativity is below the minimum requirement for a specific application. Third, our approach does not involve new security vulnerabilities and can avoid the false accusation problem. For instance, the cooperativity information measured by one node are not shared with others in our protocol, and the neighbor selection is only dependent on each node’s own knowledge to its neighborhood. By this way, one node’s cooperativity cannot be falsely rated to a low or high value by several other (might be malicious) nodes, which prevents any node from the false accusation. No information sharing also helps to avoid the complexity of deciding the actual cooperativity of one node when multiple different measurements are received. Nevertheless, the integrity of Ngbr-Rqst and Ngbr-Rply messages can be protected by traditional cryptographic techniques. Finally, the PROACtive protocol is completely distributed and localized, which makes our approach more feasible to be implemented in a real scenario. Additionally, our protocol can be run locally, in an on-demand manner, whenever a mobile node detects a significant cooperativity change among its neighborhood. Further, our protocol is light-weighted in terms of the overhead of message exchanges. For example, given a wireless multi-hop network, in the worst case, every node should send either a Ngbr-Rqst or Ngbr-Rply message to each of its adjacent nodes in order to build up a neighbor set. This implies that to generate a resilient overlay, the total number of messages needed is no more than the number of links, denoted by m, in the original network. Therefore, the message complexity of our protocol is only O(m). Until now, the PROG problem, how to generate a perfect resilient overlay, has been solved by applying the PROACtive algorithm on given networks. We will evaluate the effectiveness of our solution by simulations in the next section.

4

Simulation Evaluations

To evaluate our PROACtive protocol, we performed a considerable body of experiments by using NS2 v2.28 and MATLAB v7sp3 tools. In our simulations, nodes are distributed randomly and uniformly in an area. Distinct node pairs randomly establish constant bit rate (CBR) connections, with packet size of 512 bytes and rate of 5 packets/sec, such that nodes can measure their adjacent nodes’ coopertivities by the method described in Section 3.3. The AODV routing protocol is used. The connectivity requirement k is 3 for all simulations.

On the Resilient Overlay Topology Formation

(a)

(b)

9

(c)

Fig. 2. The topologies of the underlying network and overlays generated: (a) no topology control, (b) applied with PROACtive, (c) K-Neigh with K = 9 (Phase I)

We show how the PROACtive protocol generates overlays first, then show the effectiveness of our protocol with regard to the k-connectivity preservation and the false positive (negative) rate. 4.1

Topology Generated by PROACtive Protocol

In this simulation, 500 nodes are distributed on a 1500 m×1500 m area at random with the same transmission radius of 150 m. Among 500 nodes, 150 misbehaving nodes drop packets to be forwarded or report false routes. Fig. 2(a) illustrates the network without applying any topology control, in which a pair of nodes are connected by a link as long as their distance is no larger than 150 m. Fig. 2(b) shows the network structure after applying our PROACtive protocol, in which cooperative and misbehaving nodes are represented by solid and hollow dots, respectively. From the figure, we can see that the overlay topology generated by PROACtive excludes most of misbehaving nodes, while containing almost all cooperative nodes. Though some links are removed in the overlay due to the mutual neighbor selection, the generated overlay is still connected. To highlight the feature of our protocol, the topology generated by the K-Neigh protocol (Phase 1 only, with K = 9) [7] is shown in Fig. 2(c), where we can see that all misbehaving nodes are included in the topology. This is because the neighbor selection in K-Neigh is only based on the distance between nodes, that is each node selects K nearest adjacent nodes as its neighbors. 4.2

Preservation of k-Connectivity

One of the major tasks in our simulations is to verify that the k-connectivity should be preserved, w.h.p., for the overlays generated by PROACtive, when the original network is k-connected. To test whether the network is k-connected, we use breadth first search (BFS) to compute how many disjoint paths connecting two distinct nodes. In this simulation, the nodes are placed uniformly at random in a bounded region of 2000 m × 2000 m. The transmission radius is set to 100 m. The number of nodes N ranges from 500 to 5000 with an interval of 100,

F. Xing and W. Wang

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Probability of k−connectivity, k=3

Probability of k−connectivity, k=3

10

0.8 0.6 0.4 0.2 0 2000

2500

The original M, Pm=0.1 Overlay M−, Pm=0.1 The original M, Pm=0.3 Overlay M−, Pm=0.3 3000 3500 4000 System size N

1 0.8 0.6 0.4 0.2 0 0

Analysis for M, N=3000 Simulation for M−, N=3000 Analysis for M, N=4000 Simualtion for M−, N=4000 0.1 0.2 0.3 0.4 0.5 0.6 Ratio of misbehaving nodes in original net (Pm)

(a)

0.7

(b)

Fig. 3. The k-connectivity probabilities of the overlays generated, compared to those of the original networks: (a) against N , (b)against PM

FPR, Pm=0.1 FNR, Pm=0.1 FPR, Pm=0.3 FNR, Pm=0.3 FPR, Pm=0.5 FNR, Pm=0.5

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N=1000 N=2000 N=3000 N=4000

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0 500 1000

2000 3000 System size N

4000

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0 0

(a)

0.1 0.3 0.5 0.7 0.9 Ratio of misbehaving nodes in original net (PM)

(b)

Fig. 4. The false positive and negative ratios produced by PROACtive (a) against N , (b) against PM (FNR only)

which makes the network placements representative for both sparse and dense networks. For every value of N , a certain number of nodes are misbehaving, whose ratio to the total, denoted by PM , is ranging from about 1% up to 90% with a interval of 10%. The simulation results are shown in Fig. 3(a) and 3(b). For clarity, only the results for 2000 < N < 4000 are shown, and we omit the part for PM > 0.7 by the similar reason. From the two figures, we can see that the overlays generated preserve the k-connectivity with probability greater than 90%, when the original network is k-connected. 4.3

False Positive and Negative Ratio

As we described in Section 3.4, though the individual threshold helps nodes to reach a trade-off between system resilience and individual connectivity, it is possible that a cooperative node u cannot build up its neighbor set if c(u) < c∗ (v) ∀v ∈ Adj(u). In this case, we say u is a false positive. On the contrary, a misbehaving node u may have the chance to be added to another node v’s neighbor set if v cannot have enough neighbors without adding u. In this case, we say u is a false negative. Since the perfect resilient overlay (PRO) is an overlay

On the Resilient Overlay Topology Formation

11

that contains all and only cooperative nodes of the original network, we can use two metrics, false positive rate (FPR) and false negative rate (FNR), to evaluate the effectiveness of the PROACtive protocol in generating PROs. If let NC and NM denote the number of cooperative and misbehaving nodes in the original network, then the FPR and FNR can be calculated by F P R = NCm /NC and c c F N R = NM /NM , respectively, with NCm and NM denoting the number of false positives and false negatives. Our simulation results are reported in Fig. 4(a) and 4(b). In Fig. 4(a), the FPRs are very low (< 5%) for all networks of different system size N as well as different PM ; however, the FNRs are more significant for small N than for large N . This indicates that relatively more misbehaving nodes are added into the overlay to keep it connected when the network is sparse. Another observation is that the FNRs increase significantly when PM increases, which is further illustrated in Fig. 4(b), where the FNR for N = 4000 raises even up to 50% when PM = 0.9. This is due to the fact that more false negatives are produced to keep enough neighbors for every node when many misbehaving nodes present. These observations show that the PROACtive protocol is more “conservative” in satisfying the k-connectivity constraint.

5

Conclusion and Future Work

In this paper, we proposed a distributed and localized protocol, PROACtive, to generate perfect resilient overlays which contain all and only cooperative nodes of the original wireless multi-hop networks. The PROACtive protocol has a light-weighted message complexity, O(m), and the overlays generated achieve k-connectivity with high probability and low false positive ratio. The main advantage of applying our PROACtive protocol is that the resilient overlays generated essentially provide cooperative platforms for multi-hop routing and data transmission when misbehaving nodes are present. Based on the resilient overlays, new routing strategies and data aggregation schemes can be designed, which will be our next works. Further, more advanced cooperativity measurement schemes are also needed to be explored.

References 1. Aad, I., Hubaux, J.P., Knightly, E.W.: Denial of Service Resilience in Ad Hoc Networks. In: Proc. of ACM MobiCom ’04. (2004) 202–215 2. Xing, F., Wang, W.: Modeling and Analysis of Connectivity in Mobile Ad Hoc Networks with Misbehaving Nodes. In: Proc. of IEEE conference on communication (ICC) ’06. (2006) 1879 – 1884 3. Marti, S., Giuli, T.J., Lai, K., Baker, M.: Mitigating Routing Misbehavior in Mobile Ad hoc Networks. In: Proc. of ACM MobiCom ’00. (2000) 255–265 4. Zhong, S., Chen, J., Yang, Y.R.: Sprite: A Simple, Cheat-Proof, Credit-based System for Mobile Ad-Hoc Networks. In: Proc. of IEEE INFOCOM ’03. (Mar. 2003) 1987–1997

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5. Hu, Y., Perrig, A., Johnson, D.B.: Ariadne: A Secure OnDemand Routing Protocol for Ad Hoc Networks. In: Proc. of ACM MobiCom ’02, Atlanta, USA (Sep. 2002) 6. Ganesan, D., Govindan, R., Shenker, S., Estrin, D.: Highly-Resilient, EnergyEfficient Multipath Routing in Wireless Sensor Networks. Mobile Computing and Communications Review (MC2R) 5(4) (2001) 1–13 7. Blough, D.M., Leoncini, M.: The K-Neigh Protocol for Symmetric Topology Control in Ad Hoc Networks. In: Proc. of ACM MobiHoc ’03, ACM Press (2003) 141–152 8. Shen, C.C., Srisathapornphat, C., Liu, R., Huang, Z., Jaikaeo, C., Lloyd, E.L.: CLTC: A Cluster-Based Topology Control Framework for Ad Hoc Networks. IEEE Transactions on Mobile Computing 3(1) (2004) 18–32 9. Cardei, M., Wu, J., Yang, S.: Topology Control in Ad Hoc Wireless Networks Using Cooperative Communication. IEEE Transactions on Mobile Computing 5(6) (Jun. 2006) 711–724 10. Penrose, M.D.: On k-connectivity for a Geometric Random Graph. Random Struct. Algorithms 15(2) (1999) 145–164 11. Bettstetter, C.: On the Minimum Node Degree and Connectivity of a Wireless Multihop Network. In: Proc. of ACM MobiHoc ’02, ACM Press (Jun. 2002) 80–91 12. Li, X.Y., Wan, P.J., Wang, Y., Yi, C.W.: Fault Tolerant Deployment and Topology Control in Wireless Networks. In: Proc. of ACM MobiHoc ’03. (Jan. 2003) 117–128 13. Bettstetter, C.: On the Connectivity of Ad Hoc Networks. The Computer Journal, Special Issue on Mobile and Pervasive Computing 47(4) (2004) 432–447

Placing and Maintaining a Core Node in Wireless Ad Hoc Sensor Networks Amit Dvir and Michael Segal Department of Communication Systems Engineering Ben Gurion University of the Negev Israel azdvir, [email protected]

Abstract. Wireless Ad hoc sensor networks are characterized by several constraints, such as bandwidth, delay, power, etc. These networks are examined by constructing a tree network. A core node usually chosen to be the median or center of the multicast tree network with a tend to minimize a performance metric, such as delay or bandwidth. In this paper, we present new efficient strategy for constructing and maintaining a core node in multicast tree for wireless ad hoc sensor networks that undergo dynamic changes based on local information. The new core (centdian) function is defined by convex combination that signifies total bandwidth and delay constraints. We provide two bounds of O(d) and O(d + l) time for maintaining the centdian using local updates, where l is the hop count between the new center and the new centdian and d is the diameter. We also show a O(n log n) time solution for finding centdian in the Euclidian complete network using interesting observations. Finally a simulation is presented.1 Keywords: Sensor networks, Wireless Ad hoc Networks, Multicast tree, Core Node.

1 Introduction Wireless Ad hoc sensor networks is a network architecture that can be rapidly deployed without relying on pre-existing fixed network infrastructure . Wireless communication is used to deliver information between nodes, which may be mobile and rapidly change the network topology. The wireless connections between the nodes (which later will be referred as links or edges) may suffer from frequent failures and recoveries due to the motion of the nodes and due to additional problems related to the propagation channels (e.g. obstructions, noise) or power limitations. A wireless ad hoc sensor network consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and some level of intelligence for signal processing and networking of the data. Recently, wireless sensor networks have been attracting a great deal of commercial and research interest [13,27,29]. In particular, practical emergence of wireless ad hoc networks is widely considered revolutionary both in terms of paradigm shift as well as enabler of new applications. 1

This research has been partially supported by INTEL and REMON consortium.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 13–24, 2007. c IFIP International Federation for Information Processing 2007 

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Group communication is the basis for numerous applications in which a single source delivers concurrently identical information to multiple destinations. This is usually obtained with efficient management of the network topology in the form of tree having specific properties. For example, multicast routing refers to the construction of a spanning tree rooted at the source and spanning all destinations [3,11,25,30,36]. Delivering the information only through edges that belong to the tree generates an efficient form of group communication which uses the smallest possible amount of network resources. In contrast, with unicast routing from the source to each destination, one needs to find a path from the source to each destination and generates an inefficient form of group communication where the same information is carried multiple times on the same network edges and the communication load on the intermediate nodes may significantly increase. We notice that wireless ad hoc sensor networks pose the reliable and efficient communication services necessary for distributed computing [6, 31], while objective functions considered are the most classical that involve the minimization of the average or the maximum distance to service facilities. Generally, there are two well-known basic approaches to construct multicast trees: the minimal Steiner tree (SMT) and the shortest path tree (SPT). Steiner tree (or groupshared tree) tends to minimize the total cost of a tree spanning all group nodes with possibly additional non group member nodes. The optimal construction of the SMT is known to be a NP-hard problem [14,22]. Some heuristics that offer efficient solutions to this problem are given in [21, 37]. The best up today solution has been derived by [38] and proved factor of 1.55. In contrary, SPT tends to minimize the cost of each path from the root source to each destination. This can be achieved in polynomial time by using the well-known algorithm by Dossey et al. [12]. The goal of a SPT is to preserve the minimal distances from the root to the nodes without any attempt to minimize the total cost of the tree. Gupta and Srimani [16] present distributed core selection and migration protocols for multicast tree in MANET with dynamically changing network topology. The proposed core location method is based on the notion of median node of the current multicast tree instead of the median node of the entire network. The adaptive distributed core selection and migration method uses the fact that the median of a tree is equivalent to the centroid of that tree. Gupta and al. [17] present efficient core migration protocol for MANET that migrates the core until the multicast tree branches reflect the desired QoS requirements of the multicast application where the proposed core location method is based on the notion of center node of the current multicast tree. Bing-Hong and al. [9] gave heuristic to the minimum non-leaf multicast tree problem that reduce the number of non-leaves nodes in the multicast tree and their experimental results show that the multicast tree after the execution of their method has smaller number of non-leaves than others in the geometrically distributed network model. The bandwidth of a tree is defined as the total distance of packet transmissions required to deliver packet from core node v to all others nodes. The maximum delay of the tree is the maximum distance to traversed by any packet in traveling from core node v to other node. The transport of a node is defined as the total distance of the node to all others nodes in the tree. The corresponding solution concepts have been considered in literature as median and center [26, 28, 39]. Since the median approach is based on

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averaging, it often provides a solution in which remote and low-population density areas are discriminated against in terms of accessibility to public facilities, as compared with centrally situated and high-population density areas. For this reason, an alternative approach, involving the maximum distance between any customer and closest facility can be applied. This approach is referred to as the center solution concept [4]. The minmax objective primarily addresses geographical equity issues, which are of particular importance in spatial organization of emergency service systems. On the other hand, locating a facility at the center may cause a large increase in the total distance, thus generating a substantial loss in spatial efficiency. The problems of using only center or median as a core lead to search for a compromised solution concept called centdian, where centdian function presents some kind of trade-off between the center and the median functions ( [19]). The centdian function for node v in the network is defined by Dv = λ · sum(v) + (1 − λ) · dist(v), 0 ≤ λ ≤ 1 where dist(v) is the maximum distance from node v to other nodes in the network, sum(v) is the sum of distances from node v to all other nodes in the networks. Halpern [18] introduced the centdian model and studied the properties of the centdian in a tree. In a subsequent work, Carrizosa et al. [10] presented an axiomatic approach justifying the use of the centdian criterion. Tamir et al. [41] present the first polynomial time algorithm for the p-centdian problem on a tree with O(pn6 ) complexity where p is the number of facilities. For more results about centdian problem, see [2,7,20,32,33,40]. Other related notion of ordered median of a tree ( [5, 23, 34, 35]) generalizes the most common criteria mentioned above, e.g., median, center and centdian. If there are n demand points in a tree T , this function is characterized by a sequence of reals, κ = (κ1 , . . . , κn ), satisfying κ1 ≥ κ2 . . . ≥ κn ≥ 0. For a given subtree S ∈ T , let X(S) = {x1 , . . . , xn } be the set of weighted distances of the n points to S. The value of the ordered median objective at S is obtained as follows: Sort the n elements in X(S) in non-increasing order, then compute the scalar product of the sorted list with the sequence κ. It is easy to see that when κi = 1, i = 1, . . . , n, we get the median objective and when κ1 = 1 and κi = 0, i = 2, . . . , n, we obtain the center objective. For the case κ1 = 1 and κi = λ, i = 2, . . . , n we get the centdian objective. Unfortunately, constructing and maintaining cores by use of ordered median technique is not suitable for wireless ad hoc sensor networks, since this technique requires keeping some global information about nodes of network which is completely inconceivable in the case of wireless ad hoc sensor networks. Most protocols for constructing core node are not suitable for wireless ad hoc sensor networks, since these algorithms are not based on local updates. In this paper, we present new efficient strategy for constructing and maintaining a core node under centdian criteria in multicast tree for wireless ad hoc sensor networks with dynamic changes in the network topology. The new core node is defined by convex combination of the sum of the weighted distance paths (sum of the weighted edges in the path) of all the nodes in the tree network to the core node and the maximum weighted distance from the core node to the farthest node in the tree network satisfied center and median core functions. We also provide two bounds of O(d) and O(d + l) time for maintaining the centdian after a change (add/remove edge/node) in the topology of the tree network, where l is the hop count between the new center and the new centdian of the multicast

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tree and d is the diameter of the tree. We show an O(n log n) time algorithm for finding a centdian node in the Euclidian complete network bases on observation in [8]. Finally, we present a simulation that compare our new core solution with well known cores’ strategies to exhibit the advantages and the efficiency of our algorithms. This paper is organized as follows: Section 2 presents a new algorithm that finds and maintains a centdian core in a multicast tree. In Section 2.2 we show a solution for a static Euclidian network. Next we show our simulation results and finally, we conclude with several ideas for future work.

2 Algorithm to Find Centdian of Multicast Tree in Wireless Ad Hoc Sensor Networks We model the topology of wireless ad hoc sensor networks by weighted undirected graph G(V, E, We ), where V is the set of nodes, E is the set of edges between neighboring nodes and We is an edge weight function, e.g. squared distance between the endpoints of edges. Note that the edges represent logical connectivity between nodes, i.e., there is an edge between two nodes u and v if they can hear each other’s local broadcast. Since the nodes are mobile, the network topology graph stochastically changes. Let us define by T(V’, E’) a weighted multicast tree of G. For a node v ∈ T we define by Kv the number of nodes in the connected component containing v (created by removing e(v, x)); by Wv the total sum of weighted distances from the nodes in the connected component containing v (created by removing e(v, x)) to node v. Center of a tree T is a node c1 ∈ T such that the maximal distance from c1 to any other node in T is minimized, i.e. dist(c1 , T ) = minv∈T dist(v, T ). In order to find a center of tree T we can use the distributed algorithm described in [26] that requires r(I)+(d(T )/2) time, where r(I) is the the maximal weighted distance from the initiator node I to any other node in T and d(T ) is the weighted diameter of the tree. This algorithm finds the center node by starting form an arbitrary node I and goes from the internal nodes towards the leaves and back to the new center using the information from the leaves about the weighted distance path and the knowledge that the center of the tree lies on the diameter of the tree. Median of a tree T is a node c2 ∈ T such that the sum of the weighted distances from c2 to any other node in T is minimized, i.e. sum(c2 , T ) = minv∈T sum(v, T ). In order to find a median of a tree T we can use the distributed algorithm in [26] that requires maxx∈T (r(I) + d(x, c2 )) time, where d(x, c2 ) is the weighted distance between node x and the new median. This algorithm finds the median node by starting form an arbitrary node I and goes from the internal nodes towards the leaves. Each leave propagates the weight of its edge and each internal node propagates the sum of values obtain from its descendants plus the weight of the edge connecting him to its predecessor in the tree. Next, we show a simple algorithm to find the number of nodes in each one of node v branches. We define by Kvi , i = 1 . . . b, to be the number of nodes in the ith branch of node v, with b standing for the number of branches of node v. By convergecast process from the leaves towards the center of the tree we can find the total number of nodes in the tree. By knowing this number, we start a new process from the leaves to find for each node v its values Kvi . Each leaf sends to its father w in a rooted tree T a num(1)

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message. Each internal node w gets from his sons their num messages and sums all the values in the messages. The process converges towards c1 . It is a well-known fact that a centdian is located on the path connecting center c1 and median c2 ( [18]). The following lemma presents an efficient way of calculating centdian in a multicast tree based on knowledge about location of the center and median. Lemma 1. Dv > Dx iff λ((Kx − Kv ) + 1) < 1. Proof: A centdian node x must minimize expression Dx = λ · sum(x, T  ) + (1 − λ) · dist(x, T  ) . Denote by Dv = λ · sum(v, T  ) + (1 − λ) · dist(v, T  ) the cost of node v who is the neighbor of current centdian x with the minimum value of D from x neighbors. We should move the centdian towards v only if Dx > Dv . Notice T ) . We conclude that sum(x, T  ) = Wx + (Kv + that dist(x, T  ) = Dx −λsum(x, (1−λ) 1) · d(x, v) + Wv and sum(v, T  ) = Wv + (Kx + 1) · d(x, v) + Wx . Therefore, Dv = λ( Wv + (Kx + 1) · d(x, v) + Wx ) + (1 − λ)dist(v, T  ). It easy to see that there are 5 different cases (out of 9) that we should deal when Dx > Dv , 1) sum(v, T  ) < sum(x, T  ) and dist(v, T  ) < dist(x, T  ). 2) sum(v, T  ) > sum(x, T  ) and dist(v, T  ) < dist(x, T  ). 3) sum(v, T  ) = sum(x, T  ) and dist(v, T  ) < dist(x, T  ) . 4) sum(v, T  ) < sum(x, T  ) and dist(v, T  ) = dist(x, T  ). 5) sum(v, T  ) < sum(x, T  ) and dist(v, T  ) > dist(x, T  ). We present an analysis only for cases 1-3 that are relevant (case 4 is trivial (sum(v, T  ) = sum(x, T  ))) and case 5 is equivalent to cases 1-3. In cases 1-3 |dist(x, T  ) − dist(v, T  )| ≤ d(x, v), therefore Dv = λ( Wv + (Kx + 1) · d(x, v) + Wx ) + (1 − λ)dist(v, T  ) = λ( Wv + + (Kx + 1) · d(x, v) + Wx ) + (1 − λ)(dist(x, T  ) − dist(x, v)) = λ( Wv + T ) + (Kx + 1) · d(x, v) + Wx ) + (1 − λ)( Dx −λsum(x, − dist(x, v)) = (1−λ) = λ( Wv + (Kx + 1) · d(x, v) + Wx ) + Dx − λsum(x, T  ) − dist(x, v)(1 − λ) = = λWv + λ (Kx + 1) · d(x, v) + λWx + Dx − λsum(x, T  ) − dist(x, v)(1 − λ) = = λWv + λ (Kx + 1) · d(x, v) + λWx + Dx − λ(Wx + (Kv + 1) · d(x, v) + Wv )− −dist(x, v)(1 − λ) = λWv + λ (Kx + 1) · d(x, v) + λWx + Dx − λWx − λ(Kv + 1)· ·d(x, v) − λWv − dist(x, v)(1 − λ) = Dx + λd(x, v)(Kx − Kv ) − dist(x, v)(1 − λ).

As we stated above, we move the centdian node only if D1 > D2 . This happens when Dx − (Dx + λd(x, v)(Kx − Kv ) − dist(x, v)(1 − λ)) > 0 or in other words λ(Kx − Kv + 1) < 1. In case 4 dist(x, T  ) = dist(v, T  ), thus Dv = λ( Wv + (Kx + 1) · d(x, v) + Wx ) + (1 − λ)dist(x, T  ). Analysis similar to the previous one shows that Dx > Dv only if λd(x, v)(Kx − Kv ) < 0. In case 5 we get that Dx > Dv only if λ((Kv − Kx ) − 1) < −1. It follows that the inequality Dx > Dv holds when λ(Kx − Kv + 1) < 1  

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Based on this lemma, we can locate the centdian in the multicast tree, starting either at center or the median of the multicast tree and going over the path between them, locally improving from one neighboring node to another. Thus, the centdian in the tree can be found in O(l) time when the location of the center and the median are known, with l standing for the number of nodes in the path connecting center and median. 2.1 Maintaining a Centdian in a Multicast Tree When we have some change in the multicast tree, each node needs to update the number of nodes in each of its branches. By using Kx values of the centdian node, our maintaining algorithms running on the subtree with the highest number of the multicast group members. In what follows we show two different approaches to maintain centdian in a multicast tree. Both approaches use the fact that the centdian function is convex and therefore has only one minimum point. The first approach is to maintain the center of the tree in O(d) time using the algorithm in [24]. The new centdian lies on the path between the center and the median of the updated tree. Therefore, starting from the new center and finding the node that locally improves the centdian function value, point us the direction towards the new centdian of the tree. The centdian, thus, can be maintained in worst-case O(d + l) time, where l is the hop count between the new center and the new centdian of the multicast tree and d is the diameter of the tree. The second approach uses the fact that the neighbor of the old centdian x that the most improves the centdian function value lies on the path between the old centdian and the new centdian. Therefore, the centdian can be maintained in worst-case O(d) time. Since we want only multicast group members to be assigned the responsibility of core node, the second approach needs to be modified. If the new centdian node is a multicast member it becomes the actual new centdian of the tree. If not, we seek the path towards the old centdian in order to find a node that belongs to multicast group and declare this node to be the new actual centdian of the multicast tree. 2.2 Algorithm to Find Centdian in Euclidean Plane We model the topology of planar network as explained above by having the edge weight function defined as the squared distance between the nodes. The motivation to choose this function is the common method that power transmitting behaves quadratically to the distance between transmitting and receiving node. Using the observation in Bespamyatnikh et al. [8] we are able to solve the centdian problem in Euclidean plane in O(n log n) time. The farthest point Voronoi diagram of a collection of points S in the plane is a partition of plane into cells, each of which consists of the points further to one particular points than to any others. This diagram can be constructed in O(n log n) time supporting a query requests in O(log n) time. For a given point p, a query asks about the farthest neighbor of p in S. Thus, in O(n log n) time we can find, for each point, its farthest neighbor performing total n queries. In other words, for every node v in the network, we find dist(v) in total O(n log n) time. Bespamyatnikh et al. [8] observed that “squared” Euclidean metric is separable, i.e. the distance between two points is the sum of their squared x and y-coordinates’ differences. We follow the notations from [8]. We sort our points according to their x and

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y-coordinates. Let {p1 , . . . , pn } be the sorted points. For every point pi ∈ S we can compute the sum Σix of the x-distances from pi to the rest of the points in S. This is performed efficiently as follows. For the point p1 we compute Σ1x by computing and summing up each of the n − 1 distances. For 1 < i ≤ n we define Σix recursively: asx sume the x-distance between pi−1 and pi is δ, then Σix = Σi−1 +δ·(i−1)−δ·(n−i+1). x The sums Σi (for i  = 1, . . . n) can be computed in linear time when n the points are y sorted. The value of i is computed similarly. Next, let sumxi = j=1 (xj − xi )2 . The recursion formula for  computing all the squared x-distances is easily computed to be sumxi = sumxi−1 − 2δ xi−1 −nδ 2 , where the x-distance between pi−1 and pi is δ. Assume the point p ∈ S is ith in the x order and j th in the y order. The sum of squared Euclidean distances from p to the points in S is sum(p) = sumxi + sumyj . It remains to compute, for every node v the value of centdian function based on values of computed already sum(v) and dist(v) values. This is done in linear time. Thus, we can conclude, Theorem 1. Given a set S of n nodes in Euclidean complete graph with a cost of every edge that equals the squared Euclidean distance between nodes, we can find the centdian node in this graph in O(n log n) time.

3 Simulation This section describes the medium-scale experiment in details. The objectives of the experiment were to test whether the suggested maintaining algorithm actually works, and to compare its results to the performance of other core algorithms. For this simulation we choose to implement the second approach. As we performed our simulation we made an interesting observation about the runtime bound of the first approach of maintaining the centdian node. 3.1 Environment The following assumptions have been made: – For each node, the transmission and reception range are equal; however different nodes can have different ranges. The radius value refers to the transmission range. – All the nodes are equal in their functionalities and abilities. – The movement of each node is based on mobility model of random walk based on random directions and speeds ([15]).2 – There is no dependence between the nodes and the boundary of the network is predefined. In our simulation we used 5 different types of cores: center, median, continuous median, centdian and continuous centdian (with different values of λ). The difference between continuous and non continuous core is that non continuous core can “jump” from one node to another while continuous core keep continuous track of the path of previous core towards the newly computed core. 2

In this model each node moves from its current location to a new location by randomly choosing speed and direction in which to travel.

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3.2 Results The main goal of our simulation is to examine the influence of the multicast group on the cores’ behavior. One of the parameters we wish to exam is the period of time that cores are co-located at the same node (defined by collision). From the obtained results we can learn that the behavior of the cores in the multicast tree is sometimes similar to the behavior of the cores in regular network tree, for example: – When λ = 1 the collision between the centdian core and center core is 100%. – When λ ≤ 0.5 the collision between the centdian core and median core is 100% as has been proved in [18]. In some cases, the collision value between the median/center core and centdian core should be 1, but in our simulation the collision value in those cases are less then 1. The reason for that is the well known fact that in a tree two centers/medians may possibly exist. In the simulation we choose them in arbitrarily fashion. We simulate ad hoc sensor network with 100 nodes and 50 multicast nodes with a variety range of radius and λ values in network boundary of 600x600 meters. Figures 1–7 show the influence of the radius on the constraints’ values of the network. In particular, Figures 1–2 show the transport and delay values of the tree network as an unimodal linear function with a break point being a maximal value. The reason for that is as long as the radius is growing the network becomes more connected and more nodes are participating in the network. Starting at some point of time the network becomes to be connected and the pathes from nodes to the core contain small amount of hops. Figure 3 shows the connection between the radius and the life span of the cores, with life span being the period of time/rounds that the core does not change its location. It easy to see that as we increase the radius the life span also grows up. The collision between the cores with various values of λ and radius is depicted in Figures 4–7. In Figure 4 we focus on the collision between the centdian and median, while in Figures 5–6 we have examined the collision between the continuous centdian and the well known cores. Figure 7 shows the collision between the new centdian and the new center. From Figure 8 we can learn that for most radius’ values, the value l (number of hops between center and centdian) is small. The continuous centdian core

Fig. 1. Total transport of the cores with variety Fig. 2. Total delay of the cores with variety values of radius values of radius

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Fig. 3. Cores life span with variety values of ra- Fig. 4. The collision between the Centdian and Median with variety values of radius and λ dius

Fig. 5. The collision between the Centdian and Fig. 6. The collision between the Continuous Continuous Centdian with variety values of ra- Centdian and Continuous Median with variety dius and λ values of radius and λ

Fig. 7. The collision between the Centdian and Fig. 8. The Average Hop Count between CentCenter with variety values of radius and λ dian and Center with variety values of radius and λ

achieves improved convergence to delay performance than median core and better transportation performance than center core. The continuous centdian core achieves these properties in a well connected networks, as well as in sparse networks too.

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4 Conclusion and Future Work We have developed a new distributed algorithm for finding and maintaining centdian core in ad hoc sensor network that is based on processing local information of the network. Analytic analysis to bound value l seems to be very interesting. One interesting future direction is by adapting self-stabilizing algorithm to core selection problem in ad hoc sensor network when it gets partitioned and partitions get connected. The analysis of the model where one assumes some distribution for the velocities of the nodes seems also attractive.

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Flooding Speed in Wireless Multihop Networks with Randomized Beamforming Vasil Mizorov1 , J¨ org Widmer2 , Robert Vilzmann3 , and Petri M¨ ah¨ onen4 1

Siemens AG, Corporate Technology, D-81730 Munich, Germany 2 DoCoMo Euro-Labs, D-80687 Munich, Germany 3 Technische Universit¨ at M¨ unchen, D-80290 Munich, Germany 4 RWTH Aachen University, D-52072 Aachen, Germany

Abstract. This paper analyzes aspects of message propagation in multihop wireless networks with beamforming antennas. In particular, we focus our intention on the message propagation in the time domain. Our work uses a simulation based implementation of the 802.11 MAC protocol and a simplified version of a previously proposed MAC protocol, called BeamMAC [1]. Both protocols are compared under different network scenarios with several antenna array implementations (including an omnidirectional antenna). Our conclusions confirm the advantages beamforming antennas have over omnidirectional antennas in wireless multihop networks. Reduced hop distances and reduced time for information dissemination speed up flooding of messages. Moreover, we observe the impact network topology parameters have on the overall performance of the message propagation. Keywords: Ad Hoc Networks, antenna arrays, randomized beamforming, flooding speed.

1

Introduction

The concept of wireless ad hoc networking has been until recently considered only with omnidirectional antennas. Their advantage is that they are small, compact, spatial and radiate power omnidirectionally, i.e. equally in all spatial directions. However, they cause higher interference and block transmissions of other network nodes, significantly reducing the capacity and the throughput of the network. Seeking a way to increase the network capacity and throughput, directional (i.e. beamforming) antennas have been addressed. Their most important feature, to focus the energy into specific spatial directions, has proven to be appealing for providing higher network capacity and greater spatial reuse. There are some downsides in implementing beamforming antennas for wireless ad hoc networking. Firstly, an antenna array means increased hardware size as opposed to the small size of the wireless gadgets. However, the latest technology allows antenna arrays to be smaller in size, making their implementation easier. Secondly, beamforming antennas must “know” the direction of the intended recipient. Otherwise, they might “miss” and radiate in a nonoptimal I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 25–36, 2007. c IFIP International Federation for Information Processing 2007 

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direction. Therefore, additional signal processing algorithms, like Direction-ofArrival (DoA) or Angle-of-Arrival (AoA) algorithms, are necessary for achieving optimal performance. Despite these facts, some research papers report achieving significantly larger gains in terms of network throughput when deploying beamforming antennas. However, they are based on knowing the neighbors’ locations so each node can properly position its antenna beam ([2], [4], [3]). The location information can be obtained by means of Global Positioning System (GPS) [6] or AoA or DoA estimation algorithms ([4], [5]). Bearing in mind battery life consumption, complexity and sometimes processing capabilities of the mobile devices, we believe that these algorithms will overburden the devices and reduce their usage time. Therefore, to reduce the implementation complexity and to simplify the communication, we use randomized beamforming [7]. Nodes choose the direction of radiation randomly and avoid signal processing complexity. Thus, it turns out to be a practical approach when no a priori information is available about location of the nodes. With respect to network topology properties, authors in [7] show that the randomized beamforming improves the connectivity in the network. Due to the longer links beamforming antennas provide, it is possible to ”build a bridge” among previously isolated subnetworks [7]. In addition, [9] discusses the hop distances when randomized beamforming is implemented. It shows that the network diameter, as well as the random node pair hop distance are significantly reduced in the case of randomized beamforming. These findings are very interesting considering that the randomized beamforming introduces zigzag paths, which may increase the hop distances and lead to slower message dissemination. However, authors in [7] and [9] do not consider the effects that a Medium Access Control (MAC) layer introduces to the process of message propagation. The work presented in this paper aims to further investigate the communication features in wireless ad hoc networks with randomized beamforming. In particular, we focus our intention on the time domain of the message propagation in these networks. As this type of study was missing in the scope of the related papers [9] and [7], our work represents the first step towards more realistic approach in the investigation of the time-related features in the message propagation in networks implementing randomized beamforming. For comparison purposes, we simulate the IEEE 802.11 MAC protocol with both, omnidirectional and beamforming antennas. We use flooding speed as main performance metric. In addition, we analyze the route discovery process and discuss its impact on the message propagation. The remainder of this paper is organized as follows. Section 2 describes the proposed BeamMAC protocol. Section 3 explains the antenna and link model, as well as the scenarios used in our simulations. In section 4 we present the time aspects of the message propagation. In addition, we discuss the route discovery process and the impacts beamforming antennas have on it. Finally, Section 5 concludes the work.

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Protocol Model

Work done in the field of implementing beamforming antennas for ad hoc networking resulted in various modifications of the IEEE 802.11 MAC protocol. Some propose extending the Network Allocation Vector (NAV) into directional NAV (D–NAV) by keeping directionality related information [3]. Other proposals implement directional and omnidirectional transmission of Request-To-Send (RTS) and Clear-To-Send (CTS) messages [2]. However, as the 802.11 protocol was designed for omnidirectional transmissions, network performance can deteriorate due to issues specific to directional antennas [8]. Therefore, in order to investigate the time aspects of message propagation in wireless multihop networks, we evaluate a simplified version of the BeamMAC [1] protocol. It gains access to the wireless transmission channel using the following control packets: – Announcement (ANN) – Ready-To-Receive (RTR) – Objection. (OBJ) A node willing to initiate a data transmission, must announce it beforehand. For this purpose, it sends an ANN to inform the transmitter’s surrounding of the forthcoming transmission. In other words, each desired transmission is ”simulated” before being carried out. If the intended destination of the communication, for which the ANN packet is meant, is idle (i.e. not transmitting or receiving), it transmits an RTR packet back to the transmitter. The idea here is to inform the transmitter that the desired addressee is available. Upon reception of an ANN, each neighbor currently engaged in a parallel communication as a receiver, determines the interference that would be caused by the forthcoming transmission.

ANN

Source

Destination Other Receiving Stations

DATA

RTR

ACK

OBJ Estimation

Fig. 1. BeamMAC Channel Access

If the interference is so high to degrade the ongoing communication, the receiver sends an OBJ back to the sender of the ANN. In case the level of the interference maintains an acceptable level, the receiver does not send an OBJ back. In case an OBJ is received, the node enters a backoff state. Details of how an ANN packet is assessed by a receiving node are not discussed in this paper. Instead, we refer the interested reader to [1]. When the transmission is successfully “simulated” (ANN and RTR are sent, and no OBJ is received), the actual data packet can be sent. Upon error-free reception of the data packet, the receiving node transmits an acknowledgment (ACK) back.

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3

Network Model

3.1

Antenna Model

Antenna arrays, used in Multiple Input Multiple Output (MIMO) systems to increase the user data rate, can have different shapes; most prominently, linear or circular. Antennas with linear geometry are referred to as linear antenna array, whereas antennas with circular geometry are known as circular antenna arrays. Circular antenna arrays offer higher diversity and improved link capacity ([17], [18]. Thus, the antenna model used in our simulations is Uniform Circular Array (UCA) [7]. An UCA array comprises m identical isotropic radiators placed uniformly on a closed circumference. Each antenna element transmits with the same power pt /m at a wavelength λ = fc , with c = 3 · 108 m/s and carrier frequency f . By implementing a phase shift between the array elements, the resulting antenna beam pattern can be controlled. The shape of the resulting beam depends on the target direction Θb , known as boresight direction, and the number of antenna elements. Examples of antenna patterns for UCA antenna with m = 4 elements are shown in Figure 2. Directivity of a 4−element UCA

Directivity of a 4−element UCA 90

Directivity of a 4−element UCA

90

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5

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0

210

330

240

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◦

(a) Θb = 0

300 270

◦

(b) Θb = 30

◦

(c) Θb = 70

Fig. 2. Gain patterns of UCA with m = 4 elements

In general, an antenna pattern consists of a main lobe and side lobes. The main lobe represents the radiation in the desired direction, whereas the side lobes refer to the radiation in all other directions. It can be noted from antenna theory [10] that with an increase in the number of elements in the antenna array the radiated power in the direction of the main lobe increases. Note that due to antenna reciprocity, the gain characteristic is valid for both, transmission and reception. 3.2

Wireless Link Model

The wireless link model is based on a line-of-sight communication between two nodes, given their transmission parameters and their distance. Figure 3 depicts the implemented link model.

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Transmitter d

pt gt

gr

Receiver

pr

Fig. 3. Wireless link model

One node transmits the signal with power pt , which is received by the other node with power pr . The gain of the antenna at the transmitting node is gt . The gain of the receiver’s antenna in the corresponding direction toward the transmitter is gr . Thus, we can write  −α d pr = gt gr , (1) pt 1m where α represents the pathloss exponent of the propagation environment. The value of α is environment-dependent and is approximately α = 2 for a free space scenario and α = 3...5 for urban areas [10]. The link establishment between two nodes assumes that the received power pr is above the receiver sensitivity pr0 , that is (2) pr ≥ pro . In the following, we assume that all nodes have the same transmission power pt and reception sensitivity pro . Thus, considering the fact that antenna pattern reciprocity holds (same antenna pattern for transmission and reception), all links in the network can be considered as bidirectional (or undirected) links. That is, if a node A can communicate with node B, then node B can communicate with node A, as well. One should note that our simulation model does not implement propagation phenomena like fading. 3.3

Randomized Beamforming

As mentioned in Section 1, in order to avoid implementing complex signal processing algorithms, we use a communication paradigm referred to as randomized beamforming [7]. Its implementation is based on nodes choosing a random direction where to point their antenna beams. With choosing both, the boresight direction and the antenna array direction, uniformly distributed in the interval [0, 2π] the shape of the resulting pattern is fully described. In addition, all nodes keep their beam direction constant the whole time, i.e. once chosen it does not change. 3.4

Network Topology and Scenarios

The network topology in our simulations comprise n nodes distributed uniformly at random on a square area with side length l. For obtaining the node coordinates

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(x, y) we use the Mersenne Twister pseudo-random number generator [11]. Nodes in the network are static. We use a simple flooding mechanism to disseminate messages into the network. One node sends a packet and all other nodes forward the packet until received by every node. To more closely model reality, we borrow parameters from the IEEE 802.11 standard [12]. Namely, parameters used in our simulations are: frequency f = 2 GHz, pathloss exponent α = 3 (urban area), maximum transmission power pt = 0.1 W , communication threshold Pr0 = −111 dB, sensitivity threshold (for 802.11) Ps0 = −121 dB and transmission range for omnidirectional antenna Tx -Range = 121 m. In addition, we simulate the 802.11 protocol only with omnidirectional antennas, whereas the BeamMAC protocol with both, omnidirectional and UCA antennas (particularly UCA with m = 4 or UCA4 and m = 10 or UCA10). Table 1. Network scenarios Network size Small Medium Large

n

l (m) Area (km2 )

100 577 500 1290 2000 2580

0.33 1.66 6.65

We conduct our simulations with node density n/l2 = 300 km−2 . In order to obtain at least 95% connectivity in the network, we use calculations taken from [13] and calculate the area for a certain number of nodes so to guarantee the required connectivity. In fact, the connectivity in the network is above 99%. In order to perform a thorough analysis of the message dissemination process, we regard three scenarios in our simulations [15]. Namely, we use different network sizes, i.e. small, medium and large, as we want to clearly investigate any connection between the size of the network and the performance of the both protocols. The number of nodes for each network scenario is 100, 500 and 2000 nodes, respectively. In addition, the network area is 0.33 km2 , 1.66 km2 and 6.65 km2 , respectively. The simulated scenarios (number of nodes, length, network area) are given in Table 1.

4

Simulation Analysis

For the purpose of conducting the analysis we apply a protocol driven ad hoc network simulation tool (PANTS). It is an event-based simulator developed in C++ language, incorporating realistic models for beamforming antenna patterns, calculated using accurate formulas provided by antenna theory [10] and moreover, implementing the two investigated protocols, IEEE 802.11 and BeamMAC. For visualization purposes of the network topology and the distribution of the nodes in it, our simulation tool uses the Library of Efficient Data Types and Algorithms (LEDA) [14].

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With respect to the simulated parameters, we were interested in the flooding speed, flooding time and route reply time. The first one describes the speed with which a message propagates in the network, in terms of how many nodes have received the message until a certain time instant. The second parameter tells us how long it takes to flood a message in the network. Finally, the third parameter helps us better understand the route reply in networks implementing randomized beamforming. To be compliant with the protocol specifications, for 802.11 we implement broadcasting as defined in the IEEE 802.11 standard [12] (without RTS/CTS control handshake) and we omit the ACK packet in BeamMAC implementation. In addition, in the route reply process we make use of the 802.11 DCF function (RTS/CTS scheme) and all of the BeamMAC protocol functions. For accuracy purposes, we run a large number of simulations for every scenario. In particular, for the small and medium network we use 200 runs and for the large network we use 100 runs (due to large memory consumption). In addition, in our graphs we give a confidence interval of one sigma (σ being standard deviation) which equals to a confidence level of 68.27%. 4.1

Flooding Speed

We define the flooding speed as a curve that gives the percentage of flooded nodes depending on the time. We calculate the number of flooded nodes on every packet transmission and the result (nodes, time) is represented as a point of the flooding speed curve. Figures 4–6 depict the results we obtained from the considered scenarios given in Table 1. Flooding speed Nodes=100, Node density=300 (nodes/km2)

Percentage flooded nodes

100

80

60

40 802.11 BeamMAC-OMNI BeamMAC-UCA4 BeamMAC-UCA10

20

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Time (s)

Fig. 4. Flooding speed for 802.11 and BeamMAC protocol, small network

Figure 4 gives the results for the small network scenario (n = 100). It compares several different combinations of antenna and MAC protocol, namely the 802.11 protocol (implemented with OMNI antenna) and the BeamMAC protocol implemented with directional antenna (UCA4 and UCA10) and OMNI antenna. The obtained flooding speed curves are close to one another, which means that the advantages of the beamforming antennas are hardly noticeable.

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There is a minor difference in the Percentage of Flooded Nodes (PFN) between the BeamMAC and the 802.11 for a fixed value of the time, which is about 1%–5% for BeamMAC-UCA4 and BeamMAC-UCA10, respectively. However, the BeamMAC-UCA10 scheme has the highest flooding speed. The poor performance observed in the small network scenario is due to the impact of the border effects on the message propagation. Namely, caused by the random beamforming and the fact that the network area is relatively small, many nodes transmit outside the area. This is more visible in the beamforming case, as their transmission range is up to several times bigger than the one of the omnidirectional antennas. Flooding speed 2 Nodes=500, Node density=300 (nodes/km )

Percentage flooded nodes

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20

0 0

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 Time (s)

Fig. 5. Flooding speed for 802.11 and BeamMAC protocol, medium network Flooding speed Nodes=2000, Node density=300 (nodes/km2)

Percentage flooded nodes

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20

0.1

0.2

0.3

0.4

0.5

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0.7

0.8

Time (s)

Fig. 6. Flooding speed for 802.11 and BeamMAC protocol, large network

The medium network scenario shown on Figure 5 as well as the large network scenario on Figure 6 show an improvement in the message propagation for beamforming antennas. In particular, the larger the network size is, the better the flooding speed of the beamforming antennas. In the latter network, the difference between the BeamMAC-UCA4 and the 802.11 is more than 20%, which makes the UCA4 antenna a more sensible and more practical solution. The impact that border effects have on the message propagation is minor, as a larger portion of the nodes radiate inside the network area and less transmissions are void.

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It can be noted from Figures 4–6 that the performance of the combination of the BeamMAC protocol and omnidirectional antenna (BeamMAC-OMNI) experiences the poorest performance. The main reason for this inferior behavior is protocol related. Namely, the BeamMAC protocol takes longer to access the wireless channel (ANN and RTR) compared with the 802.11 protocol (only DIFS - Distributed Inter Frame Space). Consequently, this has proven to be reason for the slower flooding speed of the BeamMAC-OMNI implementation. 4.2

Flooding Time

The flooding time parameter is defined as time it takes to flood the whole network. It can be derived from the flooding speed curves, as the time instance when the percentage of flooded nodes is 100 (i.e. whole network is flooded). We show the obtained values from our simulations in Figure 7. The parameter flooding time can be understood as a follow-up to the flooding speed analysis. In the small network scenario there is scarcely a difference between the omnidirectional and directional antenna implementation. However, increasing the network size the difference in the flooding time between the directional and omnidirectional antenna implementation is continuously reducing, meaning that beamforming antennas have the dominance in larger network areas. The reduction of the flooding time is about 20%–30% (large network scenario) for the BeamMAC-UCA4 and for the BeamMAC-UCA10, respectively. Flooding time Node density=300 (nodes/km2) 0.7 0.6

Time (s)

0.5 0.4 0.3 802.11 BeamMAC-OMNI BeamMAC-UCA4 BeamMAC-UCA10

0.2 0.1 0

500

1000

1500

2000

Number of nodes

Fig. 7. Flooding time for 802.11 and BeamMAC protocol

4.3

Route Reply (RREP) Time

In addition to the flooding mechanism analyzed in Section 4.1, we implemented and analyzed a route reply mechanism, as well. We believe that this parameter will give us more understanding about the time it takes two nodes to establish a path. The initiator of the route discovery (i.e. the source) broadcasts a packet to the destination, which after receiving it, generates and sends a route reply packet to the source. The route reply packet is not broadcasted, but rather sent using a

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hop-by-hop unicasting. The route reply mechanism is based on a source routing concept, i.e. the reply packet follows the same path on which the route request came from. RREP Time 2 Node density=300 (nodes/km ) 1

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0.8

0.6

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0.2

0 0

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Number of Nodes

Fig. 8. Route reply time for 802.11 and BeamMAC protocol

As Figure 8 shows, the network area has a negative impact on the route discovery process, as well. On the one hand, considering the small network scenario, we confirm that there is an insignificant reduction of the route reply time with beamforming antennas. On the other hand, considering the large network scenario, we notice a superior performance of the BeamMAC protocol. The route reply time has been reduced by about 30%–40% in the case of the BeamMACUCA4 and the BeamMAC-UCA10, respectively. Due to the previous analysis of the flooding speed, we can see here that having faster message propagation ensures quicker delivery to the desired destination. This in turn, helps in achieving better route reply times.

5

Conclusion

Our work outlines the positive impact that the beamforming antennas have on the information dissemination in wireless multihop networks. Our study is performed by simulating realistic network scenarios, considering the very popular 802.11 MAC protocol and a simplified version of the BeamMAC protocol. In addition, we use parameters adopted directly from the IEEE 802.11 standard [12]. Results presented in this paper represent a performance comparison of the broadcast mechanism in multihop networks. Together with the findings in [7] and [9], we provide a thorough analysis of the parameters related to the network topology as well as to the message dissemination in these networks. Although our approach includes a simple flooding model and a simple sourcerouting based route discovery process, we show that the beamforming antennas outperform the omnidirectional antennas. They provide faster message dissemination and faster route discovery process. In addition, we identify their downsides (e.g. void transmission) when implemented in small network scenarios. However,

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in large network area scenarios the beamforming antennas have proven to be superior. This approach could be the worst case analysis when there is no other network information available. As soon as more topology related information is available, nodes can use sophisticated location finding algorithms to adapt or optimize their radiation direction. Moreover, this particular study can be of further help in service discovery scenarios in wireless multihop networks. By simply defining a threshold when the service announcement or service query is successful (e.g. 80% of nodes receive the packet), looking into Figures 4–6 we can obtain the time it takes for a certain service to be properly advertised. We strongly believe that the implementation of beamforming antennas has an enormous potential in wireless multihop networks. Therefore, more in-depth analysis is required which will investigate the actual routing and will look into issues related to cross–layer optimization.

Acknowledgment The authors would like to thank Imad Aad from DoCoMo Euro-Labs and Christian Bettstetter (previously with DoCoMo, now with University of Klagenfurt) for various discussions and very useful comments.

References 1. R. Vilzmann, C. Bettstetter, C. Hartmann: BeamMAC: A New Paradigm for Medium Access in Wireless Networks, International Journal of Electronics and ¨ Volume 60, Number 1, (Jan. 2006) 3–7. Communications (AEU), 2. M. Takai, J. Martin, A. Ren, R. Bagrodia: Directional Virtual Carrier Sensing for Directional Antennas in Mobile Ad Hoc Networks, In Proc. 3rd ACM MobiHoc, Switzerland, (June 2002) 183–193. 3. R. Roy Choudhury, N. H. Vaidya: Ad Hoc Routing Using Directional Antennas, Technical Report, Coordinated Science Laboratary, University of Illinois at Urbana-Champaign (Aug. 2002). 4. H. Singh, S. Singh: A MAC Protocol based on Adaptive Beamforming for Ad Hoc Networks, In Proc. IEEE PIMRC, China, (Sept. 2003) 1346–1350. 5. H. Singh, S. Singh: Smart-802.11b MAC Protocol for Use with Smart Antennas, In Proc. IEEE ICC, France, Volume 6, (June 2004) 3684–3688. 6. Y.-B. Ko, V. Shankarkumar, N. H. Vaidya: Medium Access Control Protocols using Directional Antennas in Ad hoc Networks, In Proc. IEEE Infocom, Israel, (March 2000), 13–21. 7. C. Bettstetter, C. Hartmann, C. Moser: How Does Randomized Beamforming Improve the Connectivity of Ad Hoc Networks?, In Proc. IEEE ICC, Korea, (May 2005), 3380–3385. 8. R. Vilzmann, C. Bettstetter: A Survey on MAC Protocols for Ad Hoc Networks with Directional Antennas, In Proc. EUNICE Open European Summer School, Spain, (July 2005), 268–274. 9. R. Vilzmann, C. Bettstetter, D. Medina, C. Hartmann: Hop Distances and Flooding in Wireless Multihop Networks with Randomized Beamforming, In Proc. ACM MSWIM, Canada, (Oct. 2005), 20–27.

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10. Constantine A. Balanis: Antenna Theory, Analysis and Design, John Wiley & Sons, Inc., 2nd Edition, (1997). 11. M. Matsumoto, T. Nishimura: Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator, ACM Transactions on Modeling and Computer Simulation, Volume 8, Number 1, (1998), 3–30. 12. IEEE Standards Board: Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, (1999), http://www.ieee802.org/11/. 13. C. Bettstetter: Mobility Modelling, Connectivity, and Adaptive Clustering in Ad Hoc Networks, PhD Thesis, Technische Universit¨ at M¨ unchen, Germany, (Oct. 2003). 14. Algorithmic Solutions: LEDA - Library of Efficient Data Types and Algorithms, (Dec. 2005), http://www.algorithmic-solutions.com/enleda.htm. 15. V. Mizorov: Routing in Ad Hoc Networks with Beamforming Antennas, Master Thesis, RWTH Aachen University, (Dec. 2005). 16. T. Korakis, G. Jakllari, L. Tassiulas: A MAC protocol for full exploitation of directional antennas in ad hoc wireless networks, In Proc. 4th ACM MobiHoc, USA, (June 2003), 98–107. 17. W. Weichselberger, G. L. de Echazarreta: Comparison of Circular and Linear Antenna Arrays with respect to the UMTS Link Level, COST 260 Management Committee and Working Groups Meeting, G¨ oteburg, Sweden, (May 2001). 18. N. Razavi-Ghods, M. Abdalla, S. Salous: Characterisation of MIMO Propagation Channels Using Directional Antenna Arrays, in Proc. IEE International Conference on 3G Mobile Communication Technologies (3G2004), London, UK, (October 2004).

Power Amplifier Characteristic-Aware Energy-Efficient Transmission Strategy Kwanghun Han, Youngkyu Choi, Sunghyun Choi, and Youngwoo Kwon School of Electrical Engineering and INMC Seoul National University, Seoul, Korea {khhan,ykchoi}@mwnl.snu.ac.kr, {schoi,ykwon}@snu.ac.kr

Abstract. The energy consumption in transmitting an information bit, i.e., energy-per-bit, has been known to decrease monotonically as the transmission time increases [1]. However, when considering the power amplifier (PA) characteristics, we learn that the energy-per-bit starts to increase as the transmission time becomes long over a certain threshold. This is caused by the fact that, in a wireless device, it is not the transmission power, which determines the energy consumed during transmissions, but the input power to the PA whose output power is used as the transmission power. Based on our new trade-off model between the energy-per-bit and transmission time, we revisit known energy-efficient scheduling algorithms. Finally, we evaluate the impact of the new tradeoff model and the performance of algorithms via simulations.

1

Introduction

For battery-powered hand-held mobile devices, it is a key concern to reduce the energy consumption in order to extend the device’s life time. Since the wireless communication module is a major source of the overall energy consumption in such devices [2, 3], many studies in the literature have tried to minimize the energy consumption by turning off the unused parts of the wireless communication module after finishing packet transmission as quickly as possible [4, 5, 6]. In the meantime, the authors in [1, 7] proposed another approach, which reduces the transmission energy by controlling the transmit power when sending a given amount of data. They showed based on Shannon’s capacity equation that the energy-per-bit monotonically decreases as the transmission time1 increases. According to this trade-off relation, the authors proposed Lazy scheduling algorithm, which minimizes the transmission energy by sending an information bit as slowly as possible, which is accomplished by using low order modulation, low code rate channel coding and low transmission power. However, their model [1] 

1

This research is in part supported by University IT Research Center (ITRC) project, and by the SNU-Samsung 4G collaboration project. Here, the exact meaning of the transmission time is the inverse of spectral efficiency. Assured that it is not confusing, hereafter, we will simply use this term to represent this meaning.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 37–48, 2007. c IFIP International Federation for Information Processing 2007 

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accounts for only the transmission energy while circuitry typically keeps consuming energy in order to maintain the transmission module consistently active even when there is no on-going packet transmission. The power required by circuitry is called electronics power, and this is almost constant irrespective of the transmission power. When considering the effect of electronics power, Yu et al. showed that the energy-per-bit does not monotonically decrease with transmission time any more, especially when the electronics power is comparable with the transmit power [8]. Such a situation can indeed happen when the transmission range is extremely small as in the sensor networks, while the transmit power dominates the electronics power in most wireless communication systems. Shuguang et al. verified through in-depth circuit-level analysis [9] that such a trade-off relation is really present in the context of sensor network. Based on this trade-off model, the authors in [8] proposed a packet scheduling algorithm, which aims at minimizing the energy dissipation in multi-hop wireless sensor networks. Then, in a long-range communication, e.g., conventional cellular network, where the transmit power dominates the electronics power, is it still reasonable to think that the energy-per-bit monotonically decreases as the transmission time increases? Our major finding, in this paper, is that even if the electronics power is ignorably small compared with the transmit power (i.e., long-range communications), the energy-per-bit does not monotonically decrease as transmission time increases when the power amplifier (PA) characteristics are considered. In a strict sense, we should note that the actual energy consumption is not caused by the transmission energy but by the energy consumed by PA to generate the transmit power. Accordingly, we revisit the energy-efficient scheduling problems, which are considered in [1, 10], based on our new trade-off model. We expect that if the algorithms derived in this paper are used for uplink scheduling, especially in a cellular network, the lifetime of battery-powered device could be extended. The rest of this paper is organized as follows: In Sect. 2, we derive the tradeoff model between the energy-per-bit and transmission time considering the PA characteristics. In Sect. 3, we formulate the energy-efficient scheduling problems, and then present not only two offline algorithms, called Modified Lazy scheduling and Modified Move Right scheduling, but also the online version of each offline algorithm, respectively. In Sect. 4, we evaluate the performance of the algorithm via simulation. Finally, in Sect. 5, we conclude the paper with some remarks on the future work.

2

Revised Trade-Off Model

The PA efficiency is defined as the ratio of the output power to the power provided by direct current (DC) voltage source. Since the PA output power is equivalent to the transmit power in the context of communication system, we denote the output power by Ptx as shown in Fig. 1.

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DC voltage source

P in

P tx

Fig. 1. A diagram for power amplifier

Let us denote the power efficiency by η(Ptx ) (< 1), which is typically nondecreasing with Ptx [11, 12]. Then, Ppa , which represents the actual power consumed by PA, is represented as Ppa =

Ptx , 0 ≤ Ptx ≤ Ptx,max , η(Ptx )

(1)

where Ptx,max is the maximum output power, a design parameter of a PA.2 Due to the dependence of η on Ptx , (1) implies that as we reduce Ptx by Δ, the decrement of Ppa is not linearly proportional to Δ, but is altered by the operating range of Ptx . Under an Additive White Gaussian Noise (AWGN) channel with noise power N , the optimal channel coding gives Shannon’s capacity as follows: C=

αPtx 1 log2 (1 + ), 2 N

(2)

where α represents the power loss due mainly to the path loss. Although the actual transmission rate should be less than C, for the convenience of discussion, we just regard C as the achievable rate. When we denote the time necessary to transmit one information bit by t = C1 , the energy-per-bit, Er (t), is given by Er (t) = tPpa = t

Ptx N 2 , Ptx = (2 t − 1). η(Ptx ) α

(3)

If η(Ptx ) is simply modeled by a constant, Er (t) is monotonically decreasing and convex in t as shown in [1,7]. Now, using a proposed power efficiency model in [13], we replace η(Ptx ) with  η(Ptx ) = ηmax 2

Ptx , Ptx,max

(4)

In fact, the additional power provided by PA amounts to Ptx − Pin , and hence those, who are interested in the PA performance itself, typically use a metric called powerin . Since the PA gain defined as added efficiency (PAE), which is defined as PtxP−P pa typically ranges from 20 to 30 dB, Ptx − Pin  Ptx , and hence PAE can be thought to be nearly equal to the power efficiency. Ptx Pin

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where ηmax is the maximum PA efficiency achieved when Ptx = Ptx,max . Accordingly, we obtain a new energy-per-bit Er (t) as follows:  Ptx Ptx,max t. (5) Er (t) = ηmax We present two theorems, which show the property of the trade-off relation between Er (t) and t. For the purpose of comparison, we denote by Ec (t) the energy-per-bit obtained when the power efficiency is considered constant, e.g., η = ηmax as in the literature. Theorem 1. Er (t) is neither monotonically decreasing nor convex in t. 1

Proof. It can be shown that as t becomes much smaller than 1, Er (t) ∼ 2 t t and 2 Ec (t) ∼ 2 t t. Therefore, Er (t) is also monotonically decreasing and convex in t as Ec (t). On the other hand, √ as t approaches infinity, Ec (t) approaches a constant ln 2 i.e., 2N while E (t) ∼ t. Thus, Er (t) becomes an increasing and concave r αηmax function in the region of large t. Since Er (t) changes from a decreasing convex function to an increasing concave function in the range of t from 0 to infinity, we can prove that there exist both minimum and inflection points of Er (t). Theorem 2. When we denote t yielding the minimum and inflection points of Er (t) by t∗ and to , respectively, t∗ < to . √  2 N Ptx,max Proof. From (5), Er (t) = β 2 t − 1t, where β = √αηmax . Differentiating

−2 r t g(t), where g(t) = (1 − lnt 2 )2 t − 1. Er (t) by t, we obtain dE dt = β(2 − 1) Since Er (t) becomes an increasing function when t is large, g(t) < 0 when t < t∗ , and g(t) > 0 when t > t∗ . 2 d 2 Er − 32 2t ln 2 r t 2 t3 k(t), where Differentiating dE dt again, we obtain dt2 = β(2 − 1) 2 k(t) = (2 ln 2 − 1)2 t − 2 ln 2. From k(to ) = 0, to = log ( 22 ln 2 )  1.0849. Since 2

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Figure 2 plots both Ec (t) and Er (t). Referring to the parameters of RF2162PA [13], we set Ptx,max to 1.41 W and ηmax to 0.5. Assuming that the received N tx signal-to-noise ratio (SNR), αP N , ranges from 0.11 dB to 20 dB, we set α appropriately in order that Ptx does not exceed Ptx,max at the highest SNR (i.e., 20 dB). Obviously, we see that Er (t) becomes quite different from Ec (t). This observation tells us that the energy-efficient scheduling algorithm should be revisited based on the new trade-off model. This motivation is differentiated from other approaches [7, 8] because the trade-off model, which considers only the effect of electronics power, just accounts for the short-range communication like sensor network, while the electronics power can be ignored in the energy consumption of many other communication systems.

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Energy Efficient Uplink Scheduling

In this section, we consider the energy efficient uplink scheduling problem in cellular systems. Based on our trade-off model, we derive offline and online algorithms. 3.1

Problem Definition

In cellular systems, most subscriber stations are battery-powered while the base station is AC-powered. Accordingly, the energy efficient transmission strategy can be beneficial, especially for uplink transmissions. In order to highlight the impacts due to our new trade-off model, we restrict the discussion to the scenario of a point-to-point communication. Let us denote by w(τ ) the amount of energy required to transmit a packet over a time duration τ by subscriber stations. We assume that M packets should be transmitted within time [0, T ), which can be considered the time allocated to a subscriber station by the scheduler. For the purpose of analytical simplicity, all packets are assumed to be of equal size. We denote the arrival time of the ith packet by ti and the packet interarrival time by di = ti+1 − ti . Without loss of generality, the first packet is assumed to arrive at time zero, i.e., t1 = 0. The scheduler determines both the packet transmission duration τ = (τ1 , · · · , τM ) and the transmission start time s = (s1 , · · · , sM ). Based on this system description and assumption, we formulate an energy-efficient scheduling problem constrained by the total allowed transmission time for a group of packets, which is equivalent to the problem considered in [1].

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Problem 1. Find the scheduling, τ and s, which minimizes the total transmission energy: M min W (τ ) = i=1 w(τi ), s.t. ti ≤ si , ∀i ∈ {1, ..., M }, sM + τM ≤ T. For the purpose of notation, we denote this problem by ESP-I. As shown in Problem 1, the transmission of the last M th packet should be completed within time T . However, since Problem 1 does not take care of the delay, which an individual packet would experience, if T is quite large, the scheduling result may not be desirable for some types of traffic. Note that packets often have transmission delay bound, and hence packets exceeding a certain delay bound would be considered a delivery failure or discarded. This is quite typical for packets out of real-time multimedia applications. Therefore, we consider another problem formulation of energy-efficient scheduling with constraints of per-packet delay bound, and denote the problem by ESP-II. Given a vector of per-packet delay bound q = (q1 , · · · , qM ), where qi represents the maximally allowed transmission time for the ith packet, ESP-II can be written as follows: Problem 2. Find the scheduling, τ and s, which minimizes the total transmission energy: M min W (τ ) = i=1 w(τi ), s.t. ti ≤ si , ∀i ∈ {1, · · · , M }, si + τi ≤ qi , ∀i ∈ {1, · · · , M }. 3.2

Offline Algorithm

Modified Lazy Scheduling Algorithm. In order to solve ESP-I, Lazy scheduling algorithm was originally developed, and it holds optimality and feasibility (see the detailed algorithm and proofs in [1]). It achieves transmission energy reduction by lowering the transmission power and increasing the transmission time as much as possible under the strictly convex and monotonically decreasing energyper-bit function. Now, since the energy-per-bit function is changed in our model, we develop a modified Lazy scheduling algorithm, which is an extended version of the Lazy scheduling. In order to devise modified Lazy scheduling, we consider the two lemmas induced from the trade-off relationship described in Section 2. Lemma 1. There exists a packet transmission time τmax , which minimizes the transmission energy consumption. Proof. From Theorem 2, there exists a transmission time-per-bit t∗ , which yields the minimum energy-per-bit value. Accordingly, we can obtain the packet

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transmission time τmax , which minimizes the energy consumption needed to transmit a packet, by multiplying the packet size and t∗ together. Lemma 1 tells us that we do not need to consider the range of τ longer than τmax in the optimization problem. The reason is that using τ (> τmax ) does not help minimizing the energy consumption nor satisfying the delay constraint. Lemma 2. When the range of τ , which we are interested in, is upper-bounded by τmax , w(τ ) can be regarded as a convex function of τ . Proof. According to Theorem 2 in Sect. 2, Er (t) is convex when the transmission time-per-bit t is less than t∗ since t∗ is less than the inflection point to . Therefore, w(τ ) is also convex in τ when τ < τmax . Our modified Lazy scheduling algorithm consists of two parts. The first part is called minimum energy transmission part, where the scheduler finds the packets which can be transmitted with the minimum energy packet transmission time τmax . The second part is called legacy Lazy scheduling part, where the Lazy scheduling is conducted for the set of the remaining packets. The modified algorithm is summarized in Algorithm 1. Algorithm 1. Modified Lazy Scheduling si ⇐ ti , i ∈ {1, ..., M } and si+1 = T tsum ⇐ s1 and smax ⇐ 0 for i = 1 to i ≤ M do tsum ⇐ tsum + τmax if tsum ≤ si+1 then smax ⇐ i and tsum ⇐ si+1 for i = 1 to i ≤ smax do si+1 ⇐ max{si+1 , si + τmax } and τi ⇐ τmax if smax < M then Do Lazy scheduling beginning from the (smax + 1)th packet

Modified Move Right Algorithm. In order to solve ESP-II, we modify the Move Right scheduling algorithm [10]. Originally, Move Right algorithm was developed to solve ESP-I when the non-identical energy-per-bit transmission function is used for each packet. However, we modify this algorithm to deal with the constraints of per-packet delay bound. The main idea of the original Move Right algorithm is to find the optimal transmission start time with an iterative manner, and this notion of iteration is maintained in our proposed algorithm as well. The modified algorithm has two additional constraints: one is a delay bound for the feasibility and the other is a transmission duration bound for the minimum transmission energy. Initially, si and τi are set to ti and min{si+1 − si , qi − si , τmax }, respectively. For a pair of packets, which arrive subsequently, we find s2  (s2 ≤ s2  ≤ s2 +τ2 ), τ1  , and τ2  such that w(τ1  )+w(τ2  )

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is minimized. This operation proceeds until the last two packets. In order to get the optimum value, the algorithm repeats this process until the scheduling results converge. The detailed algorithm is described in Algorithm 2, where the superscript k−1 k−1 k−1 k in τik and ski indicates the kth iteration. f (τik−1 , τi+1 , si si+1 ) returns the k k k k ) when updated set of values (τi , τi+1 , si+1 ), which minimize w(τi k ) + w(τi+1 k−1 k−1 k k k k k τi +τi+1 is fixed, where τi , τi+1 , and si+1 satisfy τi ≤ τmax , τi+1 ≤ τmax , and k−1 k−1 k−1 k ski+1 ≤ sk−1 +τik−1 , i+1 +τi+1 ≤ min{si+1 +τmax , qi+1 }, respectively. If si+1 > si k k k (τi , τi+1 , si+1 ) are not changed from the values at the (k − 1)th iteration. The optimality is also shown in [10] and this is maintained as well in our algorithm since we only consider the convex region of w(τ ) given when τ is less than or equal to τmax . Algorithm 2. Modified Move Right Scheduling k ⇐ 0 and f lag ⇐ 0 ski ⇐ ti , i ∈ {1, ..., M } τik ⇐ min{ski+1 − ski , qi − ski , τmax }, i ∈ {1, ..., M } while f lag = 0 do k ⇐k+1 τ k ⇐ τ k−1 and sk ⇐ sk−1 for i = 1 to i ≤ M − 1 do if ski+1 = ski + τik then k−1 k−1 k−1 k (τik , τi+1 , ski+1 ) = f (τik−1 , τi+1 , si si+1 ) k k−1 then if τ = τ f lag ⇐ 1

3.3

Online Algorithm

Online Extension of Modified Lazy Scheduling Algorithm. For the extension to online algorithm, we assume that packets arrive according to the Poisson distribution with mean rate λ. Our goal is to achieve the optimal performance in an average sense. To do so, we need a more tractable form of the scheduling result achieved by the algorithm and this was derived in [1]  offline  k 1 ∗ as τj = maxk∈{1,...,M−(j+bj )} k+bj i=1 Ci , where M is the total number of packets arriving during [0, T ), j is the current packet to be sent, and Ci , i ∈ {1, ..., M − j − bj } is the inter-arrival time between the (j + i − 1)th packet and the (j + i)th packet. bj is the number of packets backlogged at that time when the jth packet is transmitted. From this expression, one can derive   1 k , where b is the curD a random variable τ ∗ (b, t) = maxk∈{1,...,M} k+b i i=1 rent backlog, M is a random variable representing the number of packet arrivals during [t, T ), and Di is the average inter-arrival time when M = i. Finally, the transmission duration of the current packet is determined by E[τ ∗ (b, t)]. However, we simply modify it to τ ∗ = min{τmax , E[τ ∗ (b, t)]} for our algorithm by considering τmax .

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Online Extension of Modified Move Right Algorithm. Online extension of modified Move Right scheduling can also be achieved by the similar method used for the online modified Lazy scheduling case. In [10], the authors derived the online algorithm using a look-ahead buffer. In other words, the scheduler waits for gathering some packets during a determined time. However, for ESPI, this look-ahead buffer adds a constant value to the total transmission delay, and hence it can cause the actual duration of transmission to be shorter, which means that more energy consumption is required. Accordingly, instead of using the notion of the look-ahead buffer, we make use of the assumption that for a given time interval, the packets arrive with the same inter-arrival time, which is determined by the ratio of the given time interval to the average number of packet arrivals during that interval. Then, we run Move Right scheduling to decide the transmit duration of the current packet using the information of b backlogged packets and the expected packet arrivals, where each packet is assumed to have the same delay margin from its own arrival time.

4

Simulation Results

In this section, we evaluate the proposed algorithms using an MATLAB-based simulator. We assume that the size of a packet is 10 kbits and the system bandwidth is 106 symbols/sec. A simulation run lasts for 10 sec, and during the last 0.5 sec, no packet is assumed to arrive in order to prevent the transmission energy from divergence. For other parameters, we use the same values mentioned in Sect. 2. Whenever a result needs to be averaged, we repeat the simulation runs as many as 10 times. First of all, we compare Lazy and modified Lazy scheduling for the same packet arrival patterns under the proposed trade-off model. As shown in Fig. 3, when the arrival rate is less than 150 packets/sec, Lazy scheduling tends to send each packet with a transmit duration longer than τmax , and hence more energy is consumed. In the meantime, modified Lazy scheduling limits the transmit duration up to τmax , which yields the minimum energy consumption. However, as the packet arrival rate increases, the difference of energy-per-bit between two algorithms becomes marginal since both algorithms will yield the same result if the optimal transmit duration is less than τmax . Second, Fig. 4 compares the online modified Lazy scheduling algorithm with corresponding offline version. The discrepancy between two algorithms becomes larger as the packet arrival rate increases. This is due to the fact that the online algorithm only minimizes the average energy consumption. Finally, Fig. 5 shows the energy per bit achieved by offline and online modified Move Right scheduling algorithm. In this simulation, we assume that the perpacket delay bound, qi , is given by 200 msec for every packet. When the packet arrival rate is less than 90 packets/sec, the delay bound does not affect the energy-per-bit much because the energy optimal transmit duration is determined as a value less than the delay bound. However, as the packet arrival rate increases,

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the delay bound tends to limit the transmit duration, and hence the energy-perbit also increases. This remark can be confirmed by comparing them with the modified Lazy scheduling result, which does not impose per-packet delay bound constraint.

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In this work, we show that the characteristic of power amplifier leads to a non-convex energy-per-bit curve. Based on this trade-off model, we propose modified Lazy scheduling and modified Move Right scheduling algorithms, which were originally proposed in [1, 10], to solve the energy minimization problem, where the delay constraint is imposed on either a group of packets or each packet. Since the transmit power in the uplink of a cellular network should be controlled in order to deal with the near-far problem and the inter-cell interference, it is our on-going work to solve both the energy-efficient scheduling and power control problem jointly in the multi-cell environment.

References 1. Prabhakar, B., et al.: Energy-Efficient Transmission over a Wireless Link via Lazy Packet Scheduling. In: Proc. IEEE INFOCOM. (April 2001) 2. Stemm, M., et al.: Reducing Power Consumption of Network Interfaces for Handheld Devices. In: Proc. MoMuC. (September 1996) 3. Flinn, J., Satyanarayanan, M.: Energy-aware adaptation for mobile applications. In: Proc. ACM SOSP. (December 1999) 4. Krashinsky, R., Balakrishnan, H.: Minimizing Energy for Wireless Web Access with Bounded Slowdown. In: Proc. ACM MobiCom. (September 2002) 5. Anand, M., et al.: Self-tuning wireless network power management. In: Proc. ACM MobiCom. (September 2003) 6. Qiao, D., Shin, K.G.: Smart Power-Saving Mode for IEEE 802.11 Wireless LANs. In: Proc. IEEE INFOCOM. (March 2005)

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7. Schurgers, C., et al.: Power Management for Energy-Aware Communication Systems. ACM Trans. Embedded Computing Sys. 2(3) (2003) 431–447 8. Yu, Y., et al.: Energy-Latency Tradeoffs for Data Gathering in Wireless Sensor Networks. In: Proc. IEEE INFOCOM. (March 2004) 9. Cui, S., et al.: Energy-constrained Modulation Optimization. IEEE Trans. Wireless Commun. 4(5) (2005) 10. Gamal, A.E., et al.: Energy-Efficient Scheduling of Packet Transmissions over a Wireless Networks. In: Proc. IEEE INFOCOM. (2002) 11. J¨ ager, H., et al.: Broadband High-Efficiency Monolithic InGap/GaAs HBT Power Amplifiers For 3G Handset Applications. IEEE MTT-S Inter. Microwave Sympo. Digest 2 (2002) 1035–1038 12. Pedro, J.C., et al.: Linearity versus Efficiency in Mobile Handset Power Amplifiers: A Battle without A Loser. Microwave Engineering Europe (August 2004) 13. Corte, F.D.: Power Management for Energy-Aware Communication Systems. RFDesign Magazine (May 2000)

Energy Efficient Throughput Optimization in Multi-hop Wireless Networks Dan Xu and Xin Liu Computer Science Department, University of California Davis, CA 95616, USA {xud,liu}@cs.ucdavis.edu

Abstract. Throughput, fairness, and energy consumption are often conflicting objectives in multi-hop wireless networks. In this paper, we propose the notion of lexicographical maxmin energy efficiency throughput fairness that achieves throughput fairness per unit energy. Compared with maxmin throughput fairness and maxmin time fairness, the proposed scheme allocates more bandwidth to nodes with relay requirements and provides satisfactory bandwidth to nodes far from the sink. We design an optimal bandwidth allocation algorithm to achieve the proposed fairness objective. Simulation results show that the proposed scheme results in more balanced throughput among users when they exhaust energy resources, compared to other fairness schemes.

1

Introduction

One of the most challenging issues in bandwidth allocation is the conflict between fairness and throughput. In [1], the authors indicate in multi-rate 802.11 MAC, throughput-based fairness degrades network throughput considerably since most channel access time is occupied by low bit rate links. Time-based fairness proposed in [2] allows each user to fairly share time resources, which results in low throughput on low capacity links. In [3], the authors argue in multi-hop WLANs[4], maxmin throughput fairness can improve network throughput without penalizing low capacity users, and maxmin time fairness leads to an even higher network throughput by protecting high bit rate links. In multi-hop networks, some nodes need to serve as routers and relay traffic for other nodes. Routers handle more traffic and thus consume much more energy than descendant nodes. If nodes are energy constrained, such as in sensor networks or when using devices on battery power (e.g., laptops or PDAs), energy consumption needs to be considered in bandwidth allocation among users. This observation motivates the work in this paper. We consider a multi-hop wireless network where all nodes need to connect to a wired sink or AP (access point) while taking into account energy consumptions. When energy is a constraint, maxmin throughput fairness is unfair to routers 

The work was in part supported by NSF through CAREER Award #0448613 and Grant #0520126, and by Intel through a gift grant.

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because they consume more energy and die much faster than other nodes. Consider the example illustrated by Fig. 1; nodes 2 and 3 choose node 1 as their router since in (a) they both have low bit rates and therefore low throughput if directly connected to the sink. To simplify the example, we assume at each node, transmitting one unit of data costs 2J of energy, receiving one unit of data needs 1J, and no energy is consumed in an idle state. Then in (b) where node 1 is a router, it costs 8J per unit time to transmit the same amount of data as node 2 or 3, which only consume 2J per unit time. Therefore, node 1 will have a much shorter lifetime and consequently much less aggregate throughput compared with that of itself in a) and nodes 2 or 3 in b). On the other hand, maxmin time fairness severely penalizes nodes far away from the sink. This is because, in order to fairly share a router’s time, a child node’s throughput is about 12 of that allocated to the router. The larger the number of network hops, the less the bandwidth leaf nodes receive. For example, consider a chain topology with 20 nodes and a sink as shown in Fig. 2. Each node chooses the node in front of it as its router. Assume each link’s bit rate is 11Mbps. In b), by maxmin time fairness, routers receive much more bandwidth 0.282 20

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compared to their descendants. The leaf node 20 only receives a bandwidth of 1 27kbps, which is less than 20 of that of node 1. Motivated by such limitations, we propose the notion of lexicographical maxmin energy efficiency throughput fairness, which achieves maxmin throughput fairness per unit energy. With this objective, a router is allocated more bandwidth when it consumes more energy per unit time to relay traffic for other nodes. In other words, routers are compensated for providing service for other nodes. Although a router has a shorter lifetime, it can still achieve a high total throughput before using up its battery power. Another benefit is illustrated in Fig. 2, using maxmin energy efficiency throughput fairness, a router can receive higher throughput than its child nodes. Meanwhile, even the leaf node can still receive a satisfactory throughput, which is 185kbps in the example illustrated by Fig. 2. That is, maxmin energy efficiency throughput fairness provides a better balance among users than both maxmin throughput fairness and maxmin time fairness when energy is the constraint. Our contributions are as follows. We observe the limitations of maxmin throughput fairness and maxmin time fairness when energy is constrained and propose maxmin energy efficiency throughput fairness that allocates more bandwidth to routers while still not penalizing the bandwidth of children nodes severely. To achieve the objective, we design an iterative optimal bandwidth allocation algorithm. We also propose and implement a maxmin energy fair scheme for comparison. Our results show that maxmin energy efficiency throughput fairness results in more balanced throughput among all nodes when they deplete their energy compared with that of the maxmin throughput fair, maxmin time fair, and maxmin energy fair schemes. The rest of paper is organized as follows. In Section 2, we describe the network model and state the maxmin energy efficiency throughput fairness. To achieve the defined fairness objective, we design an optimal bandwidth allocation algorithm and validate its correctness in Section 3. Section 4 is performance evaluation. We present related work in Section 5 and conclusions in Section 6.

2 2.1

Formulation Network Model

We consider a wireless multi-hop network where each node chooses only one pre-determined first-hop router to be connected to the sink or AP. Therefore, the network topology can be modeled by a tree structure rooted at the sink. In sensor networks or mesh networks, nodes are often static and thus we consider a relatively stable network topology in this paper. Consider a node i, ai,Pi denotes achievable link rate between node i and its parent, which is denoted by Pi . Notice a sink has no parent node. We use an indicator function Ii to denote whether a node is a sink or not, where Ii = 1 if i

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D. Xu and X. Liu Table 1. Notions of main variables bi bandwidth of node i the set of child nodes of node i Ci Pi the first hop router of node i the subtree rooted at node i Ti total bandwidth of subtree Ti Bi Ii Ii = 1 if i is a non-sink node, Ii = 0 if i is a sink actual bit rate from node j to i aj,i energy consumed per unit time in transmit state of node i eti energy consumed per unit time in receive state of node i eri energy consumed per unit time in idle state of node i eii Wi workload of i, i.e., the sum of time length of receive and transmit state time length of idle state of node i tii Ei total energy consumption of node i 2 e bi energy efficiency throughput of node i bandwidth vector of the subtree Ti Bi E2 Bi energy efficiency throughput vector of subtree Ti

is a non-sink node, Ii = 0 otherwise. Let Ci be the set whose first-hop router is i and Ci+ be the set Ci ∪ {i}. Let the subtree rooted at node i be Ti and |Ti | be the number of nodes in Ti . Let bi denote the bandwidth allocated to node i, and Bi be the aggregate bandwidth of subtree Ti . The notions used in this paper are listed in Table 1 for reference. The time fraction of node i consumed to transmit i bi its own traffic is aIi,P . Here the time fraction represents the workload added to i node i. Notice node i also needs to relay traffic for its child node j ∈ Ci . The B I B B time fraction to relay j’s traffic is aj,ij + aii,Pj , while aj,ij is the time consumed to i receive j’s packets. Since the time fraction at each node can not exceed 1, the feaB I B i bi sible bandwidth allocation condition is given as j∈Ci ( aj,ij + aii,Pj ) + aIi,P ≤ 1. i i Similar models are widely used in previous literature [3][8][12]. In such models, the effect of inter-link interference can be ignored or taken into account in the achievable link rate ai,Pi , as in this article. In wireless networks, a node may transmit, receive, or stay idle (we ignore the sleeping state in this paper although all studies in this article can be extended to include the sleeping state). Energy consumed in all three states is taken into account in this paper. Let eti , eri , and eii be the energy consumptions per unit time of node i in transmit, receive, and idle states, respectively. These parameters are similar for the same kind of wireless terminals. In [5], the authors have measured these parameters for various wireless adapters. The reported values for a 802.11 WLAN module are used in our simulations. 2.2

Maxmin Energy Efficiency Throughput Fairness

As illustrated in the example in Fig. 1, a router node consumes much more energy, and therefore has much less overall throughput than its descendant nodes when maxmin throughput fairness is implemented. On the other hand, a node with multiple hops is severely penalized when maxmin time fairness is used. The maxmin energy efficiency throughput fairness is motivated by such limitations and formulated in this section.

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Consider a non-sink node i. Its energy consumption per unit time is

Ei =

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(1)  B B bi In (1), 1 − j∈Ci ( aj,ij + ai,Pj ) − ai,P is the idle time of node i, denoted as i i tii , where tii ≥ 0. A non-sink node i consumes Ei unit of energy per unit time and achieves a bandwidth of bi . We define its energy efficiency bandwidth as e2 bi = Ebii , which denotes the bandwidth per energy unit. Suppose there are n non-sink nodes in the network. The energy efficiency bandwidth vector is defined as E2 B=( Eb11 , Eb22 , . . . , Ebnn ), where Eb11 ≤ Eb22 ≤ . . . ≤ Ebnn . We give Definition 1 as: Definition 1(Maxmin Energy Efficiency Throughput Fairness). A feasible bandwidth allocation B is maxmin energy efficiency throughput fair if its energy efficiency bandwidth vector E2 B=( Eb11 , Eb22 , . . . , Ebnn ) is lexicographically equal or larger than that of any other feasible bandwidth allocation. Informally, a feasible bandwidth allocation is maxmin energy efficiency throughput fair if and only there is no way to increase energy efficiency throughput of any node without decreasing the energy efficiency throughput of some nodes with equal or already less energy efficiency throughput. The objective of Definition 1 is to provide maxmin fairness of the bandwidth per unit energy. Intuitively, it first allocates the bandwidth per energy unit among all nodes equally. When some nodes are not able to consume the allocated bandwidth within per unit energy, the bandwidth per unit energy can be evenly shared by the rest of the nodes. A desirable property is that a router’s energy efficiency throughput is not less than that of its child nodes. By maxmin energy efficiency throughput fairness, routers that spend more energy can get more bandwidth. If we view the energy as the cost that each node must pay for communications and the bandwidth as the revenue, the more a node contributes, the higher throughput it gets. Although routers have a shorter lifetime, they can still transmit a large amount of data before using up battery power. Therefore, compared with maxmin throughput fairness, the aggregated throughput of a router is significantly improved when it depletes its energy. This mechanism is a good incentive to encourage each node to serve others. ti times of Since maximum energy consumption at node i does not exceed eerj any other node j, maximum throughput allocated to a certain router is not excessively higher than its descendants. Compared with maxmin time fairness, the proposed fairness objective provides a satisfactory throughput to leaf nodes, even with a large number of network hops, as shown in Fig. 2. Maxmin energy efficiency throughput fairness provides the fairest overall throughput after each node depletes energy resource.

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3.1

Algorithm Design

We design an optimal bandwidth allocation algorithm named E2 TBA (Maxmin Energy Efficiency Throughput Fairness Bandwidth Allocation) to achieve the defined fairness objective. The structure of E2 TBA is based on the idea of Pump-Drain first proposed in [3]. We use Pump-Drain to convert the problem of maxmin energy efficiency throughput fairness bandwidth allocation, which can be modeled as a serial Quasi-optimization problem, to a simper problem of solving a non-linear equation set. Then we give an approximate algorithm to obtain the solution numerically. E2 TFA runs in the recursive and distributed way: a sink initializes E2 TFA, then E2 TFA recursively calls E2 TFA in the up-bottom order. After E2 TFA execution at a node j ∈ Ci returns, node i will perform Pump-Drain within Ti to achieve maxmin energy efficiency throughput fairness among all the nodes in Ti . To perform Pump-Drain, each node i maintains the following information that is locally reported by its children nodes. After performing Pump-Drain, node i also reports the information to its parent node. • The bandwidth assigned to each node k in Ti , namely bk . • The distinct amounts of energy efficiency bandwidth assigned to Ti , which are sorted in the array λi in a non-decreasing order. |λi | is the up-to-date number of the elements of λi . • In array ξi , the kth element ξi [k] is a set of nodes whose energy efficiency bandwidth is equal to λi [k]. • The structure of Ti . The details of E2 TFA are as follows. Pump: Initially, the energy efficiency bandwidth of each node is zero(Bandwidth of each node is set to zero). After the execution of E2 TFA returns from a child node of Ti . Pump is executed at node i in the following steps. I. If node i is a sink, let bi keep the value of 0 and there is no need to perform Pump. However, Drain maybe needed since bandwidth of its children nodes may make i overloaded. If node i is a non-sink node, Pump goes to Step II. II. For a non-sink node i, after the execution of E2 TFA at j ∈ Ci returns, the bandwidth allocation within Tj is maxmin energy efficiency throughput fair and node j is saturated. The aggregated bandwidth of Tj is Bj , whose time B B share at node i is aj,ij + ai,Pj . The energy efficiency bandwidth of node j i

is e2 bj =

bj Ej .

At node i, the time fraction left to support its own bandwidth is

Energy Efficient Throughput Optimization in Multi-hop Wireless Networks

1− is:



Bj j∈Ci ( aj,i

55

B

+ ai,Pj ), then the bandwidth of node i that can be supported i

⎛

⎞   Bj Bj ⎠ + φ = ⎝1 − ai,Pi aj,i ai,Pi

(2)

j∈Ci

With the bandwidth of φ, there is no idle time at node i, and the corresponding energy efficiency bandwidth of node i is e2 bi =

φ φ = eri eti Ei j∈Ci ( aj,i + ai,P )Bj + i

When



φ e e + a ti )Bj + a ti φ

eri j∈Ci ( aj,i

i,Pi

eti ai,Pi

φ

(3)

≥ λi [|λi |], i.e., energy efficiency bandwidth

i,Pi

of node i is larger than that of any other node in Ti , let bi = φ. Pump stops and there is no need to perform Drain. Otherwise, Pump goes to Step III. φ III. When  < λi [|λi |], we let the energy efficiency e e e ( ri + ti )B + ti φ j∈Ci

aj,i

ai,P i

j

ai,P i

bandwidth of node i equal to λi [|λi |]. Node i gets the bandwidth ϕ by solving the following equation, λi [|λi |] = 

Then ϕ =

λi [|λi |]



ϕ eri j∈Ci ( aj,i

eri eti j∈Ci ( aj,i + ai,P i e 1−λi [|λi |] a ti i,Pi

)Bj

+

eti ai,Pi

)Bj +

eti ai,Pi

ϕ

,

(4)

. Now node i also has the largest energy

efficiency bandwidth among all the nodes in Ti . Since ϕ > φ, node i must be overloaded, Pump stops and Drain is needed to decrease the bandwidth of Ti to make the bandwidth allocation feasible. Drain: The objective of Drain is to decease the bandwidth of each node in ξi [|λi |] to make the workload of node i feasible, i.e., let Wi =1. Meanwhile, the energy efficiency bandwidth of each node should still stay the same. However, since in ξi [|λi |], deceasing the bandwidth of a node will probably result in the decrease of energy consumption of itself and all the ancestor nodes, decreasing the bandwidth of each node while keeping their energy efficiency bandwidth the same is a non-trivial job. In ξi [|λi |], there are three kinds of nodes: first, the nodes without any children in ξi [|λi |], we use k to denote them; the second is those with children in ξi [|λi |], we denote these with l; the last is node i itself. Then mathematically, the above problem can be solved by the equation set (5). The equation in {}* appears in (5) only when i is a non-sink node. Notice in (5) the idle time and idle energy consumption of node i is 0, since i is saturated before Drain is completed. In (5), the bandwidth of each node is decreased to make the workload of node i equal to 1, while keeping energy efficiency bandwidth the same, which is denoted by η. When Wi = 1 is satisfied, if η ≥ λi [|λi | − 1], then Drain is done. If η < λi [|λi | − 1], simply decreasing

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the bandwidth of nodes in ξi [|λi |] is not enough, the bandwidth of nodes in ξi [|λi | − 1] should also be decreased. Then Drain is performed by two steps: first, decrease the bandwidth of each node in ξi [|λi |] to make their energy efficiency bandwidth equal to λi [|λi | − 1], which can be done through replacing η by λi [|λi | − 1] in (5) and deleting the final equation; second, combine the set ξi [|λi |] and ξi [|λi | − 1] and do the same operation described by (5). If at this time the current λi [|λi |] is still smaller than the current λi [|λi | − 1], repeat the above two steps. ⎧ bk −δk =η  ⎪ e e e (b −δ ) ⎪ ( a rk + a tk )Bj + tka k k +eik tik ⎪ j∈C ⎪ k j,k k,Pk k,Pk ⎪  ⎪ B −δk ) j ⎪ ⎪ tik = 1 − j∈Ck ( aBj,k + ak,Pj ) − (bakk,P ⎪ ⎪ k k ⎪ ⎪ .. ⎪ ⎪ ⎪ . ⎪ ⎪ ⎪ bl −δl ⎪ =η   e e e (b −δ ) ⎪ ⎪ ( a rl + a tl )(Bj − δm )+ tla l l +eil til ⎪ j,l l,Pl l,Pl ⎪ j∈C m∈ξ [|λ |]&m∈T ⎨ l i i l  (5)  (aj,l +al,Pl )(Bj −m∈ξi [|λi |]&m∈Tl δm ) (bl −δl ) ⎪ til = 1 − − al,P ⎪ aj,l al,Pl ⎪ l ⎪ j∈Cl ⎪ ⎪ ⎪ . ⎪ .. ⎪ ⎪ ⎪ ⎪ ⎪ bi −δi ∗ ⎪ {  eri eti  e (b −δ ) = η} ⎪ ⎪ (a +a )(Bj − δk )+ tia i i ⎪ j,i i,P i,P ⎪ i i j∈Ci k∈ξi [|λi |]&k=i ⎪     ⎪ ⎪ Bj − δn Ii ( Bj − δn ) ⎪ ⎪ j∈Ci n∈ξi [|λi |]&n=i n∈ξi [|λi |]&n=i i −δi ) ⎩ j∈Ci + + Ii (b =1 aj,i ai,P ai,P i

i

(5) is a non-linear equation set which definitely has an optimal solution. However, numerically, there is only an approximate algorithm to solve (5). Here we design an efficient distributed algorithm to perform Drain which can get approximate optimal numerical results.

Algorithm 1. The procedure of Drain of E2 TFA for (η = λi [|λi |]; η = η − Δ; η > λi [|λi | − 1]) do E2 TFA Drain (initialnode, η, initialnode) end for if initialnode s workload is still larger than 1 then Let λi [|λi |] = λi [|λi | − 1] and ξi [|λi |] = ξi [|λi |] ∪ ξi [|λi | − 1] Algorithm is performed from beginning again end if /* Here Δ is a small value which decides precision*/

3.2

Correctness Validation

Proposition 1. E2 TFA achieves maxmin energy efficiency throughput fairness. Proof is available at[6].

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57

Algorithm 2. Function E2 TFA Drain (i, η, initialnode) if i is initialnode then Let all the parameters including bandwidth, idle time as what they are before the first time the function called end if for ∀j ∈ ξi [|λi |] do if parent[j] is i then E2 TFA Drain (j, η, initialnode) end if end for if i is not initialnode then Decease bandwidth of node i by δi , which is calculated by the first equation in (5). Update parameters of each node in ξi [|λi |], including aggregate subtree bandwidth, idle time, which varied resulted from the decrease δi . else {i is initialnode and i is not a sink} Decease bandwidth of node i by δi , which is calculated by the first equation in (5), but here idletime is 0. end if

4

Evaluation

In this section, we evaluate the performance of E2 TFA and compare it with other fairness schemes including maxmin throughput fairness bandwidth allocation(MMFA), maxmin time fairness bandwidth allocation(MTFA) and maxmin energy fairness bandwidth allocation(MEFA). MMFA and MTFA have been studied in [3]. We propose MEFA for comparison. In MEFA, each router’s energy resource is fairly shared by all its descendants and itself. The idea of MEFA is similar with that of MTFA. Therefore, MEFA should have similar performance to MTFA. In E2 TFA, routes are predetermined. Because finding an optimal routing that yields best throughput is NP-hard, we implement two heuristic schemes. The first is the tree construction algorithm proposed in [3], which can iteratively improve throughput. We call it ITCA here. ITCA provides good performance for networks with a small number of hops but works slowly when the number of hops increases. Therefore, we also deploy a shortest distance routing (SDR) scheme, where a node chooses its first-hop router which is the nearest one among the nodes that have a shorter distance with the sink. SDR is efficient in large wireless networks. In the simulation, we consider an area of 150m ∗ 150m. The link bit rate is determined as follows: it is 11Mb/s when the distance between a transmitter and its receiver is smaller than 50m, 5.5Mb/s when distance is smaller than 80m, 2Mbps when smaller than 120m, and 1Mbps when larger than 120m. Unit energy consumptions in the transmit, receive, and idle states are 1.9J/s, 1.55J/s, and 0.75J/s, respectively [5]. We conduct simulations in the following two scenarios.

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• Scenario A: 4 sinks are located at each corner and 30 nodes are randomly located in the square. ITCA is used. • Scenario B: 1 sink is located at a corner and 25 nodes are randomly located in the square. SDR is used. First, we study the aggregate throughput of each node when it depletes energy. The initial energy of each node is set to be 100J. We compare MMFA, MTFA, MEFA and E2 TFA in both Scenario A and B. When a node runs out its energy, it quits the networks and the four schemes are executed for the rest of the nodes. As showed in Fig. 3, in both scenarios, E2 TFA provides the fairest aggregate throughput among all nodes. In Scenario A, using MMFA, routers only transmit a small amount of data before they deplete their energy resources. While in B, the aggregate throughput of leaf nodes in MTFA and MEFA is very low, since in Scenario B there is only a sink, the number of network hops is large, throughput of leaf nodes is severely penalized by MEFA and MTFA. Although leaf nodes have a longer lifetime, their aggregate throughput is still very low. Notice that routers have higher aggregate throughput, throughput, and energy efficiency throughput in MTFA and MEFA, which is contrary to that in MMFA. In Fig. 4, in both scenarios, MMFA results in fairest throughput(bi ) among all the nodes. On the other hand, MTFA allocates much higher throughput to routers than children nodes. In Scenario B, throughput of leaf nodes allocated by MTFA is only 14 of that given by MMFA. However, in the same scenario, compared with MTFA, E2 TFA allocates much more throughput to the leaf node, which is about 3 times more than that of MTFA. Meanwhile it still allocates similar high throughput to a router. In both scenarios, E2 TFA gives higher throughput to routers without severely penalizing any node. In Fig. 5, we show that E2 TFA achieves the fairest throughput per unit energy( Ebii ), which is the objective of E2 TFA. By MMFA, children nodes have smaller energy consumption and therefore have high energy efficiency throughput. MTFA and MEFA gives routers excessively high throughput, which result in a higher energy efficiency throughput for routers although they have relatively higher energy consumption. This aslo illustrates that MTFA is severely biased towards nodes near the sink.

Fig. 3. Each node’s aggregate throughput

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59

Fig. 4. Throughput of each node

Fig. 5. Energy efficiency throughput

5

Related Work

In [3], the authors consider both maxmin throughput fairness and maxmin time fairness in multi-hop WLANs and design an optimal bandwidth allocation algorithm for each objective. In [8][9][10][11], the authors study MAC layer scheduling or bandwidth allocation for ad hoc networks. In [12], the authors study maxmin fairness bandwidth allocation in multi-AP single-hop WLANs through association control. Since the problem is NP-hard, algorithms to determine user-AP associations are proposed that attain near-optimal maxmin fairness. In [13], the authors study maximum and maxmin fairness bandwidth allocation in multichannel wireless mesh networks. All the above works do not consider energy constraints. In [14], the authors consider maxmin fairness rate allocation in sensor networks. Flow split is allowed and thus the problem can be solved by a serial LP with lifetime constraints.

6

Conclusion

In this paper, we study throughput fairness and optimization in energy-constrained multi-hop wireless networks. We observe that maxmin throughput fairness biases against routers with heavier traffic while maxmin time fairness biases against nodes with more hops to the sink. Motivated by such observations, we propose the notion

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of lexicographical maxmin energy efficiency throughput fairness with the following properties. First, the proposed fairness objective allocates more bandwidth to routers that relay packets for others and therefore encourages them to serve others; second, the throughput discrepancy between routers and their descendants is bounded. Therefore, leaf nodes can still receive satisfactory throughput even in a large network; third, by combining energy consumption and throughput, our scheme results in the most balanced aggregate throughput when all nodes use up the energy resources. We develop a distributed algorithm to achieve the above objective and validate its advantages through extensive simulations.

References 1. M. Heusse, F. Rousseau, G. Berger-Sabbatel, and A. Duda, “Performance anomaly of 802.11b,” in IEEE Infocom, 2003. 2. G. Tan and J. Guttag, “Time-based fairness improves performance in multi-rate wireless lans,” in USENIX Annual Technical Conference, 2004. 3. Q. Dong, S. Banerjee, B. Liu, “Throughput Optimization and Fair Bandwidth Allocation in Multi-Hop Wireless LANs,” in IEEE Infocom, 2006. 4. S. Lee, S. Banerjee, and B. Bhattacharjee, “The case for a multi-hop wireless local area network,” in IEEE Infocom, 2004. 5. O.Kasten, “Energy Consumption,” available at http://www.inf.ethz.ch/personal/ kasten/research/bathtub/energy consumption.html 6. Dan Xu and Xin Liu, “Energy Efficient Throughput Optimization in Multi-hop Wireless Networks,” http://www.cs.ucdavis.edu/˜liu/. 7. T. Nandagopal, T.-E. Kim, X. Guo, and V. Bharghavan, “Achieving mac layer fairness in wireless packet networks,” in ACM MobiCom, 2000. 8. L. Tassiulas and S. Sarkar, “Maxmin fair scheduling in wireless networks,” in IEEE Infocom, 2002. 9. S. Lu, H. Luo and V. Bharghavan, A new model for packet scheduling in multihop wireless networks,in Proceedings of ACM MobiCom, 2000. 10. X. L. Huang and B. Bensaou, “On max-min fairness and scheduling in wireless ad-hoc networks: Analytical framework and implementation,” in Proceedings of in ACM MobiHoc, 2001. 11. A. Penttinen, I. Koutsopoulos and L. Tassiulas, “Low-complexity distributed fair scheduling for wireless multi-hop networks,” in IEEE Wiopt, 2005. 12. Y. Bejerano, S.-J. Han, and L. E. Li, “Fairness and load balancing in wireless lans using association control,” in Proceedings of ACM MobiCom, 2004. 13. J. Tang, G. Xue, W. Zhang, “Maximum throughput and fair bandwidth allocation in multi-channel wireless mesh networks,” in IEEE Infocom, 2006. 14. Y. Hou, Y. Shi, H. Sherali, “Rate Allocation in Wireless Sensor Networks with Network Lifetime Requirement,” in Proceedings of ACM MobiHoc, 2004.

Election Based Hybrid Channel Access Xin Wang1 and J.J. Garcia-Luna-Aceves1,2 Computer Engineering Department, University of California, Santa Cruz, Santa Cruz, CA 95064, USA 2 Palo Alto Research Center (PARC) 3333 Coyote Hill Road Palo Alto, CA 94304, USA {wangxin,[email protected]}

1

Abstract. We propose an Election based Hybrid Channel Access (EHCA) protocol for ad hoc network to achieve high throughput and bounded channel access delay at the same time. EHCA reduces the contentions during the channel scheduling formation through fair node elections, which are based on the topology information. Only the elected nodes contend for the channel and broadcast the scheduling result. Numerical analysis and simulation results show that EHCA outperforms alternative designs.

1

Introduction

The analysis about the capacity of wireless networks [5] demonstrated that perfect scheduling is the ultimate way to achieve the capacity in the MAC layer. However, in a distributed ad hoc network it is impossible to use perfect channel scheduling, and the random channel access has to be used to some extent. We propose the Election based Hybrid Channel Access (EHCA) protocol to attain both high channel utilization and bounded channel access delay. The former is important for serving data-centric applications, while the latter is critical for voice-related applications. In EHCA, channel access period is divided into four time sections. The first section is used to exchange the neighbor information. After that, all nodes do fair elections to reduce the number of nodes which will contend for the channel access. The nodes which fail in the election will follow the scheduling result of the nodes elected. In the third section, the channel scheduling is distributed in the two-hop range and contention-free transmissions happen in the fourth section. We evaluate the performance of EHCA through analysis and simulation. Compared with existing hybrid channel access scheme [3] and 

This work was supported in part by the Baskin Chair of Computer Engineering at UCSC, the National Science Foundation under Grant CNS-0435522, and the U.S. Army Research Office under grant No.W911NF-041-1-0224. Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the funding agencies.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 61–72, 2007. c IFIP International Federation for Information Processing 2007 

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X. Wang and J.J. Garcia-Luna-Aceves

IEEE 802.11, EHCA can achieve a much higher throughput and smaller channel access delay at the same time. The rest of the paper is organized as follows. We describe the related work in Section 2. We introduce the details of the proposed approach in Section 3. We analyze the properties of EHCA in Section 4. We evaluate the performance of EHCA and compare it with alternative designs in Section 5. We conclude the paper in Section 6.

2

Related Work

Medium access control (MAC) protocols of ad hoc network can be classified into contention-based channel access and contention-free channel access. In contention-based MAC protocols, each node either detects the transmission collision (collision-detection) or tries to avoid the transmission collision through random back-offs (collision-avoidance). Based on its observation of the channel status, each node contends for the channel access in a distributed fashion. Contention-based MAC protocols may experience throughput degradation at high traffic loads and due to their best effort nature, they can not provide Quality-of-Service (Qos) support for real-time applications. In contention-free MAC protocols, a set of timetables for individual nodes or links is prearranged. Each node/links can only transmit in their assigned time/frequency slots, so that the transmissions from these nodes/links are collision-free within the effective range of the transmissions. Dynamic transmission scheduling protocols can exploit spatial reuse of the wireless channel and have higher channel utilization than static scheduling approaches, e.g. TDMA. Based on whether the schedule scheme needs the topology information, the scheduling-based channel access can be further divided into topology dependent/independent scheduling. In topology dependent scheduling, global topology information is required to form the correct channel scheduling. Arikan [1] has shown that the problem of establishing an optimal interference-free schedule where the optimal is considered in term of throughput, is NP complete. Chlamtac [4] first proposed a topology-transparent scheduling algorithm for wireless ad hoc networks. It uses polynomials over a Galois field to assign time slots, which guarantees each node can transmit successfully at least once in a frame. This approach can provide a minimum performance guarantee for each node. It just needs the information of overall number of nodes in the network and the number of neighbors of each node. The frame length is also much smaller than the classic TDMA approach. Konstantinos [8] proposed probabilistic policy to increase the system throughput under various traffic loads. Ju [7] proposed an approach based on code theory to optimize the performance of Chlamtac’s algorithm in terms of minimum throughput and maximum delay. However, Carlos [11] has shown that the throughput of topology-transparent scheduling is at most the same with the slotted ALOHA.

Election Based Hybrid Channel Access

63

Hybrid channel access is proposed to take the advantages of contentionbased channel access and topology-dependent scheduling. Nodes first use the contention-based channel access to exchange the neighbor information to build the channel scheduling or reserve time slots in the scheduling-based transmission period. The examples of hybrid channel access protocol are NAMA [3] and CATA [13]. NAMA uses a hash function, which takes the node identifier and the current time slot number as input to derive a random priority for every neighbor within two hops. If a node has the highest priority, it can access the channel within the corresponding time slot. The advantage of NAMA is that it completely eliminated the communication overhead with regard to building the dynamic channel access schedule, except for collecting the two-hop neighbor information. However, NAMA has the following problems [2]: first, a node may probabilistically derive low priority for a long period of time and never get access to the channel; second, there may be chain effects to the channel access opportunities, in which the priorities of nodes cascade from high priority to low priority across the network. Chain effects will reduce the spatial reuse of the whole system; third, the channel bandwidth may also be wasted when a node does not have data to send in the allocated time slot. Because of the wasted bandwidth causing starvation to the nodes with traffic, NAMA interacts badly with certain applications that are sensitive to the delay, such as TCP congestion control [12] and AODV route update mechanisms [9]. In CATA, the transmission period is composed of a contention period and a group transmission period. During the contention period, nodes contend for the channel access and reserve a space in the group-transmission period. Then during the group-transmission period, one or more nodes can transmit data packets without collisions. The problem of CATA is that when there is a large number of nodes, the length of the contention period may not be long enough for each node to reserve a slot in the group transmission period. In this paper, we propose a hybrid channel access protocol which reduces the control overhead during the scheduling formation through nodes elections. Compared with NAMA and CATA, it can provide a bounded channel access delay and only nodes with traffic can access the channel. Since it reduces the number of contending nodes through elections, it allows more nodes to successfully reserve the channel through contentions and is more suitable for mobile scenarios.

3

Election Based Hybrid Channel Access (EHCA)

We assume that each node is synchronized on slot systems and nodes access the channel based on slotted time boundaries. Each time slot is numbered relative to a consensus starting point. We divide the channel access period into four different sections, as Figure 1 shows:

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Fig. 1. Channel access period

3.1

Neighbor Information Exchange Period

The neighbor information exchange period is used to maintain the neighbor information, send the reservation requests and distribute the reservation information in the two-hop range. All nodes adapt the 802.11 Distributed Coordination Function (DCF) to contend for the channel access during the neighbor information exchange period. It can be further divided into two sections: one-hop broadcast period and one-hop re-broadcast period. Each node will do the following in the one-hop broadcast period: – If a node does not want to reserve a slot in the scheduling-based transmission period, just send a HELLO packet to maintain the neighbor information. – Otherwise, a node needs to send a RESERVE REQUEST packet which indicates the receiver of the transmission (NULL for broadcast packet). The format of the RESERVE REQUEST is (src, dest, type). The type field indicates the previous scheduling is a failure or not. – Node i will classify the set of the RESERVE REQUEST information it has collected during the one-hop broadcast period as one-hop link set li1 . – Each node will choose the node with the largest MAC address in its onehop range as the leader of the network. The leader information is used for the time synchronization across different networks, which will be further discussed in Section 3.5. Then in the one-hop re-broadcast period, node i will forward the li1 to its neighbors, which guarantees the RESERVE REQUEST will be distributed in the two-hop range. We define the RESERVE REQUEST information node i has 2 received from node j during the one-hop re-broadcast period as lij . The final RESERVE REQUEST information node i has collected (li ) is: 2 li = {li1 ∪ lij

Ni1

∀j ∈ Ni1 }

(1)

where is the set of node i’s one-hop neighbors. After one-hop re-broadcast, each node will compare the MAC address of its two-hop neighbors with its current one-hop leader, then update the node with the largest MAC address in its two-hop range as the network leader. We denote Nmax1 as the maximum number of one-hop neighbors. Nmax1 is a predefined value to control the node density in the network. The length of the neighbor information exchange period Tne needs to be long enough to allow every nodes to broadcast twice. In this paper, we set Tne as 2Nmax1 × Tb , where Tb is the maximum time needed to send a broadcast packet using 802.11 DCF, including carrier sensing and exponential back-off.

Election Based Hybrid Channel Access

65

At the end of the neighbor information exchange period, we use one-hop link contender election to reduce the possible contentions in the channel reservation 2 period. Each node i will compare the li1 with lij for its every neighbor j. If node 1 2 1 2 i finds li = li ∪ lij and li = lij , it means node i and j have the exactly same set of one-hop links, which constitute the total RESERVE REQUEST information node i has collected, then node i will compare the MAC address of i and j. If i has a smaller MAC address, it will give up doing channel reservation and follow the scheduling result of the node j. We define the node with a larger MAC address as contender. Consider the case in which a lot of nodes are close to each other, this approach elects the node with the largest MAC address as the contender and it will do the scheduling for all the one-hop links, which reduces the possible channel access contentions. 3.2

Contention-Based Channel Reservation Period

During the contention-based channel reservation period, we extend the 802.11 DCF to form the channel scheduling in a distributed fashion. We define Dmax as the maximum delay a frame can tolerate, which is dependent on specific application. Nmt is the number of maximum transmissions a frame can support in order to satisfy Dmax . The priority of node i (iprio ) is the overall number of links it has collected in the neighbor information exchange period: iprio = |li |

(2)

The node priority will be broadcasted along with the scheduling results during the reservation information exchange period (section 3.3), then each node compares the node priority it has received. If for node i, there are two nodes j and k (j, k ∈ Ni1 ) with the same highest priority (larger than iprio ), i will define the contention link set (li1 cont ) as follows: 2 2 li1 cont = li1 ∩ lij ∩ lik 2 2 |li1 cont | = |li1 ∩ lij ∩ lik |

(3)

The contention priority of node i (icont prio ) is the number of links in the contention link set (li1 cont ): icont prio = |li1 cont |

(4)

The length of the back-off time (Tbackof f ) is decided by the number of links it has observed and the type of links, as Equation 5 shows:  Tsif s + Random × Ts if |li1 cont | > 0 (5) Tbackof f = Tsif s + (2Nmt − |li |) × Ts if |li1 cont | = 0 Each node keeps carrier sense the channel for a Short Inter Frame Space time (Tsif s ), which is defined in IEEE 802.11 [6]. If the channel is idle and |li1 cont | > 0, the back-off time equals to Tsif s + Random × Ts , where Random is

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a random variable uniformly distributed in [0, Nmt ], Ts is the minimum time slot length defined in IEEE 802.11 [6]. When |li1 cont | = 0, the back-off time equals to Tsif s + (2Nmt − |li |) × Ts . Through this approach, we divide the back-off period into two sections. One section is for the contention links while the other is for the remaining two-hop links, as Figure 2 shows. A node which has observed contention link set always has a shorter back-off period than than a node which has not.

Fig. 2. Node back-off scheme

During the back-off period, nodes which have contention links will access the channel and broadcast its scheduling result before the nodes which have not. In other words, contention links are scheduled before the normal links. After the scheduling for the contention links is formed, the node which can observe the largest number of remaining two-hop links will have the shortest Tbackof f . It will first access the channel and build the channel scheduling for the rest links. When a node gets the channel access, it will send an ordered set which indicates the corresponding scheduling as (sourcen , destn , type). After receiving the scheduling results from neighbor j, a node will first compare the jprio and jcont prio . The scheduling results of contention link set li1 cont is decided by the node with the highest contention priority (N odecont prio ). The scheduling results of rest links (li − li1 cont ) is decided by the node with the highest node priority (N odeprio ). If all the link schedules of a node are already formed by its neighbor with a higher priority, a node will give up its attempt to contend the channel. We give a simple example to show how the channel scheduling is formed, as Figure 3 shows. We assume node D is the node with the highest priority 4, which can observe links AB, BD, DE and EG. Then node D will first access the channel and broadcast the scheduling result of those four links to nodes {B, C, E, F }, which will distribute the scheduling information to nodes A and G during the reservation information exchange period (Section 3.3). We also consider the case that two nodes observe the same number of links, their node priorities (N odeprio ) are the same and their channel accesses will experience a collision. For example, we assume the nodes priorities and the contention link sets in Figure 3 are: – – – –

Aprio = Dprio = Gprio = 6 Bprio = Cprio = Eprio = Fprio = 2 1 1 lB cont = lC cont = {AB, DB} 1 1 lE cont = lF cont = {DE, GE}

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Fig. 3. Channel scheduling formation example

In the first round of scheduling, node A and D will have the same Tbackof f and collide at hidden terminals B and C, node D and G will collide at hidden terminals E and F . We use failed-link contender election to solve this problem. as Algorithm 1 shows. We define li1 f ail as the set of links which are collected by node i during 2 one-hop broadcast period with type failure. lij f ail is the set of links node i has received from node j during one-hop re-broadcast period with type failure. Then at the end of neighbor information exchange period, we compare the li1 f ail and 2 1 2 lij f ail for each neighbor j. If li f ail ⊂ lij f ail , node i will not schedule the failed 2 links, just follow the schedule result of node j. If li1 f ail = lij f ail , we compare the iprio and jprio , the one with a higher priority will be elected as the contender. If iprio = jprio , we further compare the MAC address to break the tie. Now when we revisit the previous example, node B and C will elect one node as contender. According to the backoff scheme we have introduced, this contender will access the channel before nodes A and D to build the scheduling for contention link set {AB, DB}. Then nodes A and D just need to schedule the rest four links. It is the same case for nodes {D, E, F, G}. The length of the contention-based channel reservation period (Tcr ) needs to be long enough to for nodes with the same highest contention priority to send their reservation packets, which is the worst case for the contention-based channel reservation. In this paper, based on the simulation experiment, we set Tcr as 6 × (Tmax backof f + Tr ), where Tmax backof f is the maximum back-off time and Tr is the needed to send a reservation packet. 3.3

Reservation Information Exchange Period

During the reservation information exchange period, nodes broadcast the scheduling results they have received and the related N odeprio to the neighbors, thus the scheduling results are distributed in the two-hop range. If a node receives a different channel scheduling with the same priority, it will mark the type of corresponding link as failure.

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Algorithm 1. Failed-link contender election algorithm /* First step, get the contend link sets */ 1: for each node j, k ∈ Ni1 do 2: if j prio == k prio == max(Ni1 prio ) 3: && max(Ni1 prio ) > i prio then 4:  /* node j and node k are the highest among all node i’s one-hop neighbors */; 2 2 ∩ lik ; 5: li1 cont = li1 ∩ lij 2 2 ∩ lik |; 6: |li1 cont | = |li1 ∩ lij 7: end if 8: end for /* Second step, elect the contender for the failed links */ 9: for each node j ∈ Ni1 do 2 10: if li1 f ail ⊂ lij f ail then 11: contender = j; 12: end if 2 13: if li1 f ail == lij f ail then 14: if iprio ! = jprio then 15: contender = max prio(i, j); 16: else 17: contender = max mac address(i, j); 18: end if 19: end if 20: end for

The length of the reservation information exchange period (Tre ) needs to be long enough to allow every node to broadcast once, which is half of the neighbor information exchange period (Tre = Nmax1 × Tb ). 3.4

Schedule-Based Transmission Period

In schedule-based transmission period, each node will follow the channel scheduling with the highest priority to send its own packets. The length of the schedulebased transmission period (Tst ) equals to Nmt × Tdata , where Tdata is the time needed to send a data packet with the maximum payload length. When a node experiences a collision during the schedule-based transmission, it will mark the type of corresponding link as failure. 3.5

Network Merge Consideration

Under mobile scenarios, when two networks which are in different time sections merge into one, they need to synchronize on one time section to form the correct scheduling. We address this problem by leader election. We get the network leader information during the neighbor information exchange period (section 3.1). Then we add the leader, N odepriority and current time section information in the header of each frame sent after the neighbor information exchange period. The basic principle is to detect the network merge by identifying the leader of the network, then letting the network with the fewer transmissions to synchronize on the

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time section of the network with more transmissions. If two network have the same number of transmissions, all nodes will follow the network leader with a larger MAC address. If two networks merge before neighbor information exchange period and both of them have not formed the leader and priority information, they can finally form a new network, although it may not have all the transmission information. If one network is already in the time section after the neighbor information exchange period while the other is not, the latter will synchronize on the time section of the former.

4

Performance Analysis

1 Through fair node election, each node in EHCA can reserve  Nmax2 +1  of the schedule-based transmission period and access the channel in up to two time frames, which are at the order of Θ(Nmax1 ) slots. We compare the per-node throughput and maximum channel access delay (dmax ) of EHCA with other channel access schemes through numerical analysis, as Table 1 shows, where Nmax2 is the maximum number of nodes in the two-hop range. N is the overall number of nodes in the network. It demonstrates that EHCA achieves a good balance between throughput and delay.

Table 1. Comparison with existing channel access schemes

Protocol

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dmax (unit:slot)

EHCA Θ(Nmax1 ) NAMA [3] ∞ TDMA Θ(N ) 2 2 Topology Nmax2 log2 N (N Nmax2 ) O( log ) O( log ) 2 2N Nmax2 log2 N max2 transparent [4]

5

Performance Evaluation

We have implemented the EHCA and NAMA under Qualnet [10]. We use the simulation setting that 50 nodes are uniformly distributed across a 400 × 400 square meters area. Each node uses 802.11a as the physical layer and the transmission rate is 54 Mbps. Packets are served in First-In First-Out (FIFO) order. The duration of the simulation is 90 seconds. The transmit power is set to 16 dBm, receive sensitivity to -69 dBm. We set Nmax1 equal to 20 and Nmt equal to 400. The simulations are repeated with ten different seeds to average the results for each scenario.

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Static Topology

We use twenty Constant Bit Rate (CBR) flows with varying inter-packet times to evaluate the performance for real time applications. The packet length of the CBR flow is 512 bytes. The senders and destinations are more than two-hops away from each other. This ensures that the metrics measured are reflective of multi-hop traffic. The simulation results are shown in Figure 4. Throughput

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5.2

Dynamic Topology

We use the Random-WayPoint as the mobility model and the way point speeds randomly varying from 1 to 10 meters/second. The pause time is 10 seconds. DSR is used as the routing protocol. The results are shown is the Figure 5. Through the comparisons of Figure 4(a) - 4(d) and Figure 5(a) - 5(d). We can see that under light traffic loads, EHCA performs similar to IEEE 802.11 and NAMA, but with the increase of the traffic load, the contention-based protocol begins to perform badly. EHCA also outperforms NAMA because NAMA does not have enough spatial reuse. The end to end delay of EHCA remains almost constant. 5.3

Interaction With TCP

We generate a traffic scenario which integrates the CBR traffic and TCP traffic to evaluate the interaction between EHCA and TCP. Twenty FTP flows are

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pumped into the network along with twenty CBR flows. The sources and destinations of the FTP flows are randomly chosen such that they are more than 2 hops away from each other. The results are shown in Figure 6(a) - 6(b), which indicate that EHCA performs well with TCP traffic.

6

Conclusion

This paper introduced an election based hybrid channel access (EHCA) protocol. The advantage of EHCA is that reduces the number of contention nodes through node elections, thus reducing the additional control overheads during the channel scheduling formation. EHCA can achieve high system throughput and bounded

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channel access delay at the same time. It is particularly suited for multi-hop ad hoc networks over which both voice and data services must be provided. We have shown through analysis and simulation that EHCA outperforms TDMA, IEEE 802.11 and existing hybrid channel access scheme.

References 1. E. Arikan. Some Complexity Results about Packet Radio Networks. IEEE Transactions on Information Theory, 30(4):681–685, Jul 1984. 2. L. Bao. MALS: Multiple Access Scheduling Based on Latin Squares. In IEEE MILCOM 2004, October 31-November 3, 2004. 3. Lichun Bao and J. J. Garcia-Luna-Aceves. A New Approach to Channel Access Scheduling for Ad Hoc Networks. In ACM Seventh Annual International Conference on Mobile Computing and networking(Mobicom), 2001. 4. I. Chlamtac and A. Farago. Making Transmission Schedules Immune to Topology Changes in Multi-hop Packet Radio Networks. IEEE/ACM Transactions on Networking, 2(1):23–29, February 1994. 5. P. Gupta and P. R. Kumar. The Capacity of Wireless Networks. IEEE Trans. on Inf. Theory, 46:388–404, March 2000. 6. IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Nov 1997. 7. Ji-Her Ju and Victor O. K. Li. An Optimal Topology-Transparent Scheduling Method in Multi-hop Packet Radio Networks. IEEE/ACM Transactions on Networking, 6(3):298–306, June 1998. 8. Konstantinos Oikonomou and Ioannis Stavrakakis. Analysis of a Probabilistic Topology-Unaware TDMA MAC Policy for Ad-Hoc Networks. IEEE JSAC Special Issue on Quality-of-Service Delivery in Variable Topology Networks, 22(7):1286– 1300, September 2004. 9. C. Perkins, E. Belding-Royer, and S. Das. RFC 3561- Ad hoc On-Demand Distance Vector (AODV) Routing, Jul 2003. 10. Qualnet Simulator. Scalable Network Technologies, http://www.scalable-networks. com/. 11. Carlos H. Rentel. Network Time Synchronization and Code-based Scheduling for Wireless Ad Hoc Networks. Ph.D. Thesis, Carleton University, January 2006. 12. T. Socolofsky and C. Kale. RFC 1180 - A TCP/IP Tutorial, Jan 1991. 13. Z. Tang and J.J. Garcia-Luna-Aceves. A Protocol for Topology-Dependent Transmission Scheduling. In Proceedings of IEEE Wireless Communications and Networking Conference(WCNC), September 21-24, 1999.

Asynchronous Data Aggregation for Real-Time Monitoring in Sensor Networks Jie Feng, Derek L. Eager, and Dwight Makaroff Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada {jif226,eager,makaroff}@cs.usask.ca

Abstract. Real-time monitoring applications for sensor networks can require high sampling rates and low-delay forwarding of the sensor values to a sink node at which the data is to be further processed. High data collection rates can be efficiently supported by aggregating data as it is being forwarded to the sink. Since aggregation requires that some sensor data be delayed at intermediate nodes, while waiting for other data to be received, a key issue in the context of real-time monitoring is how to achieve effective aggregation with minimal forwarding delay. Previous work has advocated synchronous aggregation, in which a node’s position in the aggregation tree determines when it transmits to its parent. This paper shows that asynchronous aggregation, in which the time of each node’s transmission is determined adaptively based on its local history of past packet receptions from its children, outperforms synchronous aggregation by providing lower delay for a given end-to-end loss rate. Keywords: sensor networks, aggregation protocols, real-time monitoring, performance evaluation.

1

Introduction

Data aggregation is an important technique for reducing sensor network traffic and energy consumption [1,2,3,4,5,6]. Various aggregation protocols have been proposed for different applications, such as monitoring and periodic data collection [7,8,9], dynamic event detection[10], and target tracking[11]. This paper considers data aggregation in the context of sensor networks supporting realtime monitoring, specifically real-time monitoring systems where sensor data is sampled periodically and forwarded to a single sink node. A high sampling rate and a low delay in forwarding data to the sink are required in such systems so as to maintain a current “view” of the environment being monitored. Aggregation protocols for sensor networks with periodic traffic transmit sensor values over a tree or cluster topology, rooted at the sink [12,7,8,9]. Previous work has advocated synchronous aggregation protocols, in which a sequence of time intervals are statically defined for each round (collection of one set of sensor values), with each interval dedicated to transmissions from particular sensor nodes. TAG is an example of an aggregation service using synchronous I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 73–84, 2007. c IFIP International Federation for Information Processing 2007 

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aggregation [7]. In TAG, each node, beginning with the sink node, informs its children in the aggregation tree of the interval during which it will be receiving data. A child’s transmission interval is fixed as the receiving interval of its parent, and the child’s own receiving interval is chosen as the immediately preceding interval. Thus, all of the sensors at the i’th level of the aggregation tree, 1 ≤ i ≤ H, share transmission interval H − i, where H denotes the height of the tree and where the first interval in a round is numbered as interval zero. All intervals are of identical duration. A potential disadvantage of synchronous aggregation is increased delay, since the interval duration must be conservatively chosen so as to provide a high probability that each node will be able to successfully transmit its data to its parent prior to the end of its transmission interval. A second potential disadvantage is that the constraints imposed on node transmission times may result in suboptimal use of spatial multiplexing. Solis and Obraczka have described and evaluated two asynchronous aggregation protocols, called periodic simple and periodic per-hop aggregation [8]. In periodic simple aggregation, each node waits for a period of time equal to the round duration, aggregating all of the data received from its children over that period, before transmitting to its parent. This approach does not provide low delay; in fact, data generated during one round may not be received at the sink for a number of rounds equal to the height H of the aggregation tree. Periodic per-hop aggregation is similar to periodic simple aggregation in that nodes may wait for a period of time equal to the round duration before transmitting to their parent, but each node may transmit earlier if data is received and aggregated from all children prior to the end of the round. Again, this approach may result in long delays, with the data generated in one round not being received at the sink until some subsequent round. These simple asynchronous protocols were found to yield poorer performance than synchronous aggregation. In this paper, improved asynchronous aggregation protocols are designed through use of more aggressive methods for determining when a node should transmit to its parent. If a node receives data from all of its children prior to sending its own data to its parent, in a given round, all of this data is aggregated and sent to the parent at that point. A node will also transmit to its parent if the time it has been waiting for its children exceeds an adaptively determined timeout value. In this case, any “late arrivals” from its children are simply dropped. The choice of timeout value is critical, since a long timeout value may cause excessive delay, while substantial data loss may be incurred if the timeout value is too short. In the proposed protocols, timeout values are adaptively determined based on local history of past packet receptions. The performance of the new asynchronous protocols, as well as that of synchronous aggregation, is evaluated using simulation. Asynchronous aggregation is found to outperform synchronous aggregation. Performance comparisons of the asynchronous protocols show that adaptation of timeout values based on a weighted average of history information from multiple rounds is preferable to adaptation based only on the immediately previous round. It is also found that randomizing the transmission times of leaf nodes to avoid congestion at

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the beginning of each round, and the duration of the randomization interval, have a great impact on delay and end-to-end loss rate. A method is proposed for adaptively determining the duration of the randomization interval. The remainder of the paper is organized as follows. The new asynchronous aggregation protocols are presented in Section 2. Section 3 presents simulation results evaluating the performance of the new asynchronous protocols and of synchronous aggregation. Section 4 concludes the paper.

2

Asynchronous Aggregation

The main goal is to design asynchronous aggregation protocols that maximize aggregation efficiency by ensuring that as much aggregation occurs as possible, while still providing timely arrival of aggregation results at the sink. Three asynchronous protocols are proposed in the following subsections, beginning with the simplest of these protocols, and then making enhancements that yield improved performance as shown by our performance results in Section 3. The proposed protocols run above the network layer. Aggregation is performed as data packets are forwarded to the sink. The union of the routes to the sink forms an aggregation tree with the sink as its root node. For simplicity, it is assumed that a node can aggregate data from its subtree, together with its own data, into a fixed-size packet. 2.1

Basic Asynchronous Aggregation Protocol

In our basic asynchronous protocol, each non-leaf node sets a timeout in each round, establishing the maximum time it will wait to receive data from its children. The timeout value is determined adaptively, based on the timings of packet receptions from its children in the immediately preceding round. The node transmits its data packet for this round to its parent (aggregating its own data with whatever it has received from its children) either when it hears from all of its children, or when the timeout expires. For simplicity, it is assumed that all nodes agree on the same base time T0 defining the beginning of the first round. (In Section 3.5, however, it is shown that the proposed protocols are tolerant of substantial variability in the values of T0 used at different nodes.) To avoid concurrent transmissions, each node i (other than the sink node) picks a random value ri between 0 and R at T0 , where R is a protocol parameter. At time T0 + ri , node i sends a packet containing its sensor data for the first round to its parent. At each subsequent round j, each node i that is a leaf in the aggregation tree sends a packet containing its sensor data at time T0 + ri + (j − 1) × t, where t is the time between successive sensor readings at each node (i.e., the inverse of the sensor sampling rate). Each non-leaf node operates as follows. Let Lji denote the time by which non-leaf node i receives the last packet for round j. Let T Oij denote the timeout for round j at node i. Node i sets its timeout for the second round to T Oi2 = L1i + t.

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For round j + 1, j + 1 > 2, the timeout of node i is updated as follows: 1. If node i received data packets for round j from all of its children before T Oij , it sets the timeout for round j + 1 to T Oij+1 = Lji + t + e (since a packet from each child should arrive approximately once every time t). The protocol parameter e allows for some variance in the times at which packets are successfully transmitted. 2. If the timer for round j went off before node i received packets from all of its children, node i sets the timeout for round j + 1 to T Oij+1 = T Oij + t. If node i receives one or more packets for round j after time T Oij (“late arrivals”), it updates T Oij+1 to Lji + t. Such late arrivals have been received too late to be aggregated in node i’s round j transmission to its parent, and are simply dropped, since only up-to-date data is of interest in real-time monitoring. The choice of the protocol parameter e impacts the timeliness of the arrivals of data packets at the sink, and the number of late arrivals at the intermediate nodes in the aggregation tree. If e is set too small, timeouts may be set too aggressively, and data packets that experience normal variability in transmission times may arrive after the expiry of the respective timeout and be dropped. When e is set too large, latency may build up as nodes wait for data packets that will never be received owing to transmission failures. Our experiments show, however, that e can be set to a fixed value that yields good performance over a wide range of conditions. In contrast, we find that tuning the parameter R according to the particular network scenario can yield substantial improvements in performance. A method is proposed in Section 2.3 to adaptively determine the value for R. The above protocol supports adaptivity to dynamics in the topology of the aggregation tree, as long as nodes have some mechanism for dynamically altering when necessary their set of child nodes and their parent. The timing of transmissions can be quickly adjusted according to the above rules. 2.2

Asynchronous Aggregation Protocol with EWMA

While the basic protocol is straightforward, it may cause a “timeout chain” phenomenon under certain circumstances. Suppose, for example, that a node 1 has only one child, node 2, and only one grandchild, node 3. Suppose that node 2 receives the packet for round j from node 3 at time Lj2 , and sets its timeout for round j + 1 to Lj2 + t + e. Suppose further that the aggregate sent by node 2 arrives at node 1 after a transmission delay d, causing node 1 to set its timeout for round j + 1 to Lj2 + d + t + e. If the round j + 1 packet from node 3 is not successfully received by node 2, node 2 will time out and send its packet for round j + 1 at time Lj2 + t + e. If the transmission delay of this packet exceeds d, node 1 will also time out, causing the packet to be a late arrival and be dropped. The main reason for the above phenomenon is that the timeout for the next round at a node is set too aggressively when packets are received from all children prior to timeout expiry. In the second asynchronous protocol that we propose, an Exponentially Weighted Moving Average (EWMA) strategy is used to adjust the

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timeout value in this case. Specifically, if a node i heard from all of its children before timeout in round j, it sets its timeout for round j + 1 to T Oij+1 = (1 − δ) × (T Oij + t) + δ × (Lji + t + e)]. The parameter δ controls how quickly the protocol reacts to changes in the network. The above adjustment method bears some similarity to the Additive Increase Multiplicative Decrease (AIMD) algorithm in TCP. Both react slowly to “good news” while aggressively to “bad news”. In our case, the timeout for the next round is adjusted cautiously when packets are received from all children prior to timeout expiry, but more aggressively when there is a late arrival. 2.3

Adaptive Asynchronous Aggregation Protocol

It is important to randomize the transmission times of leaf nodes to avoid congestion at the beginning of each round. The parameter R controls the duration of the randomization interval. Choosing an appropriate value of R requires balancing the risk of congestion (if R is set too small) versus increased delay (if R is set too large). The best value is network dependent. In our third asynchronous aggregation protocol, R is determined adaptively. Based on the observation that the “asynchronous with EWMA” protocol achieves good performance when the ratio of R to the average data collection delay D is within a certain range, the adaptive protocol works by calculating the average data collection delay at the sink and adjusting R when the ratio is out of range. The data collection delay for a round is defined as the maximum delay from when a sensor value is captured, until the corresponding aggregate arrives at the sink. Although sensor values may be captured at somewhat different times at the various nodes, in our simulation implementation the capture times are approximated for each node and round j as T0 + (j − 1) × t. The average data collection delay is calculated as D = αD + (1 − α)D∗ , where ∗ D is the latest measurement for the data collection delay and α is a parameter determining the weight given to the previous value of the average. Suppose the desired range of R/D is [β −Δ, β +Δ]. When the sink observes that R/D is out of range, it updates R as follows. If R/D < β −Δ, R is updated to R = D×(β +Δ). If R/D > β + Δ, R is updated to R = D × (β − Δ). With suitable parameter value selections, changes to R with this protocol would be relatively rare, and we do not model any particular technique for communicating changes in R from the sink to the other network nodes.

3 3.1

Comparative Evaluation Synchronous Aggregation

The synchronous aggregation protocol used here for comparison uses a similar synchronization structure as that in TAG [7] and the cascading timeout protocol [8]. In particular, it is assumed that each node i knows its hop count to the sink, hi , and accordingly chooses its transmission interval within each round. Let I be the duration of the interval. For each round j, node i picks a random value

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rij between 0 and λ × I, 0 ≤ λ ≤ 1, aggregates the data it has received for this round and sends out its packet at T0 +t×(j −1)+(H −hi )×I +rij . Randomizing transmissions over λ × I yields better performance than when all nodes at the same tree level attempt to transmit at the same time. Parameter λ is set to 0.8 in all experiments. Alternative values were tried, but did not yield better performance. The duration of the interval is the decisive performance factor once the network configuration is fixed. In the performance evaluation experiments, different interval durations are used to explore the best achievable performance. 3.2

Goals, Metrics, and Methodology

The performance of the asynchronous protocols and the synchronous protocol is evaluated through ns2 simulation. The primary metrics are the end-to-end loss rate, equal to the ratio of the number of samples not included in the aggregates arriving at the sink to the total number of samples the nodes generate, and the maximum data age, which measures how old the data at the sink can be by the time the next samples arrive. The maximum data age is approximated by t plus the average data collection delay. An additional metric for which some results are presented is the average number of MAC layer data packet transmissions per round, which may yield insight into relative energy usage. Different sensor networks are generated by randomly scattering nodes in square areas with different sizes. The sink is located in the center of the network unless otherwise stated, and the aggregation tree is constructed as a shortest path tree. The physical layer packet loss rate is specified as a simulation input parameter. The uniform random error model is used for all experiments except those in which the two-state Gilbert error model is used to simulate channel errors (Section 3.5). An 802.11 MAC layer is simulated, without RTS/CTS [13], with a transmission range of 40 meters and rate of 2Mbps, and a fixed packet size of 52 bytes. A data packet is retransmitted up to three times before being discarded if an ACK is not received. 3.3

Parameter Analysis

While the asynchronous protocols have more protocol parameters than the synchronous protocol, experimental results show that e and δ can be easily fixed at 0.1 second and 0.05 respectively for all network settings. For adaptive aggregation, α, β, and Δ are fixed at 0.875, 0.7, and 0.15 respectively. Fig. 1 shows the impact of e on end-to-end loss rate and maximum data age. Fixing e at 0.1 second yields good performance for different physical layer loss rates. Experiments are also conducted with different sensor networks and the results show that good performance is achieved for all sensor networks when e is 0.1 second. Similar results are obtained showing that the chosen values of the other parameters (δ, α, β, and Δ) yield good performance across all of the simulated network configurations. Figures showing the impact of these parameters are omitted.

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The performance of the synchronous protocol is very sensitive to the choice of the duration of the interval. Experimental results show that link quality and aggregation tree structure have a great impact on the choice of I. In practice, it may be difficult to set this parameter in a manner yielding consistently good performance. 3.4

Principal Performance Comparison

Fig. 2 shows the performance of the aggregation protocols at three different sampling rates. Each point for basic asynchronous and asynchronous with EWMA is achieved at a specific R. For t = 0.25, R ∈ [0, 0.1, 0.2, 0.25]. For t = 0.5 and 0.75, R ∈ [0, 0.1, 0.2, 0.3, 0.4, 0.5] and [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75] respectively. Similarly, each point for the synchronous protocol is achieved at a specific I. Two different values, 0 and t, are used as the initial value for R in adaptive asynchronous. For all three sampling rates, asynchronous aggregation achieves lower maximum data age than synchronous aggregation for a given end-to-end loss rate. The performance of basic asynchronous and asynchronous with EWMA is very close when t is 0.5 and 0.75 second. Fig. 2 shows that both protocols get end-toend loss rate close to 1% at similar maximum data age. At such a low end-to-end loss rate, almost all packet transmissions are triggered when a parent hears from all children. The timeout strategy doesn’t have much impact. When t is 0.25 second, the end-to-end loss rate gets higher and the difference between the two protocols becomes bigger as more packet transmissions are triggered by timeout. Fig. 3 shows the performance of the considered protocols with alternative sink placement. The same sensor network as for Fig. 2 is used, but with the sink at the corner. Fig. 3 (compare with Fig. 2) shows that the maximum data age of the synchronous protocol substantially increases when the sink is located at the corner while that of the asynchronous protocols stays around the same. A close look at the tree structure shows that the aggregation tree with the sink at the corner is more than twice as long as the one with the sink in the center, but the maximum number of nodes at the same tree level is quite similar in both cases.

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Thus, for synchronous aggregation, a similar interval duration is required in both cases, but twice as many intervals are required when the sink is at the corner. Another observation from Fig. 2 and Fig. 3 is that with the same 0.5 and 0.75 second sampling periods, asynchronous with EWMA performs much better than basic asynchronous with the sink at the corner. The reason for the difference can be traced back to the tree structure as well. When the sink is located at the corner, the sink only has four children and only one of these children has three children. Moreover, only one of these three grandchildren of the sink

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has its own children. The performance of basic asynchronous is very susceptible to the “timeout chain” phenomenon mentioned in Section 2.2 with such a tree structure. The number of late arrivals now differs enough to make a more significant difference in the end-to-end loss rate. The relative difference between basic asynchronous and asynchronous with EWMA, however, doesn’t vary much with different sink placement when t is 0.25 second. This is because packet losses due to congestion now play an important role in the network. The loss caused by the defects of basic asynchronous is less dominant. Fig. 4 shows the average number of MAC layer data packet transmission per round of the considered protocols with 10% and 30% physical layer loss rate. Fig. 4 shows that the performance improvements shown in Fig. 2 and Fig. 3 are achieved without impact on the number of MAC layer packet transmissions. The number of MAC layer data packet transmissions is very similar with all of the considered protocols, and in fact even slightly better with the asynchronous protocols when the end-to-end loss rate is low. 3.5

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Fig. 5 shows that the maximum data age and the end-to-end loss rate of both synchronous and asynchronous aggregation get worse as the physical loss rate increases. Asynchronous aggregation outperforms synchronous aggregation for different physical layer loss rates. When the number of nodes is fixed, the performance of the asynchronous protocols is not very sensitive to the size of the area. For the synchronous protocol, the average data collection delay increases as the tree gets longer and skinnier with a lower density. The maximum data age increases accordingly. As shown in Fig. 6, the performance improvement asynchronous aggregation achieves over synchronous aggregation increases as the size of the area increases.

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When the size of the area is fixed, the maximum data age for all of the considered protocols increases as the number of nodes increases. As seen in Fig. 7, the asynchronous protocols outperform the synchronous protocol for different numbers of nodes. For both synchronous and asynchronous aggregation, it was assumed that there is a common base time T0 that defines the beginning of the first period at all sensor nodes. Here this assumption is relaxed by assuming that there is some variable clock shift away from this common base time, so that different nodes consider the first period to begin at somewhat different times. Fig. 8 (compare with Fig. 1(b)) shows that the asynchronous protocols are much more tolerant of clock shift than the synchronous protocol. Fig. 9 considers the impact of a more bursty physical layer packet loss process on the relative performance of the aggregation protocols. The two-state Gilbert error model is used, with a “good” state in which there is no physical layer packet loss, and a “bad” state in which there is a 40% physical layer packet loss rate. When in each state, after a time duration of 5 seconds on average,

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a transit decision is made with 20% probability of moving to the other state. Each link independently transits between the two states. As seen in the figure, the relative performance of the various protocols is qualitatively consistent with that observed in the earlier experiments. Similar results have been obtained with other settings of the Gilbert error model parameters.

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This paper presents three asynchronous protocols and compares them against each other and against synchronous aggregation for the context of real-time monitoring systems. Simulation results show that asynchronous aggregation outperforms synchronous aggregation in its ability to keep data “current” while achieving a low end-to-end loss rate. Results also show that the per-node transmission adaptation strategy is crucial in asynchronous aggregation.

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References 1. Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: MobiCom ’00, Boston, MA (2000) 56–67 2. Krishnamachari, B., Estrin, D., Wicker, S.B.: The impact of data aggregation in wireless sensor networks. In: ICDCSW ’02, Vienna, Austria (2002) 575–578 3. Intanagonwiwat, C., Estrin, D., Govindan, R., Heidemann, J.: Impact of network density on data aggregation in wireless sensor networks. In: ICDCS ’02, Vienna, Austria (2002) 457–458 4. He, T., Blum, B.M., Stankovic, J.A., Abdelzaher, T.: AIDA: Adaptive applicationindependent data aggregation in wireless sensor networks. Trans. on Embedded Computing Sys. 3(2) (2004) 426–457 5. Shrivastava, N., Buragohain, C., Agrawal, D., Suri, S.: Medians and beyond: new aggregation techniques for sensor networks. In: SenSys ’04, Baltimore, MD (2004) 239–249 6. Nath, S., Gibbons, P.B., Seshan, S., Anderson, Z.R.: Synopsis diffusion for robust aggregation in sensor networks. In: SenSys ’04, Baltimore, MD (2004) 250–262 7. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: a tiny aggregation service for ad-hoc sensor networks. SIGOPS Oper. Syst. Rev. 36(SI) (2002) 131– 146 8. Solis, I., Obraczka, K.: The impact of timing in data aggregation for sensor networks. In: ICC ’04, Paris, France (2004) 3640–3645 9. Madden, S., Szewczyk, R., Franklin, M.J., Culler, D.: Supporting aggregate queries over ad-hoc wireless sensor networks. In: WMCSA ’02, Washington, DC (2002) 49–58 10. Fan, K.W., Liu, S., Sinha, P.: On the potential of structure-free data aggregation in sensor networks. In: INFOCOM ’06, Barcelona, Spain (2006) 11. Zhang, W., Cao, G.: DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks. IEEE Trans. Wireless Commun. 3(5) (2004) 1689– 1701 12. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: HICSS ’00, Maui, HI (2000) 8020–8029 13. Xu, K., Gerla, M., Bae, S.: Effectiveness of RTS/CTS handshake in IEEE 802.11 based ad hoc networks. Ad Hoc Netw. 1(1) (2003) 107–123

A Novel Agent-Based User-Network Communication Model in Wireless Sensor Networks Sang-Sik Kim and Ae-Soon Park Electronics and Telecommunications Research Institute {pstring,aspark}@etri.re.kr

Abstract. Wireless sensor networks generally have three kinds of objects: sensor nodes, sinks, and users that send queries and receive data via the sinks. In addition, the user and the sinks are mostly connected to each other by infrastructure networks. The users, however, should receive the data from the sinks through multi-hop communications between disseminating sensor nodes if such users move into the sensor networks without infrastructure networks. To support mobile users, previous work has studied various user mobility models. Nevertheless, such approaches are not compatible with the existing data-centric routing algorithms, and it is difficult for the mobile users to gather data efficiently from sensor nodes due to their mobility. To improve the shortcomings, we propose a view of mobility and propose a model to support a user mobility that is independent of sinks. The proposed model, finally, is evaluated by simulation of delivery ratio, latency, and network lifetime. Keywords: User Mobility Support, Wireless Sensor Networks.

1 Introduction Wireless sensor networks typically consist of three objects, as shown in Fig. 1: user, sink, and sensor node [1]. Firstly, a user is an object that disseminates an interest in the sensor field and collects data about the interest from sensor nodes. Secondly, a sink is an object that collects data. The sink receives an interest from a user and disseminates the interest inside sensor fields. The sink receives sensing data from sensor nodes and forwards the sensing data to the user. Lastly, a sensor node is an object that generates data about the interest and delivers the data to a sink. As shown in Fig. 1, the user and the sinks are mostly connected to each other by infrastructure networks. The users, however, should receive the data from the sinks through multi-hop communications between sensor nodes if such users move around the sensor networks without infrastructure networks. Recently, applications transmitting data to moving users inside sensor fields, such as rescue in a disaster area or maneuvers in a war zone, have been on the rise in large-scale sensor networks [5]. (Firefighters and soldiers are users gathering data from sensor networks.) To support mobile users in wireless sensor network, previous work has studied various user mobility models. But, until now, only three models supported the mobility I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 85–96, 2007. © IFIP International Federation for Information Processing 2007

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of users for those applications: the direct user-network communication model, the GPS-based user-network communication model, and the topology-control-based usernetwork communication model.

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The direct user-network communication model (D-COM) is shown in Fig. 2. It supports the mobility of a user on the assumption that the user communicates directly with sinks through infrastructure networks, namely, the Internet, such as communication systems in traditional sensor networks [1]. And, users can communicate directly with the networks via the sinks. But, in applications such as rescues in a disaster area or maneuvers in a war zone, circumstances without infrastructure networks, except sensor networks, are more prevalent. Hence, the assumption that a user and a sink can communicate directly through the Internet is not entirely accurate. The GPS-based user-network communication model (G-COM) is seen in Fig. 3. G-COM is source-based topology [5], [6], [7]. In G-COM, sensor nodes proactively construct a GRID system with GPS receivers. G-COM assumes that all sensor nodes have their own GPS receivers and an ability that constructs a GRID when a stimulus is detected. A sensor node, i.e. source, with a stimulus is going to make a GRID in a sensor field. Once a GRID is set up, mobile user floods its interests within a cell only where the user is located. When a sensor node on a GRID receives interests, it sends interests to the source along a GRID path and data from the source are forwarded to the user along the reverse path. The topology-control-based user-network communication model (T-COM) is seen in Fig. 4. It also identifies a user with a sink. This model supports the mobility of the user by reflecting the movement of the user [8], [9]. In T-COM, the user and sensor nodes construct a tree that is rooted at the user. The user always maintains the tree and gathers data from sensor nodes. Intuitively, G-COM and T-COM seem to be suitable for the aforementioned applications. But, these models also have various problems. First of all, they cannot use existing effective data collection algorithms [2], [3], [4] between a sink and sensor nodes based on the data in static sink sensor networks because of low protocol compatibility. Accordingly, such algorithms can hardly be exploited if users in sensor networks have mobility.

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The other problem is that the cost of the overhead to reorganize the network topology and reconstruct dissemination paths from sensor nodes to the mobile user is expensive. In G-COM, all sensor nodes make the topology based on location information. Accordingly, each sensor node must have its own GPS receiver. The cost of GPS receivers is decreasing, but the overall cost is still high. In T-COM, similarly, user mobility causes topology reconstruction. Users in T-COM have a tree that is rooted at each mobile user. If users move into a new location, then the root of trees must be changed, as seen in Fig. 4. This leads to enormous overhead to sensor nodes with constrained energy. Hence, this paper proposes a novel agent-based user-network communication model (A-COM). A-COM collects data through a temporary agent and delivers the data to mobile users. In A-COM, if a user intends to obtain data while moving, the user appoints a sensor node to act as an agent, and the agent forwards interests to the sink via a sensor network. The sink, having received interests from the user, collects data from sensor nodes using the existing data collection algorithm in static sink sensor networks [2], [3], [4]. The collected data are finally forwarded to the user. (If there is no sink, the agent directly disseminates interests to the whole networks and collects data.) Table 1. Taxonomy of Mobility Type Mobility Type

Compatibility Feasibility GPS Control Control Help of with Existing receivers Overheads Overheads to infrastructure Static Sink for sensors according to support networks Routing Protocols user mobility multiple users D-COM High Low Needless Low Low Mandatory G-COM Low Middle Mandatory Middle Low Needless T-COM Low High Needless High High Needless A-COM High High Needless Low Low Needless

A-COM has various advantages, as can be seen in Table 1. First of all, A-COM has the compatibility with existing static sink routing protocols without infrastructure networks. In addition, the users in A-COM do not make a topology (tree or GRID) and communicate only with agents. So, the users, while moving, are free from

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topology control. The user’s freedom saves much energy, and enables more users to participate in the proposed model even if the sensors have no GPS receivers. Our simulation verifies that the lifetime of sensor networks is prolonged because the user’s freedom decreases the energy consumption of the sensor nodes. Also, we verified that the performance of the data delivery ratio and the delay never decreases. Nevertheless, the movement of the user is supported by only sensor nodes without infrastructure networks. The rest of this paper is organized as follows. Section 2 describes the proposed model. Simulation results are presented in Section 3 to evaluate the effectiveness of our design and to analyze the impact of important parameters. Section 4 concludes the paper.

2 Model Analysis In our model, if a user intends to obtain data while moving, the user appoints a sensor to act as an agent and forwards an interest to the agent. If there is one or more sink(s), the agent forwards interests to sensor networks via sink(s). The number of sinks, however, depends on the network policy. A network administrator might want to set a single or more sinks in the sensor field, or alternatively the sensor field may be hazardous as he cannot reach the field. Hence, we consider three scenarios according to the number of sinks and describe the scenarios based on following assumptions. • A user can communicate with static sinks only through sensors because networks within sensor fields are infrastructure-less networks. • It is possible that multiple sinks are deployed in sensor networks and are connected to each other via the Internet. How to connect a sink with other sinks is out of the range of this paper, but this helps maximize the efficiency of gathering data. • Multiple static sinks are located in the outskirts of sensor fields. • The data which one sink collects is aggregated by the sinks. The aggregated data is shared by every multiple static sinks through the infrastructure network, namely, Internet. • The interest from a user describes how many times the sink forwards the gathered data set to a user.

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2.1 Scenario 1: Sensor Fields with Only One Sink Dissemination of sink announcement message. In the initial stage of the sensor network, the network administrator sets a sink in a suitable position: center, outskirt, or a special position according to his or her policy. If a sink is located in an arbitrary position in sensor fields, it floods a sink announcement message to announce itself inside the whole sensor field (Fig. 5). As a result of the flooding announcement message, every sensor node knows the hop counts and next hop neighbor sensor node to the sink. Dissemination of the user interest. While moving inside the sensor fields, if a user wants to collect data, the user selects the nearest node as a first agent, as shown in Fig. 6. The user delivers an interest to the first agent. The first agent, to which the user has delivered the interest, forwards the interest to the next hop neighbor node toward the sink. The next hop sensor node, which has had the interest delivered to it, also forwards the interest to the next hop neighbor node toward the sink. This process continues until the sink receives the interest of the user. Also, a route for the interest from the sink to the user has been established through this process. Based on our assumptions, the established route vanishes from the network when the described period in the interest is over. Data collection of sink. A sensor network with a static sink is a network where sensing data from sensor nodes should be transmitted to the static sink through multihop communication. So, existing routing algorithms for a static sink can be used (e.g., routing algorithms collecting data by periods, routing algorithms collecting a minority event, or routing algorithms detecting a moving object.) Hence, the network administrator can support a suitable routing protocol for users, and besides, a user can select and use the most appropriate routing algorithm with a static sink according to the policy of the networks. Such research has already been advanced [2], [3], [4]. So we will not mention it further in this paper. Rather, we use one of the existing routing algorithms to collect data in this paper. In Fig. 7, the static sink can forward interests from users to sensors and gather data from sensor networks according to the existing routing protocols. If all data are gathered by routing protocols, the static sink aggregates all data and forwards an aggregated data to the first agent. Mobility support of the user. A user may move to another place after sending an interest to the first agent. In this case, the user selects another agent that can communicate with the first agent. Also, the user makes a new connection between the newly selected agent and the original agent. (While moving inside the sensor field, the user can make more agents and connections.) These agents and connections are used for forwarding the aggregated data from the sink. Data propagation of sink. A sink delivers the aggregated data to the first agent through the reverse path of interest forwarding. The first agent delivers the aggregated data to the last agent through the connection of agents. The last agent delivers the

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aggregated data to the user. As shown in Fig. 8, the user can surely receive the aggregated data from the sink. 2.2 Scenario 2: Sensor Fields with Multiple Sinks Separation of the sensor fields. Basically, the difference between Scenario 1 and Scenario 2 is only the number of sinks. If there are more than one sink in the sensor field, this means a separation of the sensor fields. As a result of sink announcement message dissemination in this case, all sensor nodes know the nearest sink according to the hop counts. Accordingly, Interest dissemination of the user targets the nearest sink from the agent, as shown Fig. 9. The targeted sinks can be changed whenever the user wants to send its interests (see Fig. 10). Nevertheless, mobility support of the user and data propagation of the sink is still the same with Scenario 1. In addition, users may not be able to recognize how many static sinks are in the sensor fields. This means that the proposed model is independent of the number of sinks. Interest

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Data sharing of multiple static sinks. As shown in Fig. 1, a sink in typical sensor networks takes charge of the function as a gateway for a connection with infrastructure networks [1]. Various papers in relation to multiple static sinks also indicate the connection between a sink and an infrastructure network and the connection between all sinks as an assumption [10], [11].

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Therefore, in this paper, it is assumed that each sink placed in the edge of a sensor field can communicate with the other sinks via the infrastructure networks. Hence, the proposed model of this paper collects data by one sink through sensor fields. And the aggregated data of the collected data will be shared by multiple static sinks through the infrastructure network. Advantages with multiple static sinks. The proposed model can obtain various advantages with multiple static sinks. First of all, a user can receive the data from the nearest sink to its position. Therefore, short hops communications between a user and a sink are possible. This saves energy, enhances the data delivery ratio, and reduces delay. Also, because a user requires a dissemination of interests through multiple static sinks, the locations of data collection are diverse. It relieves the hot spot problem, which has carried a disproportionate amount of traffic to sensor nodes near the sink [13]. As a result, the lifetime of the sensor networks will increase because a balance of energy consumption of the sensor nodes is made possible. 2.3 Scenario 3: Sensor Fields with No Sink Hazardous sensor fields. Sensor fields without a sink are a special type of sensor networks. If the sensor field is hazardous such that network administrator cannot reach the field (e.g., a battlefield), the sensor field may not have any sinks. In a battlefield, users (or soldiers) may move into a sensor field that has no sink and gather data from sensors. In this case, users must gather data for themselves. Dissemination of sink announcement message. Because there is no sink in the sensor field, the sensor network cannot perform the sink announcement message dissemination process for itself. In this case, users appoint the nearest sensor node as first agent, and the first agent disseminates the sink announcement message. As shown in Figs. 11 and 12, users that want to gather data from sensors examine nearby sensor nodes whether there is a sink in the sensor field or not. If there is no sink, users appoint the nearest sensor node as first agent. Once a sensor node becomes the first agent, it acts as the sink of Scenario 1. Hence, other processes such as sink announcement message dissemination, interest dissemination of the user, mobility support of the user, and data propagation of the sink are the same as in Scenario 1. New Agent Move Report

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Advantages with idle sensor network. Based on our assumptions, the proposed model in this scenario may create many first agents periodically. The first agents must return to the original state after the described period. This means that the first agents are appointed whenever users want to send their interests. Then, the first agents are reactively selected and perform all processes for user mobility. In the whole network, therefore, the sensor network can remain in an idle state in case there is no user in the sensor field. From the standpoint of the whole network, this is a positive effect because there is no control of messages and interests in the idle state sensor network.

3 Performance Evaluation In this section, we evaluate the performance of a proposed model through simulations. We first describe our simulation model and simulation metrics. We next evaluate how environmental factors and control parameters affect the performance of a proposed algorithm. 3.1 Simulation Model and Metric

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We evaluate the proposed model in Qualnet, a network simulator [12]. A sensor node’s transmitting and receiving power consumption rate are 0.66 W and 0.39 W. The transceiver in the simulation has a 50 m radio range in an outdoor area. Each interest packet is 40 bytes long, and the data packet has 64 bytes. The sensor network consists of 100 sensor nodes, which are randomly deployed in a 300m x 300m field. We consider three scenarios for the proposed model according to the number of sinks. Hence, the number of sinks and users is changed for this evaluation. The multiple static sinks are located in the outskirts of sensor fields. And the user, which follows a random waypoint model of 10m/s speed and 10 second pause time, moves into the sensor field. The user disseminates an interest at an interval of every 10 seconds. Every sensor node receives the interest and generates only one sensing data for the interest. This is defined as one interest round. Namely, one interest round is 10 seconds. The simulation lasts for 500 seconds.

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We use metrics to evaluate the performance of the proposed algorithm. The network lifetime is defined as the number of the interest round when the first sensor node dies. The data delivery ratio is the ratio of the number of successfully received reports by a user to the total number of reports generated by every sensor node. The delay is defined as the average time between the time a user transmits an interest and the time the user receives the report. We compare three mobility types (D-COM, G-COM, and T-COM) in Table 1 with the proposed model in the simulation. 3.2 Impact of the Number of Static Sinks

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Scenarios 1 and 2 of A-COM can be compared with the D-COM because G-COM and T-COM have no static sink. We first study the impact of the number of sinks on A-COM’s performance. The number of sinks varies from 1, 2, 3, 4 to 5. And there is only one user in the sensor field. In this part, we compare Scenarios 1 and 2 to D-COM regarding lifetime, delay, and delivery ratio. A difference between A-COM and the D-COM is how to communicate between a user and a sink. Fig. 13 shows the number of interest rounds, namely, network lifetime. As shown in Fig. 13, the number of interest rounds shows little difference between A-COM and D-COM. This means that A-COM can manage sensor fields as well as D-COM without infrastructure that connects users with sinks. In addition, the lifetime is increased according to the number of sinks. This is a side effect of multiple sinks. Multiple sinks separate the sensor field, and besides, users only use the nearest sink to send interests and receive replies. Users can use the shortest path to communicate with multiple sinks. As a result of the shortest communication, the lifetime in A-COM is enhanced according to the number of sinks. The delay is also enhanced by this side effect of multiple sinks. A-COM basically has some delay due to multi-hop communication between users and sinks. However, the delay is diminished according to the number of sinks, as shown in Fig. 14. Nevertheless, the data delivery ratio of A-COM is comparable with D-COM, as shown in Fig. 15. This also proves that the proposed model can manage sensor fields as well as D-COM without infrastructure.

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3.3 Impact of the Number of Users The number of users only results in path increase between users and sinks. D-COM uses direct communication between users and sinks, and A-COM uses multi-hop communication. A-COM has more paths and consumes more energy. (e.g., five users in A-COM consumes five times of the energy that is consumed by one user.) However, it is a tradeoff between energy and infrastructure. Although A-COM has more energy consumption and delays than D-COM, the merit of A-COM is infrastructure-less communication systems. Scenario 3 of A-COM can be compared with G-COM and T-COM because Scenario 3 of A-COM, G-COM, and T-COM have no static sinks. There are no sinks, and the number of users varies from 1, 2, 3, 4 to 5. In this part, we compare Scenario 3 of A-COM to G-COM and T-COM regarding lifetime, delay, and delivery ratio. G-COM and T-COM make and change the topology proactively, but Scenario 3 of A-COM reactively makes and shares it among users. Generally, users move about the sensor field only and generate its interest occasionally. Hence, sensors in Scenario 3 can save considerable energy. Alternatively, sensors in G-COM and T-COM maintain a topology continuously. Fig. 16 shows each lifetime of these sensor networks. As shown in Fig. 16, the lifetime of T-COM is considerably low due to frequent topology change and that of G-COM is relatively low due to GRID maintenance. In Fig. 17, G-COM has little delay due to proactive GRID topology by the GPS receiver. T-COM proactively creates the topology, but frequent topology changes of TCOM delay data delivery considerably. The delay of Scenario 3, as shown in Fig. 17, however, is only a little high due to the reactive first agent selection and topology construction. In the case of the data delivery ratio, A-COM and G-COM in Fig. 18 are similar except for T-COM. The reason is frequent topology change. Topology change messages disturb the data delivery ratio. 3.4 Impact of the Number of Sensor Nodes Evaluating A-COM with other models according to the number of sensors is of no real consequence. It is closely related to the performance of routing protocols that are used in the network. Hence, we do not evaluate the proposed model with this factor. 100

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3.5 Impact of the User Mobility We lastly evaluate the impact of user speed on A-COM. We vary the maximum speed of a user from 8, 10, 12, 14 to 20m/s. We assume that there is one user in the sensor field. In this part, we compare Scenario 3 to G-COM and T-COM because D-COM is independent of user speed. Fig. 19 shows the delay in data delivery, which slightly increases as the user moves faster. The delay depends on a movement operation that is processed by the user. The faster a user moves, the more the time is needed to establish a connection between the user and the network. Nevertheless, the delay of A-COM is comparable with G-COM because A-COM creates only one communication path between the user and its first agent. The delay of T-COM, on the other hand, is relatively higher than the others due to frequent topology changes. And, Fig. 20 shows the data delivery ratio when the user’s moving speed changes. The data delivery ratio of A-COM is slightly decreased according to the delay. But the data delivery ratio remains around 0.8 - 0.9; nevertheless, the user moves faster. Besides, the data delivery ratio of G-COM remains high because the GPS receiver may help the user with geographical routing. On the other hand, the data delivery ratio of T-COM is relatively lower than the others because it has too many topology changes when moving. The results in Fig. 19 and Fig 20 mean that A-COM is fast and stable without GPS receiver.

4 Conclusion and Further Work In this paper, we propose a novel agent-based user-network communication model to support the mobility of users in wireless sensor networks. In the proposed network model, the user can receive data with a higher data delivery ratio and in a faster time without infrastructure. We verified that the lifetime of sensor networks is prolonged because the reactive path construction decreases the consumption of sensor nodes. Also, we verified that performance of the data delivery ratio and the delay never falls; nevertheless, communication between the user and the network for guaranteeing movement of the user is supported by only sensor nodes without infrastructure networks. There is further work that is related to this research. In a mobile environment, many sensor nodes can shift from one place to another frequently. The mobile sensor node environment makes more dynamic sensor networks. The issue of node mobility requires further study.

References 1. I.F. Akyildiz, et al., "A survey on sensor networks," IEEE Communications Magazine, Vol. 40, pp. 102-114, Aug. 2002. 2. C. Intanagonwiwat, et al., "Directed diffusion: A scalable and robust communication paradigm for sensor networks," ACM Mobicom, 2000. 3. C. Schurgers and M.B. Srivastava, "Energy efficient routing in wireless sensor networks," IEEE MILCOM 2001.

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4. W. R. Heinzelman, et al., "Adaptive Protocols for Information Dissemination in Wireless Sensor Networks," ACM Mobicom, 1999. 5. F. Ye, et al., “A Two-Tier Data Dissemination Model for Large-scale Wireless Sensor Networks,” ACM MobiCOM, Sept. 2002. 6. S. Kim, et al., “SAFE: A Data Dissemination Protocol for Periodic Updates in Sensor Networks,” Distributed Computing Systems Workshops 2003. 7. H. L. Xuan and S. Lee, “A Coordination-based Data Dissemination Protocol for Wireless Sensor Networks,” IEEE ISSNIP, Dec. 2004. 8. K. Hwang, et al., "Dynamic sink oriented tree algorithm for efficient target tracking of multiple mobile sink users in wide sensor field," IEEE VTC, Sep. 2004. 9. S. R. Gandham, et al., "Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile Base Stations," IEEE GLOBECOM, Dec. 2003. 10. H. Ferriere, et al., “Efficient and Practical Query Scoping in Sensor Networks,” IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Oct. 2004. 11. E. I. Oyman and C. Erso, “Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks,” IEEE ICC, Jun. 2004. 12. Scalable Network Technologies, Qualnet, [online] available: http://www.scalablenetworks.com. 13. Hui Dai and Rechard Han, “A node-centric load balancing algorithm for wireless sensor networks,” IEEE GLOBECOM, Dec. 2003.

Realistic Mobility and Propagation Framework for MANET Simulations Mesut G¨ une¸s, Martin Wenig, and Alexander Zimmermann Chair of Computer Science, Informatik 4, RWTH Aachen University {guenes,wenig,zimmermann}@i4.informatik.rwth-aachen.de

Abstract. Two main steps on the way to more realistic simulations of mobile ad-hoc networks are the introduction of realistic mobility and sophisticated radio wave propagation models. Both have strong impact on the performance of mobile ad-hoc networks, e.g. the performance of routing protocols changes with these models. In this paper we introduce a framework which combines realistic mobility and radio wave propagation models. Our approach consists of a zone-based mobility generator and a high accuracy radio wave propagation model. For the mobility generation a wide variety of well understood random mobility models is combined with a graph based zone model, where each zone has its own mobility model. To achieve a realistic radio wave propagation model a ray tracing approach is used. The integration of these two techniques allows to create simulation setups that closely model reality.

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Introduction

A mobile ad-hoc network is created by a collection of nodes which communicate using radio interfaces and do not rely on any pre-installed infrastructure. Furthermore, it is supposed that ad-hoc networks are inherently adaptive and auto-configured. Therefore, ad-hoc networks offer immense flexibility. In recent years the interest in the deployment of ad-hoc networks for real world scenarios grew. Still the number of real world ad-hoc networks is quite low and most of the testbeds [1] consist only of a small number of nodes. The development and testing of new algorithms and methods nowadays relies heavily on network simulations. Simulating wireless networks, and especially mobile adhoc networks, is not a trivial task and consequently there have been discussions about the validity of presented simulation results [2,3]. This work does not deal with the methodological background used to analyze the output of the simulation, instead it deals with the simulator’s accuracy. A key factor of accurate simulation results are accurate simulation models. To the belief of the authors the main weak points are 1) the unrealistic assumptions concerning the radio wave propagation [2], 2) the currently used simplistic mobility models [4,5] and 3) the assumed workload of the network. This work proposes a solution to the first two mentioned problems. I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 97–107, 2007. © IFIP International Federation for Information Processing 2007

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Our contribution in this work is an integrated framework which allows the definition and control of the movement and the radio wave propagation model in higher detail than previous approaches. We propose a generation process which based on partitioning the simulation area into zones with different independent mobility models together with a high accuracy radio wave propagation model. The need for such a generation framework might be illustrated by a quick literature overview: Taking the publications of the MobiHoc conferences of the last two years as an example, it is obvious that there is a need for better tool support for simulation designers. Out of 52 papers 35 presented simulation results (around 67%). Six papers did not give any information about the used mobility model, 10 used random waypoint to model mobility and 14 considered static scenarios. Only two papers showed results obtained from considering more than one mobility model. Only two papers mention the used radio wave propagation model, ten papers gave no indication about the used model and 22 used a fixed radius. Assuming that all papers which did not specify their propagation model used a fixed range it can be concluded that all papers used circular, bidirectional links. None of the presented papers used a small scale (fading) model. The structure of the paper is as follows: In section 2 mobility and radio wave propagation models are presented. In section 3 our approach is discussed in detail and in section 4 some simulation results obtained with ns-2 are discussed.

2 2.1

Related Work Random Mobility Models

There are many random mobility models proposed in literature. Detailed descriptions of these models are given in [6,7,8,9,10]. The most simple random mobility model is called Random Walk. In this model, a node selects randomly a direction and speed from predefined ranges [ϕmin : ϕmax ] and [vmin : vmax ], respectively. Each movement is bounded either by travel time or distance. The Random Waypoint mobility model is an extension of Random Walk and integrates a pause time between two consecutive moves. A disadvantage of this model is the concentration of nodes in the center of the simulation area [11]. Besides these entity mobility models, there are group mobility models which specify how a set of nodes move in respect to each other [6]. In the Nomadic Community Mobility Model, all mobile nodes move to the same location in the same order but by using different entity mobility models. The Reference Point Group Mobility model specifies the movement of the group as well as the movements of the nodes within the group. There are also models which match the characteristics of car movements. In the Freeway model, there is at least one lane in each direction of a street. The nodes move on the lanes. The speed of a node depends on other nodes on the same lane. In the Manhattan model the lanes are organized around blocks of buildings. A node can change its direction only at intersections. All mobility models discussed so far share the assumption that there are no obstacles. In [12] a refinement of random mobility models by integrating obstacles

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is proposed. The obstacles represent buildings. Upon the definition of buildings, paths between them are calculated. The mobile nodes are randomly distributed on the paths and the destinations of the nodes are selected randomly among the buildings. The nodes move on the defined paths from building to building. Additionally, the communication characteristic is also affected by the obstacles. A mobile node inside a building cannot communicate with a mobile node outside the building. 2.2

Models from Cellular Network Research

In cellular networks the geographical area is divided into cells. There is a base station in each cell which provides communication service for the nodes. The mobility models in this area describe the mobility of the nodes regarding the cell topology, i.e., when a node moves from a particular cell to another cell. In [13] a hierarchy of such models with regard to metropolitan, national, and international mobility is presented. Their results cannot directly be applied to MANET simulations, since here movements have to be described with higher granularity. 2.3

Mobility Models from Real User Traces

The best input for simulations would be derived from real traces. However, it is very difficult for the research community to obtain those data. Therefore, there are few studies reported which are based on real data [14]. In [15] the authors describe how real user traces can be used to build simulation models. It is based on the trace collection at Darthmouth College. Their interesting research can be used as input for our mobility generator. But using it to evaluate our models is not meaningful, since we could set up our model to deliver similar results by simply creating a similar geometry and using their parameters as input. 2.4

Radio Wave Propagation

Radio channels are more complicated to model than wired channels. Their characteristics may change rapidly and randomly and they are dependent on their surrounding (buildings, terrain etc.). Nevertheless, most wireless network simulators use very simplified propagation models. In general, propagation models can be characterized into two groups: large-scale and small-scale propagation models. Large-scale models characterize how the transmission power between two nodes changes over long distances and over a long time. Small-scale models account for the fact that small movements (in the order of the wavelength) may have large influence on the transmission quality. Also, due to multipath propagation, the signal varies heavily even if the nodes do not move. Common used propagation models are the Free Space model, the Two-Ray Ground model and the Shadowing model [16]. In addition, Ricean and Rayleigh fading are often used as small-scale models [17]. None of these models is able to correctly model complex scenarios with obstacles. One way to overcome this

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limitation is the use of ray tracing technologies. In [18] an approach using this technique is described. It allows the definition of obstacles in a graphical editor and this scenario description is used in the simulation to feed a ray tracing algorithm. The algorithm is started once for every new position the node takes up. The authors state that this approach slows down the simulation by a factor of up to 100. Also, no movement information is generated by this tool. Other approaches [19,20] either do not scale well or their accuracy is highly dependent on the selected grid resolution of the calculated scenario. Additionally, these models have been developed for fixed wireless networks.

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Generation Framework

The proposed framework named CosMos addresses the generation of i) realistic mobility patterns, and ii) accurate radio wave propagation information for the specific scenario. The whole process needs three steps. First the designer has to create a scenario for the desired simulation setup. In the next two steps the energy density maps for the radio wave propagation model are precomputed and the movement files are generated. 3.1

Scenario Creation

The scenario consists of movement zones (MZ) and obstacle zones (OZ). Both can basically be described as polygons and they divide the simulation area into smaller parts. The designer assigns a mobility model to each MZ. All models have their own set of parameter (e.g. maximum speed) and are independent of each other. When MZs overlap nodes can change from one zone to the other. The probability to leave the current zone can also be set by the designer. The obstacles have three parameters: their height, their reflection, and transmission coefficients. The position of the zones can be set up by the designer as wished, e.g to model an indoor scenario. The values for the mobility models must be decided individually according to the intended scenario. Here, only very limited experience has been gathered by researchers. The approach presented in [15] can be integrated in our framework. For the radio wave propagation model there have been measurements which can be used, e.g. [16]. Another approach is to perform own measurements and use these as input for the ray tracer. 3.2

Mobility Generator

The movement zones create a weighted and directed graph: the zones are the vertices (V), there is an edge between two vertices if the corresponding movement zones overlap. The set of edges (E) has weights attached to them. The function w : E → − (0 . . . 1) defines weights for all edges of the graph G(V, E). The weight wi,j of a directed edge ei,j ∈ E from zone i to zone j corresponds to the probability for nodes to leave zone i to zone j.

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Initially all nodes are distributed randomly in the movement zones. During the generation process nodes move inside the zones according to the mobility model of their current zone. If a node decides to leave to a neighbouring zone (depending on the exit probability) it moves towards the overlapping region of the two zones. When it arrives there, the mobility model of the new zones takes over and the node starts to move according to the new mobility model. Our approach allows the calculation of the spatial distribution of nodes on the simulation area as well as the distribution of nodes on the movement zones. Since initially all nodes are distributed randomly in all movement zones, there will be a point in time in which zones with a high exit probability will ‘loose’ some nodes to the zones with a lower exit probability. After a while, the distribution of nodes should become stable. Figure 1 shows a small example with three movement zones: two large rectangles on each side and a narrow street connecting them. Zone A has a higher exit probability than Zone B. The street is only used to travel from zone A to B and vice versa. 1

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Fig. 1. Visualizing the steady state

Figure 1(a) shows the (long-term) spatial probabilities of the simple scenario. It shows the probability of a node being at a specific place over a simulation run of 10000 seconds. Figure 1(b) shows that initially all zones contain approximately one third of all nodes. During the simulation run the distribution slowly changes: all nodes in the street zone leave this zone to one of the neighbouring zones. The distribution stabilizes when most of the nodes are inside the zone with the lower exit probability. Nevertheless, still nodes travel from one zone to the other, but the long term average remains relatively constant from second 2000 onwards. If this ‘steady-state’ behaviour is important for the considered simulation scenario then the first seconds will have to be cut off. The proposed framework per default creates several independent variants of the movements according to the given models. This helps the researcher to conduct simulations with independent replications.

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Radio Wave Propagation Model

The radio wave propagation model used in this work is based on a ray tracing approach. The obstacles defined in CosMos are used as input for the ray tracer. Triggering a ray tracing run for every position of the current sender is unfeasible in mobile ad-hoc networks. Instead, our approach uses a set of predefined starting points for the ray tracing approach. The ray tracer is then started once for each of this points creating an energy distribution map for each one. During the simulation the energy distribution between the sender and the receivers is calculated using weighted interpolation, as detailed below. The ray tracer accounts for the following propagation phenomenas: reflection, diffraction, and scattering. To use the generated energy distribution maps during the simulation, we modified the ns-2 network simulator [21]. We added a propagation model which reads in a given set of maps and the corresponding starting points. During the simulation, whenever a node nt wants to transmit a packet, a k-nearest neighbor search is started1 . This search finds the k nearest starting points and their corresponding energy distribution maps to the sender’s position. For each node inside the maximum interference range of an unobstructed radio wave the transmission power is calculated. The formula used for the weighted interpolation is given below: k−1 si i=0 posi −post p 1 i=0 posi −post p

st−r = k−1

,

where st−r is the signal strength between the transmitter node nt and the receiver node nr . The position of the transmitter is given as post , posi denotes the position of the starting point of the i-th closest map. Note that si is the predicted signal strength of map i at the position of the receiver posr . The exponent p controls how much influence is given to further away maps2 . The benefits of our approach are that it is not necessary to rerun the ray tracing algorithm during simulation time, it is not necessary to divide the simulation area into evenly sized squares, and the accuracy can be increased in areas with a lot of obstacles, simply by adding more starting points. A real-time evaluation tool has been developed to show the result of the interpolation. Our approach increases the simulation speed and allows the designer to choose between high accuracy and reduced memory needs [22].

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Results

In this Section we discuss some simulation results created with ns-2. The simulation scenario was created with CosMos. The presented Scenario models the office building in which the authors’ chair is in. The intention of the studies was 1 2

Our experiments showed k equal to 3 gave good results. In our experiments p was set to 3.

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Fig. 2. Indoor scenario

to show the impact of the mobility and radio wave propagation models on the performance of MANET routing protocols. Figure 2 shows the scenario outline. Only the ground floor is modeled here. All nodes are equipped with IEEE 802.11 radio interfaces with a transmission rate of 11 Mbit/s and a transmission power of 0.1 mW. The receiving threshold was set to -88 dBm, a value taken from the specification of the Cisco Aironet 1240AG Series access point. The AODV implementation of the university of Uppsala [23] and the current version of DSR [11] in ns2 were used. Thirty connections between randomly selected nodes were started, each one offering 32 kBytes of load. Movements inside of offices are seldom and relatively slow (max. speed was set to 1 m/s). Movements inside of the hallways on the other hand are faster and follow the freeway model (max. speed was set to 2 m/s). Since we had detailed plans of our building, we were able to model it with high detail. To check the accuracy of the radio wave propagation model, we conducted some measurements with real-life systems and compared the results to the calculated values. The mean error between the predicted values and the calculated is 3.5 dB which is as good as the results for approches mentioned in section 2. We conducted simulations using the AODV and the DSR routing protocols in which the following combinations were considered: CosMos mobility model together with Two-Ray Ground propagation model and CosMos mobility model with ray tracing propagation model. Figure 3 shows a comparison of the throughput achieved using AODV and DSR in the presented scenario. It is clear to see that the measured values without the ray tracer propagation model can be considered as equal. But using the ray traced propagation model, the DSR protocol suffers more heavily from performance loss. AODV seems to be able to cope better with the situation. The decreasing performance for larger number of nodes can be explained by the higher number of hops between sender and destination.

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The routes are getting longer because the node density is higher and farther away nodes can also be reached. The reason for the worse performance of DSR compared to AODV seems to be a larger number of discovered paths which were actually already invalid (stale paths) when they should be used for the first time. Figure 4 compares the average end-to-end delay for packets between source and destination. As expected, the values using TwoRay Ground can again be considered as equal. Using the ray tracer the delay of course grows due to longer routes, higher number of transmission errors, and thus higher routing overhead. Again, we see a strong influence on DSR. As a rule of thumb, one can say that if more than 90% of all packets have a delay of less than 150ms, VoIP is possible with reasonable quality. If scenarios with more than 60 nodes are considered, DSR is not able to fullfil this criterion. This is yet another example why accurate simulation models are absolutely neccesarry. If one would have based the decission on the simple simulation setup both algorithms would have been judged as equal but in reality only AODV is actually able to fullfil the delay bound. 35

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Another result of our simulation study is that the mobility model is more important for larger scenarios. The smaller the simulation area compared to the transmission range of the nodes, the smaller the influence of the mobility model. We also measured the run-time of the simulations with and without our propagation model. Table 1 shows the times for the indoor simulation. The increase in run-time is relatively small since during the simulation runtime only lookups in the kd-tree have to be done. The preprocessing time, namely the time needed to create the energy distribution maps, is dependent on the complexity of the scenario. For the presented indoor scenario 112 starting points were used and the ray tracer needs around 12 seconds for each point (shooting 50000 photons).

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Conclusion

In this paper we have introduced a mobility and radio wave propagation scenario generator for MANET. The goal was to aid researchers in the design of realistic simulation scenarios. The framework is very general and can be deployed to design scenarios with special requirements. Our approach combines a wide variety of well understood random mobility models with a graph based zone model and a sophisticated ray traced radio wave propagation model. Each zone can have a different mobility model. The framework allows to generate the mobility definition and the ray tracer results from one common scenario. So the combination of realistic movement models and accurate radio wave propagation models becomes an easy task for the researcher. Furthermore, our approach allows the calculation of the spatial distribution of nodes on the simulation area as well as the distribution of the nodes on the defined zones. This allows us to figure out the time when the stationary state is reached. Since, trustworthy MANET simulations should begin when the stationary state is reached.

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References 1. G¨ une¸s, M., Bouazizi, I.: From Biology to Technology: Demonstration Environment for the Ant Routing Algorithm for Mobile Ad-hoc Networks. In: Tenth Annual Int. Conference on Mobile Computing and Networking (ACM MobiCom 2004), Philadelphia, USA (September 2004) 2. Kotz, D., Newport, C., Gray, R.S., Liu, J., Yuan, Y., Elliott, C.: Experimental evaluation of wireless simulation assumptions. Technical Report TR2004-507, Dept. of Computer Science, Dartmouth College (June 2004) 3. Pawlikowski, K., Jeong, H.D.J., Lee, J.S.R.: On credibility of simulation studies of telecommunication networks. IEEE Communications 40(1) (January 2002) 132– 139 4. Bettstetter, C., Hartenstein, H., Perez-Costa, X.: Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate. In: MSWiM ’02: Proc. of the 5th ACM int. Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems, NY, USA, ACM Press (2002) 7–14 5. Bettstetter, C., Resta, G., Santi, P.: The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Trans. Mobile Computing 2(3) (2003) 257–269 6. Hong, X., Gerla, M., Pei, G., Chiang, C.C.: A group mobility model for ad hoc wireless networks. In: Proc. of the ACM Int. Workshop on Modeling and Simulation of Wireless and Mobile Systems (MSWiM). (August 1999) 53–60 7. Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications 2(5) (2002) 483–502 8. Sanchez, M.: Mobility models. http://www.disca.upv.es/misan/mobmodel.htm (2005) 9. Bai, F., Sadagopan, N., Helmy, A.: The important framework for analyzing the impact of mobility on performance of routing for ad hoc networks. AdHoc Networks Journal - Elsevier Science 1(4) (November 2003) 383–403 10. Lin, G., Noubir, G., Rajaraman, R.: Mobility models for ad hoc network simulation. In: Proc. of the 23rd Conference of the IEEE Communications Society, Hon Kong, IEEE, IEEE (March 7-11 2004) 11. Johnson, D.B., Maltz, D.A.: Dynamic source routing in ad hoc wireless networks. In: Mobile Computing. Volume 353. Kluwer (1996) 12. Jardosh, A., Belding-Royer, E.M., Almeroth, K.C., Suri, S.: Towards realistic mobility models for mobile ad hoc networks. In: The Ninth Annual Int. Conference on Mobile Computing and Networking (ACM MobiCom 2003), San Diego, USA, ACM (September 14-19 2003) 13. Lam, D., Cox, D.C., Widom, J.: Teletraffic modeling for personal communications services. IEEE Communications Magazine 35 (Feb. 1997) 79–87 14. Hsu, J., Bhatia, S., Takai, M., Bagrodia, R., Acriche, M.J.: Performance of mobile ad hoc networking routing protocols in realistic scenarios. In: MilCom 2003, Boston, Massachusetts (October 13-16 2003) 15. Kim, M., Kotz, D., Kim, S.: Extracting a mobility model from real user traces. In: Proc. of the 25th Joint Conference of the IEEE Computer and Communications Societies, Barcelona, Spain, IEEE Computer Society Press (April 2006)

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16. Rappaport, T.S.: Wireless Communications, Priciples & Practice. Prentice Hall (1999) 17. Punnoose, R.J., Nikitin, P.V., Stancil, D.D.: Efficient simulation of ricean fading within a packet simulator. In: Vehicular Technology Conference. (Sep. 2000) 18. Dricot, J.M., Doncker, P.D.: High-accuracy physical layer model for wireless network simulations in ns-2. In: Proc. of the Int. Workshop on Wireless Ad-hoc Networks. (2004) 19. Catedra, M., Perez, J., de Adana, F.S., Gutierrez, O.: Efficient ray-tracing techniques for three-dimensional analyses of propagation in mobile communications: application to picocell and microcell scenarios. Antennas and Propagation Magazine 40(2) (Apr 1998) 15–28 20. Schmeink, M., Mathar, R.: Preprocessed indirect 3D-ray launching for urban microcell field strength prediction. In: AP 2000 Millennium Conference on Antennas and Propagation. (April 2000) 21. Fall, K., Varadhan, K.: The ns-2 manual. Technical report, The VINT Project, UC Berkeley, LBL and Xerox PARC (2003) 22. Schmitz, A., Wenig, M.: The effect of the radio wave propagation model in mobile ad hoc networks. In: MSWiM ’06: Proc. of the 9th ACM international workshop on Modeling, Analysis and Simulation of wireless and mobile systems, Torremolinos, Spain, ACM Press (2006) 23. Wiberg, B., Nordstr¨ om, E.: Ad-hoc on-demand distance vector routing - for real world and simulation. http://core.it.uu.se/core/index.php/AODV-UU (October 2006)

Localization for Large-Scale Underwater Sensor Networks Zhong Zhou1 , Jun-Hong Cui1 , and Shengli Zhou2 1 2

Computer Science & Engineering Dept, University of Connecticut, Storrs, CT, USA, 06269 Electrical & Computer Engineering Dept, University of Connecticut, Storrs, CT, USA, 06269 {zhong.zhou,jcui,shengli}@engr.uconn.edu

Abstract. In this paper, we study the localization problem in large-scale underwater sensor networks. The adverse aqueous environments, the node mobility, and the large network scale all pose new challenges, and most current localization schemes are not applicable. We propose a hierarchical approach which divides the whole localization process into two sub-processes: anchor node localization and ordinary node localization. Many existing techniques can be used in the former. For the ordinary node localization process, we propose a distributed localization scheme which novelly integrates a 3-dimensional Euclidean distance estimation method with a recursive location estimation method. Simulation results show that our proposed solution can achieve high localization coverage with relatively small localization error and low communication overhead in large-scale 3-dimensional underwater sensor networks.

1 Introduction Recently, there has been a rapidly growing interest in monitoring aqueous environments for scientific exploration, commercial exploitation and coastline protection. The ideal vehicle for this type of extensive monitoring is a distributed underwater system with networked wireless sensors, referred to as Underwater Wireless Sensor Network (UWSN) [1,9]. For most UWSNs, localization service is an indispensable part. For example, in the long-term non-time-critical aquatic monitoring service [9,13], localization is a must-do task to get useful location-aware data. Location information is also needed for geo-routing which is proved to be more efficient than pure flooding in UWSNs [20]. In this paper, we investigate the localization issue for large-scale UWSNs. Localization has been widely explored for terrestrial wireless sensor networks, with many localization schemes being proposed so far. Generally speaking, these schemes can be classified into two categories: range-based schemes and range-free schemes. The former covers the protocols that use absolute point-to-point distance (i.e., range) estimates or angle estimates to calculate locations [12,14,6,5,18,15], while the latter makes no assumptions about the availability or validity of such range information [7,17,16,11,19]. Although range-based protocols can provide more accurate position estimates, they need additional hardware for distance measures, which will increase the network cost. On the other hand, range-free schemes do not need additional 

This work is supported in part by the NSF CAREER Grant No.0644190.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 108–119, 2007. c IFIP International Federation for Information Processing 2007 

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hardware support, but can only provide coarse position estimates. In this paper, we are more interested in accurate localization, which is requested by a range of applications, such as estuary monitoring and pollutant tracking [9]. Moreover, in UWSNs, acoustic channels are naturally employed, and range measurements using acoustic signals are much more accurate than using radio [9,20]. Thus, range-based schemes are potentially good choice for UWSNs. Due to the unique characteristics (such as low communication bandwidth, node mobility, and 3-dimensional node deployment) of UWSNs [1,9], however, the applicability of the existing range-based schemes is yet to be investigated. There are also several schemes proposed for the localization service in underwater acoustic networks [4,3,21,10]. These solutions are mainly designed for small-scale networks (usually with tens of nodes or even less). For large-scale UWSNs, hundreds or thousands of sensor nodes are deployed in a wide underwater area. Directly applying these localization schemes proposed for small scale underwater networks in large-scale networks is often inefficient and costly. In this paper, for the first time, we explore the localization problem in large-scale UWSNs. We propose a hierarchical approach, dividing the whole localization process into two sub-processes: anchor node localization and ordinary node localization. Many existing approaches can be used in anchor node localization. For ordinary node localization, we propose a novel distributed method based on a 3-dimensional Euclidean distance estimation method and a recursive location estimation method. Simulation results show that our localization scheme can achieve high localization coverage with accurate location estimation and low communication overhead in large-scale 3-dimensional underwater sensor networks. The rest of this paper is organized as follows. In Section 2, we describe our localization scheme. Simulation results are then presented in Section 3. And finally we draw conclusions in Section 4.

2 Localization for Large-Scale UWSNs 2.1 Overview We consider a typical UWSN environment as shown in Fig. 1. There are three types of nodes in the network: surface buoys, anchor nodes, and ordinary nodes. Surface buoys are nodes that drift on the water surface. These buoys are often equipped with common GPS and can get their absolute locations from GPS or by other means. Anchor nodes are those who can directly contact the surface buoys to get their absolute positions. These nodes can also communicate with ordinary nodes and assist them to do localization. Ordinary nodes are those who can not directly talk to the surface buoys because of cost or some other constraints but can communicate with the anchor nodes to estimate their own positions. To handle the large scale of UWSNs, we propose a hierarchical localization approach. In this approach, the whole localization process is divided into two sub-processes: anchor node localization and ordinary node localization. At the beginning, only the surface buoys know their locations through common GPS or by other means. Four or more buoys are needed in our system. These buoys work as the “satellites” for the whole network, and anchor nodes can be localized by

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Fig. 1. A typical large-scale underwater sensor network setting

these surface buoys. Using surface buoys to locate underwater objects has been extensively investigated and many existing systems, such as [4] and [3], can be employed in the anchor node localization process. In this paper, we will not contribute to this part. Instead, we mainly tackle the problem of ordinary node localization, for which we propose a distributed range-based scheme, novelly integrating a 3-dimensional Euclidean distance estimation method and a recursive location estimation method. We describe this scheme in the following section. 2.2 Ordinary Node Localization In 3-dimensional UWSNs, for a range-based localization scheme, ordinary nodes have to estimate their distances to more than 4 anchor nodes and calculate their locations by triangulation methods, which are commonly used in GPS systems. In a large-scale UWSN, however, not all ordinary nodes can directly measure their distances to 4 or more anchor nodes, thus some multi-hop distance estimation methods have to be developed. In [18], the authors proposed and compared three multi-hop distance estimation methods: DV-Hop, DV-Distance and Euclidean. Even for two dimensional terrestrial sensor networks, the performance of DV-Hop and DV-Distance degrades dramatically in anisotropic topologies, while the Euclidean method can achieve much more accurate results and behave more consistently in both anisotropic and isotropic networks than other methods [18]. In a UWSN, since the sensor nodes are constantly moving due to many environment factors, the network topology may change unpredictably with time and space. Thus, the Euclidean method is expected to be more suitable for UWSNs than other approaches. In our scheme, we employ a hybrid approach based on a 3-dimensional Euclidean distance estimation method and a recursive location estimation method to get the ordinary node positions. When combined with the recursive method, the inherent problems of the Euclidean method such as high communication cost and low localization coverage can be greatly alleviated. Next, we first discuss these two methods, examining why they can be seamlessly integrated together. Then we describe the ordinary node localization process in detail.

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3-Dimensional Euclidean Distance Estimation. In [18], a Euclidean distance propagation method is proposed for two dimensional sensor networks. Here, we extend it into 3-dimensional networks. We use an example to illustrate the method. Referring to Fig. 2, if an ordinary node E wants to estimate its distance to anchor node A, it needs to know at least three (onehop) neighbors (e.g., B, C, and D) which have distance estimates to A. Note that nodes A, B, C and D should not be co-plane and any three nodes out of A, B, C, D and E should not be co-line. Moreover, E needs to know its two-hop distance estimates, that is, E should have the length information of EB, BA, EC, CA, ED, DA, DB, DC, and BC. The 3-dimensional Euclidean distance estimation works as follows: First, node E uses edge BA, CA, BC to construct the basic localization plane. Since the lengths of edges DB, DA and DC are already known (to E), the position of D can be easily estimated. There exist at most two possible positions for D. Because E knows the lengths of edges ED, EB and EC, corresponding to the two possible positions of D, there will be at most four possible solutions for E’s position. The choice among these four possibilities is made locally by voting when E has more immediate neighbors with estimates to A. If it cannot be decided, the distance estimate to A is not available until E gets more information from its neighbors.

Fig. 2. 3-dimensional Euclidean estimation

Recursive Location Estimation. In [2], the authors propose an iterative framework to extend the position estimation from a few reference nodes throughout the whole network. System coverage increases recursively as nodes with newly estimated positions join the reference node set, which is initialized to include anchor nodes. This recursive location estimation method is illustrated in Fig. 3. In the figure, node 1 can get its location information from four neighboring anchor nodes A, B, C and D. If the location estimation error is small enough, node 1 can be regarded as a new reference node for other nodes. Then, it will broadcast its own location information. When node 2 gets to know the locations of C, D, E and 1 as well as the distances to these nodes, it can calculate its own location. On the other hand, if the location estimation error is large, node 1 cannot be treated as a reference node and will not broadcast its location. In our scheme, the following formula is used to estimate the location error δ:

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δ=

  (u − xi )2 + (v − yi )2 + (w − zi )2 − l2 , i

(1)

i

where (u, v, w) are the estimated coordinates of the unknown node, (xi , yi , zi ) are the reference node i’s location, li is the measured distance between the unknown node and node i.

Fig. 3. Recursive location estimation

In order to alleviate the error propagation effect, every reference node in the system has a confidence value η. For the initial reference nodes (i.e., the anchor nodes), η is set to be the largest, while for a new reference node, η is associated with its location error. In our scheme, η is calculated as follows ⎧ 1 ⎪ ⎨

if node is the initial anchor δ η = 1−  others ⎪ ⎩ (u − xi )2 + (v − yi )2 + (w − zi )2

(2)

i

We can see that η is essentially a normalized δ. A critical value λ (referred to as “confidence threshold” later) is set. When η > λ, the unknown node can become a reference node. Otherwise, it will continue to be non-localized. When a node gets to know its distances to more than four nodes, it will choose four according to their η values and calculate its location. Ordinary Node Localization Process. In the ordinary node localization process, there are two types of nodes: reference nodes and non-localized nodes. In the initialization phase, all anchor nodes label themselves as reference nodes and set their confidence values to 1. All the ordinary nodes are non-localized nodes. With the advance of the localization process, more and more ordinary nodes are localized and become reference nodes. There are two types of messages: localization messages and beacon messages. Localization messages are used for information exchange among non-localized nodes and reference nodes, while beacon messages are designed for distance estimates. During the localization process, each node (including reference nodes and non-localized nodes) periodically broadcasts a beacon message, containing its id. And all the neighboring

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Fig. 4. Ordinary node localization process

nodes which receive this beacon message can estimate their distances to this node using techniques, such as TOA (time of arrival). We describe the actions of the two types of nodes as follows. Reference Nodes: Each reference node periodically broadcasts a localization message which contains its coordinates, node id, and confidence value. Non-localized Nodes: Each non-localized node maintains a counter, n, of localized messages it broadcasts. We set a threshold N (referred to as “localization message threshold”) to limit the maximum number of localization messages each node can send. In other words, N is used to control the localization overhead. Besides, each nonlocalized node also keeps a counter, m, of the reference nodes to which it knows the distances. Once the localization process starts, each non-localized node keeps checking m. There are two cases: (1) m < 4. This non-localized node broadcast a localization message which contains all its received reference nodes’ locations and its estimated distances to these nodes. Its measured distances to all one-hop neighbors are also included in this localization message. Besides, this node uses the 3-dimensional Euclidean distance estimation approach to estimate its distances to more non-neighboring reference nodes. After this step, the set of its known reference nodes is updated. Correspondingly, m is updated and the node returns to the m-checking procedure. (2) m ≥ 4. This non-localized node selects 4 reference nodes with the highest confidence values for location estimation. After it gets its location, it computes confidence

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value η. If η is larger than or equal to the confidence threshold λ, then it is localized and labels itself as a new reference node. Otherwise, if η is smaller than λ, the node will take the same actions as described in case (1). The complete localization procedure of an ordinary node is illustrated in Fig. 4.

3 Performance Evaluation In this section, we evaluate the performance of our proposed localization scheme through simulation. 3.1 Simulation Settings In our simulation experiments, 500 sensor nodes are randomly distributed in a 100m × 100m × 100m region. We define node density as the expected number of nodes in a node’s neighborhood, hence node density is equivalent to node degree. We control the node density by changing the communication range of each node while keeping the area of deployment the same. Range (i.e., distance) measurements between nodes are assumed to follow normal distributions, with real distances as mean values and standard deviations to be one percent of real distances. 5%, 10% and 20% anchor nodes are considered in our simulations. Besides our scheme, we also simulate a Euclidean scheme and a recursive scheme for comparison. The recursive scheme here is the same as in [2]. As for the Euclidean scheme, we use the three dimensional Euclidean distance estimation as the distance propagation method and then use the triangulation method to estimate an ordinary node’s position if it gets to know four or more reference nodes. It works almost the same as the Euclidean scheme for two dimensional networks [18]. We consider three performance metrics: localization coverage, localization error and average communication cost. Localization coverage is defined as the ratio of the localizable nodes to the total nodes. Localization error is the average distance between the estimated positions and the real positions of all nodes. As in [18,8], for our simulations, we normalize this absolute localization error to the node communication range R. Average communication cost is defined as the overall messages (including beacon messages and localization messages) exchanged in the network divided by the number of localized nodes. 3.2 Performance in Static Networks In this set of simulations, nodes in the network are fixed. The confidence threshold λ is set to 0.98, and the localization message threshold N is set to 5. We change the node density (i.e., node degree) from 8 to 16 and compare our scheme with the Euclidean scheme and the recursive scheme. The results are plotted in Fig. 5, Fig. 6, and Fig. 7. Localization Coverage. Fig. 5 shows that our scheme outperforms both Euclidean scheme and recursive scheme in terms of localization coverage. This is reasonable since any node which can be located by either Euclidean scheme or recursive scheme can also be located by our scheme. The localization coverage of our scheme increases monotonically with the node density. But when the node density is relatively large, the coverage

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reaches a relatively high value and will not change much after that. For example, when the anchor percentage is 20%, the localization coverage reaches 94% at node density 12 and does not increase much with the node degree lifted. And we can also see that the more the anchors, the higher the localization coverage. For example, if the anchor percentage is 5%, the localization coverage can only reach 0.4 when the node density is 13, but if the anchor percentage is 10%, the localization coverage can reach 0.8 when the node density is 13. This suggests us that in sparse networks, we can increase the number of anchor nodes to achieve higher localization coverage. Localization Error. Fig. 6 plots the relationship between the localization error and the node density. We can observe that when the node density is relatively small, the localization error of our scheme is almost the same as that of the other two schemes. With the increase of the node density, the localization error of our scheme will increase and become a little larger than recursive scheme but much smaller than Euclidean scheme. This is because with the increase of the node density, the localization coverage of our scheme increases much faster than the other two schemes, as leads the growth of the localization error. But this growth is much slower rate than that of the localization coverage. As the node density continues to increase beyond some point, the localization error of our scheme will decrease slowly. This can be explained as follows. When the node density reaches a certain point, most sensor nodes can localize themselves. If we continue to increase the node density, ordinary nodes will get to know more anchor nodes and have more choices to calculate their locations. Thus, the localization error will decrease. But, as show in Fig. 6, this decrease is very limited. For example, when the anchor percentage is 5%, if we increase the node density from 13 to 16, the localization error only decreases from 0.3 to 0.27. Thus, in practice, we cannot expect to reduce the localization error by simply lifting the node density. Fig. 6 also shows us that the localization error will decrease observably with the anchor percentage. For example, at node density 13, when the anchor percentage is 5%, the localization error is 0.3. But when the anchor percentage is enlarged to 20%, it reduces to 0.05. Thus, more anchor nodes can translate into smaller localization errors. Communication Cost. Fig. 7 shows the average communication cost with the changing node density. In the recursive localization scheme, only nodes with known locations broadcast messages and other nodes keep silent. Therefore, the average communication cost of this scheme is very small. For our scheme, when the node density is small, it introduces larger communication cost than the recursive scheme. This is because in our scheme, when the network is sparse, although many nodes exchange beacon messages, they cannot finally localize themselves. In other words, these beacon messages are actually “wasted” in the localization process. But with the increase of the node density, this waste becomes smaller and smaller, and the average communication cost of our scheme becomes closer and closer to the recursive scheme. From the figure, we can also observe that the average communication cost of our scheme decrease with the increase of anchor percentage. Compared with the Euclidean localization scheme, our scheme can always achieve much lower communication cost. This is due to that fact that the recursive component in our scheme help to find more reference nodes much faster than the Euclidean localization scheme.

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(a) Anchor percentage=5%

(b) Anchor percentage=10%

(c) Anchor percentage=20%

Fig. 5. Localization coverage

(a) Anchor percentage=5%

(b) Anchor percentage=10%

(c) Anchor percentage=20%

Fig. 6. Localization error

Discussions. It is shown in [8] that range-based ad hoc localization schemes have high requirements on the node density of the networks. The paper also shows that in a two dimensional network, the node density needs to be at least 11 in order to localize 95% nodes with less than 5% localization error when 20% anchor nodes are present in the network. From Fig. 6(c), we can observe that when there are 20% anchors, our scheme can localize more than 95% nodes with less than 5% localization error if the node density is 12 in a 3-dimensional UWSN. Compared with the results in [8] for two dimensional networks, our scheme can achieve the same performance in 3-dimensional networks, with the connectivity requirement increased from 11 to 12. This indicates the good performance of our proposed scheme. On the other hand, this connectivity requirement of 12 may be still a little high for UWSNs with expensive sensor nodes or sparse deployment. One possible solution is to distinguish between the sensor’s localization range and communication range. This means that we can increase the transmission power for the localization and beacon messages. In this way, the localization connectivity requirement can be satisfied while the contention among data will not increase much. Besides the aforementioned results, we also study the impact of confidence threshold λ, the impact of the localization message threshold N , and the performance in mobile networks. In the following, we briefly summarize our findings for each aspect. Due to space limit, however, we do not include the detailed results in this paper. Interested readers can refer to our technical report [22].

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(b) Anchor percentage=10%

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(c) Anchor percentage=20%

Fig. 7. Average communication cost

Impact of Confidence Threshold: This study suggests us that by changing the confidence threshold, we can control the tradeoff between the localization error, the localization coverage and the average communication cost. For example, with the increase of the confidence threshold, the localization coverage and the localization error will decrease, while the average communication cost will increase. For UWSNs where location information is only used for geo-routing, high localization accuracy is not required [11], but a high localization coverage is desired. For this type of networks, the confidence threshold can be set to a relatively small value. While for UWSNs which require high precise location information, the confidence value should be set to a relatively large value. Some adaptive algorithms can be used to control this important parameter to provide performance guarantees. Impact of Localization Message Threshold: This study tells us that for a network setting, there is a critical value of N . When N is smaller than this value, the localization coverage, the localization error and the average communication cost will increase rapidly. When N is larger than this value, the localization coverage and the localization error will not change much and are relatively stable. But the communication cost will continue to increase. This indicates that beyond the critical value, increasing N will only increase the communication cost and will not bring any benefits. Thus, in practice we need to carefully choose N according to the network environments. In our previously presented simulations, we set N to 5, which is the critical value of N for the considered network setting. Performance in Mobile Networks: We also conduct simulations to evaluate the performance of our scheme in mobile networks, and the results show that the localization coverage and average communication cost are not affected much by the node mobility, while the localization error increases noticeably with the node moving speed. This is mainly due to that fact that the average distance measurement error increases with the average moving speed, as naturally causes the increase of the final localization error.

4 Conclusion In this paper, we presented a hierarchical localization approach for large-scale UWSNs. In this approach, the whole localization process consists of two sub-processes: anchor

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node localization and ordinary node localization. We focused on the ordinary node localization, for which we proposed a distributed scheme which novelly integrates a 3-dimensional Euclidean distance estimation method and a recursive localization method. Simulation results showed that our scheme can achieve high localization coverage with relatively small localization error and low communication cost. Besides, we also investigated the tradeoffs among the node density, the anchor percentage, the localization error, the localization coverage and the communication cost in our scheme. Different networks may have different requirements for these parameters. Via changing the confidence threshold parameter of our scheme, we can well control these tradeoffs.

References 1. I. F. Akyildiz, D. Pompili, and T. Melodia. Challenges for efficient communication in underwater acoustic sensor networks. ACM SIGBED Review, 1(1):3–8, Jul 2004. 2. J. Albowitz, A. Chen, and L. Zhang. Recursive position estimation in sensor networks. In Proceedings of IEEE ICNP, pages 35–41, Nov 2001. 3. T. C. Austin, R. P. Stokey, and K. M. Sharp. PARADIGM: a buoy-based system for auv navigation and tracking. In Proceedings of MTS/IEEE Oceans, 2000. 4. C. Bechaz and H. Thomas. GIB system: The underwater GPS solution. In Proceedings of 5th Europe Conference on Underwater Acoustics, May 2000. 5. P. Biswas and Y. Ye. Theory of semidefinite programming relaxation for sensor network localization. To appear in matehmatical programming. 6. P. Biswas and Y. Ye. Semidefinite programming for ad hoc wireless sensor network localization. In Proceedings of IPSN, pages 46–54, Apr 2004. 7. N. Bulusu, J. Heidemann, and D. Estrin. GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, pages 28–34, Oct 2000. 8. K. K. Chintalapudi, A. Dhariwal, R. Govindan, and G. Sukhatme. Ad-hoc localization using range and sectoring. In Proceedings of IEEE Infocom, pages 2662–2672, Mar 2004. 9. J.-H. Cui, J. Kong, M. Gerla, and S. Zhou. Challenges: building scalable mobile underwater wireless sensor networks for aquatic applications. IEEE Network, Special Issue on Wireless Sensor Networking, pages 12–18, May 2006. 10. J. E. Garcia. Ad hoc positioning for sensors in underwater acoustic networks. In Proceedings of MTS/IEEE Oceans, pages 2338–2340, 2004. 11. T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher. Range-free localization schemes for large scale sensor networks. In Proceedings of 9th annual internatonal conference on mobile computing and networking, pages 81–95, Sep 2003. 12. Kenneth and D. Frampton. Acoustic self-localization in a distributed sensor network. IEEE Sensors Journals, 6:166–172, Feb 2006. 13. J. Kong, J.-H. Cui, D. Wu, and M. Gerla. Building underwater ad-hoc networks and sensor networks for large scale real-time aquatic application. In Proceedings of IEEE Military Communications Conference (MILCOM’05), Atlantic City, New Jersey, USA, pages 1535– 1541, Oct 2005. 14. A. Mahajian and M. Walworth. 3-D position sensing using the differences in the Timeof-Flights from a wave source to various receivers. IEEE Transactions on Robotics and Automation, 17:91–94, Feb 2001. 15. D. Moore, J. Leonard, D. Rus, and S. Teller. Robust distributed network localization with noisy range measurements. In Proceedings of Sensys, pages 50–61, Nov 2004. 16. R. Nagpal, H. Shrobe, and J. Bachrach. Organizing a global coordinate system from local inforamtion on an ad hoc sensor network. In Proceedings of IPSN, Apr 2003.

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17. D. Nichulescu and B. Nath. DV based positioning in ad hoc networks. Springer, Telecommunication Systems, pages 267–280, Oct 2003. 18. D. Niculescu and B. Nathi. Ad hoc positioning system (APS). In Proceedings of IEEE Globecom, pages 2926–2931, Nov 2001. 19. H. Wu, C. Wang, and N.-F. Tzeng. Novel self-configurable positioning technique for multihop wireless networks. IEEE/ACM Transaction on Networking, pages 609–621, Jun 2005. 20. P. Xie, L. Lao, and J.-H. Cui. VBF: vector-based forwarding protocol for underwater sensor networks. In Proceedings of IFIP Networking, May 2006. 21. Y. Zhang and L. Cheng. A distributed protocol for multi-hop underwater robot positioning. In Proceedings of IEEE International Conference on Robotics and Biomimetics, pages 480– 484, Aug 2004. 22. Z. Zhou, J.-H. Cui, and S. Zhou. Localization for large-scale underwater sensor networks. UCONN CSE Technical Report: UbiNet-TR06-04, http://www.cse.uconn.edu/˜jcui/ publications.html, Dec. 2006.

Location-Unaware Sensing Range Assignment in Sensor Networks Ossama Younis, Srinivasan Ramasubramanian, and Marwan Krunz Department of Electrical & Computer Engineering University of Arizona, Tucson, AZ 85721 {younis,srini,krunz}@ece.arizona.edu

Abstract. We study field-monitoring applications in which sensors are deployed in large numbers and the sensing process is expensive. In such applications, nodes should use the minimum possible sensing ranges to prolong the “coverage time” of the network. We investigate how to determine such minimum ranges in a distributed fashion when the nodes are location-unaware. We develop a distributed protocol (SRAP) that assigns shorter ranges to nodes with less remaining batteries. To handle location-unawareness, we develop a novel algorithm (VICON) for determining the virtual coordinates of the neighbors of each sensor. VICON relies on approximate neighbor distances and 2-hop neighborhood information. Our simulations indicate that SRAP results in significant coverage time improvement even under inaccurate distance estimation.

1 Introduction Sensor monitoring applications require node collaboration to maximize the network “coverage time,” defined as the time during which a specified fraction of the area is continuously monitored. In this work, we focus on applications in which the sensing process is the dominant source of energy consumption and sensing ranges are adjustable. Examples of such applications are those requiring sensors to send continuous longrange pulses for object detection (e.g., RADAR systems). In these applications, sensing is a continuous active process, while communication and processing are only invoked whenever an object of interest is detected. Other example applications are those requiring each sensor to analyze the collected data (e.g., environmental traces or images) before reporting it. Reducing the sensing range in such applications results in significant reduction in the data set to be analyzed, thus conserving energy. Currently, some commercially available sensors are capable of adjusting their sensing levels to control the cost associated with the sensing process (e.g., the Osiris photoelectric sensor [6]). We study how to assign the minimum possible sensing range to every sensor without degrading field coverage. Selecting the optimal sensing ranges for all the sensors is an NP-hard problem [10] (the simplified version of this problem in which each sensor is either ON or OFF is also NP-hard [2]). In previous research that considered nodes with adjustable ranges, greedy techniques were proposed for target monitoring [1] or 

This work was supported in part by the National Science Foundation under grants CNS0627118, CNS-0313234, 0325979, and 0435490.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 120–131, 2007. c IFIP International Federation for Information Processing 2007 

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constructing connected covers [10]. However, the problem is more challenging in location-unaware networks in which a sensor is not capable of determining its location or the directions of the incoming signals. This occurs when the sensors can not perform network-wide localization based on location-aware anchor nodes (e.g., in forests or outer space). 1.1 Contributions We develop a distributed sensing-range assignment protocol (SRAP) for locationunaware sensor networks, assuming that every node can tune its sensing range to one of an available set of ranges. In such networks, nodes are not aware of the field “boundary,” and therefore the objective of every sensor is to cover its own maximum sensing region. SRAP employs a novel localized algorithm (VICON) for determining the virtual coordinates of the neighbors of every node prior to range selection1 . At a node v, VICON exploits the 2-hop neighborhood information and the estimated distances between v and its neighbors. VICON employs conservative heuristics to place as many neighbors of v as possible when the estimated distances are inaccurate or the graph of v’s neighbors is disconnected. To prolong the lifetime of every sensor, SRAP assigns sensing ranges based on the remaining sensor batteries. SRAP is also superior to previous work in eliminating redundancy. 1.2 Related Work Under fixed sensing ranges, a node can be either ON or OFF. All previously proposed protocols for this model assumed that node locations or directions of neighbors can be estimated (refer to [8] for a list of these protocols). More recent proposals assumed variable sensing ranges. Cardei et al. [1] proposed centralized and distributed heuristics for maximizing the number of set covers (AR-SC) under this model. Their approach assumes synchronized nodes, base station intervention, and knowledge of node positions. We do not assume any of these capabilities and study a more general model. However, we use the greedy approach in [1] as a baseline for comparison. Zhou et al. [10] proposed another greedy algorithm for selecting a connected cover to optimize query execution under variable sensing and communication ranges. They focused on maintaining both network connectivity and field coverage. Our approach can be integrated with the one in [10] to maintain connected covers in location-unaware networks. The rest of the paper is organized as follows. Section 2 provides the problem formulation. Section 3 introduces the VICON (VIrtual COordinates of Neighbors) algorithm. Section 4 provides details of the SRAP protocol and its properties. Section 5 evaluates the performance of SRAP. Finally, Section 6 gives concluding remarks.

2 Problem Statement Assumptions: Let the maximum transmission range of each node be Rt . We refer to a node within distance ≤ Rt as a “neighbor.” We assume the following: (1) nodes 1

Note that SRAP is independent of VICON.

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are stationary; (2) each node has a set of k usable sensing levels, which correspond to sensing ranges R1 , . . . , Rk , where Rk is the maximum sensing range. Turning off the sensing component corresponds to R0 = 0; (3) energy depletion is proportional to Rim , where 1 ≤ i ≤ k and m is a constant ≥ 1; (4) a node can sense an event within a circular “sensing region” around it; (5) the sensing component in each node is continuously active and the sensing process is energy-intensive. The radio component, however, employs a low duty cycle; (6) neighbor locations and directions of received signals cannot be estimated; and (7) a node can estimate the distance between itself and a neighbor based on well-known techniques such as the time of arrival, received signal strength, etc [9]. For simplicity, we assume that Rt ≥ Rk . We use a conservative approach to estimate neighbor distances in which Rt is divided into a discrete set of nd distances and every range of signal strengths maps to one of these distances. Every node broadcasts the estimated distances to its neighbors so that every node is aware of its 2-hop neighborhood. We account for the inaccuracy in distance estimation in our algorithm presented in Section 3 and evaluate its effect in Section 5. Objectives: Given a set of N deployed sensors, it is required to assign every sensor i, 1 ≤ i ≤ N , the minimum sensing range Rj , where 0 ≤ j ≤ k, such that i’s sensing region is covered. Because the field boundary is unknown to individual sensors, the objective of every sensor is to ensure that its maximum sensing region is covered.

3 The VICON Algorithm In VICON, a node computes “virtual” coordinates of its neighbors. A virtual coordinate space (VCS) of node v’s neighbors is one that keeps the connectivity profile of the real coordinate space (RCS). That is, the distances and angles between the neighbors of v are preserved. However, the VCS can have the neighbors rotated, which does not affect the coverage properties. The problem of assigning neighbor coordinates is a special instance of the “graph embedding” problem, which was studied extensively in the literatures of graph theory and computational geometry [3]. Computing virtual coordinates was also studied in the networking literature, e.g., [5, 7]. In these studies, the objective was to assign coordinates to all the nodes in the network. Such approaches are not suitable for our work for three reasons. First, we do not have anchor nodes in the network since the entire network is location-unaware. Second, basic triangulation techniques do not handle disconnected graphs and fail when distances are inaccurate. Finally, we only require each node to compute the virtual coordinates of its neighbors, and do not need to compute network-wide coordinates. Our approach is a lightweight algorithm that can be easily employed in dynamic networks where new nodes are deployed at any time. We first describe VICON assuming accurate estimates of distances. Then, we extend it to mitigate the negative effects of inaccurate distance estimation. 3.1 Details of VICON Prior to executing VICON, each node is aware of its 2-hop connectivity information (reachability and distances) through neighbor broadcasts. A node v executing VICON

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proceeds as follows. Assume that v has three neighbors v1 , v2 , and v3 , as depicted in Fig. 1(a). Node v assumes that it is positioned at the origin and places its first neighbor (v1 ) at (d1 ,0), where d1 is the distance between v and v1 (see Fig. 1(b)). Using v1 , v2 , v, v1 , and v, v2 , v can compute the angle g1 shown in Fig. 1(a). To determine the virtual coordinates of v2 , v2 is rotated by an angle g1 from the origin in the counter-clockwise direction. Similarly, v3 is rotated in the counter-clockwise direction with an angle g2 and assigned a tentative coordinate. The validity of this coordinate is then tested against all the already-placed sensors to determine whether the original connectivity is preserved. In this example, rotating v3 in the counter-clockwise direction causes it to be a neighbor of v2 , which contradicts with the RCS. Therefore, v3 is rotated by an angle g2 in the clockwise direction. Figure 1(b) illustrates that v is still covered by three nodes that are within g1 + g2 total angle, and are all on one side of v.

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Two problems have to be considered. The first problem is depicted in Figure 2(a), where v’s neighbors form more than one connected component. This results in having a subset of the neighbors unable to find reference nodes that are already placed in the VCS. VICON handles this problem as follows. First, v’s neighbors are divided into groups, where each group represents a connected component (e.g., Fig. 2(a) shows two groups: {v1 , v2 } and {v3 , v4 }). Second, the coordinates of the neighbors in each group are computed independently from the other groups. Finally, each group, other than the first one, is rotated to preserve the RCS connectivity. This is depicted in Fig. 2(b), where the two groups are placed closest to each other while preserving their disjointedness.

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The second problem is that a node may satisfy the connectivity requirements with the already-placed neighbors in both the clockwise or counter-clockwise direction. The

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problem is demonstrated in Fig. 3(a), where node v3 is a neighbor of node v1 but not of v2 or v4 . In the VCS, v1 and v2 are placed first. Node v3 is placed in the counterclockwise direction from v1 (as shown in Fig. 3(b)) and it also satisfies the connectivity of the RCS when placed in the clockwise direction. As a result, v fails to determine a virtual coordinate for v4 . v4

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The above problem can be addressed using the following recursive approach. Assume that node v has a list of Nnbr neighbors. Node v processes these neighbors in sequence and pushes the IDs of the successfully placed neighbors in a stack named FinishedNbr. A neighbor that can be successfully placed in two positions is marked “UNSURE” in FinishedNbr, while a neighbor that can only be placed in one position is marked “SURE.” If v fails to compute coordinates for a neighbor i (2 < i ≤ Nnbr ), then it pops neighbor IDs from FinishedNbr until it finds one referring to an UNSURE neighbor. This neighbor is then placed in the alternative direction, marked SURE, and pushed back in FinishedNbr. VICON then attempts to re-process the pushed-out neighbors. This approach ensures that incorrectly selected coordinates are corrected as more neighbors are placed. In our example depicted in Fig. 3(b), node v3 is marked UNSURE when placed. When v fails to place v4 , it pops v3 from FinishedNbr, places it in clockwise direction relative to v1 , then successfully places v4 . VICON does not preserve the directions of neighbors, which is not a problem since the objective of every node is to determine “how much” area is uncovered, and not “which” area. Pseudo-code and proof of correctness of VICON can be found in [8]. 3.2 VICON Under Inaccurate Distance Estimation Inaccurate distance estimation may cause failures in node placement due to either magnifying or shrinking the angles between a node and its neighbors. We conducted numerical experiments under different settings to study the reasons behind this failure. These experiments revealed two important observations: (1) placement inaccuracy within a maximum inaccuracy I = Rt /nd can be tolerated without sacrificing coverage, and (2) our distance estimation is overconservative for some distances, and less conservative for others. Based on these observations, we extend the basic VICON algorithm as follows. – Assume that the distance d between node v and one of its neighbors u corresponds ˆ to the discrete distance dˆ (dˆ ≥ d). We set u’s distance from v to be d−I/2 to achieve average uncertainty in distance estimation instead of maximum uncertainty.

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– The computed virtual coordinate of a neighbor u is acceptable if it preserves neighborhood within a distance ≤ I of all u’s neighbors. Note that under high densities, the shift in the angles can add up and result in failure to place some neighbors. Thus, the above measures do not ensure that all the neighbors will be eventually placed. In [8], we show the effect of node density on successful neighbor placement.

4 SRAP Protocol Protocol Design: SRAP assigns longer ranges to nodes with higher “weights,” where a dynamic parameter is used to represent the weight of a node (e.g., remaining energy). In addition, SRAP is re-triggered at fixed intervals of time, referred to as the cover update interval tcu , to efficiently balance the load among sensors. The SRAP protocol is executed at every node in the network, typically via timer expiration2. Since sensor clocks are typically unsynchronized, the node with the fastest clock in its 1-hop neighborhood sends a message to its neighbors to trigger the execution of SRAP. Consequently, every node that receives this message sends a similar triggering message prior to executing SRAP. We assume that a node can be in one of two states: DECIDED or UNDECIDED and all the nodes start in the UNDECIDED state. SRAP has three phases: Phase I is for initialization, Phase II is the core operation of SRAP in which a node v decides on a sensing range R, and Phase III is for the optimization of R. A summary of the three phases is shown in Fig. 4. Phase I. In the first phase of SRAP, v computes a real-valued weight wgt(v) as: wgt(v) = E(v)/Emax , where E(v) is the remaining energy in v’s battery and Emax is the maximum battery capacity. A neighbor discovery process is then initiated in which v broadcasts wgt(v). Based on the replies that v receives, it broadcasts its neighborhood table (which includes the estimated neighbor distances). In the second step of this phase, v executes VICON to compute the virtual coordinates of its neighbors (this step is independent of SRAP). The final step is to check whether v has to use its maximum sensing range Rk or not. This is done by having v assume that all its neighbors are using Rk , and check if any part of its sensing region is not covered. If v passes this test, it quits SRAP and uses Rk . Otherwise, v executes Phase II. Phase II. Node v computes its sensing range R based on its weight and the weights of its neighbors. Node v does not make a decision on R unless it has the highest weight among all of its undecided neighbors. This gives a chance for nodes with higher weights to decide first and choose longer ranges. At the same time, v sets a timer T1 (similar for all nodes) for this phase. If T1 expires before a decision is made, v computes its range assuming that all undecided neighbors use R0 . The function Compute R(v) proceeds as follows. Node v first sets its range R to Rk−1 and sets the range of every undecided neighbor u to the largest Rj smaller than [(wgt(u)/wgt(v))1/m × R], where j ≤ k − 1 and m is a constant. Note that 2

SRAP can be triggered asynchronously when detecting events such as node failures or when new nodes are deployed.

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wgt(u)/wgt(v) < 1, which means that v’s undecided neighbors are assumed to use sensing ranges < Rk−1 . Decided neighbors are set to the ranges that they have decided on. If this assignment results in covering the sensing region of v, v sets its range R to Rk−2 and the same process is repeated. If range Ri , 0 ≤ i < k, fails to ensure complete coverage of v’s region, then v uses R = Ri+1 , changes its state to DECIDED, and advertises R to its neighbors3. Phase III. After Phase II, v can terminate SRAP and use its selected R. However, redundancies may have been introduced due to the order of the decision-making process in Phase II. Therefore, we propose an iterative approach for removing redundancies. When the node with the least weight in its neighborhood selects its sensing range, it sends a token to its neighbors, allowing them to proceed with Phase III. A node v starts a timer T2 (of a few seconds granularity) when it receives the first token from one of its neighbors. It waits to receive tokens from all the neighbors with less weights than its own. Once these tokens are available, v computes its final sensing range based on the advertised ranges of its neighbors, and releases a token that advertises the new range of v. If T2 expires before v has received enough tokens, it keeps its range as computed in Phase II and releases its token. Analysis of SRAP: We analyze the SRAP protocol in terms of its correctness, computational complexity, and message overhead. 3

Note that loss of messages in Phase II may only result in more conservative estimation of R. However, termination is not affected.

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Lemma 1. When SRAP terminates, the sensing region of every node with non-depleted battery is completely covered (Coverage property). Proof. When SRAP is executed at node v, two operations affect the final coverage of v’s sensing region: 1. Selection of v’s sensing range in Phase I and II. In Phase I, if v determines that its sensing region can not be completely covered by its neighbors, it sets its sensing range to Rk and terminates SRAP. If v goes through Phase II, it selects its sensing range based on both advertised ranges of its decided neighbors and hypothetical ranges of its undecided neighbors. An undecided neighbor will not be able to select a sensing range less than the largest hypothetical range made by any of its neighbors, unless its region is covered. This ensures that v’s sensing region is completely covered. 2. Reduction of sensing ranges in Phase III. A “hole” in field coverage may occur when two neighboring nodes (e.g., v1 and v2 ) are allowed to reduce their sensing ranges simultaneously. Such scenario implies that both v1 and v2 had all the tokens they need from their neighbors with less weights to start Phase III simultaneously. This is not possible since we assume that the weight is a real number and either v1 or v2 will have less weight than the other. 2 Lemma 2. Every node v selects the minimum sensing range that satisfies coverage of its sensing region if accurate distances are used and the neighbors of v do not form multiple disjoint components (Minimality property). Proof. Let us first assume that the estimated neighbor distances are accurate; i.e., VICON computes virtual coordinates for all the neighbors of v. Also assume that v has selected R = Ri although R = Rj (j < i) was sufficient to have v’s region covered. This may occur in Phase II depending on the order of SRAP execution among neighboring nodes of v. However, when Phase III is executed, v will be able to compute the minimum range R based on its neighbors final decisions. Since nodes getting tokens after v are only allowed to reduce their sensing ranges, the selected R is minimal (this applies to all nodes). Along with selecting covers from higher-weight nodes and refreshing covers, this result has a significant impact on the perceived coverage time. If accurate distances are used for computing virtual coordinates, minimality can only be violated if the neighbors of v form multiple disjoint components. This is unlikely to occur, however, in dense networks. On the other hand, if inaccurate distances are used for obtaining virtual coordinates, minimality can be violated. The redundancy introduced in this case depends on node density and distribution in the field. Message overhead. Four types of message exchange are required: (1) neighbor discovery, which requires O(1) messages, (2) advertisement of node’s weight, which requires only one message whenever SRAP is re-triggered, (3) advertisement of the selected range, which requires O(1) messages, and (4) token exchange, which requires one message. Therefore, the total message overhead of SRAP is O(1) per node. Note that if in future applications more parameters are added to the node weight computation (e.g., mobility, remaining uncovered area, etc.), then a node v’s weight has to be advertised whenever one of v’s neighbors decides its range. This raises the message overhead to O(Nnbr ), where Nnbr is the average number of neighbors per node

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(to ensure connectivity, Nnbr must be = O(log N ) in randomly deployed networks [4]). Note that Phase III may have to be modified in this case since it relies on a parameter that is assumed to be fixed during the range assignment process. Time complexity. The time complexity of SRAP has two components: (1) convergence speed of any node in the network (ignoring the timers in the protocol), and (2) processing complexity. Phase I has O(1) convergence speed. The average-case convergence speed is proportional to the average number of neighbors of any node. The worst-case convergence speed for phases II and III can be proportional to the number of nodes (under very pessimistic distribution of nodes’ remaining battery levels). (In our experiments, we found that the convergence speed of SRAP is significantly less than the worst case.) This justifies the use of timers T1 and T2 to limit the convergence speed and avoid indefinite waits in case of failures. The main processing complexity in SRAP is in testing whether the node’s sensing region is covered. This test is performed once in phases I and III, and every time a neighbor selects its range in Phase II. If we discretize the sensing region into a number of P points, then the complexity of the test is O(P Nnbr ), where Nnbr is the average number of neighbors. The other source of complexity is the VICON algorithm, which 2 is executed only once in static networks. VICON has a complexity of O(αNnbr ) where 5 α = 2 in the worst case since there can exist at most five neighbors of a node that are pairwise non-neighbors. Each of these neighbors can be assigned to at most two positions. Therefore, VICON does not introduce significant computational complexity.

5 Performance Evaluation We study an operational scenario in which a number of sensors send their reports to a base station via multi-hop communication. We assume that nodes that are randomly distributed in a field from (0,0) to (50,50). A base station is placed at (25,25). All the nodes start with full batteries and the network is considered dead when the base station is disconnected. The simulation parameters used in our experiments are as follows: N = 900 nodes, Rt = 5 meters, number of discretized distances nd = 5, k = 4, Rk = 5 meters, battery capacity = 1.0 Joule, communication energy Ecomm = 10−6 Watt, energy consumption parameter m = 2, and cover update interval tcu = 2000 seconds. For radio communications, we assume that a fixed amount of power is consumed from every active node during its operation. We set the energy consumed in communication to correspond to the energy consumed at R1 . We developed a discrete event-driven simulator that is scalable and efficient for largescale networks. We compare SRAP to AR-SC [1]. AR-SC is a distributed protocol proposed for target coverage. However, we extend it to area coverage by discretizing the field into a large number of points. AR-SC gives priority in decision-making (range assignment) to nodes seeing more uncovered targets. This is similar to typical set cover algorithms that aim at reducing the size of the selected set (e.g., [2]). We assume ideal conditions for the operation of AR-SC, which include full node synchronization, optimal sequence of decision-making according to node priorities, and knowledge of the exact node coordinates.

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We also compare SRAP to a generic centralized greedy algorithm (which we refer to as “CentralizedApp”). In CentralizedApp, a centralized entity that is aware of the locations of all the nodes in the network is responsible for range assignment. The network operation is divided into phases of equal duration. Given the energy spent by each sensor at the end of phase i, the minimal cover for phase i + 1 is chosen such that the maximum energy spent by a sensor at the end of phase i + 1 is minimized. The algorithm selects a minimal cover as follows. All the sensors are assumed to employ the maximum sensing range for phase i + 1. The sensors are arranged in the descending order of the expected energy spent at the end of phase i + 1. The algorithm selects the sensor with the highest value (say v). If reducing v’s range by one step (i.e., from Rj to Rj−1 , 0 < j ≤ k) violates coverage, then v’s sensing range is kept at Rj . Otherwise, v’s sensing range is reduced to Rj−1 . The expected energy spent at the end of phase i + 1 is updated, as well as the ordered set of sensors. The procedure is repeated until a minimal cover is obtained. Although, the algorithm is described here as a centralized manner, it may be distributed in a distributed manner using only one-hop neighborhood information. As the sensors reduce their range one step at a time, the worst-case running time of the algorithm is O(N k). We study the operation of SRAP under accurate distance measurements (SRAP-A) and under discretized (inaccurate) distances (SRAP-D). For a fair comparison, we assume that the network boundary is not known by the application employing any of the compared techniques. We assume no packets are lost at the MAC layer. Packet losses may only add some redundancies to field coverage but have no impact on the operation of SRAP. The results provided below are the average of 10 experiments.

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Properties of SRAP and VICON. We first focus on the selection of one cover by any of the compared algorithms. All the nodes are assumed to be alive. We compute the number of nodes selected at the k sensing levels (in addition to R0 = 0), the cost of the selected cover (energy consumed in the network during its operation), and the ratio of successfully placed neighbors per node for SRAP-D. Figure 5(a) demonstrates the number of nodes at each sensing level for different node densities. SRAP shows more collaborative behavior under both accurate and discretized

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distances than AR-SC. CentralizedApp shows the best collaborative behavior because it can reduce the ranges iteratively and not at one step per node as in SRAP and AR-SC. Figure 5(b) shows the cover cost for all algorithms. With maximum sensing ranges, the energy consumed in the network can be computed as (R4 × R4 + 1) × Ecomm (which is 0.0234 Joule for N = 900). As expected, CentralizedApp gives the smallest cover cost. SRAP significantly reduces the cover cost over AR-SC, especially when distances are accurately estimated. SRAP-D and AR-SC show, respectively, about 10-20% and 30-70% increase in cover cost over SRAP-A.

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Network Operation. We now evaluate the network operation when sensing range assignment is employed. We focus on three metrics. The first metric is the duration while the coverage quality of the field is within a specified range. The second metric is the coverage quality over time as the network operates. Coverage quality is the fraction of field coverage at specific instances of time. The third metric is coverage redundancy, which is the number of sensors covering the least covered point within a sensor’s region. A coverage redundancy of 1 means that there is at least one point within any sensor’s region that is covered by only one sensor. This metric indicates how minimal the selected cover and ranges are. Figure 6(a) shows the coverage time during which the percentage of field coverage is within a specific range of coverage quality. CentralizedApp shows about 50% coverage time improvement over SRAP. SRAP-A and SRAP-D significantly improve coverage time over AR-SC, especially at the higher coverage quality ranges (60-80% and 80100%). This is a desirable effect for applications that try to maximize field coverage for the longest possible time. Figure 6(b) demonstrates the coverage quality of the field over time as the network is operating. We include results of the application when operated without sensing range adjustment (referred to as “No-Adjust”), i.e., all the sensors use their maximum sensing ranges (Rk ). The figure shows that under CentralizedApp and SRAP nodes die smoothly over time because of periodically refreshing the selected sensing ranges based on a dynamic parameter (battery level). We also study the redundancy in the selected covers under SRAP and AR-SC. CentralizedApp guarantees no

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redundancy in the selected cover and thus is not included in this experiment. Results (reported in [8]) indicate that for SRAP-A, the redundancy does not exceed 1 by more than 2-3%. We closely examined these redundancies and found that they occur at sensors which are assigned range R0 . For SRAP-D, redundancy may reach 9-10% due to the failure of VICON to place some neighbors for each node.

6 Conclusion We studied the problem of sensing range assignment in location-unaware networks. To handle location-unawareness, we proposed a novel localized algorithm (VICON) that each node uses to compute virtual coordinates of its neighbors. We then proposed a distributed protocol (SRAP) which periodically assigns sensing ranges to nodes based on their remaining battery powers. SRAP has negligible message overhead and computational complexity. Our simulation results indicate that SRAP significantly improves coverage time, even under inaccurate distance estimation. To extend the functionality of SRAP for different applications, we plan to study how to incorporate other parameters in the node weights, such as mobility, node degree, or potential coverage.

References [1] M. Cardei, J. Wu, M. Lu, and M. Pervaiz. Maximum network lifetime in wireless sensor networks with adjustable sensing ranges. In Proc. of the IEEE Intl. Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), August 2005. [2] U. Feige. A threshold of ln n for approximating set cover. Journal of the ACM, 45(4): 634–652, July 1998. [3] J. L. Gross and T. Tucker. Topological Graph Theory. John Wiley and Sons, 2001. [4] P. Gupta and P. R. Kumar. Critical power for asymptotic connectivity in wireless networks. Stochastic Analysis, Control, Optimizations, and Applications: A Volume in Honor of W.H. Fleming, W.M. McEneaney, G. Yin, and Q. Zhang (Eds.), Birkhauser, 1998. [5] T. Moscibroda, R. O’Dell, M. Wattenhofer, and R. Wattenhofer. Virtual coordinates for ad hoc and sensor networks. In Proc. of DIALM-POMC, October 2004. [6] Osiris Photoelectric Sensors, http://schneider-electric.ca/www/en/products/sensors2000/ html/osiris.htm, 2007. [7] A. Rao, C. Papadimitriou, S. Ratnasamy, S. Shenker, and I. Stoica. Geographic routing without location information. In Proc. of the ACM MobiCom Conference, September 2003. [8] O. Younis, M. Krunz, and S. Ramasubramanian. Sensing range assignment in locationunaware networks. Technical report, University of Arizona, November 2006. [9] M. Youssef and A. Agrawala. The Horus WLAN location determination system. In Proc. of the ACM International Conference on Mobile Systems, Applications, and Services (ACM MobiSys), June 2005. [10] Z. Zhou, S. Das, and H. Gupta. Variable radii connected sensor cover in sensor networks. In Proc. of the IEEE Communications Society Conference on Sensors and Ad Hoc Comm. and Networks (SECON), September 2004.

A Distributed Energy-Efficient Topology Control Routing for Mobile Wireless Sensor Networks Yan Ren, Bo Wang, Sidong Zhang, and Hongke Zhang School of Electronics and Information Engineering, Beijing Jiaotong University, 100044 Beijing, China {yren,bwang,sdzhang,hkzhang}@center.njtu.edu.cn

Abstract. One of the fundamental issues in wireless sensor networks (WSNs) is the topology control (TC) problem, which reflects how well the energy consumption is reduced and the network capacity is enhanced. In this paper, by using computational geometry theoretic, we present a fully distributed routing protocol, Cooperative Energy-efficient Topology Control (Co-ETC), whose goal is to achieve energy efficiency in mobile wireless sensor networks. Based on underlying routing graph, the proposed scheme allows each node (with or without mobility) to locally select communication neighbors and dynamically adjust its transmission radius accordingly, such that all nodes together self-form a energy-efficient topology. The simulation results indicated that the proposed scheme was feasible. Compared with existing state-of-the-art algorithms and protocols, Co-ETC has better energy-efficiency. Moreover, it can adapt to mobile environment well. Keywords: topology control, energy efficiency, routing protocol, wireless sensor networks.

1 Introduction In recent years, extensive research has been conducted on wireless sensor networks (WSNs), considered one of the top research topics [1]. Such environments may have a large number of small sensor nodes, each capable of collecting, storing, processing observations and communicating over short-range wireless interfaces and multiple hops to central locations called sinks. Since sensors may be spread in an arbitrary manner, one of the fundamental issues that arise naturally in WSNs is topology control (TC) problem. Topology control technique is to let each wireless node locally adjust its transmission range and select certain neighbors for communication, while maintaining a structure that can support energy efficient routing and improve the overall network performance [2]. In general, it can be considered as a measure of the quality of service of a WSN. For instance, one may ask how well the network can maintain some global graph property (e.g., connectivity). Furthermore, TC formulations can reduce energy consumption and enhance network capacity. Due to mobility, sensor nodes (even sink) may change their locations after initial deployment in many scenarios. Mobility can result from environmental influences I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 132–142, 2007. © IFIP International Federation for Information Processing 2007

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(e.g., wind or water), available mobile platforms in the deployment area (e.g., robots in object tracking, soldiers in battlefield surveillance) or mobile devices can be incorporated into the design of the WSN architecture (e.g., airborne and vehicles) [3]. The impact of mobility on the effectiveness of TC is twofold: Increased message overhead (especially in the case of high mobility scenarios) and nonuniform node spatial distribution. Considering the impact above, deriving results regarding mobile WSNs is even more challenging. Several TC algorithms or routing protocols [3], [4] which use different underlying routing graphs with several good properties have been proposed in last few years. However, to our knowledge none of them has been defined to explicitly deal with all four key properties for unicast routing graph on WSNs simultaneously: power spanner, sparse, localized and degree-bounded. By using computational geometry theoretic techniques, we present a cooperative topology control routing protocol with following properties: 1.

2.

3. 4. 5.

Energy-efficient. It ensures that the routes calculated on given routing graph are at most a constant factor (defined as power stretch factor that will be specified later in this paper) away from the power-optimal routes. Sparse. There is linear number of edges in given network. It eases the task of finding path and maintaining the route path in the presence of node mobility, and it can reduce the communication overhead. Bounded node degree. Bounded node degree can reduce bottleneck and neighbor signal interference in network. Running in distributed fashion. Every node compute the scalable routing graph cooperatively using the information only provided by its neighbor. Adaptive to mobile environment. This ensures that topology requires little maintenance in the presence of mobility that could change routing graph to some extent.

The remainder of this article is organized as follows: In the next section, we first give some preliminaries and define the network model. The basic design of Co-ETC protocol is presented in Section 3. We then discuss several extensions in Section 4. Specifically, we consider how to find an energy-efficient-path and how to avoid “bottleneck” node in communication graph. Finally, we evaluate the performance of Co-ETC in Section 5, conclude the paper and discuss possible future research directions in Section 6.

2 Preliminaries and Network Model In our subsequent discussions, network topology is represented by an undirected simple graph G = (V, E) when all the nodes transmit at maximum power, where V = {v1, v2, …, vn} is the set of n wireless sensor nodes and E is the set of links in the WSN. For sake of simplicity, we will assume that every node has the same maximum transmission range Rmax. A node can reach all nodes (called neighbors) inside its transmission region. We also assume that each node is assigned a unique identifier, ID and knows its location.

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Before we design the Co-ETC protocol, we first give some computational geometry concepts, which will be used as the foundations of geometry for Co-ETC: Definition 1 (Voronoi Diagram and Delaunay Triangulation). The Voronoi diagram, denoted by VD(V) (real lines in Fig. 1), of a set of discrete nodes partitions the plane into a set of convex polygons (called voronoi regions) such that all sites inside a voronoi region are closest to only one node. This construction effectively produces polygons with edges that are equidistant from neighboring nodes. The Delaunay triangulation, denoted by DT(V) (dotted lines in Fig. 1), is the dual graph of the Voronoi Diagram. It is the unique triangulation such that the circumcircle of every triangle contains no nodes of V in its interior.

Fig. 1. Voronoi Diagram and Delaunary Triangulation

Definition 2 (Gabriel Graph). The Gabriel Graph, denoted by GG(V), consists of all edges uv such that the disk(a, b) does not contain any node from V, where disk(a, b) is the closed disk with diameter ab (see Fig. 2).

Fig. 2. Gabriel Graph

Definition 3 (Power Stretch Factor). The power stretch factor, denoted by ρp, respect to G is the maximum over all possible node pairs of the ratio between the cost of the minimum-power path in G’ (Any arbitrary subgraph of G, e.g., DT(V) and GG(V)) and in G, where is represented by pG’(u, v) and pG(u, v) respectively. In other words,

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ρ p = max u ,v∈V

p G ′ (u ,v ) pG (u ,v )

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(1)

Notice that, generally, we would like to use a subgraph G’ (also called a routing graph) which has a low-power stretch factor and which is sparser than the original graph G. Such a routing graph can be used to compute routes with guarantee that the energy needed to communicate along is almost minimal. In addition, computing optimal routes in G’ is easier and has less communication overhead than in G. Moreover, such a sparse routing graph requires little maintenance in the presence of node mobility. The power stretch factor and maximum node degree of the graphs defined previously have been analyzed in [5], and are reported in Table I. Table 1. Power Stretch Factor and Maximum Node Degree of DT and GG

Power Stretch Factor DT GG

⎛1 + 5 ⎞ ⎜ ⎟ ⎜ 2 π⎟ ⎝ ⎠ 1

Maximum Node Degree

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Θ(n) n-1

As shown, the Gabriel Graph is energy-optimal since it has a power stretch factor of 1. And neither DT(V) nor GG(V) has constant maximum node degree. We will utilize these attractive routing graphs for extensions of our Co-ETC protocol in Section 4.

3 Design of Co-ETC Protocol In this section, we consider base design of the Co-ETC protocol, which enables each node to maintain the relevant part of the Voronoi routing graph efficiently and consistently, with the knowledge only provided by its neighbors, and manage topology changes due to mobility, node joining/leaving. The motivation of the proposed protocol stems from the commonality encountered in the mobile WSNs. CoETC is composed of three main procedures: route graph setup, node joining and leaving, and node movement. 3.1 Routing Graph Setup Initially, when a node wants to communicate with another one, no prior knowledge of the routing graph is available. For sake of simplicity, we will assume that every node has the same initial transmission range R. This ensures routing graph is consistently for adjacency neighbors. To avoid message collision, every node waits for a random back-off time period Ti, then simply flood a query with its own ID and coordinates to neighbors. After every node has all its neighbors’ coordinates according to the definition of VD in Section 2, each node generate the local VD itself, and the union of local graphs corresponds to all the sensors in V (see Fig. 3). Notice that, each node has to have enough R to reach its neighbors at the beginning. Generally, we assign it equal to the maximum transmission range Rmax.

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Fig. 3. Voronoi Diagram in WSN

In above routing graph, each node maintains a Voronoi diagram of all neighbors and directly connects to them with minimal latency. As only a few neighbors are kept, the cost to maintain such a routing graph at each node is low. 3.2 Node Joining and Leaving A joining node (mark as ▲) first sends a Query message with its joining coordinates and ID to neighbors. Any acceptors (the node whose region contains the joining node’s coordinates) will respond by sending a list of joining node’s adjacency neighbors (the node whose Voronoi regions border the given node’s, e.g., we mark such nodes as ● in Fig. 4). Notice that, there must be only one acceptor except it on the Voronoi lines. The joining node first computes and organizes its local Voronoi diagram, and then connects to each adjacency neighbor of itself (affected by joining). Related neighbors also update their Voronoi diagram to account for the joining node. When a node will leave from WSN due to node failures or power duty cycling reasons etc., the leaving node simply disconnects. Its adjacency neighbors will update their Voronoi diagrams, replacements are learned via other still-connected adjacency neighbors. The procedure can be seen as the converse procedure of Fig. 4 (node ▲ leave the network).

Fig. 4. Node Joining Procedure. (Left) Before node joining. (Right) After node joining.

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3.3 Node Movement When a node moves, its position updates and transmission radius are sent to all connected neighbors. If the recipient is a periphery neighbors (the node whose adjacency neighbors may partially lie outside transmission range, we mark such nodes as ■. Notice that, some adjacency neighbors may be periphery neighbors at the same time.). it will check whether any of its adjacency neighbors tend to be visible to this moving node and send a Pre-register message with position update. In this way, new neighbors can estimate the connecting time and enhance the resilience of routing graph. When a new neighbor is detected, notifications are sent to the moving node for initiating communications. The moving node also disconnects any periphery neighbors that have left its transmission range. This procedure can be explained in Fig. 5.

Fig. 5. Node Movement. (Left) Before node movement. (Right) After node movement.

The three main procedures of Co-ETC protocol above illustrate that nodes communicate with each other efficiently and the changes to routing graph are localized. Besides, protocol is scalable and adaptive to mobile environment.

4 Extensions In this section, we consider two extensions of the Co-ETC protocol and present efficient distributed algorithms: 1) find an energy-efficient-path between nodes u and v in V; 2) adjust node degree of existing routing graph dynamically to avoid “bottleneck”. From section 2, we have known that the Gabriel Graph is energy-optimal since ρp(GG) = 1. However, we cannot use this graph directly in our extension because: 1) the Gabriel Graph may have long edges, while we are only allowed to connect points within limited transmission range and 2) the empty-disk rule is a global rule and is not suitable for local computation. To deal with those two problems, we consider the following algorithm to find an energy-efficient-path constructed by edges of Gabriel Graph according to existing Voronoi routing graph:

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Algorithm 1: Energy-Efficient-Path (V, u, v) 1. From the source point u, node u adds an edge uv1 if and only if the disk(u, v1) does not contain any node from V. 2. Continually add all such edges into new routing graph until there is no edge which is belonged to the Gabriel Graph GG(V). Notice that, because the original graph is a subgraph of G, so not every edge of GG(V) can be added into new routing graph. 3. Assign each constructed edge vivj the weight equal to ||dij||α. 4. Run the Bellman-Ford distributed shortest path algorithm [6] to compute the shortest path connecting u and v, which has the minimum weight among all paths between u and v. The correctness of the algorithm is based on the following observation: If there is a sensor node vk inside disk(vi, vj), then ||dik||α ≤ ||dij||α and ||djk||α ≤ ||dij||α. It is obvious that the path vivkvj is in the GG(V). Thus, the path by substituting edge vivj with edges vivk and vkvj consumes less energy, which is a contradiction. Consequently, edge vivj must be a Gabriel edge. For this reason, those extra long edges that are not belong to old Delaunay Triangulation also will not be added into our new routing graph. According to the analyses in Section 2, neither DT(V) nor GG(V) has constant maximum node degree. It means that there will be bottleneck nodes in our routing graph, and these nodes are forced to connect beyond their capacities. So we adjust node degree of existing routing graph dynamically as following: Algorithm 2: Dynamic-Node-Degree-Adjustments (V) 1. For all nodes whose neighbors exceed the upper bound value Nmax, decreases its transmission power by ∆P once a step, until it connects Nmax neighbors. At the same time, original node sends Hello message and attaches to it with current link neighborhood list and current transmission power, denoted by Poriginal-node. 2. Whenever another node, which so far does belong to the neighborhood list, hears the Hello message of the original node for the first time. For avoiding onedirected link, it first compares Poriginal-node and Pitself. z If Poriginal-node > Pitself, it leaves its transmission power as before, whichever is smaller. z Or else it set its transmission power to the original node equal to Poriginal-node (adopts directional antennas for transmission) and then begins the same procedure like original node. 3. Node answers Hello message with Reply message and attaches to it only with current transmission power. 4. Node will restores to the preferred transmission power if the number of neighbors falls below the lower bound Nmin.

5 Evaluation and Simulation Results To demonstrate the feasibility and effectiveness of our Co-ETC protocol, we have done an across-the-board performance evaluation of this protocol according to several

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vital criteria including network connectivity, network-lifetime, and robustness to mobility. We implement our Co-ETC protocol on the NS-2 simulator using an idealized MAC layer with fixed link error probability. The simulation environment is generated by using MATLAB. In simulation, the network nodes are uniformly distributed in a square area A of size 1000*1000. 5.1 Network Connectivity Perhaps, the most basic requirement of a topology is that it be connected. More precisely, to ensuring routing graph efficiently constructed, nodes in network must have enough initial transmission range R to reach its neighbors at the Route-GraphSetup stage (Section 3.1). In simulation, we assign it equal to the maximum transmission range Rmax. For every value of R considered in the simulations, we generated 100 random placements and, for every placement, we evaluated the percentage of nodes belong to the largest connected component. With different R, the average percentage of nodes which are in the largest strongly connected component related to the number of nodes is shown in Fig. 6.

Fig. 6. Percentage of nodes that belong to the largest connected component

As shown in Fig. 6, there are critical thresholds under different transmission range R. The sharpness of the threshold depends on the R. Based on these results; we can choose appropriate R for the initial scheme of different numbers of nodes. Fig.7 has shows the actual topologies for one simulated network with 100 nodes with maximum transmission range Rmax=250. Fig.7(a) and (b) are the topologies resulting from original graph G and our Co-ETC routing graph. As can be seen, Co-ETC maintains network connectivity in its routing graph.

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5.2 Network-Lifetime We compared the Co-ETC protocol (after adding extensions) presented in the previous sections to K-NEIGH protocol [3] and CBTC algorithm [4]. Notice that, the CBTC algorithm (with α=2/3π) and K-NEIGH protocol were all with pruning optimization in simulation. Since CBTC algorithm does not provide routing by itself, we used the AODV [7] routing protocol for it in simulation. The same network setup was used to compare the implementations of routing protocols. 100 sensor nodes were randomly placed in area with R=250. Each node had initial energy of 0.5 Joule. Five of them were source nodes, which produce CBR data traffic. The length of a data packet was 64 bytes. One node was assign as sink node. Nodes moved randomly with constant speeds 10/ time. Node bounced back once hitting the boundary. Original topology

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Fig. 7. Topology graph. (Left) Original topology. (Right) Co-ETC routing graph.

In WSNs, the metric of actual interest is not the energy-efficient of per packet, but the whole operational lifetime of WSN. So we use network lifetime as the metric to evaluate the energy-efficient performance of protocols. We consider the network to be down until there is no data packet can be received by sink node. Fig. 8 illustrates the normalized network lifetime simulation results of Co-ETC, and the reference K-NEIGH and CBTC+AODV (α=2/3π) under different network loads in both static and mobile scenarios. It is shown that Co-ETC extends the network-lifetime significantly in the mobile scenario. Lifetime at most a factor of 2.2 those of K-NEIGH and CBTC+AODV could be reached. Although Co-ETC is designed to be efficient in dynamic WSNs, it also prolongs the lifetime 10-45 percent in static scenario. Contrary to K-NEIGH and CBTC, our Co-ETC protocol is almost independent of the choice of scenario: static or mobile. This is due to the fact that the constructed routing graph of Co-ETC has good property adapt to mobile environment.

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Fig. 8. Network lifetimes of different schemes

6 Conclusion In this paper, we consider energy-efficient topology control routing in mobile wireless sensor networks. We present a distributed routing protocol, Co-ETC, and indicate that base on underlying routing graph, it has better energy-efficiency. Moreover, it can adapt to mobile environment well. Although Co-ETC outperforms other existing state-of-the-art algorithms and protocols in terms of network lifetime, and has desirable property including mobility, sparse, and localized. However, we have assumed that all nodes have circular communications ranges. In practice, this is often violated due to fading and multipath effects. It would be interesting to extend results of this paper to more realistic scenarios. Furthermore, we will further do research on using more complex mobility models such as group mobility. Acknowledgments. This work is supported by National Science Foundation of China. (No. 60473001, 60572037, 60573001), and the Innovation Foundation of Science and Technology for Excellent Doctorial Candidates of Beijing Jiaotong University (No. 48013).

References 1. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer Networks, Vol. 38. (2002) 393-422 2. Rajaraman, R.: Topology control and routing in ad hoc network: A survey. SIGACT News, Vol. 33. (2002) 60-73 3. Blough, D.M., Leoncini, M., Resta, G., Santi, P.: The k-neighbors protocol for symmetric topology control in ad hoc networks. Proc. ACM Mobihoc’03, (2003) 141-152

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4. Li, L., Halpern, J.Y., Bahl, P., Wang, Y.M., Wattenhofer, R.: A cone-based distributed topology-control algorithm for wireless multi-hop networks. IEEE/ACM Transactions on Networking (TON), Vol. 13. (2005) 147-159 5. Wang, W., Li, X., Moaveninejad, K., Wang, Y., Song, W., The spanning ratio of β-skeletons. Proc. CCCG, (2003) 35-38 6. Cormen, T.J., Leiserson C.E., Rivest, R.L.: Introduction to Algorithms. Massachusetts: MIT Press/ New York: McGraw-Hill (1990) 7. Perkins, C.E., Royer, E.M.: Ad-hoc on-demand distance vector routing. Proc. IEEE WMCSA’99, (1999) 90-100

Integrated Clustering and Routing Strategies for Large Scale Sensor Networks Ataul Bari, Arunita Jaekel, and Subir Bandyopadhyay School of Computer Science, University of Windsor 401 Sunset Ave. Windsor, ON N9B 3P4, Canada {bari1,arunita,subir}@uwindsor.ca

Abstract. In two-tiered sensor networks using relay nodes, sensor nodes are arranged in clusters and the higher-powered relay nodes can be used as cluster heads. The lifetime of the networks is determined primarily by the lifetime of the relay nodes. Clustering techniques and routing schemes play a crucial role in determining the useful network lifetime. Traditionally, the clustering and the routing problems, for these networks, have been considered independently and solved separately. In this paper, we present a new integer linear program (ILP) formulation that jointly optimizes both clustering and routing to maximize the lifetime of such networks. We show that our integrated approach can lead to significant improvements over techniques that consider clustering and routing separately, particularly for the non-flow-splitting (single-path) routing model. We also propose a heuristic, based on LP-relaxation of the routing variables, which can be used for larger networks.

1

Introduction

A wireless sensor network (WSN) is a network of low-powered, multi-functional sensor nodes, each consisting, at a minimum, of a sensing circuit, a digital signal processor, and radio links [1], [2], [3]. It is extremely important to design routing protocols and algorithms that are energy efficient, so that the overall lifetime of the network can be extended as much as possible. In a two-tier hierarchical architecture, the network is organized as a number of clusters where each sensor node belongs to only one cluster. Some nodes are treated as cluster heads and have additional responsibilities (e.g. data gathering, data aggregation and routing) compared to the remaining nodes. Recently, relay nodes, acting as cluster heads, have been proposed in two-tier sensor networks [2], [4], [5], [6], [7], [8], [9] for energy-efficient data gathering, load-balancing and fault tolerance [2], [5], [7]. In relay-based networks, each relay node collects data from the sensor nodes in its cluster and forwards this data to the base station (or sink) using either single-hop or multi-hop communication model. The multi-hop data transmission model (MHDTM), [7], [8], [9], [10] is particularly suitable for larger networks and is the model used in this paper. A number of routing schemes for two-tiered networks have been proposed in the literature [2], [4], [5], [7], [8], [10]. Most of these adopt the flow-splitting (also I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 143–154, 2007. c IFIP International Federation for Information Processing 2007 

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called multi-path routing) model. In contrast, in a single-path routing model, a node is not allowed to split the traffic, and forwards all its data to a single neighbor. This model avoids many limitations of the flow splitting model [7]. An important factor affecting the lifetime of a two-tier network is the clustering scheme used to assign sensor nodes to the appropriate clusters [6], [11], particularly for single-path routing. Previous approaches to clustering and routing have considered the two problems independently. Typically, the assignment of sensor nodes to clusters is done first, and then a routing scheme to maximize the network lifetime is calculated. In this paper, we present a new integer linear program (ILP) formulation that jointly optimizes both clustering and routing to maximize the lifetime of the upper-tier relay node network. We have assumed a network model where a) the roles of the sensor nodes and the relay nodes are not interchangeable, b) the relay nodes do not perform sensing tasks and are provisioned with higher energy, c) the relay nodes can transmit over larger distances, compared to regular sensor nodes, d) each sensor node, is located close enough to some relay node so that the sensor node can transmit directly to the relay node, e) sensor nodes only communicate to their respective cluster heads and do not take part in the routing, and f) both the sensor nodes and the relay nodes communicate through an ideal shared medium where communication between nodes is handled by appropriate MAC protocols (as in [2], [5]). We focus on the non-flow-splitting (single-path) routing model and show that our integrated approach can lead to significant improvements over techniques that consider clustering and routing separately. To the best of our knowledge, this is the first technique that combines the clustering and the routing problem to maximize the network lifetime. We assume that the clustering and the routing scheme is computed at the base station (or at some centralized entity where such computation may be carried out). There are two possible scenarios for determining the positions of the relay nodes and the sensor nodes as follows: Case i) We place the sensor and the relay nodes at predetermined locations. Before the deployment of the network, we can compute the clustering and the routing decisions at some centralized location. The sensor nodes and the relay nodes may be pre-configured with this information. Case ii) We can find the locations of the nodes using a GPS system. GPS equipped nodes has been widely proposed in the literature [1], [2], [5], [6]. The GPS system needs to be operated for a very short period of time, to know the locations of the sensor and the relay nodes. We also assume that the nodes are stationary after deployment. We can then compute, at some central location, the clustering and the routing decisions and broadcast the result to the entire network. Since the communication from each sensor or relay node as well as the communication broadcast from the base station to the sensor and relay nodes will be a single, small packet, the energy

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dissipated for sending/receiving these two packets is insignificant, compared to the subsequent transmissions, and will not have any substantial impact on the lifetime of the network. In this paper we have i) presented an integrated ILP for optimal clustering and single-path routing to maximize network lifetime, giving a significant increase in network lifetime compared to solving the two problems separately. ii) proposed a LP-relaxation of the routing variables so that our approach can be used for multi-path routing as well. iii) extended the above ILP to develop a new heuristic for single-path routing capable of handling large networks, and have shown, through simulations, that the network lifetimes that may be achieved by the heuristic solutions is close to the theoretical upper bound.

2 2.1

Review Load Balanced Clustering

Many researchers have investigated clustering of nodes in a wireless network [2], [3], [5]. In [2], the problem of forming clusters around a few high-energy gateway nodes has been investigated. In Fig. 1, the sensor nodes in the shaded region can be assigned to any one of clusters A, B or C. The routing scheme and/or the energy dissipation of relay nodes A, B and C, may favor one assignment over the others. A load balanced clustering algorithm assigns each sensor node to an appropriate cluster to maximize the lifetime of the network. In [9], a minimum number relay nodes have been used as cluster heads to cover all sensor nodes. However, [9] does not address the issue of clustering after the placement of the relay nodes. In [2], the “cardinality” of a cluster (the number of sensor nodes associated with the cluster) is used in a heuristic to minimize the variance of the cardinality of each cluster in the network. In [6], it is demonstrated that suitable clustering techniques can be used to increase the lifetime of the network. 2.2

Routing in Sensor Networks Using Relay Nodes

The problem of routing in wireless sensor networks, under the “flow-splitting” model, has been extensively covered in the literature. In [8], Hou et al. have attempted to maximize the lifetime of a sensor network by provisioning relay and sensor nodes with additional energy using a mixed-integer non-linear program and have proposed a heuristic. In [10], the authors have formulated the lifetime optimization problem, under the flow-splitting model. In [12], Falck et al. have addressed the issue of balanced data gathering in sensor networks and have proposed a LP formulation that enforces some balancing constraints in the data gathering schedule. In [2], Gupta and Younis have focused on load balanced

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clustering and have proposed a heuristic solution for the optimization problem. Routing without flow splitting (i.e., single-path routing) has been studied in [7], [13], and [14]. In [7], the authors have presented a transformation algorithm to convert a multiple outgoing flow routing model to a single outgoing flow routing model. In [14], the authors have investigated the problem of maximizing network lifetime by appropriately placing nodes which are not energy constrained (e.g., connected to a wall outlet). In [13], the authors propose a formulation for constructing minimum-energy data-aggregation trees, for a flat architecture.

3 3.1

ILP Formulations for Optimal Clustering and Routing Network Model

In this paper, we have assumed that the communication energy dissipation is based on the first order radio model [3], where the energy required to transmit (receive) b bits, at a distance d, is given by ETx (b, d) = α1 b + βbdm (ERx (b) = α2 b), where α1 (α2 ) is the energy coefficient for transmitter (receiver), β is the energy coefficient for the transmit amplifier and q is the path loss exponent. For our network model, we consider a two-tiered wireless sensor network with n sensor nodes, m relay nodes and one base station. Each sensor node belongs to only one cluster and each relay node acts as the cluster head of exactly one cluster. We assign each node a unique label as follows: i) for each sensor node, a label i, 1 ≤ i ≤ n, ii) for each relay node, a label j, n < j ≤ n + m, and iii) for the base station, a label n + m + 1. Let S be the set of all sensor nodes. Each sensor node belongs to only one cluster and each relay node acts as the cluster head of exactly one cluster. In

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other words, if S j , n + 1 ≤ j ≤ m + n, is the set of sensor nodes belonging to the j th cluster, then S = S n+1 ∪ S n+2 ∪ . . . ∪S m+n and S j ∩ S k = ∅, ∀j = k, n + 1 ≤ j, k ≤ m + n. The set S j will constitute the cluster with the relay node having label j as the cluster head. As mentioned in the introduction, we assume that the locations of all the sensor nodes and the relay nodes are known (or can be determined), and the average amount of data generated by each sensor node is also known a priori. The data rate for all sensor nodes need not be uniform, but can vary from node to node. We also assume that the placement strategy, applied during the deployment phase of the network, ensures proper coverage of each sensor node and the connectivity of the relay node network. In our model, data gathering is proactive, i.e., data are collected and forwarded to the base station periodically, following a predefined schedule. We refer to each period of data gathering as a round [10]. In each round of data gathering, each relay node gathers the data it receives from its own cluster and transmits that data, either directly to the base station (single hop model) or forwards the data towards the base station using a multi-hop path (multi-hop model). In the case of multi-hop routing, in addition to the data generated by its own cluster, each relay node also relays any data it receives from neighboring relay nodes. We will measure the lifetime of the network by the number of rounds the network operates from the start, until a relay node depletes its energy completely and ceases to function. 3.2

Notation Used

In our formulation we are given the following data as input: • • • • • • • • •

α1 (α2 ): Energy coefficient for transmitter (receiver). β: Energy coefficient for amplifier. q: Path loss exponent. bi : Number of bits generated by sensor node i per round. n (m): Total number of sensor (relay) nodes, with each sensor (relay) node having a unique index lying between 1 and n (n + 1 and n + m). n + m + 1: Index of the base station.  C: A large constant, greater than bi , the total number of bits received by the base station in a round. rmax (dmax ): Transmission range of each sensor (relay) node. di,j : Euclidean distance from node i to node j.

We also define the following variables: • Xi,j : Binary variable defined as follows: ⎧ 1 if sensor node i belongs to the cluster ⎪ ⎪ ⎨ of relay node j, Xi,j = ∀i, j : 1 ≤ i ≤ n, n + 1 ≤ j ≤ n + m, ⎪ ⎪ ⎩ 0 otherwise.

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• Yj,k : Binary variable defined as follows:  1 if relay node j transmits to relay node k, Yj,k = 0 otherwise. • Bj : Total number of bits generated by the sensor nodes belonging to cluster j in one round. • fj,k : Number of bits sent by relay node j to relay node k in one round. • Rj : Number of bits received by relay node j from other relay nodes in one round. • Tj : Number of bits transmitted by relay node j in one round. • Fmax : The total energy spent per round by the relay node which is being depleted at the fastest rate. 3.3

ILP Formulation for Non Flow-Splitting Model (ILP-NFS)

Given the network as described in Section 3.1, the objective of this formulation is to maximize the lifetime of the network by finding an optimal clustering and routing scheme. Minimize Fmax

(1)

Subject to: a) A sensor node i can transmit to a cluster-head j, only if the distance between i and j is less than the range rmax of the sensor node. Xi,j · di,j ≤ rmax ,

∀i, 1 ≤ i ≤ n,

(2)

∀j, n < j ≤ n + m b) A sensor node must belong to exactly one cluster. n+m 

Xi,j = 1,

∀i, 1 ≤ i ≤ n

(3)

j=n+1

c) Compute the total number of bits Bj received at relay node j from its own cluster in one round of data gathering. n 

bi · Xi,j = Bj ,

∀j, n < j ≤ n + m

(4)

i=1

d) Relay node j can only transmit to one relay node or to the base station (non-flow-splitting constraint). n+m+1  k=n+1

Yj,k = 1,

∀j, n < j ≤ n + m

(5)

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e) Compute the total number of bits Tj transmitted by relay node j in one round of data gathering. Tj =

n+m+1 

fj,k ,

∀j, n < j ≤ n + m

(6)

k=n+1

f) Compute the total number of bits Rj received at node j (a relay node or the base station) from other relay nodes in one round of data gathering. Rj =

n+m 

fk,j ,

∀j, n < j ≤ n + m + 1

(7)

k=n+1

g) Ensure that relay node j is transmitting to relay node k, only if relay node k is the next node in the multi-hop path from j to the base station (or k is the base station). fj,k ≤ C · Yj,k ,

∀j, k, n < j ≤ n + m,

(8)

n
∀j, n < j ≤ n + m

(9)

i) A source relay node can transmit to a destination relay node (or base station) if the destination node is within the transmission range of the source node. (10) Yj,k · dj,k ≤ dmax , ∀j, k, n < j ≤ n + m, n
n+m+1  k=n+1

fj,k · dqj,k ≤ Fmax ,

(11)

∀j, n < j ≤ n + m

In evaluating ILP-NFS, we have computed the lifetime as the number of rounds until one relay node becomes completely depleted. Although ILP-NFS minimizes the maximum load in any relay node in a round, it is possible that, after the first relay node is depleted, other relay nodes might still have some residual power. We could have used the surviving relay nodes to further extend the lifetime of the network. The rescheduling strategy we have proposed in [4] may be used, where the routes are recomputed at periodic intervals1 , taking into consideration the available residual energy of each relay node. We have shown in [4] that it is possible to increase significantly the lifetime of the network by this technique. We may apply the same technique to this formulation as well. We omit the details since the technique is essentially the same. 1

Clustering was not considered in that paper.

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LP-Relaxation Based Heuristic

The ILP formulation presented in the previous section guarantees an optimal solution that maximizes the lifetime of the upper-tier relay node network. However, this formulation becomes computationally intractable for larger networks. In this section, we propose a heuristic approach based on a LP-relaxation of the routing variables Yj,k . In this approach, we first solve the ILP (ILP-FS) for the combined clustering and routing problem under the flow-splitting model. This means that we allow the traffic to be split among different nodes, and no longer require the integer variables Yj,k . The solution obtained for the flow-splitting model is then used to guide the ILP for the non-flow-splitting model. The idea is that, we define a small set P j of “promising” relay nodes, for node j, for all j, n < j ≤ n + m. The nodes in P j are selected, based on the nodes that j transmits to, in the solution generated by ILP-FS. When looking for the next node, in the path from relay node j to the base station, the new formulation only considers the nodes in P j . 4.1

ILP for Flow-Splitting Model (ILP-FS)

The formulation for the flow-splitting model is similar to ILP-NFS presented in the previous section, with the following modifications. 1. The variables Yj,k are eliminated. Since, we are allowed to arbitrarily split the traffic, the integer routing variables Yj,k are no longer needed and traffic flows can be determined by the continuous flow variables fj,k . 2. Since a relay node can transmit to any number of other nodes, constraint (5) is no longer needed, and is removed. 3. Since there can be non-zero flow to more than one node, constraint (8) is not needed, and is removed. 4. Constraint (10) is modified as follows: fj,k = 0,

∀j, k,  dj,k > dmax

(12)

All other variables and constraints, as well as the objective function are identical to ILP-NFS. 4.2

Non Flow Splitting-Heuristic (NFS-H)

In the heuristic, we find the routing scheme in three steps. The first step in the heuristic is to run ILP-FS to identify the set, P j , of promising relay nodes for the j th relay node. In the second step, we set Yj,k = 0, for all relay nodes not in P j . Finally, we run ILP-NFS to generate a solution for combined clustering and routing, under non-flow splitting model. By setting Yj,k = 0, in step 2, for all relay nodes not in P j , ILP-NFS is forced to select one of the nodes in P j as the next node in the path from node j to the base station. By extensive simulation, we have observed that the size of P j , for each node j, is typically from 1 to 4. This considerably reduces the complexity for ILP-NFS and we find that ILP-NFS quickly converges to a solution.

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151

Experimental Results Experimental Setup

We have carried out a number of experiments to test the effectiveness of our formulations, with different sensor and relay node distributions. We have measured the achieved lifetime of a network by the number of rounds until the first relay node runs out of battery power. The ILP formulations were solved using CPLEX version 9.1 [15]. In the first set of experiments, we consider networks with up to 15 relay nodes, and sensor nodes randomly distributed over the region. We varied the number of sensor nodes from 100 to 500 nodes. To evaluate the performance of our approach, we compared the achieved lifetime with standard multi-hop routing schemes such as the minimum hop (MH) routing and the minimum transmission energy (MTE) routing. We also considered an optimal single-path routing scheme (ILP-R) [4], which maximizes the network lifetime for a specified clustering strategy. For each routing scheme, we experimented with a number of standard clustering techniques [11] such as: i. Greedy Clustering (GC): Each relay node greedily selects all sensor nodes which can communicate with it, and has not yet been assigned to a cluster. ii. Least Distance Clustering (LDC): Each sensor node transmits to the closest relay node. iii. Minimum Variance Clustering (MVC): The clustering algorithm attempts to distribute sensor nodes uniformly over the relay nodes, such that the variation in cluster size is minimized. In the following sections, we have shown the results corresponding to the MVC clustering algorithm, which is the best performing algorithm as reported in [6]. The improvements obtained with respect to GC and LDC are comparable to MVC, or even higher. The first set of experiments was used to calibrate the performance of the heuristic with respect to the optimal. In the second set of experiments, we considered much larger networks with up to 44 relay nodes and 5000 sensor nodes. For such networks, it was not possible to obtain optimal solutions using the ILP for the non-flow-splitting model. So, we compared our heuristic with MTE and MH routing. We have assumed that the communication energy dissipation is based on the first order radio model, described in Section 3.1. The values for the constants are taken as α1 = α2 = 50nJ/bit, β = 100pJ/bit/m2 and the path-loss exponent, q = 2, which are similar to [3]. The range of each sensor (relay) node is set to 40m (200m) and the initial energy of each relay node is set to 5J, as in [9]. 5.2

Performance Evaluation for Moderate Sized Networks

In this section, we present the results for a 160m × 160m area network with 12 relay nodes. Figure 2 shows the network lifetime obtained by using our integrated approach versus the MH, MTE, and ILP-R routing strategies combined with MVC clustering scheme, for the non-flow-splitting model. For each data set, we

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represent the lifetimes achieved by the different strategies in the following order: minimum-hop routing (MH), minimum transmission energy routing (MTE), optimal routing performed separately after clustering (ILP-R), LP-relaxation based heuristic for combined clustering and routing (NFS-H), and optimal solution for combined clustering and routing (ILP-NFS). 4

5

x 10

MH MTE ILP−R NFS−H ILP−NFS

4.5

4

Lifetime in rounds

3.5

3

2.5

2

1.5

1

0.5

0

100

200

300

400

500

Number of sensor nodes

Fig. 2. Comparison of achieved lifetimes with MVC scheme

The data clearly indicate that an integrated approach performs significantly better than traditional routing schemes. The average achieved lifetime obtained by ILP-NFS increased by over 300% (over 200%) compared to traditional routing schemes such as MH (MTE). Even when compared to the optimal routing generated by ILP-R, ILP-NFS produced an average improvement of 23%. The results also show that the performance of our heuristic (NFS-H) is quite close to the optimal (within 10% - 15%) in all cases. 5.3

Performance Evaluation for Large Scale Networks

In the previous sections we compared our heuristic (NFS-H), with the optimal solution and have shown that its performance is close to optimal for small networks. For large networks, it was not possible to generate optimal solutions for the non-flow-splitting model, using ILP-NFS. The optimal routing formulation (ILP-R) in [4] was also unable to generate solutions for larger networks. Therefore, in this section, we compare the performance of our heuristic with traditional MH and MTE routing schemes. We also report the achieved lifetime obtained using ILP-FS. This provides an upper bound on the maximum achievable lifetime for each experimental run.

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4

3

x 10

MH MTE NFS−H ILP−FS

Lifetime in rounds

2.5

2

1.5

1

0.5

0

12

15

24

36

44

Number of relay nodes

Fig. 3. Comparison of achieved lifetimes for moderate to large networks

Figure 3 shows how the achieved lifetime varies with the size of the network. For a specific size of relay node network, we selected the number of sensor nodes so that the average cluster size remains the same for all networks. The sensing area varied from 160m × 160m for a network with 12 relay nodes to 400m × 280m for a network with 44 relay nodes. As before, Figure 3 confirms that the combined approach clearly performs better than both MH and MTE for large networks. The average improvement of our combined approach is 300% and 200% over MH and MTE respectively. The overall lifetime decreases with network size, since the sensing area as well as the total amount of data to be transmitted increases, which in turn increases the load on critical nodes close to the base station.

6

Conclusion

In this paper we have proposed an integrated approach that jointly optimizes clustering and routing in large scale two-tier sensor networks. We have presented an ILP formulation that maximizes the lifetime of the upper-tier relay node network, as well as a LP-relaxation based heuristic that can be used for large networks with thousands of nodes. We have calibrated the performance of the heuristic, by comparing with optimal solutions for smaller networks. We have demonstrated that our combined approach significantly increases network lifetime for non-flow-splitting (single-path) routing. Our proposed heuristic clearly outperforms traditional routing schemes such as minimum-hop routing and minimum-transmission-energy routing, for large scale networks.

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Acknowledgment The work of A. Jaekel and S. Bandyopadhyay have been supported by research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC).

References 1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: a survey. Computer Networks, vol. 38, pp. 393-422, 2002. 2. G. Gupta and M. Younis. Load-balanced clustering of wireless sensor networks. In IEEE Intl. Conf. on Comm., vol. 3, pp. 1848-1852, 2003. 3. W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy effcient communication protocol for wireless micro-sensor networks. In Proc. of the 33rd HICSS, pp. 3005-3014, 2000. 4. A. Bari, A. Jaekel and S. Bandyopadhyay. Maximizing the Lifetime of Two-Tiered Sensor Networks. In IEEE EIT, pp. 222–226, 2006. 5. G. Gupta and M. Younis. Fault-tolerant clustering of wireless sensor networks. Proceedings of IEEE WCNC. pp. 1579–1584, 2003. 6. G. Gupta and M. Younis. Performance evaluation of load-balanced clustering of wireless sensor networks. 10th Inter. Conf. on Telecomm., vol. 2, pp. 1577–1583, 2003. 7. Y. T. Hou, Y. Shi, J. Pan, and S. F. Midkiff. Lifetime-optimal data routing in wireless sensor networks without flow splitting. In Workshop on Broadband Adv. Sensor Networks, San Jose, CA, 2004. 8. Y. T. Hou, Y. Shi, H. D. Sherali, and S. F. Midkiff. On Energy Provisioning and Relay Node Placement for Wireless Sensor Networks. In IEEE SECON, vol. 32, 2005. 9. J. Tang, B. Hao, and A. Sen. Relay node placement in large scale wireless sensor networks. Comp. Comm., vol. 29(4), pp. 490–501, 2006. 10. K. Kalpakis, K. Dasgupta, and P. Namjoshi. Maximum Lifetime Data Gathering and Aggregation in Wireless Sensor Networks. IEEE Inter. Conf. on Network., 2002. 11. A. Bari, A. Jaekel and S. Bandyopadhyay. Optimal load balanced clustering in two-tiered sensor networks. In IEEE BaseNets, 2006. 12. E. Falck, P. Floren, P. Kaski, J. Kohonen, and P. Orponen. Balanced data gathering in energy-constrained sensor networks, vol. 3121 LNCS, pp. 59-70, Berlin Heidelberg, 2004. Springer-Verlag. 13. M. Borghini, F. Cuomo, T. Melodia, U. Monaco and F. Ricciato. Optimal data delivery in wireless sensor networks in the energy and latency domains. WICON, pp. 138–145, 2005. 14. M. Yarvis, N. Kushalnagar, H. Singh, A. Rangarajan, Y. Liu nad S. Singh. Exploiting heterogeneity in sensor networks. INFOCOM 2005, vol. 2, pp. 878–890, 2005. 15. ILOG CPLEX 9.1 Documentation. Available at the website http://www.columbia. edu/˜dano/resources/cplex91 man/index.html

On-Demand Routing in Disrupted Environments Jay Boice, J.J. Garcia-Luna-Aceves, and Katia Obraczka Department of Computer Engineering University of California at Santa Cruz

Abstract. While current on-demand routing protocols are optimized to take into account unique features of mobile ad hoc networks (MANETs) such as frequent topology changes and limited battery life, they often do not consider the possibility of intermittent connectivity that may lead to temporary partitions. In this work, we introduce the Space-Contentadaptive-Time Routing (SCaTR) framework, which enables data delivery in the face of both temporary and long-lived MANET partitions. SCaTR takes advantage of past connectivity information to effectively route traffic towards destinations when no direct route from the source exists. We show through simulations that, when compared to traditional on-demand protocols, as well as Epidemic routing, SCaTR increases delivery ratio with lower signaling overhead in a variety of network scenarios with intermittent connectivity. We also show that SCaTR performs as well as on-demand routing in scenarios that are well-connected, and/or have no mobility predictability (e.g., scenarios with random mobility).

1

Introduction

The price, performance, and form factors of sensors, processors, storage elements, and radios today are enabling the development of network-supported applications in very disrupted environments, i.e., environments where end-to-end connectivity is not guaranteed at all times because of either the characteristics of the environment or the normal operation of the network nodes. Examples of such applications and environments include monitoring of disrupted phenomena (e.g., wild fires), object tracking, establishment of on-demand network infrastructure for disaster relief or military purposes (in which case, the ad hoc network can be disrupted by terrain, weather, and other natural phenomena, as well as jamming, interference, etc.), peer-to-peer vehicular or interpersonal networks [1] with very sparse connectivity, and mobile ad hoc networks (MANETs) that need not be connected at all times in order to limit interference and contention. In these scenarios, network disconnection is the normal state of operation rather than an exception. The demand for networking in environments prone to intermittent connectivity poses a challenge because the architects of the IP Internet and MANETs have assumed that physical connectivity exists on an end-to-end basis between sources and destinations for extended periods of time, or at least for the duration of a transaction among communicating parties. This assumption has had I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 155–166, 2007. c IFIP International Federation for Information Processing 2007 

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profound implications on how communication bandwidth is shared, how routing is accomplished, and how messages are disseminated across computer networks. In particular, routing in packet-switching networks has been based on routing tables that specify the next hop to one or more destinations. Such routing information is derived entirely from topology (or connectivity) information that represents only a snapshot of the state and characteristics of network links at particular instants. Regardless of the specific mechanisms used in a routing protocol today (e.g., proactive or on-demand routing), computing the routing table entry for a given destination can be viewed as a particular form of searching a database. The routing database can be replicated (as it is done in such topology broadcast approaches as TBRPF [2] and OLSR [3]) or distributed, as in AODV [4] or DSR [5]. Depending on whether the routing database is replicated or distributed, the search algorithm can be centralized (e.g., using Dijkstra’s shortest path first algorithm) or distributed (e.g., using a flood search based on route requests and route replies). The routing databases constructed by traditional routing algorithms specify the instantaneous status of a link (up or down), and the value of its parameters such as delay and bandwidth at some specific point in time. The search for routes in such databases produces snapshot paths that have no temporal dimension. Hence, if the network connectivity or link parameters change, multiple paths to destinations may be affected; the only way most current routing protocol can recover is to search for new paths. This time-independent, reactive approach to changes in network connectivity and link parameters works well as long as the disruptions in network connectivity due to environmental or operational reasons are not so frequent and/or long-lived that they prevent the routing protocol from obtaining time-independent paths to intended destinations. Starting with the work in the Interplanetary Internet Research Group (IPNRG) [6] of the IRTF (Internet Research Task Force), considerable effort has recently been devoted to the study of networks with intermittent connectivity or very long latencies. Perhaps most prominent in this area is the work by the DTNRG (Delay Tolerant Networking Research Group) [7], which started in 2002 under the IRTF. Section 2 summarizes prior related work on routing in disrupted environments. From our summary of related work, it becomes apparent that no complete solution exists for on-demand routing that incorporates the network topology’s time dependency. In this paper we describe the SCaTR (Space Content adaptive Time Routing) framework to enable on-demand routing in MANETs with intermittent connectivity. Section 3 describes SCaTR which we currently implement by extending the On Demand Ad Hoc Distance Vector Routing Protocol [4] (AODV). Our current instantiation of SCaTR is such that, if the network is connected, it operates exactly as regular on-demand routing, in this case AODV. However, if no direct route is available from source to destination, a node that is deemed closer to the destination than the source will advertise itself as a proxy. In this manner, we are assured that the resulting protocol will do no worse than standard AODV in well-connected environments, and better in partitioned networks.

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Section 5 addresses the performance of SCaTR compared to on-demand and Epidemic routing. Scenarios involving both random and predictable node mobility are investigated. In predictable mobility scenarios, node schedules or trajectories are not assumed to be global knowledge. Instead, the routing algorithm in SCaTR uses mobility histories to improve performance. Our simulation results show that the added functionality of proxies in SCaTR improves delivery rates in both predictable and random mobility situations, while incurring lower signalling overhead. Given enough time, the protocol delivers all possible packets to their intended destinations, achieving optimal reliability. Section 6 summarizes our contributions and discusses ideas for future work.

2

Related Work

Recently, Message Ferrying [8] has been investigated for use in highly partitioned networks. The approach utilizes special nodes called ferries whose mobility can be controlled to maintain communication between partitions. Much of the work has focused on route scheduling of the ferries, and synchronization between their routes, since a well-chosen schedule will have a great impact on timely and reliable message delivery. It has also been shown that the ferries can be used as an energy saving device for other nodes in the network; if there are no ferries nearby, nodes can be turned off to conserve energy. This work makes the assumption that controllable nodes exist in the network, however, there are situations in which these nodes are not available. Our work addresses these situations. In the Epidemic routing [9] approach, data are transmitted through message exchanges between neighboring nodes. The premise behind the work is that by duplicating a message and sending it to all neighbors, it can quickly and reliably reach its destination. Similar work [10] has been done to improve Epidemic routing by limiting the number of nodes to which messages are sent. Epidemic routing, however, relies on data duplication to deliver messages which can be prohibitively expensive in terms of energy consumption. Delay Tolerant Networking [7] has attracted considerable interest from the network research community for several years now. Additional layers atop the routing and transport layers have been proposed to provide store and forward capabilities, as well as interoperability services to partitioned networks. This work has also examined the idea of custody [11], which is a hop-by-hop method of reliability for disconnected networks. This is a much-needed approach to the issue of reliability, as end-to-end reliability is unmanageable in these scenarios. A more general framework for routing using knowledge of network mobility has also been proposed [12]. This approach adds the time dimension to routing tables, and selects routes based on a combination of the data’s destination and the time of message arrival. It uses global knowledge of node mobility schedules to construct these routing tables. In some scenarios, these schedules may not be available, or too expensive to maintain. Spray and Wait [13] is a recent improvement over a pure flooding protocol such as Epidemic routing. In this work, only the source can replicate a message, and

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the amount of replication is proportional to the number of nodes in the network. It is shown that the method can bound delay proportional to the optimal delay. After ’spraying’ several copies of a message, the host ’waits’ until one is delivered. Location information is a useful tool for routing in disrupted networks. Both MobySpace [14] and MV routing [15] are methods that use location information to aid routing decisions. They assume that a node who has visited a particular location is likely to revisit it, and therefore is a good candidate to carry messages to that location. MV routing uses location information to facilitate buffer management, while MobySpace is a framework for generating probabilities that a nodes will move to specific locations in the future. Both methods require some sort of localization method such as GPS. While there has been significant prior work on the topic of partitioned networks, most of the approaches have made one of three assumptions. One assumption is global topology knowledge, which has proved to be useful in determining future connectivity. Another assumption is the existence of controllable nodes, which can be extremely useful in aiding delivery in sparse networks. The third assumption is that data can be duplicated freely among nodes, which can lead to excellent delivery ratios. Our work is an attempt to do away with these assumptions, yielding a more general framework applicable to any network scenario where any such assumption may be unrealistic.

3

SCaTR

The SCaTR framework extends on-demand routing, taking action only when direct routes cannot be established by the underlying protocol. In the case of a route discovery failure, i.e., the source and destination are in separate partitions, SCaTR tries to route data to the node or nodes in the source’s partition that are likely to have a route to the destination in the near future. These nodes act as proxies for the destination and buffer messages until either the destination is discovered, or another node is selected as a better proxy for those messages. Messages are replicated at most one time, resulting in minimal data replication and duplicate message filtering overhead. Proxies are selected based on past connectivity information which nodes keep in content-adaptive contact tables. As it will become clear, contact tables, which are the equivalent of traditional routing tables, use time-dependent and spacedependent routing metrics. These metrics differ for different types of content or local constraints such as buffer size. For instance, if a proxy is running low on buffer space, it may decide to select as the next proxy for that destination the first node it hears from that has been in contact with the destination; this is done even if the node’s contact value is lower that its own; however, if the node has higher buffer availability, it can carry the data for a longer interval. Because SCaTR takes no action if routes are successfully established, we are guaranteed that it will perform no worse than the underlying on-demand routing protocol in any situation. This diversity makes it well suited for adoption in any network scenario. SCaTR consists of several phases: contact table maintenance,

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route discovery, route selection, and proxy rediscovery. Each phase is described in detail below. Each node in the network maintains a contact table containing a measure of time-dependent distances to other nodes in the network. Each entry in the table consists of a destination address and its current contact value. Nodes maintain these tables with information that is piggybacked onto hello messages; when a node receives a hello message from a neighbor, it also receives that node’s contact table. It uses this table to make changes to its own contact table. Because it can be very expensive to maintain contact information about all nodes in a network, SCaTR initializes its contact tables on-demand. Each node starts with an empty table, and adds destinations only when it receives a request for that destination, or meets another node who has an entry in its table for that destination. This method of initialization results in a delay for the first messages introduced into the network because the contact table request must propagate to the destination and then back to the source before proxies are advertised. In large networks with predefined sinks or a limited number of destinations, however, this method saves significant computational and communication overhead. The mechanisms used to maintain and update contact values have several important characteristics that must be addressed: – Ordering: The highest contact value advertised for any node must be advertised by the node itself. A node that will reach a destination sooner than another node should have a higher contact value for that destination. – Stability: Contact values should not fluctuate greatly over time in order to minimize ’leapfrogging’ of contact values (the case in which nodes alternately select each other as proxies due to asynchronous contact value updates). – Simplicity: Contact value calculation should not be computationally complex, as it must be frequently invoked. To address these issues, we propose an algorithm that is initiated with a maximum contact value advertised by destinations themselves. Time is broken into hello intervals. During a hello interval, nodes maximize their neighbors’ advertised contact values for each destination. These values are averaged over a hello period, and each node maintain a window of periods, used to generate the next interval’s contact value for each destination. A more detailed description of contact value maintenance follows. The length of a hello interval is the amount of time between hello message advertisements. During each hello interval, a node maintains the maximum contact value it receives from its neighbors for each destination. The hello period consists of a sequential group of hello intervals as defined by the parameter τ . At the end of a hello period, a node averages the values obtained during each hello interval to obtain a single value for the entire period for each destination. The length of time for which a node retains contact information is specified by the period window κ. At the end of each hello period, the value obtained during the most recent period is added to a queue, and the oldest value is removed. Each node’s contact values are recalculated at the end of each period by averaging the

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most recent κ periods. Until the node’s contact value is updated again, it will advertise this value to its neighbors. The main mechanisms for route discovery in the SCaTR framework are the Proxy Request (PREQ) and Proxy Reply (PREP) messages. When the underlying routing protocol is unable to establish a route to a destination, the source node will issue a PREQ to its current partition. This is a request for a candidate node or nodes in the source’s partition to buffer messages and carry them towards their destination. The PREQ can request one or more destinations to reduce signaling overhead. Instead of sending out an individual PREQ for each destination, the node will send a single PREQ with all destinations for which it has buffered data. The PREQ also contains the source node’s contact value for each destination that is being requested. The contact value is included so that only nodes with a significantly better value reply to the request. In our implementation, we have used a threshold of 5% to determine whether or not a proxy is better; a proxy is only selected if its contact value is 5% higher than the node at which the messages currently reside. Different threshold values could be used and will determine how selective a node is in choosing proxies. In preliminary experiments, we found 5% to be an effective threshold to balance overhead and delivery ratios. The PREP is a message that advertises the responding node as a possible proxy for one or more of the destinations in the PREQ. The source can then decide which proxy or proxies to use for each destination. The PREP contains the proxy’s contact value for the destination, as well as its remaining buffer space, and the number of messages it has already buffered for the source-destination pair. It is important to note that the PREP specifies the destination, not the proxy itself. The source need not know the address of the proxy; it must only know the next hop towards the destination. The source-destination pair obtained from the PREQ packet is entered into a buffering table at the proxy, and any packets that are later received by the proxy with this source-destination pair are buffered. As the source collects PREPs from the nodes in its partition, it compares contact values for each of the destinations. If a new PREP has a higher contact value than the one currently in its routing table, it replaces the entry. Routing tables may now contain two types of routes. One is an active, connected route, while the other is a route to a destination that was advertised by a proxy. These are distinguished only because a reply that advertises a direct route will always take precedence over a proxy route. After a proxy buffers packets, it must ensure that those packets reach their destination. Proxies have several methods of initiating the route discovery process for buffered messages. One method is ’listening’ as updates to its contact table are received. If it receives an improved contact value for a destination for whom it has buffered packets, the node assumes that a better route to the destination is available in its partition, or that the destination itself is nearby. This information initiates route discovery behavior in the proxy. The proxy sends out a single PREQ for all nodes for which it has buffered packets. In the same

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manner as the route discovery process described above, nodes reply to this request, and the proxy selects the best next destination for the data. The proxy also listens to any relevant RREPs or PREPs that it relays, and if route information for destinations in its buffer is relayed, a route is established along the advertised path. In the case that a proxy does not make any updates to the contact value for a buffered destination, or relay a reply for that destination, it sends a PREQ periodically. There are many approaches to message replication, ranging from the minimal approach of a single copy of each message, to the unlimited duplication (up to the number of nodes in the network) of Epidemic routing. More message redundancy requires increased overhead in the form of replicated data, as well as the computational and memory requirements required to maintain buffers and detect duplicate messages. One approach to the problem is to allow each proxy to replicate a message a certain number of times. However, as the network scales, it is clear that this number will grow exponentially. In addition, a proxy holding a message has no indication of whether another proxy has successfully delivered the message already. Thus, any replicas made by this proxy are in waste. This clearly is not a feasible solution. It is also possible to control replication in a tree fashion with the message source as the root of the tree. There can be a defined branching factor or replication degree that each level of the tree is permitted to use. Using the depth of a message in the tree and the branching factor, one can specify an exact number of duplications for each message. This reduces message duplication, but has the same problem of communicating message deliveries as mentioned above. In SCaTR, we have decided to minimize the amount of message replication. Prior work by one of the authors [16] has indicated analytically that under constrained conditions, the optimal number of message duplications is one. The overhead resulting in any further duplication is not justified by the increase in delivery ratio. For these reasons, the source can select at most two initial proxies for message transmission, and proxies may not duplicate messages.

4

Simulation Setup and Mobility Models

The SCaTR framework is implemented as an extension to the AODV routing protocol and simulated in GloMoSim [17] with several mobility scenarios and connectivity models. Epidemic routing was implemented for comparison to SCaTR, in which messages are passed to all neighbors. Throughout the experiments, 18 CBR data flows generate messages at ten second intervals for the first 400 seconds of the simulation. The experiments run for 2000 seconds to give messages time to propagate to their destination, and use an 802.11 MAC layer. All experiments were run with 5 seed values and the results averaged. The two mobility scenarios described below, in addition to the random waypoint model, show the effectiveness of the protocol over varied topologies and connectivity models. In the gridded random waypoint setup, nodes are arranged in a square field with a 377m radio propagation range. Within each square, a fixed number of

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nodes move according to the random waypoint mobility model; random locations are selected within the square, and a node moves there at a rate of between 5m/s and 10m/s. After reaching its destination, a node pauses for 20s. The flows exist between nodes on opposite ends of the grid to provide maximum route lengths. Throughout the experiments with this mobility model, the number of nodes and flows are fixed, while the dimensions of the scenario are varied to provide more or less connectivity in the network. In the scheduled routes setup, 50 nodes are arranged in a square field with a radio propagation range of 377m. Ten nodes are positioned around the perimeter of the network, and act as sources and destinations. All other nodes are assigned a randomly sized and positioned rectangle over which to travel at a random speed of between 5m/s and 20m/s. Using these parameters, links will be somewhat predictable, although they will not always occur at the same interval. Throughout the experiments, the size of the scenario is increased to provide less connectivity in the network. We also run simulations with the random waypoint mobility model to illustrate the performance of SCaTR in a purely random network; in this case, past mobility information gives no indication of future topologies. This scenario is especially interesting as it is not favorable to SCaTR. 40 nodes move according to the random waypoint mobility model within a square area 3000m x 3000m. Due to the sparseness of the network, the connectivity is poor. The experiments vary the speed of each node to change the duration of connected paths in the network. Pause time remains a constant 30s throughout the simulations.

5

Performance Comparison

In this section we show that the addition of SCaTR to an on-demand routing protocol results in significantly improved performance in terms of delivery ratio and signaling overhead in all mobility scenarios that were tested. In these experiments, signaling overhead includes control messages such as route requests and replies, as well as data packet replicas. Because the networks simulated are sparse, it is often not possible to deliver all messages in the allotted time. All simulations were run for 2000 seconds, and it should be noted that undelivered packets after this time were not lost; they have not yet reached their destination. It would be possible to run much longer simulations, and have much higher delivery ratios, but the time selected gives a good indication of the relative performance between protocols. In a subsequent experiment, we show how lengthening simulation time affects performance. We first compare routing methods in the scheduled routes mobility scenario. Connectivity is varied by increasing the size of the scenario; clearly a larger scenario size results in less connectivity. Experiments with scheduled routes show that SCaTR takes advantage of predictability to provide high delivery ratios with low signaling overhead. Figure 1 illustrates delivery rates and overheads for the various protocols. At small scenario sizes with good network connectivity, all protocols successfully deliver some messages. Epidemic routing delivers, as

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expected, 100% of messages, while SCaTR delivers approximately 96%. AODV, due to frequent route disruptions, delivers only 6% of messages. Epidemic routing maintains a 100% delivery ratio until the scenario size reaches 2500m x 2500m. Most notably, SCaTR sustains similar delivery ratio to Epidemic routing throughout the experiment. At 4500m x 4500m, connectivity is extremely poor, and none of the protocols deliver more than 40% of messages. The plot of signaling overhead in Figure 1 shows that SCaTR maintains its high delivery rates with little overhead. Since those packets that reach their destination with Epidemic routing likely reach all nodes in the network, overhead remains at nearly 50 messages per delivery. The addition of SCaTR enables AODV to maintain fairly constant overhead, much lower than that of Epidemic routing. The ’hump’ in the plot for AODV occurred because partitions at the start of the simulations surrounding the sources were large. Thus, as AODV tried its route establishment attempts, they reached a high number of nodes (but not the destination). This behavior had a large impact on overhead since so few packets were delivered.

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Fig. 1. Delivery ratio (left) and signaling overhead (right) for the scheduled routes mobility model with varied network connectivity

The results for the gridded random waypoint model, shown in Figure 2, are similar to that of the scheduled routes scenario. AODV’s performance decreases sharply as connectivity diminishes, while SCaTR maintains delivery ratios similar to Epidemic routing. In addition, Epidemic routing and SCaTR both show very high delivery ratios until the network is extremely disconnected. Notably, in both scenarios, the results show that adding a second proxy to SCaTR does not have a significant impact on its delivery ratio, although it does incur additional overhead. It is likely that a network with many more nodes would show the benefit of the second proxy more clearly, however these results indicate that it is not a significant factor. When buffer limitations are introduced, SCaTR’s performance significantly improves compared to that of Epidemic routing. All protocols employed a FIFO drop strategy for buffered messages. As shown in Figure 2, the duplicate messages in Epidemic routing have a detrimental effect with a small buffer. Only once

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the size of the buffer reaches approximately 25% of the number of messages originated in the network does Epidemic routing’s delivery ratio surpass that of SCaTR. This improvement, however, is still offset by the high overhead of Epidemic routing. We can also see that with a very constrained buffer of 5% to 10%, it is beneficial to select only one proxy for each message.

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Fig. 2. Left, delivery ratio for the gridded random waypoint mobility model with varied network connectivity. Right, delivery ratio for scheduled routes mobility model with limited buffer space.

SCaTR, because it utilizes mobility predictability, was not designed with random mobility in mind, but we include this scenario to illustrate that it will perform at least as well as the underlying on-demand protocol in any situation. Figure 3 illustrates performance when node speeds were varied to provide more or less connectivity in the network. SCaTR shows a higher delivery ratio than standard AODV despite the fact that it is unable to take advantage of historical information to predict future topologies. In addition, this experiment shows the benefit of additional message replication that can be provided with SCaTR. Epidemic routing has excellent delivery ratios at the expense of very high overhead. In power constrained environments, this would likely not be acceptable. It should be noted that the overhead of AODV is extremely high because it delivers so few messages. Figure 4 shows that SCaTR’s performance improves as the length of simulation increases. As the scenario time increases, delivery rates increase and signaling overhead remains constant after an initial drop. Epidemic routing quickly achieves nearly a 100% delivery rate, while SCaTR is able to achieve better than 80% after approximately 2000 seconds. Despite the fact that SCaTR takes longer than Epidemic routing to deliver messages, it does so with far less signaling. After an initial drop in signaling overhead due to low delivery rates (most packets are en route to their destination at this point), SCaTR settles at approximately 10-20 messages per delivered packet. Epidemic routing settles at approximately 38 messages per delivered packet, since each message reaches a large portion of the network.

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6

Discussion and Future Work

We have introduced the Space-Content-adaptive-Time Routing (SCaTR) framework to efficiently and effectively route data in networked environments where connectivity is intermittent. SCaTR extends on-demand routing to operate in environments where, often times, there may not be a direct route between source and destination. Thus, if the network is connected, SCaTR operates exactly as regular on-demand routing. However, if source and destination do not have a connected route, SCaTR chooses a node that is deemed closer to the destination than the source as a proxy for that destination. The proxy will either deliver the data to the destination directly, or choose another proxy closer to the destination than itself. In summary, the resulting protocol will do no worse than standard AODV in well-connected environments, and far better in partitioned networks. Through extensive simulations in environments with varying connectivity and mobility patterns, we showed that SCaTR can yield considerably higher delivery ratio with lower signaling overhead than traditional on-demand routing.

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References 1. Davis, J., Fagg, A., Levine, B.: Wearable computers as packet transport mechanisms in highly-partitioned ad-hoc networks. In: Proceedings of the International Symposium on Wearable Computing, Zurich, Switzerland, October 2001. (2001) 141–148 2. Ogier, R., Templin, F., Lewis, M.: Rfc 3684: Topology dissemination based on reverse-path forwarding (tbrpf) (2004) 3. Clausen, T., Jacquet, P.: Rfc 3626: Optimized link state routing protocol (olsr) (2003) 4. Perkins, C., Belding-Royer, E.: Rfc 3561: Ad hoc on-demand distance vector (aodv) routing (2003) 5. Johnson, D.B., Maltz, D.A.: Dynamic source routing in ad hoc wireless networks. In Imielinski, Korth, eds.: Mobile Computing. Volume 353. Kluwer Academic Publishers (1996) 6. Cerf, V., Burleigh, S., Hooke, A., Torgerson, L., Durst, R., Scott, K., Fall, K., Weiss, H.: Delay-tolerant network architecture (2004) 7. dtnrg: Delay tolerant networking research group (2006) 8. Zhao, W., Ammar, M., Zegura, E.: A message ferrying approach for data delivery in sparse mobile ad hoc networks. In: Proceedings of ACM Mobihoc 2004. (2004) 9. Mitchener, W., Vahdat, A.: Epidemic routing for partially connected ad-hoc networks (2000) 10. Lindgren, A., Doria, A., Schelen, O.: Probabilistic routing in intermittently connected networks. In: The First International Workshop on Service Assurance with Partial and Intermittent Resources (SAPIR 2004). (2004) 11. Fall, K., Hong, W., Madden, S.: Custody transfer for reliable delivery in delay tolerant networks (2003) 12. Merugu, S., Ammar, M., Zegura, E.: Routing in space and time in networks with predictable mobility (2004) 13. Spyropoulos, T., Psounis, K., Raghavendra, C.: Spray and wait: An efficient routing scheme for intermittently connected mobile networks. In: Proc. SIGCOMM 2005. (2005) 14. Leguay, J., Friedman, T., Conan, V.: Dtn routing in a mobility pattern space. In: Proc. SIGCOMM 2005. (2005) 15. Burns, B., Brock, O., Levine, B.N.: Mv routing and capacity building in disruption tolerant networks. In: IEEE INFOCOM 2005. (2005) 16. de Moraes, R., Sadjadpour, H., Garcia-Luna-Aceves, J.: Throughput-delay analysis of mobile ad-hoc networks with a multi-copy relaying strategy. In: Proc. IEEE SECON 2004: The First IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks. (2004) 17. glomosim: Global mobile information systems simulation library (2001)

Delivery Guarantees in Predictable Disruption Tolerant Networks Jean-Marc Fran¸cois and Guy Leduc Research Unit in Networking (RUN) DEECS — Institut Montefiore, B28 — Sart-Tilman University of Li`ege 4000 Li`ege, Belgium {francois,leduc}@run.montefiore.ulg.ac.be

Abstract. This article studies disruption tolerant networks (DTNs) where each node knows the probabilistic distribution of contacts with other nodes. It proposes a framework that allows one to formalize the behaviour of such a network. It generalizes extreme cases that have been studied before where either (a) nodes only know their contact frequency with each other or (b) they have a perfect knowledge of who meets who and when. This paper then gives an example of how this framework can be used; it shows how one can find a packet forwarding algorithm optimized to meet the delay/bandwidth consumption trade-off: packets are duplicated so as to (statistically) guarantee a given delay or delivery probability, but not too much so as to reduce the bandwidth, energy, and memory consumption.

1

Introduction

Disruption (or Delay) Tolerant Networks (DTNs, [1]) have been the subject of much research activity in the last few years, pushing further the concept of Ad Hoc networks. Like Ad Hoc networks, DTNs are infrastructureless, thus the packets are relayed from one node to the next until they reach their destination. However, in DTNs, node clusters can be completely disconnected from the rest of the network. In this case, nodes must buffer the packets and wait until node mobility changes the network’s topology, allowing the packets to be finally delivered. A network of Bluetooth-enabled PDAs, a village intermittently connected via low Earth orbiting satellites, or even an interplanetary Internet ([2]) are examples of disruption tolerant networks. The atomic data unit is a group of packets to be delivered together. In DTN parlance, it is called a message or a bundle; we use the latter in the following. Routing in such networks is particularly challenging since it requires to take into account the uncertainty of mobiles movements. The first method that has 

This work has been supported by the Belgian Science Policy in the framework of the IAP program (Motion P5/11 project) and by the IST-FET ANA project.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 167–178, 2007. c IFIP International Federation for Information Processing 2007 

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been proposed in the literature is pretty radical and proposes to forward bundles in an “epidemic” way ([3,4]), i.e., to copy them each time a new node is encountered. This method of course results in optimum delays and delivery probabilities, at the expense of an extremely high consumption of bandwidth (and, thus, energy) and memory. To mitigate those shortcomings, the epidemic routing has been enhanced using heuristics that allow the propagation of bundles to a subset of all the nodes ([5,6]). More advance heuristics have been introduced to cope with the nodes limited memory. Cache mechanisms have been proposed, where the most interesting bundles are kept (i.e. those that are likely to reach their destination soon) and the others are discarded when the cache is full ([7,8,9,10]). Few papers explore how the expected delay could be more precisely estimated (notable exceptions are [11,12]). It has been proved ([13]) that a perfect knowledge of the future node meetings allows the computation of an optimal bundle routing. This short introduction emphasizes two shortcomings: – Previous works suppose either that nodes contacts are perfectly deterministic, or that only the contact frequency is known for each pair of nodes. In this paper, we introduce a framework which generalizes those extreme cases and formalizes the nodes contact predictability. It allows one to compute the expected impact of a particular bundle forwarding strategy; – Previous works only propose bundle forwarding heuristics. In what follows, we give an example of how the above-mentioned framework can be used to find a bundle routing strategy that fulfills delivery guarantees while limiting bandwidth/energy consumption.

2

Predictable Future Contacts

The network is composed of a finite set of wireless nodes N that can move and thus, from time to time, come into contact. In the sequel, a contact between two nodes happens when those nodes have setup a bi-directional wireless link between them. A contact is always considered long enough to allow all the required data exchanges to take place1 . 2.1

Contact Profiles

We expect the mobiles motion to be predictable, yet obviously the degree of predictability varies from one network to another. Sometimes nodes motion is known in advance because they must stick to a given schedule (e.g. a network of buses) or because their trajectory can easily be modelled (e.g. nodes embedded in a satellite). Other networks are less predictable, yet not totally random: colleagues could be pretty sure to meet every day during working hours, without 1

This is a major difference with [13] which does not neglect bundle transmission times.

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Fig. 1. Contact profile and first contact distribution: example. Top: A contact profile: the height of a bar gives the probability that two nodes meet (at least once) during the corresponding 12-hour time period. Bottom: The corresponding first contact distribution; each bar corresponds to a 12-hour period.

any other time guarantee. Mobile nodes behaviour could also be learnt automatically so as to extract cyclical contact patterns. We therefore suppose that each node pair {a, b} ⊂ N can estimate its contact probability for (discrete) each time step in the near future. We call it a contact profile and denote it Cab : N → [0, 1]. In the following, we suppose the profile known for each node pair. Contact profiles can easily represent situations usually depicted in the literature: – A constant profile Cab (t) = k describes a node pair that only knows its contact frequency. For example, the profile Cab (t) = 1/30 (contact probability per day) corresponds to two nodes a and b meeting once a month on average. – Perfect knowledge of meeting times results in a profile made of peaks: ∀t ∈ N : Cab (t) ∈ {0, 1}. In practice, unknown contact profiles could be replaced by a constant function equal to zero on its domain to get a defensive approximation of their behaviour. The following sections aim at studying how bundles propagate from one node to another in a network whose nodes’ contact profiles are known. 2.2

First Contact Distribution

It is easy to deduce the probability distribution of a (first) contact at time t between nodes a and b ∈ N given their profile Cab ; we denote this distribution

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dab . Since the probability of a first contact at time t is the probability of meeting at time step t times the probability not to meet at time steps 0, 1, . . . , t − 1. We have (∀a, b ∈ N ): dab (t) = Cab (t)

t−1 

  1 − Cab (i)

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(1)

i=0

The distributions domain is N since contact profiles have been defined using discrete time steps. We extend the distributions to R to get rid of this artifact. Notice that dab is not a well-defined probability distribution since its integral over its domain is not equal to 1: two nodes might never meet. Definition 1. The first contact distribution set, C, is the set of functions2 ∞ f : R+ → R+ such that 0 f (x) dx ≤ 1. Contact profiles do not allow us to express contact interdependencies; for example, they cannot model that two nodes are certain to meet exactly once during the weekend without knowing exactly which day (if a probability of .5 is assignated to Saturday and Sunday, there is a .25 probability that the nodes will meet twice). First contact distributions have no such limitations. Therefore, when it is possible, one could find preferable to generate them directly without relying on contact profiles. Figure 1 gives an example contact profile Cab (top) and the corresponding first contact distribution dab (bottom). Notice that if a bundle is delivered directly from a to b, knowing the first contact distribution allows an easy verification of a large spectrum of guarantees, such as the average delay or the probability of delivery before a certain date.

3

Delivery Distributions

3.1

Definition

First contact distributions can be generalized to take into account the knowledge that no contact were made before a certain date. Let Dab (T, t) be the probability distribution that a and b require a delay of t time steps to meet for the first time after time step T . Since these distributions will be the building blocks that allow us to compute when a bundle can be delivered to its destination, we call them delivery distributions. Dab can directly be derived from the contact profile Cab (∀a, b ∈ N ): Dab (T, t) = Cab (T + t)

T +t−1 

  1 − Cab (i)

∀T, t ∈ N

i=T 2

As before, the domain of these functions can be extended to R+ . 2

R+ denotes the set of positive reals.

(2)

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Definition 2. The delivery distribution set, D, holds all the functions ∞ 2 f : R+ → R+ such that ∀T ∈ R+ : 0 f (T, x) dx ≤ 1. Notice the inequality. D (%/day) 60 50 40 30 25

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Figure 2 draws the Dab (T, t) distribution corresponding to the contact profile given in figure 1. Notice that the D(T, ·) functions of course belong to C (∀T ≥ 0). Notice that Dab (T, ·) is the expected delivery delay distribution for a bundle sent directly from a source a to a destination b if a decides to send it at time T . 3.2

Order Relation on Distributions

We define an order relation between first contact distributions. Intuitively, this relation allows one to compare two distributions to find which one represents more frequent or predictable contacts. A rigorous definition is given below. Definition 3. The first contact distributions d1 ∈ C is greater (or equal) than d2 ∈ C (denoted d1  d2 ) if and only if:  x  x ∀x ≥ 0 : d1 (t) dt ≥ d2 (t) dt (3) 0

0

This relation is a partial order (but not a total order as there exist d1 , d2 ∈ C such that neither d1  d2 nor d1 d2 ; see [14] for more details). It appears difficult to define a total order on C: comparing two distributions that cannot be ordered using the  relation is a matter of choice and depends on the bundle delivery guarantees one wants to enforce. The  relation is thus a least common denominator, and could be replaced in what follows with a more restrictive order definition. The worst (smallest) element of C is the ⊥ (bottom) distribution: ⊥(t) = 0 (∀t ≥ 0). The best (greatest) first contact distribution is denoted  (top): (t) = δ(t) (∀t ≥ 0); the δ symbol denotes the Dirac distribution.

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The  relation can be extended to D. For all D1 , D2 ∈ D: D1  D2 ⇐⇒ ∀T ≥ 0 : D1 (T, ·)  D2 (T, ·) The D⊥ delivery distribution is such that ∀T ≥ 0 : D⊥ (T, ·) ≡ ⊥. The definition of D follows immediately.

4 4.1

Delivery Distribution Operators The Forwarding Operator

Let Dsbd be the delivery distribution associated with the delivery of a bundle from a source node s to a destination d via node b. More precisely, if s decides to send a bundle at time T , it will reach d after a delay described by the Dsbd (T, ·) distribution. Dsbd can be computed thanks to Dsb and Dbd : Dsbd ≡ Dsb ⊗ Dbd

(4)

The ⊗ (or forwarding) operator is a function defined for all distribution pair. We have ⊗ : D2 → D:  t   D1 ⊗ D2 (T, t) = D1 (T, x) D2 (T + x, t − x) dx (5) 0

It is easy to see that this operator is associative but not commutative. Equation (5) simply states that since the total delivery delay is equal to t, if the delay to reach b is equal to x, then the delay from b to d is t − x. Equation (4) can be generalized: a bundle could be forwarded through several intermediate hops before reaching its destination. We denote Ds−d (notice the dash) the delivery delay distribution for a bundle sent from a source s to a destination d at time T ; from now on, ⊗ will thus be applied to any kind of delivery distributions. For example, the graph below depicts a simple delivery path, i.e. a sequence of forwarding nodes; the corresponding delivery distribution is also given. s

/a

/b

/d :

Ds−d ≡ Dsa ⊗ Dab ⊗ Dbd

We say that two delivery paths with a common source s and destination d are disjoint if the intersection of the set of nodes they involve is {s, d}. 4.2

The Duplication Operator

Let D s Ed be the delivery distribution associated with the delivery of a bundle d

from s to d if it is duplicated so as to follow the disjoint delivery paths described  by the distributions Ds−d and Ds−d . We have:  D s Ed ≡ Ds−d ⊕ Ds−d d

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The ⊕ (or duplication) operator is a function ⊕ : D2 → D, defined as follows:   D1 ⊕ D2 (T, t) =

   t D1 (T, x) dx D2 (T, t)+ 1− 0    t 1− D2 (T, x) dx D1 (T, t) (7) 0

The expected delay computed is that of the first bundle to reach the destination d. It is easy to see that ⊕ is associative and commutative. Operators ⊗ and ⊕ can be combined to consider more complex forwarding strategies, assigning a higher precedence to ⊗. Equation (7) is the sum of two terms. Each term is the probability that the bundle reaches the destination after a delay t using one path and that the bundle following the other path is not arrived yet. It can be proven that we have both D1 ⊕ D2  D1 and D1 ⊕ D2  D2 . This means that, contrary to what happens in deterministic networks, duplicating a bundle to send it along two paths can improve performance: it is not the case that the best path always delivers the bundle first. Figure 3 shows an example of the distributions obtained using the “duplication” operator. As expected, duplicating bundles shortens the delays and increases the delivery probability. D (%/day) 250 200 150 100 25 50 0

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Fig. 3. Duplication (⊕) operator: example. We denote D1 the delivery distribution depicted in figure 2. Let D2 be a distribution that corresponds to nodes that are certain to meet on day 9, 16 and 25. This plot depicts D1 ⊕ D2 .

4.3

The Scheduling Operator

Let D s

d d

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 . We have: chooses the best delivery strategy out of Ds−d and Ds−d

Ds

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The definition of  is straightforward. It is a function  : D2 → D such that:   D1 (T, t) if D2 (T, ·)  D1 (T, ·) D1  D2 (T, t) = (9) D2 (T, t) otherwise If s sends a bundle at time T , it is delivered using D2 (T, ·) if and only if D2 (T, ·)  D1 (T, ·). This operator is not commutative since  is not a total order: when D1 (T, ·) and D2 (T, ·) cannot be compared, D1 (T, ·) is chosen. This new operator can be combined with the other two (⊗ and ⊕) assigning it a lower precedence. The following example involves all the operators defined above. Two plain arrows leaving a node depict a duplication. Two arrows leaving a node, one of them dotted, depict a scheduling operation. The dotted arrow leads to the second argument of , emphasizing the operator’s non-commutativity. ;e s GG / b G# c 4.4

/f

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Delivery Schemes

We have defined a delivery path as a delivery strategy that only involves forwarding. A delivery scheme with source s and destination d is a general delivery strategy that allows a bundle to be delivered from s to d. It can use an arbitrary number of forwarding, duplication and scheduling operations.

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Knowing the delay distribution d ∈ C associated with the delivery of a bundle allows us to verify a large range of conditions on permissible delays or on delivery probabilities. For example, the condition  ∞ d(t) t dt ≤ dmax 0

imposes a maximum expected delay dmax , while 



1h

d(t) dt ≥ .9 0

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and 0

matches distributions delivering a bundle in less than one hour nine times out of ten, and in less than a day with a probability of 99%. We naturally impose that a condition fulfilled for a certain delivery scheme must be fulfilled for better schemes.

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Definition 4. A delivery condition C is a predicate: C : C → {true, false} iff ∀d1 , d2 ∈ C such that d1  d2 , we have C(d2 ) =⇒ C(d1 ). A condition C can be extended to a delivery distribution D ∈ D:   C(D) ⇐⇒ ∀T ≥ 0 : C D(T, ·)

6 6.1

Delivering Bundles with Guarantees Probabilistic Bellman-Ford

Algorithm 1 adapts the Bellman-Ford algorithm to predictable disruption tolerant networks. In this section, we do not allow bundle duplication. Notice that, in general, the concept of “shortest path” is meaningless since the relation is a partial order.

Algorithm 1. Probabilistic Bellman-Ford Data: d is the destination node 1 2

∀ x ∈ N \ {d} : Bx ← D⊥ ; Bd ← D ;

10

repeat stabilized ← true; forall x ∈ N do forall y ∈ N do Dxy−d ← Dxy ⊗ By ; if Bx = Bx  Dxy−d then stabilized ← false; Bx ← Bx  Dxy−d ;

11

until stabilized ;

3 4 5 6 7 8 9

Similarly to the Bellman-Ford algorithm, algorithm 1 computes, for every node n ∈ N , the best distribution leading to the destinations found so far (Bn ). This distribution is propagated to its neighbours (i.e. all the other nodes since the network is infrastructureless). Once node x receives the best delivery distribution By found by y, it computes the delivery distribution obtained if it would send the bundle directly to y, and if y would forward it according to By . The resulting distribution is denoted Dxy−d (line 6). Dxy−d is compared to the best known distribution to the destination (Bx ) by means of the  operator. If Dxy−d is better than Bx on some time intervals, Bx is updated (line 9). The algorithm terminates once no more Bx distribution is updated.

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As mentioned before, this algorithm generalizes both [15] (i.e. converges to the “shortest expected path”) and [13]3 (i.e. finds the exact shortest path in the case of perfectly predictable networks). The delivery computed by this algorithm depends on the order at which the elements of N are picked up (lines 5 and 6). In practice, it might be preferable to rely on a heuristic to choose the preferred elements first. 6.2

Guarantees

Our aim is now to find a way to deliver bundles that fulfills a given condition C as specified in definition 4, while trying to minimize the network’s bandwidth/energy/memory consumption. Ideally, the DTN is predictable enough to enforce condition C without duplicating any bundle. We thus propose to rely on algorithm 1 to find a first delivery scheme (and, thus, a first delivery distribution D1 ). If C is not fulfilled by D1 , we search for another fast bundle forwarding scheme using algorithm 1; let D2 be its delivery distribution. We then duplicate the bundle on both delivery schemes, yielding a distribution D1 ⊕ D2 . We have already pointed out that D1 ⊕ D2  D1 , thus C(D1 ⊕ D2 ) is more likely to be true then C(D1 ). This process is iterated until C is finally fulfilled; see algorithm 2. As mentioned in section 4.2, the distribution computed by the “duplication” (⊕) operator is biased if its operands are not independent distributions. To avoid this bias, we ensure that D1 and D2 are independent by forbidding D2 to rely on the nodes involved in D1 (source and destination nodes excluded, line 5). More details can be found in [14].

Algorithm 2. Constrained probabilistic delivery Data: Delivery condition C Data: Bundle source s and destination d 1 2 3 4 5 6

B ← D⊥ repeat Using nodes in N , compute D ∈ D via algorithm 1 B ←B⊕D   N ← N \ {nodes involved in D} ∪ {s, d} until C(B) or N = {s, d}

Nothing guarantees of course that there exists a way to deliver bundles that satisfies C: even an epidemic broadcasting might not suffice. 3

To be fair, this work also deals with message transmission delays, which are not considered here.

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Conclusion and Future Works

We propose to model contacts between a disruption tolerant network’s mobile nodes as a random process, characterized by contact distributions. Such a description is more general than those generally encountered in the literature. We have setup a framework that shows how such contact distributions can be combined to compute the bundle delivery delay distribution corresponding to a given delivery strategy (i.e. a description of the nodes forwarding decisions). This framework is formally defined and quite generic; it can be used to evaluate quantitatively the performance of new routing protocols. It could be expanded with new operators describing other (more subtle) forwarding schemes. A significant improvement would be to modify the framework so as to deal with bundles transmission delays. As a future work, real network traces can be analysed so as to quantify their predictability; the delivery strategies elaborated using this framework could then be compared with the heuristics proposed in the literature. To demonstrate the applicability of the framework, we have used it to build a new routing algorithm. It uses a modified Bellman-Ford algorithm adapted to DTNs and asks the source to duplicate bundles. It tries to compute a routing strategy that fulfills a given delivery condition without consuming too many resources.

References 1. Zhang, Z.: Routing in intermittently connected mobile ad hoc networks and delay tolerant networks. In: IEEE Surveys and Tutorials. Volume 8-1. (2006) 24–37 2. Burleigh, S., Hooke, A., et al.: Delay-tolerant networking — an approach to interplanetary internet. IEEE Communications Magazine 41(6) (2003) 128–136 3. Vahdat, A., Becker, D.: Epidemic routing for partially connected ad hoc networks. Technical Report TR CS-200006, Duke University (April 2000) 4. Juang, P., Oki, H., et al.: Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with zebranet. In: ASPLOS, San Jose, CA. (October 2002) 5. Spuropoulos, A., Psounis, K., Raghavendra, C.: Single-copy routing in intermittently connected mobile networks. In: Proceedings of IEEE SECON. (October 2004) 6. Spyropoulos, T., Psounis, K., et al.: Spray and wait: An efficient routing scheme for intermittently connected mobile networks. In: Proc. of SIGCOMM’05. (2005) 7. Wang, Y., Jain, S., Martonosi, M., Fall, K.: Erasure-coding based routing for opportunistic networks. In: WDTN ’05: Proceeding of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking, New York, NY, USA, ACM Press (2005) 229–236 8. Lindgren, A., Doria, A., Schel`en, O.: Probabilistic routing in intermittently connected networks. SIGMOBILE Mob. Comput. Commun. Rev. 7(3) (2003) 19–20 9. Jones, E., Li, L., Ward, P.: Practical routing in delay-tolerant networks. In: Proc. of WDTN’05, New York, NY, USA, ACM Press (2005) 237–243 10. Leguay, J., Friedman, T., Conan, V.: DTN routing in a mobility pattern space. In: Proc. of WDTN’05, New York, NY, USA, ACM Press (2005) 276–283

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11. Shen, C., Borkar, G., Rajagopalan, S., Jaikaeo, C.: Interrogation-based relay routing for ad hoc satellite networks. In: IEEE Globecom, Taipei, Taiwan (November 17-21 2002) 12. Musolesi, M., Hailes, S., Mascolo, C.: Adaptive routing for intermittently connected mobile ad hoc networks. In: Proc. of WoWMoM’05. (2005) 183–189 13. Merugu, S., Ammar, M., Zegura, E.: Routing in space and time in networks with predictable mobility. Technical Report GIT-CC-04-7, Georgia Tech. Inst. (2004) 14. Fran¸cois, J.M., Leduc, G.: Predictable disruption tolerant networks and delivery guarantees. Technical Report (arXiv:cs.NI/0612034 v1) (2006) 15. Tan, K., Zhang, Q., Zhu, W.: Shortest path routing in partially connected Ad Hoc networks. In: Proc. of IEEE GLOBECOM’03. Volume 2. (December 2003) 1038– 1042

PWave: A Multi-source Multi-sink Anycast Routing Framework for Wireless Sensor Networks Haiyang Liu1 , Zhi-Li Zhang1, , Jaideep Srivastava1 , and Victor Firoiu2 1 2

Dept. of Comp. Sci. and Eng., Univ. of Minnesota, Minneapolis, MN 55414 Advanced Information Technologies, BAE Systems, Burlington, MA 01803

Abstract. We propose a novel routing framework called PWave that supports multi-source multi-sink anycast routing for wireless sensor networks. A distributed and scalable potential field estimation algorithm and a probabilistic forwarding scheme are proposed to ensure low overhead and high resilience to network dynamics. Key properties of this framework are proved through theoretical analysis and verified through simulations.Using network lifetime maximization problem as one example, we illustrated the power of this framework by showing a 2.7 to 8 times lifetime extension over Directed Diffusion and up to 5 times lifetime extension over the energy-aware multipath routing.

1 Introduction Wireless sensor networks (WSNs) are generally deployed to support specific missions or applications such as habitat monitoring, object tracking. Traffic are generated from a number of sensing sources and collected by (any one of) a few sinks [1]. Hence data communications in WSNs exhibit multi-source, a multi-sink anycast pattern, which is fundamentally different from that in general-purpose communication networks (whether wired or wireless), where any two nodes may serve as the two ends of an end-to-end communication. In addition, WSNs often operate in challenging environments and are subject to frequent disruptions and node failures. These unique settings and constraints call for a robust routing framework for WSNs that can quickly adapt to changes in traffic pattern, network conditions and environments. Existing routing schemes (e.g., [2]) used in WSNs are either variations, or even direct adoption, of routing algorithms for general-purpose wired networks, or mobile wireless ad hoc networks (MANETs), which are typically designed using the single shortest path unicast routing paradigm1. While several multi-path routing schemes (see, e.g., [3,4,5]) have been proposed, they tend to be extension of the single shortest-path routing paradigm with use of additional paths – choice of these alternative paths is often decided based on somewhat ad hoc mechanisms. More importantly, these techniques 

1

This work was supported in part by the NSF grant CNS-0435444 and a DoD Army High Performance Computing Research Center grant. While offering some desirable local properties, the more recent geographic or trajectory-based routing paradigm is still unicast based, using “shortest distance” paths in an Euclidean or metric space. In addition, it requires some type of location information, which may not be easy to obtain.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 179–190, 2007. c IFIP International Federation for Information Processing 2007 

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only support single-sink configuration (i.e., unicast routing) with no direct and easy extension to support multi-source, multi-sink anycast routing. In this paper we propose a novel framework – referred to as PWave to support multisource, multi-sink anycast routing that is inherent in WSNs. Inspired by the analogy between WSNs and electric networks, PWave constructs a potential field by assigning a “potential” (analogous to “voltage” in an electric network) to each node: a source or an intermediate node routes traffic (proportionally) to neighboring nodes with lower potentials towards the sinks, which have the lowest (zero) potentials. The PWave framework is designed with strong theoretical underpinnings. First of all, the constructed potential field realizes scalable, robust, proportional multi-source, multi-sink traffic allocation that optimizes a customizable quadratic function (based on appropriate definition of “link costs”). Furthermore, it guarantees that there is no local minima (i.e. packets are never stuck in a local dead-end) and thus ensures loop-free routing. It adapts to local changes rapidly, while dampening their global impact. These features enable compact and efficient protocol design with low execution overhead. We develop a fully distributed algorithm for constructing the potential field and implement PWave using probabilistic forwarding to achieve the properties described above. PWave scales to the density of the network because only one-hop neighborhood information exchange is needed. In addition this algorithm is resilient to network dynamics in that local perturbations only have local effect. These features make PWave a suitable routing framework for WSNs. In a nutshell, the research contribution of this paper lies in the proposal of a novel routing framework that supports global optimization of custom objectives in a multi-source multi-sink anycast routing settings via fully localized computations. To the best of our knowledge, this is the first systematic routing framework for WSN with this capability. The remainder of this paper is organized as follows. We detail the PWave framework design in section 2. The potential field estimation algorithm is described in section 3, followed by the experimental evaluations in section 4. We describe related work in section 5 and conclude in section 6.

2 The PWave Routing Framework 2.1 System Model and Problem Formulation In order to formally define the PWave routing framework, we first introduce the system model, notations and assumptions. We assume that a WSN can be represented as a weighted (undirected) graph G = (N, E), where N is the set of nodes and E the edges (i.e., links) between nodes which are assumed to be symmetric. Asymmetric links are blacklisted as suggested in literatures. We use Rx,y denote the weight of an edge e connecting nodes x and y, which is strictly positive, i.e., Rx,y > 0. This weight represents some measure of unit cost for transmitting one bit of information between x and y defined in certain manners depending on the applications and routing design objectives. (We will provide some examples of Rx,y later.) Hence if Ix,y amount of data is transmitted from x to y, the total cost would be Ix,y Rx,y . For simplicity, we assume that data traffic only flow along one direction of an edge. The cost for acknowledgements is implicitly accounted for in Rx,y . Under this assumption, Ix,y denote the data rate

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flowing from x to y, we define Iy,x = −Ix,y . The same relation also holds if Iy,x is the data rate flowing from y to x. Let S ⊂ N denote the set of source nodes, and D ⊂ N be the set of sink nodes. For each s ∈ S, Is denote the data rate that may be generated by source node s. More generally, to account for potential in-network processing at intermediate nodes in a WSN that may increase or decrease the data rate flowing through them, for each x ∈ (N − D), we use Ix to denote the (internal) data generation/consumption rate at node x. Note here Ix > 0 means that data is generated at node x while Ix < 0 means that data is consumed at node x. For each node x ∈ N, we use Z(x) to denote the set of its neighboring nodes. Then the flow conservation law requires that for any node x that is not a sink, the total of data rates flowing into node x is equal to the total of data flowing out of node x plus or minus the data rate generated or consumed at node x itself. Namely,  Ix,y = Ix , x ∈ (N − D). (1) y∈Z(x)

Given the graph G representing a WSN and I := {Ix |x ∈ N}, we refer to the tuple (G, I) a network configuration NC. Given a NC, routing for a WSN can be casted as a global multi-source multi-sink anycast flow allocation optimization problem to determine the flows {Ix,y } along the links under the flow conservation constraints (1) and boundary conditions (2) such that certain global objective function F (G, I, {Ix,y |(x, y) ∈ E}) can be optimized. Single path (or minimum cost) routing that computes a minimum cost path for each source to one of the sinks is such a flow allocation scheme that allocates flows based only on the cost of the paths, but not on the flow rates. 2.2 PWave Routing Framework Intuitions and Principles. From physics, it is well known that if the energy level of a physical system is minimized, the system would be in most stable state, i.e. the system will tend to go back to this state after disturbances. A routing framework designed this way will thus be robust. With this intuition, we design our PWave routing framework to solve this optimization problem by minimizing a natural quadratic objective function (4), which is equivalent to the total energy of a corresponding electric network system (see Fig. 1). PWave solves the flow allocation optimization by assigning a potential field to the nodes in a WSN, namely, a function V : N → R+ , where R+ denote the set of nonnegative real numbers. The potential function V satisfies the following boundary conditions at the sink nodes (2) Vd = 0, d ∈ D and the flow distribution conditions at non-sink nodes Ix,y =

(Vx − Vy ) Rx,y

as well as the flow conservation constraints (1) at the non-sink nodes.

(3)

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Fig. 1. Analogy between WSNs and Resistive Electric Networks

The equation (3) specifies a localized rule on how data are routed at each node x based on local information, namely, its and its neighbors’ potentials: data only flow from node x towards one of its nodes y with lower potentials (Vy < Vx ) and the amount of data routed along the edge (x, y) is inversely proportional to R(x, y), or equivalently, proportional to gx,y := 1/Rx,y , which is referred to as the conductance of edge (x, y). The boundary conditions (2) ensure that the sink nodes have the lowest possible potential (namely, zero potential) so that data will always flow towards the sinks and are eventually “absorbed” at the sinks. As will be shown shortly, the potential field defined above guarantees the existence of a unique flow allocation {Ix,y : (x, y) ∈ E} such that it minimizes the following global objective function: E=

1 1 2 Ix,y Rx,y = gx,y (Vx − Vy )2 . 2 x,y 2 x,y

(4)

Moreover, we will see that the potential-based PWave routing framework also allows for a probabilistic routing/forwarding implementation at the packet level. More specifically, equation (3) allocation can be achieved in practice through forwarding a packet from node x to one of its lower potential neighbors y with probability given by px→y =

I x,y

Ix,i

(5)

i:i∈Z(x)∧V (i)
where Ix,i is computed from local potential values using eq.(3). In summary, the potential-based PWave routing framework enables us to achieve goals at three different levels: i) at the network-wide macroscopic level, it minimizes a natural global objective function (4); ii) at the intermediate flow level, it provides a localized rule to determine how data flows are routed; and iii) at microscopic packet level, it allows for a simple probabilistic packet forwarding mechanism to achieve both flow-level routing and network-wide design objectives.

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Example Applications. Before we leave this section, we remark that by choosing different interpretations (and thus different values) for the edge costs Rxy (or equivalently, the edge conductance, gxy ), we can use eq.(4) to optimize different design objectives in routing for WSNs. For example, if we set Rxy = 1, then data flows are approximately proportionally distributed and routed based on path hop counts. If we set Rxy equal to the data loss rate on an edge, then minimizing eq.(4) would yield a flow allocation/routing strategy that attempts to approximately equalize the data losses among different paths. As another example, if we set Rx,y as a combination of the per unit power consumed by transmitting one bit of data, denoted as CEi , and the current energy level, denoted as ENi , as follows: Ri,j =

1 CEi CEj =( + ) gi,j ENi ENj

(6)

the PWave routing framework yields a solution that approximately equalizes the power consumption among various paths to maximize the network lifetime. See [6] for detailed derivations.

3 Potential Field Estimation 3.1 Principles and Algorithm The potential field estimation problem is governed by eqs.(1, 3) under boundary conditions specified in eq. (2). The existence and uniqueness of the solution to this problem are well known from electric network theory [7]. Traditional way of solving this problem is to rewrite the problem in matrix form and compute the inverse of the matrix. This is obviously infeasible in WSN environment as centralized data collection and processing are needed. Inspired by the random walk interpretation of electric networks from [7,8], we propose an iterative and localized algorithm based on random walk games to progressively estimate the potential field . Consider experiments of random walks in graph G, illustrated in Fig. 2, where every node is marked with a fixed face value m. Starting from an arbitrary node x, a walker, Terminal

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Fig. 2. Illustration of the Random Walk Game

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initially with 0 money in hand, goes to one of its adjacent nodes with probability px,i for i ∈ Z(x). This walker keeps going in the same fashion inside the graph until it reaches one of the sink nodes, i.e. d1 or d2 . Every time the walker passes one node i, it collects a fixed amount of money equal to mi . After a large number of experiments, central limit theorem guarantees that the expectation of the total money collected by the walker, f (x), converges [8] to: deg(x) px,i f (i) + mx (7) f (x) = i=1

Obviously f (i) = 0, i ∈ D as a walker starting from a sink node immediately stops. After rewriting eqs. (1, 3) to yield the expression of the voltages, we observe that eq. (7) is equivalent to eqs. (1, 3) when px,i and mx are designed as follows: gi,x px,i = deg(x) (8) k=1 gk,x Ix mx = deg(x)

(9) gk,x This mathematical equivalence warrants that the expectation of the total money collected by a walker starting from node x converges to the potential value at node x. While this analogy provides a distributed way for individual nodes to estimate its potential value, this method is still impractical in a WSN environment because of the need for extra routing infrastructure support, due to route-back of the final collected amount, and the huge communication overhead due to required large number of experiments and long duration for each experiment. Motivated by the Relaxation Method [9], we address the issues listed above by restricting the random walk game to be within one-hop neighborhood of the starting point and apply equation (7) iteratively on all nodes until the whole network reaches equilibrium state. With this method, only local broadcasting among adjacent nodes is needed. The overall communication overhead can be reduced through adjusting of accuracy requirement. In addition, the nature of potential field being a smooth harmonic function ensures localized effect of perturbations, which we prove it in section 3.2, that further reduces potential field maintenance overhead. The pseudo code of this algorithm is presented in Algorithm 1. This pseudo code describes how the entire network reaches global equilibrium. Each node only needs to periodically execute the steps from line 12 to line 19. Though PotentialFieldConstruct only specifies a uniform absolute tolerance threshold for all nodes. In practice, non-uniform relative thresholds may be used to better reflect estimation accuracies at different potential levels. Our experimental results in section 4 show that significantly low overhead can be achieved by combining a coarsegrained estimation of the entire potential field with on-demand localized potential field refinements. k=1

3.2 Properties We now summarize the key properties of this algorithm that are of great value in ensuring efficient routing protocol design and executions in WSN. All formal proofs of these properties are described in [6] and omitted in this paper due to space constraint.

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Algorithm 1. PotentialFieldConstruct (N, I, Sinks, tolerance) Require: Set of all nodes N, array of packet generation rate I, set of sink nodes Sinks, error tolerance tolerance Ensure: The equilibrium potential field 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22:

Variables: P nodePotentials, x nodeId, for each x ∈ N if x ∈ Sinks then P [x] ← 0 else P [x] ← arbitrary non-negative number endif endfor equilibrium ← f alse while (equilibrium = true) equilibrium ← true for each x ∈ N if x ∈ / Sinks then newP ← apply eq. (7) else newP ← 0 endif if |P [x] − newP | ≥ tolerance then equilibrium ← f alse endif P [x] ← newP endfor endwhile return P

Properties: 1) Convergency:The PotentialFieldConstruct algorithm, when tolerance → 0, converges to the unique solution of the potential field estimation problem with arbitrary non-negative initial guess of potential values and in any iteration order. 2) Loop Free: Forwarding over equilibrium potential field is loop free. 3) No Local Minima: The equilibrium potential field does not have local minima in that node x has the minimum potential value among its neighbor nodes if and only if x is a sink node. 4) Proportional Traffic Allocation:Given two disjoint paths sharing only the starting and ending nodes, P ath1 and P ath2 , with corresponding effective resistances Re1 and Re2 . If Re1 < Re2, the flow rate allocated on P ath1 will be higher than that allocated on P ath2 . 5) Rich Links Attract More Traffic:When more parallel links are added to a path, higher flow rate will be allocated to that path. 6) Perturbation Spatial Decay:In a dense network, the absolute potential value changes caused by event at node x are attenuated to zero over the number of hops from node x. Property 1) guarantees the correctness of the algorithm. Property 2) and 3) enable efficient protocol design in PWave as there is no need for loop-preventing mechanisms and last resort of flooding for getting packets out of a local dead-end. Property 4) comes directly from Ohm’s Law and effectively guarantees that capable paths take more traffic. Property 5) comes from Rayleigh’s Monotonicity Law [7] and warrants that any new

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nodes or links always help to take more traffic share. Property 6)is the key property that ensures the scalability and network dynamics resilience of PWave since it guarantees that only local areas of the potential field need to be updated in react to local events.

4 Experimental Evaluations 4.1 Experimental Setup and Performance Metrics We have implemented the PWave routing algorithm and protocol in TinyOS and evaluated its performance using TOSSIM [10] simulation environment. Recognizing the asynchronous nature of PWave protocol and the lack of precise MAC timing and interference models in TOSSIM, we developed our own plug-in modules to count the protocol iteration steps. We conducted our experiments with a network setting with grid layout, denoted as NS1 and a setting with random layout, denoted as NS2. Both layouts contain 400 nodes. In NS1, the nodes are placed on grid locations of a (200m X 200m) square. In NS2, the nodes are randomly distributed in a (400m X 400m) square. The regular structure of NS1 enables better illustration of key concepts, while NS2 simulates a sensor layout closer to environmental monitoring applications.

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We evaluated the performance of PWave in terms of convergence time, communication overhead, and locality of effects of network changes (spatial effect). The convergence time is measured by the number of iterations of the PWave algorithm until equilibrium is reached (all changes in potential are below the given threshold). The overhead is measured by the total number of messages broadcasting needed to reach equilibrium. The spatial effect of a single network change is measured by the maximum number of hops from the point of change where there is a node with potential value affected by the initial network change. 4.2 Evaluation of the PWave Protocol Here we investigate the dynamic performance of PWave protocol in reacting to network dynamics. The other aspect of PWave’s performance, the initialization of the potential field, has lesser impact on the overall performance of PWave since it is a one-time cost that can be minimized through pre-computed distribution guess and amortized through long communication session. We first evaluate the locality of impact following a network event and then the sensitivity of PWave performance to the estimation accuracy and network density. Locality of Perturbations (Spatial Effect). Fig. 3(a) shows the spatial decay of a 10% data rate increase at (50,50) inside network NS1 with source at (90,90), sink at (0,0) and the radio range of 13m. We observe that this change has an exponentially spatial decay, essential for the scalability and resilience of PWave. We also observe that the event perturbation decays faster with larger radius range, which corresponds to higher network density. Thus better robustness through locality can be achieved with denser deployment of senor nodes. Fig. 3(b) shows the spatial decay effect for a network event of 1/3 link cost increase around node x at (193,190) inside NS2 network with source at (389,379), sink at (32,1) and the radio range of 25m. Observe that PWave still shows exponential decay while the event in Smallest Cost Field (SCF) propagates with constant value throughout the network. This result shows the resilience of PWave to network event. Last, observe that the link const change event is general in that it can include link outages and new nodes joining the network, and thus the above observations hold for a wide range of network events. PWave Sensitivity. Using the same setting from which Fig. 3(b) was generated, Fig. 3(c) shows the PWave and SCF performance for a range of relative accuracy requirements. First observe that SCF is insensitive to the relative accuracy requirement since SCF has constant event propagation. On the other hand, PWave shows a clear decrease in convergence time, communication overhead, and reduced spatial effect for higher levels of error tolerance. In particular, for relative tolerance greater than 0.6%, PWave incurs lower overhead and faster convergency than SCF, primarily due to the tighter affected region. From our experiments, this accuracy is sufficient for traffic allocation purpose. Using the same setting with a fixed relative accuracy of 0.6%, Fig. 3(d) shows that denser networks (larger radio range) have decreased convergence times, require smaller refreshing overhead, and have a smaller number of nodes impacted by a network change, in both PWave and SCF. But PWave incurs less overhead

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and converges much faster at higher density than SCF. We also observe that SCF’s impacted area (spacial effect) is larger than PWave. 4.3 Network Lifetime Maximization In the following we illustrate performance of PWave when applied in the context of network lifetime maximization (see section 2.2) and compare with other two state-ofthe-art protocols, Directed Diffusion [2] and Energy-aware Routing [4]. Our experiments use the NS1 network setting with four source nodes located closely at (144,135), (153,135), (153,144) and (144,144). Three sink nodes are located at (27,27), (144,27) and (27,144). All nodes in network have the same amount of battery energy level initially except sink nodes and source nodes which are set to have high energy level to guarantee that source and sink nodes will not run out of battery before intermediate nodes given that the purpose of this experiment is to evaluate the efficiency of the traffic balancing obtained from PWave and its competitors. To reduce simulation time, the battery energy level of each non-sink, no-source node is set to be able to handle(either receive or transmit) at most 10,000 packets. The life time of the network is defined as the duration from the start of experiment until the first node runs out of energy. 4

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We illustrate in Fig. 4(a) the potential field constructed by PWave under relative accuracy of 0.6% with 31 iteration rounds and 2314 message overhead, and observe its properties of monotonicity and lack of local minima. Fig. 4(b) shows that PWave achieves 2.7 to 8 times longer lifetime compared to the baseline shortest path Directed Diffusion. and up to 5 times longer lifetime over the energy-aware routing scheme. PWave achieves the major lifetime extension through better traffic balancing and take advantage of existence of multiple sinks none of the existing schemes are exploring. Fig. 5 shows the traffic distribution normalized to the total traffic. Observe that less traffic is allocated on nodes that are along longer paths from sources to sinks. For example, the sink at (27,27) received less traffic than the other two sinks due to its longer distance from the sources. While all nodes around a sink received traffic allocation, the ones that face the source nodes received more allocation due to their smaller distance to sources.

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This experiment thus verifies that PWave achieve balanced traffic allocations with more traffic allocated on shorter paths.

5 Related Work Well-studied network optimization is generally related to our work. The classic work in this area can be traced back to [11,9]. All of these work formulated the optimization problem over traditional unicast/multicast communication pattern with multiple commodity flow model. PWave address a single commodity flow between multiple sources and multiple sinks with anycast communication pattern. The key mathematical difference between these two settings lies in the end-to-end flow rate constraints. Traditional mode requires that traffic (one commodity) generated by a source must be equal to the traffic received by the corresponding destination while PWave only requires the sum of the traffic generated by all sources is equal to the sum of the traffic received by all destination nodes. Many multipath routing schemes for WSNs [3,4,5] are related to our work. But these schemes are based on heuristic alternative paths selection without clear global optimization objective. And they did not address the anycast problem. The traffic-aware routing scheme proposed in [12] is also relevant. [12] builds local potential field (using a taut elastic membrane analogy), around shortest path as an accessory to route data around hot spots. This scheme builds on top of link-state routing protocol and aims at Internet type networks. It requires substantial message exchanges and intensive computations, thus not applicable in WSNs.

6 Conclusion and Future Work In this paper, we presented a novel anycast routing framework that supports global optimization of custom objectives via a fully distributed, highly scalable and resilient protocol. Key properties of this framework are proved through theoretical analysis and verified through simulations. Using network lifetime maximization problem as one example, we illustrated the power of this framework by showing a 2.7 to 8 time lifetime

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extension over Directed Diffusion and up to 5 times lifetime extension over the energyaware multipath routing scheme proposed in [4]. As our immediate future work, we plan to pursue systematic performance evaluation in real world settings.

References 1. Intel: Intel Heterogeneous Sensor Networks Project (2004) http://www.intel.com/ research/exploratory/heterogeneous.htm. 2. Intanagonwiwat, C., etc.: Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. In: Proc. of ACM Mobicom’00. (2000) 3. Ganesan, D., etc.: Highly-Resilient Energy-Efficient Multipath Routing in Wireless Sensor Networks. ACM Mobile Computing and Communication Review 5(4) (2001) 4. Shah, R.C., Rabaey, J.: Energy Aware Routing for Low Energy Ad Hoc Sensor Networks. (In: Proc. of WCNC’02) 5. Ye, F., Zhong, G., Lu, S., Zhang, L.: GRAdient Broadcast: A Robust Data Delivery Protocol for Large Scale Sensor Networks. J. of Wireless Networks 11(3) (2005) 285–298 6. Liu, H., Zhang, Z., Srivastava, J., Firoiu, V.: PWave: Flexible Potentila-based Routing Framework for Wireless Sensor Networks. Technical report, (http://www-users.cs.umn. edu/˜hliu/pwave.pdf) 7. Doyle, P.G., Snell, J.L.: Random Walks and Electric Networks. Mathematical Assn. of America (1984) 8. Qian, H., Nassif, S., Sapatnekar, S.: Power Grid Analysis Using Random Walks. IEEE Tran. on CAD of Integrated Circuits and Systems 24(8) (2005) 1204–1224 9. Stern, T.: A Class of Decentralized Routing Algorithms Using Relaxation. IEEE Trans on Comm. (25(10)) 10. Levis, P., Lee, N., Welsh, M., Culler, D.: TOSSIM: Accurate and Scalable Simulation of Entire TinyOS Applications. In: Proceedings of SenSys’03. (2003) 126–137 11. Gallager, R.: A Minimum Delay Routing Algorithm Using Distributed Computation. IEEE Trans on Comm. (25(1)) 12. Basu, A., Lin, A., Ramanathan, S.: Routing Using Potentials: A Dynamic Traffic-Aware Routing Algorithm. Proceedings of ACM SIGCOMM’03. (2003)

Simple Models for the Performance Evaluation of a Class of Two-Hop Relay Protocols Ahmad Al Hanbali1,3 , Arzad A. Kherani2, and Philippe Nain1 1

INRIA, Sophia-Antipolis, Cedex 06902, France Dept. of Computer Science and Engineering, IIT Delhi, New Delhi, India 3 Dept. of Computer Science, Universit´e de Nice-Sophia Antipolis, France

2

Abstract. We evaluate the performance of a class of two-hop relay protocols for mobile ad hoc networks. The interest is on the multicopy two-hop relay (MTR) protocol, where the source may generate multiple copies of a packet and use relay nodes to deliver the packet (or a copy) to its destination, and on the twohop relay protocol with erasure coding. Performance metrics of interest are the time to deliver a single packet to its destination, the number of copies of the packet at delivery instant, and the total number of copies that the source generates. The packet copies at relay nodes have limited lifetime (time-to-live TTL). Via a Markovian analysis, the three performance metrics of the MTR protocol are obtained in closed-from in the case where the number of the copies in the network is limited. Also, we develop an approximation analysis in the case where the inter-meeting times between nodes are arbitrarily distributed and the TTLs of the copies are constant and all equal. In particular, we show that exponential intermeeting times yield stochastically smaller delivery delays than hyper-exponential inter-meeting times, and that exponential TTLs yield stochastically larger delivery delays than constant TTLs. Finally, we characterize the delivery delay and the number of transmissions in the two-hop relay protocol with erasure coding and compare this scheme with the multicopy scheme. Keywords: Mobile ad hoc network, Two-hop relay protocol, Erasure coding, Mobility model, Analytical model, Markovian analysis, Performance evaluation.

1 Introduction In mobile ad hoc networks (MANETs), since there is no fixed infrastructure and nodes are mobile, routes between nodes are set up and turn down dynamically. For this reason, MANETs often experience route failures and network disconnectivity, especially when the nodes are moving frequently and the network is sparse. Grossglauser and Tse [9] propose to make the mobile nodes serve as relays in order to increase the network throughput in MANETs. Their relay mechanism, called two-hop relay protocol, is simple: if there is no direct route between the source node and the destination node, the source node transmits its packets to the nearest neighbor node (called relay node) for delivery to the destination. Then, it was shown that with this protocol it is possible 

The authors acknowledge the support of the European IST project BIONETS and of the Network of Excellence (NoE) EuroNGI.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 191–202, 2007. c IFIP International Federation for Information Processing 2007 

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to schedule Θ(N ) concurrent successful transmissions per time-slot, where N is the number of nodes [9]. After that, it was observed that the delay experienced by the packets under this protocol is large [7,13,17]. In order to reduce this delay it is proposed in [5,13] to allow the source transmits the packet to all its neighboring nodes (not only to the nearest neighbor). In this paper we evaluate the performance of two variants of the basic two-hop relay protocol: the multicopy two-hop relay (MTR) protocol and the two-hop relay protocol with erasure coding when N is finite. Before describing these two variants that aim at improving the delay, let first introduce the nodes mobility model. All nodes move independently of each other according to the same random mobility model inside a two-dimensional bounded region. Two nodes may only communicate at certain points in time, called meeting times. The time that elapses between two consecutive meeting times of a given pair of nodes is called the inter-meeting time. The following will be assumed throughout: (A1) Transmission times are instantaneous. (A2) All inter-meeting times are independent and identically distributed (iid) random variables (rvs) with a common cumulative distribution function (CDF) G(·). Assumption (A1) will be justified in delay tolerant networks, where the incurred delay to send a packet may be very large with respect to the transmission times [1]. For Assumption(A2), in [15] it has been shown that when nodes move independently on a sphere and they have uniform (stationary) spatial distribution, the inter-meeting time distribution is approximately the exponential distribution with mean 1/λ for λt  1. Furthermore, in [8] the exponential approximation of inter-meeting times distribution was validated for the random mobility models inside a square of non-uniform spatial distribution such as the random waypoint model [3] and of uniform spatial distribution such as the random direction model [12]. Moreover, it was observed that assumption (A2) is “reasonable” as long the node transmission range is not “too large” with respect to the area where nodes move. On the other hand, if nodes are humans moving in a conference space it has been found that the inter-meeting times distribution shows a heavier-than-exponential tail [4]. In this paper, we will show the impact of considering arbitrary and exponential inter-meeting times distribution on the performance of the MTR protocol. See Section 3 for more details. We consider the scenario consisting of N + 1 mobile nodes: one source node, one destination node, and N − 1 relay nodes. The source has a single packet to transmit to the destination. We now introduce the two variants. MTR protocol. In the MTR protocol the source node may either transmit the packet directly to the destination node when both nodes come within transmission of one another, or use the relay nodes. In the latter case, if the source meets a relay node before meeting the destination, then it sends a copy of the packet to this relay node; this relay node will only transmit the packet to the destination when it comes close to it (as opposed to the epidemic routing protocol [20], also called the unrestricted relay protocol [8], where a relay node is allowed to send a copy (of its copy) to another relay node). Section 2 evaluates the performance of the MTR protocol in the case where the number of copies in the network is limited. We define, Td , the (packet) delivery delay as the first time when the destination receives the original packet or a copy, whichever arrives first to the destination. We assume

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that the packet at the source cannot be dropped before the transmission has taken place, i.e., before time Td if the packet joins the source at time t = 0. On the other hand, each copy has a time-to-live (TTL) associated with it: when a TTL expires, the relay node that holds the copy drops it. This relay node then becomes eligible to receive another copy. We assume that the source cannot transmit a copy to a relay node that already holds a copy. Two-hop relay protocol with erasure coding. The relay protocol is the basic two-hop relay protocol. A node may only transmit a piece of information either directly to the destination or to at most one relay node. In the two-hop relay protocol with erasure coding, the source introduces some redundancy in its transmissions and sends more data than the actual information. Upon receiving a piece of data (packet), the source produces n blocks of data. The transmission of the packet is completed when the destination receives the kth block, regardless of the identity of the k ≤ n blocks it has received [15,19]. More details are provided in Section 4, where the Laplace-Stieljes transform (LST) of the delivery delay and the z-transform of the number of transmissions are derived in closed-form. Note that under the above assumptions, the delivery delay obtained in our setting gives a lower-bound, as a consequence of the instantaneous transmission time. Second, the total number of copies per-packet generated gives an upper-bound. This is so because in the realistic context the source will not systematically transmit a packet to a relay node that it encounters. The rest of the paper is organized as follows: Section 2 evaluates the performance of the MTR protocol with the assumptions that the number of packet copies in the network is limited, and that inter-meeting times and TTLs are distributed exponentially. In Section 3, we show the impact of considering arbitrary distribution of inter-meeting times and constant TTLs on the delivery delay of MTR. Section 4 finds the delivery delay and the number of transmissions for the two-hop relay protocol with erasure coding, and compares with erasure coding scheme.

2 Performance of MTR Protocol with Limited Number of Copies In this section we consider the MTR protocol, with exponentially distributed node intermeeting times with parameter λ (i.e., G(t) = 1 − e−λt ), exponentially distributed TTLs with parameter μ, and where the number of copies of a packet in the network may not exceed K (including the packet at the source), where K is an arbitrary integer less than or equal to N (in [2, Sec. 3] K = N ). We recall that we only focus on the transmission of a single packet between a given source and a given destination, and that the packet at the source has no TTL (only copies have a TTL). The performance metrics of interest are: Td the time needed to deliver the packet to the destination, Cd the number of copies of the packet before the delivery to the destination, and Gd the total number of transmissions before the delivery to the destination. Note that the latter metric is related to the energy needed to deliver a packet to the destination. In this section we derive closed-form expressions for the E[Tdn ] for all n ≥ 1, P (Cd = j), and E[Gd ]. We conclude this section by showing how these results can be used to find the value of K that minimizes E[Gd ], subject to a constraint on E[Td ].

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Under the above assumptions it is easily seen that the system can be modeled as a finite-state absorbing Markov chain I = {I(t), t ≥ 0}, where I(t) ∈ {1, 2, . . . , K} gives the number of copies of the packet at time t < Td , and I(t) = a if t ≥ Td . The states 1, 2, . . . , K are the transient states and the state a is the absorbing state of I. Let q(i, j) denote the (i,j)-entry of, Q, the infinitesimal generator of I. It is easily found that q(i, i + 1) = (N − i)λ,

i = 1, . . . , K − 1,

q(i, i − 1) = (i − 1)μ, i = 2, . . . , K, q(i, i) = −[N λ + (i − 1)μ], i = 1, . . . , K − 1, q(K, K) = −[Kλ + (K − 1)μ], q(i, a) = iλ, i = 1, . . . , K, q(i, j) = 0, otherwise. The matrix Q can be written as  Q=

QK R 0 0

 ,

(1)

where QK = [q(i, j)]1≤i,j≤K , R = (q(1, a), . . . , q(K, a))T , and 0 is a K-dimensional row vector with all entries equal to 0. We will show below that for any initial state I(0), E[Tdn ], P (Cd = j), and E[Gd ] can be derived in closed-form if one has a closed-form expression for Q−1 K , the inverse of QK . We now derive Q−1 K in closed-form. We note that QK can be decomposed as QK = ˆ K + buuT , where Q ˆ K is the K-by-K sub-matrix composed of the first K rows and Q columns of the matrix QN , u = (0, · · · , 0, 1)T , and b = λ(N − K). By applying the Sherman-Morrison formula [16, P. 76] we find that ˆ −1 Q−1 K = QK −

b ˆ −1 u 1 + buT Q K

ˆ −1 uuT Q ˆ −1 . Q K K

(2)

ˆ −1 . EquivLet qK (i, j) be the (i, j)-entry of Q−1 ˆK (i, j) be the (i, j)-entry of Q K and q K alently, (2) rewrites qK (i, j) = qˆK (i, j) −

qK (K, j) λ(N − K)ˆ qK (i, K)ˆ . 1 + λ(N − K)ˆ qK (K, K)

(3)

ˆ −1 . It remains to find the entries qˆK (i, j) of Q K −1 The matrix QN was obtained in closed-form in [2, Appendix I]. On the other hand, ˆ −1 is related to Q−1 through the relation simple algebra shows that the (i, j)-entry of Q K N qˆK (i, j) = qN (i, j) +

λ(N − K)qN (i, K)qN (K + 1, j) 1 − λ(N − K)qN (K + 1, K)

(4)

for 1 ≤ i, j ≤ K. A closed-form expression for qN (i, j) was obtained in [2, Eq. 25] for any i and j.

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2.1 The Performance Metrics Given that I(0) = i, the nth order moment of Td , which is by definition equal to the time to absorption of I, is given by [14, Chap. 2, Eq. 2.2.7] n Ei [Tdn ] = (−1)n n!(αi Q−n K e) = (−1) n!

K 

(n)

qK (i, j)

(5)

j=1

for i = 1, . . . , K, where e is a K-dimensional column vector with all entries equal to 1, αi is a K-dimensional row vector with all entries equal to 0 except the ith one that is (n) equal to 1, and qK (i, j) is the (i, j)-entry of Q−n K that can be expressed in closed-form in terms of qK (i, j) which are given in (3)-(4). Given that I(0) = i, the probability distribution of the number of copies just at delivery time is [2, Sec. 3.2] Pi (Cd = j) = −jλqK (i, j),

i = 1, . . . , K.

(6)

Given that I(0) = i, the expected total number of transmissions before delivery is given by [2, Sec. 3.3]  1 Ei [Gd ] = αEi [Td ] + Ei [Cd ] + bqK (i, K) + δ , (7) 2 where α := (λN − μ), b = λ(N − K), and δ :=

1 ρ

− i − 1.

2.2 Minimizing the Consumed Energy The total number of copies that are transmitted is directly related to the energy consumed to deliver the packet to the destination. Our objective now is to use the above results to find, Kopt , the optimal value of K which minimizes the expected total number 25

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of copies that are transmitted before the delivery of the packet to the destination, subject to a constraint on the expected delivery delay, that is, min

{K:E (K) [Td ]≤C}

E (K) [Gd ],

where E (K) [Td ] := E1 [Td ] and E (K) [Gd ] := E1 [Gd ]. The superscript (K) emphasizes the dependency in the variable K. Since the integer mappings K → E (K) [Td ] and K → E (K) [Gd ] are strictly decreasing and strictly increasing, respectively, the solution to this constrained optimization problem is obtained for the smallest integer K in [1, N ] such that E (K) [Td ] ≤ C if E (N ) [Td ] ≤ C, and has no solution if E (N ) [Td ] > C. Figure 1 reports Kopt as a function of C for ρ = 1 and for three values of λ. We observe that Kopt has a sharp decay as C increases.

3 Impact of Arbitrary Inter-meeting and Constant TTLs on MTR In [4], it has been observed that the inter-meeting times distribution has heavier-thanexponential tail. This finding was the motivation to investigate the impact of arbitrary inter-meeting times distribution on the delivery delay of the MTR protocol. Throughout this section we assume that for any pair of nodes their inter-meeting times are iid with distribution G(t), and all inter-meetings are mutually independent. Let X be a generic rv with distribution G. Also define G∗ (s) = E[e−sX ] the LST of X. We assume that TTLs are constant and all equal to T . As a result, the stochastic process I is no longer a Markov process and a different approach has to be used in order to evaluate the delivery delay of the MTR protocol. For sake of simplicity we consider the case where K = N , i.e., there is no restriction on the number of packet copies in the network. For convenience we label the nodes so that node 0 is the source, node N is the destination, and nodes 1, 2, . . . , N − 1 are the relay nodes. Since K = N , we have st

Td = min(Xsd , D1 , . . . , DN −1 ),

(8)

st

where Xsd = X represents the inter-meeting time between the source and the destination, and Di is the time needed for relay node i = 1, 2, . . . , N − 1 to deliver a copy of the packet to the destination. Moreover, the rvs Xsd , D1 , . . . , DN −1 are mutually independent and the rvs D1 , . . . , DN −1 are identically distributed. Hence, P (Td < t) = 1 − (1 − G(t))P (Di > t)N −1 .

(9)

We need to determine P (Di > t). We shall actually find an approximation formula for P (Di > t) since finding an exact expression is a very difficult task, unless G(t) is the exponential distribution that is considered in the end of this section. From now on i is fixed in {1, . . . , N − 1}. We assume that the source, destination and relay node i are in steady-state at time t = 0, and that the relay node i does not hold a copy of the packet at t = 0 (only the source holds the original packet at t = 0). Let R record the number of times the relay node i has dropped a copy of the packet before it transmits it to the destination. On the event R = m + 1, let ak > 0 be the

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arrival time of the kth copy to the relay node i for k = 1, . . . , m + 1, let dk > ak be the time where the kth copy is dropped by relay node i for k = 1, . . . , m, and let em+1 be ˆ = a1 , Zk = ak+1 − dk the time where copy m + 1 reaches the destination. Define X ˆ for k = 1, . . . , m, and Z = em+1 − am+1 . Clearly, ˆ + Z1 + · · · + Zm + mT + Zˆ Di = X

(10)

ˆ Z1 , . . . , Zm , Yˆ are mutually on the event R = m + 1. Given that R = m + 1, the rvs X, independent; moreover the rvs Z1 , . . . , Zm are iid. Let D∗ (s) := E[e−sDi ] be the LST of Di . We have ˆ ˆ  −sT D∗ (s) = E[e−sX ]E[e−sZ ] (e E[e−sZk ])m P (R = m + 1). (11) m≥0

1. Evaluation of ZT∗ (s) := E[e−sZk ]: Recall that X denotes a generic inter-meeting time and that its density probability is g(·). Let hT (t) := dP (Zk < t)/dt be the probability density of Zk . The reason why we indicate the dependency on the parameter T in hT (t) will soon become apparent. If the st source does not meet the relay node i in (ak , ak +T ) then Zk = ak+1 −ak −T = X −T ,   otherwise Zk = ak+1 − ak − (T − (ak − ak )) where ak is first time the source meets the st st relay node i in (ak , ak + T ). The latter rewrites Zk = X1 + X2 − T with Xj = X for ∞ j = 1, 2. From this we deduce that ZT∗ (s) = 0 hT (t)e−st dt satisfies the following renewal equation  ∞  T ZT∗ (s) = e−st g(T + t)dt + g(u)ZT∗ −u (s)du. (12) 0

0

that ZT∗ (s)

satisfies an integral equation (of Fredholm type) from which We have shown ZT∗ (s) can be obtained numerically using standard techniques [10]. 2. Probability distribution of R: Finding the probability distribution of R is difficult. We will first assume that R is a geometric rv with parameter π = 1 − P (R = 1). It is possible to find an integral equation for π. However, for sake of simplicity, we will assume that the destination node is at equilibrium at time a1 , so that π = 1−Ge (T ), with Ge (t) t the excess probability distribution of G(t), that is, Ge (t) = (1/E[X]) 0 (1 − G(u))du. In summary, P (R = m + 1) ≈ (1 − π)π m , for m ≥ 0. Plugging the approximation of P (R = m + 1) in (11) gives that D∗ (s) ≈

ˆ

1 − π (1 − G∗ (s)) E[e−sZ ] , sE[X] 1 − πe−sT ZT∗ (s)

(13)

where ZT∗ (s) is the solution of the integral equation (12), and π = 1 − Ge (T ) (Hint: ˆ ˆ E[e−sX ] = (1 − G∗ (s))/sE[X]). It remains to evaluate E[e−sZ ]. Again, this is not ˆ an easy task. Clearly, e−sT ≤ E[e−sZ ] ≤ 1. For sake of simplicity, we will replace ˆ E[e−sZ ] by 1. This gives the final approximation D∗ (s) ≈

(1 − G∗ (s)) 1−π . sE[X] 1 − πe−sT ZT∗ (s)

(14)

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P (Di > t) is obtained by inverting (1 − D∗ (s))/s (since (1 − D∗ (s))/s =  ∞Finally, −st e P (Di > t)dt) with the help of the complex inversion formula [18, Chap. 7], 0 which yields  γ+i∞ 1 − D∗ (s) 1 ds, t > 0, (15) ets P (Di > t) = 2πi γ−i∞ s where the integration has to be performed along a line s = γ in the complex plane (in (15) i denotes the imaginary complex number). The real number γ must be chosen so that s = γ lies to the right of all singularities. Note that since P (Di > t) is a bounded function it is sufficient to take γ > 0. The approximation for FTd (t) := P (Td > t) has been computed when G(t) is an H hyper-exponential distribution, namely, G(t) = 1 − l=1 pl e−νl t , and compared to simulation results. The evaluation of the integral in (15) has been performed by using the procedure described in [6]. Numerical results are reported in Figure 2. Two hyper-exponential distributions, represented by the t-tuple (H, ν1 , . . . , νH , p1 , . . . , pN ), have been considered: (H1) (3, 0.09, 0.08, 0.07, 0.6, 0.3, 0.1) with mean 22.83sec., and (H2) (3, 0.05, 0.04, 0.03, 0.6, 0.3, 0.1) with mean 11.84sec.. The numerical results of the mathematical model were done using a C program for a network composed of one source, one destination, and N − 1 relay nodes. Let Td (app) and Td (sim) be the approximate and simulated delivery delays, respectively. Let FTd (app)(.) (resp. FTd (sim)(.)) denotes the complementary cumulative distribution function (CCDF) of Td (app) (resp. Td (sim)). Figure 2.(a) displays the mappings t → FTd (app) (t) and t → FTd (sim) (t) for the hyper-exponential distributions H1 and H2. We observe that the approximation is accurate for moderate value of N . Figure 2.(b) compares FTd (sim) (t) with the CCDF of Td in the case where the intermeeting times are exponentially distributed, where the latter distribution has been obtained in closed-form. See (16) in the following for details. We conclude from these results that Td under exponential inter-meeting times is stochastically smaller than Td under hyper-exponential inter-meeting times. This is related to the fact that the hyperexponential distribution has a fatter tail than the exponential distribution. We conclude this section by briefly addressing the simple case where the intermeeting times are distributed exponentially with rate λ, and the TTLs are constant and all equal to T . In this case we derive the closed-form expression of the delivery delay of the MTR protocol as follows T  m    (Am )k+1 − (Bm )k+1  1+ , (k + 1)! m=0 t

P (Di > t) = e

−λt

(16)

k=0

where Am := λ(t − mT ), Bm := λ[t − (m + 1)T ]+ , x designates the largest integer ≤ x, and [x]+ := max(0, x). Plugging (16) in (9) gives the CDF of Td . By comparing the CCDF of Td with constant TTL= T and with TTLs distributed exponentially with mean T , we deduced that Td with constant TTL is stochastically smaller than Td with TTL exponential.

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4 Two-Hop Relay with Erasure Coding We now consider a system where the source introduces some redundancy in its transmissions and sends more data than the actual information. The advantage of this mechanism is that it can considerably reduce the variance of the delivery delay at the expense of an increase of its expectation. One of these techniques is known as erasure coding [11]. Erasure coding with replication factor r works as follow. Upon receiving a packet of size M , the source produces n = r · M/b equal sized code blocks of size b, such that any of the k = (1 + ) · M/b code blocks can be used to reconstruct the packet. Here is a small constant, close to zero [11]. Thus, the destination is able to decode the packet if it receives k ≤ n blocks. On the other hand, when k = 1, the size of a block becomes almost equal to M , the packet size, and in this case the destination needs to receive a block in order to R(t)

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decode the packet. Thus for k = 1, the erasure coding scheme is the same as a simple multicopy scheme in which the source sends exactly one copy of a packet to n different relay nodes [19]. We will exploit this observation to compare erasure coding with the multicopy scheme in the following. Throughout this section the stochastic model is the following. There are N relay nodes, one source node, and one destination node. We assume that the source cannot send directly a packet to the destination. Inter-meeting times between any pair of nodes are exponentially distributed with rate λ, except for the pair source-destination. Under this setting, the only way to forward the data from the source to the destination is through the relay nodes. The source has only one packet to send to the destination, and the source implements the erasure coding algorithm with replication factor r and parameter k. Hence, the destination needs to receive k ≤ n of the blocks in order to decode the original packet. The forwarding mechanism used to deliver the blocks to the destination is the standard two-hop relay protocol. We assume that there is only one copy of a block in the network. A relay node can only relay one block at a time, and it is possible for a relay node that already delivered a block to the destination receives a new block when it again encounters the source. There is no TTL associated with the blocks. Let Td and Gd be the delivery delay and the total number of source-relay transmissions at the time when the kth block reaches the destination, respectively. Introduce the joint transform H(s, z) := E[e−sTd z Gd ], s ≥ 0, |z| ≤ 1. We now evaluate H(s, z). Let A(t) = (B(t), R(t)) denote a two-dimensional process such that A(t) = (m, l), 1 ≤ m ≤ n, 1 ≤ l ≤ k − 1, m + l ≤ n, if there are m relay nodes that hold m blocks (one block for each relay node) and the destination has received l blocks at time t < Td , and A(t) = a when t ≥ Td (a is an absorbing state). Under the above assumptions, {A(t), t ≥ 0} is a finite-state absorbing Markov process. Figure 3 displays the transition diagram of this Markov chain, where the y-axis represents R(t) and the x-axis represents the sum B(t) + R(t). More precisely, a point (i, l), i ≥ l, in the transition diagram means that the destination has received l blocks and that there are i − l relay nodes that hold i − l blocks (one block for each). Let ji ≥ 0 denote the number of jumps (transitions) along the horizontal line of index i ∈ {0, · · · , k − 1}. Let Si denote the total number of jumps along the lines of index less than or equal to i. Given Si−1 , the probability of making ji jumps along the horizontal line i is

−Si−1 +i)! Si −i P1 (ji ) = (N (N −Si +i)! N ji +1 , Si < n (17) (N −Si−1 +i)! 1 P2 (ji ) = (N −n+i)! N ji , Si = n, for 0 ≤ i ≤ k − 1. Let m∗ denote the index of the horizontal line such that Sm∗ −1 < n and Sm∗ = n. Conditioned on all the possible paths before absorption at {a} and given that A(0) = (0, 0), H(s, z) can be written as H(s, z) =

n−1 

···

j0 =1 ∗

×

jk−1 =0

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n−Sm∗ −2 n k−1   zN λ Sk−1 +k P1 (jl ) + ··· P2 (n − Sm∗ −1 ) s + Nλ j =1 j ∗ =0 l=0

0

 zN λ n+m∗ k−1

λ(n − l) , P1 (jl ) s + Nλ s + λ(n − l) ∗ l=m

m −1

(18)

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We now evaluate theexpectation and the variance of Td for different values of k, n, and N . Let σTd := var(Td )/E[Td ] denote the normalized standard deviation of Td . As noted earlier, erasure coding when k = 1 is similar to the multicopy scheme. Table 1 shows that E[Td ] increases with k and that σTd decreases with k. For instance, for N = 20 and n = 10 when k increases from 1 to 5 E[Td ] increases by a factor of 3 while σTd decreases by a factor of 7.5, thereby showing that erasure coding has a much lower variability than the multicopy scheme. A similar result was found in [19] under the assumption that the source instantaneously transmits all its n blocks to n different relay nodes. Table 1. Erasure coding (k > 1) vs multicopy scheme (k = 1) for different value of n and N (N, n) (20,10) (40,10) k 1 2 5 1 2 5 E[Td ] (sec.) 31.51 42.04 96.69 22 33.7 79.76 σTd 8.7 4.9 1.17 2.01 1.38 0.63

We conclude this section by investigating the behavior of TN∗ (s) := E[e−sTd ], the LST of Td , as N is large. We observed that as N becomes large the most probable path (MPP) is where all n blocks are first transmitted to n relay nodes, and then these relay nodes start to deliver these blocks to the destination. Further, it is easy to see that as N is large the system has a deterministic path MPP with probability one. Therefore, TN∗ (s) ≈

 N λ n k−1

λ(n − l) s + Nλ s + λ(n − l)

(19)

l=0

5 Concluding Remarks In this paper, we have studied a class of two-hop relay protocols. The interest was on the multicopy two-hop relay (MTR) protocol and on the two-hop relay protocol with erasure coding. Closed-form expressions have been derived for nth order moment of the time to deliver a packet to its destination, the distribution of the number of copies at delivery, and the expected total number of copies generated before delivery in the case where copies have limited lifetime (TTL) and where the number of copies in the network is limited. Also, We investigated the impact of arbitrary inter-meeting times distribution and constant TTLs on the delivery delay of the MTR protocol. In particular, we show that exponential inter-meeting times yield stochastically smaller delivery delay than hyper-exponential inter-meeting times. Finally, for the two-hop relay protocol with erasure coding the joint generating function of the delivery delay and of the number of transmissions was derived in closed-form. By analyzing these results, we found that the delivery delay in the case of erasure coding has much lower dispersion than the delivery delay of the multicopy scheme. As future work, we will study the delay when there are multiple sources with multiple packets to transmit to a set of destinations and where the relay nodes may have different mobility.

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References 1. Delay Tolerant Research Group, http://www.dtnrg.org. 2. A. Al Hanbali, P. Nain, and E. Altman, Performance of Two-hop Relay Routing Protocol With Limited Packet Lifetime, Proc. of Valuetools 2006, Pisa, Italy, Oct. 2006. 3. C. Bettstetter, H. Hartenstein, and X. Prez-Costa. Stochastic Properties of the Random Waypoint Mobility Model. ACM/Kluwer Wireless Networks, Special Issue on Modeling and Analysis of Mobile Networks, vol. 10, no. 5, pp. 555-567, Sept. 2004. 4. A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, Impact of Human Mobility on the Design of Opportunistic Forwarding Algorithm, Proc. of INFOCOM 2006, Barcelona, Spain, Apr. 2006. 5. R. De Moraes, H. Sadjadpour, and J. Garcia-Luna-Aceves, Throughput-Delay Analysis of Mobile Ad-hoc Networks with a Multi-Copy Relaying Strategy, Proc. of IEEE SECON, Santa Clara, CA, Oct. 2004. 6. H. Dubner and J. Abate, Numerical inversion of Laplace transforms by relating them to the finite Fourier cosine transform, Journal of the ACM, Vol. 15, No. 1, pp. 115-123, Jan. 1968. 7. A. El Gamal, J. Mammen, B. Prabhakar, and D. Shah. Throughput-Delay Trade-off in Wireless Networks Proc. of INFOCOM 2004, Hong Kong, Apr. 2004. 8. R. Groenevelt, P. Nain, and G. Koole, The Message Delay in Mobile Ad Hoc Networks, Proc. of Performance 2005, Juan-les-Pins, France, October 2005. Published in Performance Evaluation, Vol. 62, Issues 1-4, Oct. 2005, pp. 210-228. 9. M. Grossglauser and D. Tse, Mobility Increases the Capacity of Ad hoc Wireless Networks, IEEE/ACM Transactions on Networking, Vol. 10, No. 4, Aug. 2002, pp. 477-486. 10. S. G. Mikhlin, Integral Equations. Pergamon Press, Oxford, 1964. 11. M. Mitzenmacher, Digital Fountains: A Survey and Look Forward, Proc. of IEEE Information Theory Workshop, TX, USA, Oct. 2004. 12. P. Nain, D. Towsley, B. Liu, and Z. Liu, Properties of Random Direction Models Proc. of INFOCOM 2005, Miami, FL, USA, Mar. 2005. 13. M. J. Neely and E. Modiano, Capacity and Delay Tradeoffs for Ad-Hoc MobileNetworks, IEEE Transactions on Information Theory, Vol. 51, No. 6, June 2005. 14. M. Neuts, Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach. Johns Hopkins University Press, 1981. 15. E. Perevalov and R. Blum, Delay Limited Capacity of Ad hoc Networks: Asymptotically Optimal Transmissiom and Relaying Strategy, Proc. of INFOCOM 2003, San Francisco, USA, Apr. 2003. 16. W. H. Press, B. P. Flannery, S. A. Teukolsky and W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1988. 17. G. Sharma, R. Mazumdar, and N. Shroff Delay and Capacity Trade-offs in Mobile Ad Hoc Networks: A Global Perspective Proc. of INFOCOM 2006, Barcelona, Spain, Apr. 2006. 18. M. R. Spiegel, Schaum’s Outline of Theory and Problems of Laplace Transforms. McGrawHill, New York, 1965. 19. Y. Wang, S. Jain, M. Martonosi, and K. Fall, Erasure-coding based routing for opportunistic networks Proc. of SIGCOMM Wokshop on DTN, Philidelphia, PA, USA, Aug. 2005, 20. X. Zhang, J. Neglia, J. Kurose, and D. Towsley, Performance Modeling of Epidemic Routing Proc. of NETWORKING 2006, Coimbra, Portugal, May 2006.

Maximum Energy Welfare Routing in Wireless Sensor Networks Changsoo Ok1, Prasenjit Mitra2, Seokcheon Lee3, and Soundar Kumara1 1

The Department of Industrial Engineering, The Pennsylvania State University, University Park, PA 16802, USA {cuo108,skumara}@psu.edu 2 College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA [email protected] 3 The School of Industrial Engineering, Purdue University, West Lafayette, IN 47907-20232, USA [email protected]

Abstract. Most routing algorithms for sensor networks focus on finding energy efficient paths to prolong the lifetime of sensor networks. As a result, the sensors on the efficient paths are depleted quickly, and consequently the sensor networks become incapable of monitoring events from some parts of their target areas. In many sensor network applications, the events have uncertainties in positions and generation patterns. Therefore, routing algorithms should be designed to consider not only energy efficiency, but also the amount of energy left in each sensor to avoid sensors running out of power early. This paper introduces a new metric, called Energy-Welfare, devised to consider average and balance of sensors’ remaining energies simultaneously. Using this metric, we design the Maximum Energy Welfare Routing algorithm, which achieves energy efficiency and energy balance of sensor networks simultaneously. Moreover, we demonstrate the effectiveness of the proposed routing algorithm by comparing with three existing routing algorithms. Keywords: Sensor Network, Distributed algorithm, Energy aware routing, Energy Welfare, Social welfare, Energy balance.

1 Introduction Sensor networks report predetermined events or transmit sensed data to the base station for further analysis [1, 2]. The sensors contain a fixed amount of stored power and the process of sending data consumes some of that stored power. It is desirable that the sensors run as long as possible. In this work, we propose a routing algorithm that attempts to route messages efficiently so as to maximize the life of a sensor network. Consequently, the design of the routing algorithm for sensor networks should also incorporate the following factors [2]. z Due to sensors’ limited power, the routing algorithm should have a design to allow finding efficient paths to prolong the lifetime of the sensor network. I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 203–214, 2007. © IFIP International Federation for Information Processing 2007

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z However, it is inevitable for most energy efficient routing algorithms to drive some sensors, which are close to the base station or on energy efficient paths, drained power quickly. As a result, the sensor networks become unable to detect events from regions where all sensors are nonfunctioning. Thus, in the sensor networks, data traffic should be dispersed and distributed over the whole network to extend its lifetime. z Although most existing routing algorithms assume that events are generated uniformly at each sensor, events could occur randomly [3], uniformly [4] over the target area, or repeatedly [5] at a specific part of the target area. Event patterns can change from one type to another over time. Therefore, the routing algorithm should be sufficiently robust for diverse event generation functions. This problem can be addressed by routing so as to utilize the energy uniformly over the entire sensor network. z In addition, a sensor network can consist of a large number of nodes for which a central control architecture does not apply. Therefore, the routing algorithm should adopt a local decision making scheme. Although the literature includes several routing algorithms, such as direct communication approach, hierarchical routing methods [6, 7], self-organized routing algorithm [4], and the other routing algorithms [8], little evidence exists for effectiveness and efficiency of these algorithms with respect to the considerations mentioned earlier.

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Fig. 1. An explanatory example for Energy Welfare Routing algorithm: Node 1 routes data to a path to maximize an Energy Welfare (Average×Equality) of sensor 1 and 2

We assume that the neighbors only get the information about the energy left in each neighbor and the energy required to transmit to the base station from that neighbor periodically. Individual sensors forward messages to neighbors that they think are on the “best” route to the base station. The determination of the optimal route is difficult because the individual sensors do not have the information about the

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dynamic topology of the entire network and the dynamic energy balances of each node on the network. This study proposes a new heuristic metric, called Energy Welfare, to achieve the efficiency and balance of energies of sensors simultaneously. Based on this metric, we propose a localized routing algorithm, the Maximum Energy Welfare (MaxEW) routing, to accomplish the two objectives. Figure 1 gives a simple example of the MaxEW routing. Ei and eij represent the residual energy of sensor i and energy required to transmit from node i to node j respectively. Sensor 1 has two paths to reach to the base station. Path 2 is more energy efficient than Path 1. However, if sensor 1 keeps using path 2, sensor 2 will run out its power while sensor 1 has sufficient energy. In MaxEW, sensors can avoid the traffic concentration to a sensor by using a metric, Energy Welfare (Average Equality), as a decision criterion. The Equality is in inverse proportion to the difference between energy levels of sensor 1 and 2. Based on the metric, sensor 1 chooses path 1 which causes higher energy welfare. The rest of this paper has the following organization: Section 2 presents the details of the considered sensor network. Section 3 defines energy equality and the welfare of sensor network as new metrics. After describing the details of the maximum energywelfare routing algorithms in Section 4, Section 5 presents extensive simulation results, and Section 6 details conclusions.

×

2 Sensor Network Model With n homogenous sensors randomly and uniformly distributed over a target area, every sensed data must be sent to the base station. Each sensor has limited battery power. Sensors can control their respective transmission power for minimal consumption to transmit to a destination [6, 7] and they have discrete adjustable transmission power levels [9-12]. This ability is necessary to allow the routing algorithm to maximize sensor networks’ operational times. Therefore, sensors can send data to either a neighbor or the base station directly, according to their routing policies [4, 6, 7]. The details of the problem are: 2.1 Energy Consumption Model Each sensor uses a fixed transmission power for communicating with its neighboring sensors while each sensor transmits data to the base station. The neighboring distance is defined as the maximal reachable distance with the fixed transmission power for neighboring sensors. For a given sensor the sensors within its neighboring distance are its “neighboring sensors” or “neighbors”. In this scheme, each node can be aware of the current energy level of its neighbors or energy required to transmit from its neighboring nodes to the base station by anticipating and/or eavesdropping data from the neighbors. Generally, sensors use their energy when they sense, receive and transmit data. However, the amount of energy consumption for sensing is unaffected by the routing algorithm and only a small difference exists between the power consumption of idle and receiving modes [13]. Therefore, consideration is only energy consumption by

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transmission in the design of the routing algorithms to maximize the lifetime of the sensor network. By normalization of the radio characteristic constant and the size of sensed data [4], the energy consumption model is simplified to E=d2, where E and d are the required energy and the transmission distance respectively. 2.2 The Lifetime of Sensor Network Validating the effectiveness of the proposed MaxEW routing algorithm uses the lifetime of sensor network as the performance measure [4, 6, 8, 11]. The definition of lifetime of a sensor network is the time or number of rounds that occur until the first node or a portion of nodes become incapable, due to energy depletion, of sending data to the base station directly or indirectly via its neighbors [4, 6, 8, 11, 14]. The portion (number of depleted nodes) can vary depending on the context of the sensor networks. In this paper, the lifetime of a sensor network is the number of rounds until the first (L1), 10% (L10), or 20% (L20) node(s) expend all their power [8, 11]. 2.3 Event Generation Functions For evaluation purposes, many previous studies of routing algorithms assumed that all sensors have uniform data or event generation rates [4, 6, 7]. In infrastructure monitoring applications, each sensor performs a sensing task every time, T, and has a homogeneous event generation function. However, in many sensor network applications, this assumption becomes unrealistic. In a monitoring application for the migration of a herd of animals, the animals might move along a path in the target area repeatedly [5]. While, in the case of forest fire detection, events can occur rarely and randomly over the target area [3]. Furthermore, some event generation functions can be a combination of uniform, random, and repeated types. Therefore, more reasonable is the consideration of several event types for evaluation of routing algorithms. The results section demonstrates that our algorithm is robust for the different types of event generation functions.

3 Energy Equality and Welfare To keep detecting events at an unknown position in a target area for as long as possible, routing algorithms should have a design to enable finding efficient paths, and, at the same time, prevent a particular set of sensors from being depleted early by a concentration of data traffic. In other words, the routing algorithms must achieve an energy balance for sensor networks while guaranteeing that sensors use their energies efficiently. Designing the routing algorithm first required a new measure for considering energy balance of sensor networks, as well as energy efficiency of routing algorithms. For this purpose, two definitions of Energy-Equality (EE) and EnergyWelfare (EW) apply.

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3.1 Energy-Equality (EE) To measure how well energy-balanced a sensor network is, we define the EnergyEquality (EE) of a given sensor network is Equation (1) and (2):

EE ( t , ε ) = 1 − I N ( t , ε ) IN

⎡1 (t , ε ) = 1 − ⎢ n ⎣⎢

∑

i∈ N

⎛ E i (t ) ⎞ ⎜⎜ E ( t ) ⎟⎟ ⎝ ⎠

1− ε

.

(1)

1

⎤ 1− ε . ⎥ ⎦⎥

(2)

I N (t , ε ) is the inequality(explained below) of the sensor’s residual batteries at

time, t. N and εare the set of nodes in the sensor network and the inequality aversion parameter. n and Ei(t) are the number of sensors and the remaining energy of sensor, i, at time, t, respectively. Basically, the derivation of the energy inequality index, I N (t , ε ) , is from the Atkinson inequality index [15]. Social scientists use the index to measure the inequality among entities with respect to their income. The aversion parameter ε reflects the strength of society’s penalty for inequality, and can take values ranging from zero to infinity. When ε equals zero, no penalty for inequality accrues. As ε rises, society penalizes inequality more. The values of ε that are typically used include, 1.5 and 2.5 [16, 17]. This aversion parameter provides a flexibility to apply this metric to diverse sensor network applications. 3.2 Energy-Welfare (EW) A drawback exists for only considering energy balance of a sensor network. If a routing algorithm only pursues the energy balance without considering energy efficiency, sensors’ residual energies might converge to a lower value. That is, sensors possibly use their energy in an inefficient way to achieve the energy-balance of the sensor network. Therefore, Energy-Welfare (EW) is the consideration of energy efficiency and energy balance of sensor networks simultaneously. EW is a simple form of weighting the average of sensors’ residual energies by EE. We can calculate EW using equation (3).

EW (t ) = E (t ) × EE (t , ε ) .

(3)

The equation for EW has the same form as Atkinson welfare function [18]. The EW is high where the average and equality of sensors’ remaining battery power are both high. A low average, or EE, leads to a low EW. Therefore, the EW is an appropriate metric to design a routing algorithm that improves sensor networks with energy efficiency and energy balance perspectives. Additionally, since a sensor

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network having a high EW, can monitor unknown position events for a long time, the EW has consideration as the preparedness of sensor networks for upcoming events.

4 Maximum Energy Welfare (MaxEW) Routing In this paper, we assume that each sensor uses a fixed transmission power for communicating with its neighboring nodes. To send a message from a sensor to the base station the total transmission power required is minimized if the sensor communicates directly with the base station. The sensors are aware of the minimum transmission power required to send a message to the base station and the current battery level of its neighbors. The basic idea of the proposed routing algorithm is simply to use a path which maximizes the EW of the sensors. When a node i needs to send data to the base station, it can transmit data to the base station directly or route the data to one of its neighbors (Ni). For evaluating these alternatives, node i calculates the EW of a local society which consists of its neighbor and itself for each alterative. That is, the node can anticipate the residual energies of its neighboring nodes and itself when the data is routed according to its decision as in (4).

{E (t ) : j ∈ N j

+ {i}} → {E j (t + 1) : j ∈ N i + {i}} . Di ( t )

i

(4)

Also, this node can calculate the EWij of these expected residual energies, Ej(t+1), for each decision, Di(t), by (5). By comparing these expected EWs, the node routes data to the path allowing the maximum EW of the local society, Ni+{i}.

EWij (t + 1) = E (t + 1)(1 − I N (ε ) (t + 1) ) .

(5)

Through this decision making scheme, MaxEW tries to maximize the energy welfare of the entire sensor network. In MaxEW, each sensor keeps a small size routing table only for its neighboring nodes. The routing table contains node identification number, minimum transmission power to the base station, and available energy, for each neighbor node. The details of the algorithm are: Initialize routing table. During the setup period, each sensor finds its minimum transmission power to the base station. Then, each sensor broadcasts a setup message to neighboring nodes with a pre-set transmission power. This setup message includes node ID., minimum transmission power to the base station, and available energy. Every node receiving this broadcast message registers the transmitting node as one of its neighbors. Since all nodes have an identical neighbor distance, two nodes within the neighbor distance are neighbors to each other. After the setup period, all sensors initialize their routing tables.

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Algorithm 1. MaxEW routing algorithm (at node i and time t)

∈

For all j Ni+{i} do If j = i then For all k Ni+{i} do If k = i then Ek(t+1) = Ek(t) - TEBk Else Ek(t+1) = Ek(t) End If End For Else For all k Ni+{i} do If k = i then Ek(t+1) = Ek(t) - TENk Else If k = j then Ek(t+1) = Ek(t) – TEBk Else Ek(t+1) = Ek(t) End If End For End If Compute EWij by (4) End For Choose J = ArgMax EWij and route data

∈

∈

j∈N i +{ BS }

(

)

Update routing table. A change of a neighbor’s energy level should be reflected in the routing table. When a sensor transmits data, all of its neighbors receive this data and get the current battery level of the transmitting sensor. As a result, whenever a sensor’s battery level changes, all routing tables including the corresponding sensor information are updated. Localized routing decision. Based on their routing tables, every node makes a local routing decision. Algorithm 1 gives a high-level description of the MaxEW algorithm of node i, at time, t. TEBk and TENk are the required transmission energies from node, k, to the base station and neighboring nodes, respectively. Also, EWij is the expected energy welfare of Ni +{i} when node i routes data to node, j. Based on this algorithm, node i selects J as the best candidate for transmitting data to the base station without considering whether J sends data directly to the base station or not. If the selection is that node i itself is the best node, it sends the data to the base station, finishing the routing process. Otherwise, the data is routed to node J and J performs the same process. This process continues until the base station receives the data. This localized decision making process results in a monotonic increase of EW because the best candidate can have a better indirect path than direct transmission.

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Base station ni nj

nk Routing path Possible path

Fig. 2. An example routing path: ni sends data to nj, nj to nk, then nk sends to the base station directly

Fig. 2 shows how the MaxEW algorithm operates over a sensor network. For a given data, ni chooses nj among several possible routes. After the data passes to nj, energy level of ni changes and the routing table of nj also changes. nj performs the same process sequentially. In the figure, nk sends data to the base station directly because the transmission of nk, itself results in the maximum EW of nk’s local society compared to other neighbors. MaxEW guarantees elimination of loops in any routing path. In MaxEW, a sensor routes data to a neighbor only if the neighbor incurs more energy welfare than the sensor itself. As this routing mechanism continues, the expected energy welfare of the original node is apparently greater than that of the next down-stream node. Therefore, MaxEW always assures finding a routing path to the base station without loops.

5 Experimental Results In this section, several experimental results validate the effectiveness of the MaxEW algorithm. The comparison of the algorithm is with three other algorithms discussed in [4, 7]: Direct Communication (DC), Minimum Transmission Energy (MTE), and Self-Organized routing (SOR). In DC, every sensor simply transmits data directly to the base station without considering any energy-efficient indirect path. MTE and SOR consider indirect routing to save sensor power but make routing decisions based on energy efficiency only. The MaxEW algorithm tries to achieve an energy balance of the network by maximizing the EW of a local society in a decentralized manner. Experimental results show that this approach is valid for extending the lifetime of sensor networks and robust for different event. The experiments use a sensor network in which 100 nodes have random and uniform deployment in a 100m×100m square area with the base station located at (50, 150)(see Fig. 3). In the sensor network, sensors have an initial battery level of 250,000. The initial energy levels are established by determining the amount of energy needed for the farthest node to transmit data to the base station 100 times with DC and also used in [4]. To discuss the effect of different event generation types on the lifetime of

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a sensor network, performed simulations uses uniform, random, and repeat event generation functions. In the case of the random distribution, 25% of sensors have events randomly occurring in each round. While, for the repeat events, the assumption is that sensors from (0, 0) to (50, 50) incur repeated events. Because a sensor network is generated randomly, 100 repeated experiments for each condition provides an average of the results. Lastly, neighbor distance of sensors (for MTE and MaxEW) and the aversion parameter ε (for MaxEW) have settings of 15m and 2.5 respectively. Table 1. Lifetime (L1, L10, L20) for Direct, MTE, SOR, and MaxEW with Uniform, Random, and Repeat Events

Direct MTE SOR MaxEW

L1 105.3 14.4 28.7 202.5

Uniform L10 123.4 67.1 145.9 258.3

L20 142.3 115.5 109.9 268.9

L1 492.7 74.6 111.3 1012.7

Random L10 L20 618.1 717.2 337.4 581.1 562.9 771.4 1239.9 1346.7

L1 107.8 30.0 154.1 685.6

Repeat L10 L20 145.8 193.3 223.7 415.6 316.9 418.2 970.6 1020.6

Table 1 gives the result for the lifetime of sensor network (L1, L10, L20) for Direct Communication, MTE, SOR, and MaxEW algorithms with three different event generation types. As shown in Table1, MaxEW shows a dominant performance compared with Direct, MTE and SOR over the time. Especially, in the case of L1, MaxEW gives approximately two to eight times better performance than the others. Fig. 4 shows how well MaxEW achieves the energy balance of sensors over the network. As discussed in [7], in the Direct (Fig. 4(a)), SOR (Fig. 4(c)) and MTE (Fig. 4(b)) routing scheme sensors far away and close to the base station ran out their energies around 150 round. While, in MaxEW, all sensors remain live and even have sufficient energy for responding upcoming events (Fig. 4(d)). Also notable is that Direct, MTE, and SOR missed some events during the first 150 rounds. However, MaxEW guaranteed that all data is transmitted to the base station for the same period.

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Fig. 5 shows the routing paths for four algorithms with repeated events in the regions from (0, 0) to (50, 50). In the case of Direct, MTE, and SOR, data traffic concentrates in specific sensors which have location in the region or on the efficient path. On the other hand, MaxEW tries to dissipate energy usage over the whole network to achieve energy balance. As a result, MaxEW can keep all sensors operating for as long as possible.

6 Conclusion and Future Works Sensor networks should be able to achieve energy balance as well as energy efficiency. Most energy-aware routing algorithms are only concerned about energy efficiency. This paper has presented a performance measure, called Energy Welfare, to consider energy balance and efficiency of sensor networks simultaneously. Based on this metric, the proposal is for a Maximum Energy Welfare (MaxEW) routing algorithm. We demonstrate the superiority of this routing algorithm to Direct Communication, MTE, and SOR with a lifetime metric, generally accepted for evaluation of routing algorithms. Additionally, from the experimental results, the conclusion is that MaxEW is robust for several event generation functions. To build the metric EW and MaxEW algorithm, we here use the Atkinson welfare function and set the inequality aversion parameter to 2.5. Many alternative welfare functions are available in social science, and this inequality aversion parameter is tunable. In the future, we will apply alternate social welfare functions and different aversion parameters to enhance our results. Currently, we used three types of event generation function for evaluation of our routing algorithm. Future work will involve development of more diverse and detailed event generation functions. In addition, we can consider a general multi-hop communication scenario where only a few sensors can communicate with the base station. In this scenario, the required transmission energy from a sensor to the base station can be calculated by the number of hop to the base station. As a future work, we will investigate how MaxEW works well in the general multi-hop scenario. Acknowledgments. This work was supported in part by NSF under the grant NSFSST 0427840. Any opinions, findings, and conclusions or recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References 1. D. Estrin, R. Govindan, J. Heidemann, and S. Kumar, Next century challenges: scalable coordination in sensor networks, Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, Seattle, Washington, United States, 263270 (1999). 2. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, A survey on Sensor Networks, IEEE Communications Magazine, 38(8), 102-114 (2002).

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3. D. Braginsky and D. Estin, Rumor Routing Algorithm for Sensor Networks, the proceedings of the first Workshop on Sensor Networks and Applications (WSNA), 22-31 (2002). 4. A. Rogers, E. David, and N. R. Jennings, Self-organized routing for wireless microsensor networks, IEEE Transactions on Systems, Man and Cybernetics, Part A, 35(3), 349-359 (2005). 5. Z. Butler and D. Rus, Event-based motion control for mobile-sensor networks, IEEE Pervasive Computing, 2(4), 34-42 (2003). 6. S. Lindsey, C. Raghavendra, and K. M. Sivalingam, Data Gathering Algorithms in Sensor Networks Using Energy Metrics, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 13(9), 924-935 (2002). 7. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Microsensor Networks, Proceedings of the 33rd Hawaii International Conference on System Sciences - 2000, (2000). 8. J. Chang and L. Tassiulas, Maximum Lifetime Routing in Wireless Sensor Networks, IEEE/ACM TRANSACTION ON NETWORKING, 12(4), 609-619 (2004). 9. J. Wanger and R. Cristescu, Power Control for Target Tracking in Sensor Networks, 2005 Conference on Information Sciences and Systems, The Johns Hopkins University, (2005). 10. R. Ramanathan and R. Rosales-Hain, Topology Control of Multihop Wireless Networks using Transmit Power Adjustment, INFOCOM 2000, 404-413 (2000). 11. H. Zhang and J. C. Hou, Maximizing α-Lifetime for Wireless Sensor Networks, SenMetrics 2005, 70-77 (2005). 12. R. Wattenhofer, L. Li, P. Bahl, and Y. Wang, Distributed topology control for power efficient operation in multihop wireless ad hoc networks, INFORCOM 2001, Anchorage, AK, USA, 1388-1397 (2001). 13. M. Stemm and R. H. Katz, Measuring and reducing energy consumption of network interface in hand-held devices, IEICE Transactions on Communications, E80-B(8), 11251131 (1997). 14. S. R. Gandham, M. Dawande, R. Prakash, and V. S., Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile Base Stations, Proceedings of IEEE Globecom 2003, 377-381 (2003). 15. A. B. Atkinson, On the measurement of inequality, Journal of Economics Theory, 2(3), 244-263 (1970). 16. D. A. Seiver, A note on the measurement of income inequality with interval data, Review of Income and Wealth, 25(2), 229-233 (1979). 17. J. G. Williamson, Strategic wage goods, prices, and inequality, American Economic Review, 67(2), 29-41 (1977). 18. A. K. Sen and J. E. Foster, On Economic Inequality, (Oxford: Clarendon Press, 1997).

Analysis of Location Privacy/Energy Efficiency Tradeoffs in Wireless Sensor Networks Sergio Armenia1 , Giacomo Morabito2 , and Sergio Palazzo2 1

2

CNIT - Research Unit of Catania, Italy [email protected] Universita’ di Catania, DIIT, V.le A.Doria, Catania, Italy [email protected], [email protected]

Abstract. In this paper an analytical framework is proposed for the evaluation of the tradeoffs between location privacy and energy efficiency in wireless sensor networks. We assume that random routing is utilized to improve privacy. However, this involves an increase in the average path length and thus an increase in energy consumption. The privacy loss is measured using information theory concepts; indeed, it is calculated as the difference between the uncertainties on the target location before and after the attack. To evaluate both privacy loss and average energy consumption the behavior of the routing protocol is modeled through a Markov chain in which states represent the nodes traversed by a packet in its way to the sink. The analytical framework can be used by designers to evaluate the most appropriate setting of the random routing parameters depending on the privacy and/or energy efficiency requirements.

1

Introduction

It is well known that wireless networks have serious privacy problems. This is mainly because of the broadcast nature of the radio channel that allows all stations in proximity of the sender to overhear the frames sent. Even if network devices make use of encryption algorithms, confidentiality is usually provided for the data field only, whereas the header/tail fields remain in plain text. Therefore, given that during the normal activity there are frequently packets sent using broadcast address as destination, an eavesdropper can receive and process them without any effort and thus can obtain information about the sender. This, joined to the fact that wireless devices usually have a fixed address, gives the attackers the possibility to link device address to user identity or to device position as well to the type of application utilized. In wireless sensor networks (WSNs) the above problems are amplified and new issues arise. In fact, WSNs are based on the wireless multihop communication paradigm and therefore, eavesdropping attacks can be accomplished more easily. Furthermore, WSN applications are pervasive by nature and as a consequence, a lot of user sensible information can be stolen by attackers. In the recent past a lot of attention has been devoted to the key distribution in the WSN cryptography domain. Accordingly, several solutions have been proposed for pre-distributing keys or for reducing their size. I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 215–226, 2007. c IFIP International Federation for Information Processing 2007 

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However, secure cryptography does not guarantee privacy, as we have already said. Indeed, some research work has recently appeared that deals with the relationship between routing and location privacy in WSN. In fact, radio activity at an intermediate node can be used to obtain information about the position of the information source. In [3] a formal model of the source-location privacy problem is provided, and two popular classes of routing protocols, namely, flooding protocols and single path protocol, are analysed from the privacy and energy consumption standpoints. Based on such analysis a new technique called phantom routing is proposed that combines the advantages of both the above mentioned classes of routing protocols and provides suitable protection of the source location while not causing a noticeable increase in energy consumption. In [5] the authors propose GROW (Greedy Random Walk): a two way random walk to reduce the chance an eavesdropper can collect the source-location information. Note that both the above research contributions are simulations-based. Differently, in this paper we introduce an analytical framework for the evaluation of the tradeoff between location privacy and energy efficiency in wireless sensor networks. To this end we extend the definition of privacy loss based on information theory concepts, proposed in [1] for data mining systems, to the case of location privacy in sensor networks. More specifically, we focus on the relationship between random routing design choices and privacy loss as well as energy efficiency. Accordingly, we will derive a Markov-based model of the random routing behavior that allows to calculate the privacy loss as well as the average energy consumption. Numerical results confirm that, as expected, energy efficiency and privacy are competing requirements. The framework can be used by protocol designers to set appropriate tradeoffs between the two above requirements. The remainder of this paper is organised as follows: in Section 2 we present the system model along with a statement of the problem. In Section 3 we evaluate the privacy loss and the energy consumption. Some numerical results are provided in Section 4 and, finally, conclusions are drawn in Section 5.

2

Problem Statement and System Model

In this section we first state the problem of location-privacy in wireless sensor networks (WSNs). More in detail, in Section 2.1 we will define the problem using the panda-hunter game scenario, then, in Section 2.2 we introduce the system model that will be utilized in the following of the paper. 2.1

Statement of the Problem: The Panda-Hunter Game

The panda-hunter game is a well known reference scenario utilized for the study of source location privacy in WSNs [4,3]. Suppose that a set of sensor nodes has been deployed by the Save The Panda Organisation, in a random way within a large area in order to study and monitor

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panda habit. Sensor nodes are able to detect panda’s presence. At any time, while the panda freely moves, there is always a sensor node, called source node, that detects panda’s position. Such an observation must be periodically reported to a sink node, via multihop routing techniques. In this way the current position of the panda is approximately the position of the current source node. Thus, when the sink node receives a message from the source node, it will know the panda position. We suppose that transmissions are encrypted, so the source node ID field cannot be read by attackers. Moreover we assume that relationship between node ID and node location is known only by the sink node. In the area there is a hunter as well, with the role of adversary. He aims to catch the panda, thus he is an enemy from the Save The Panda Organisation standpoint. The hunter is not able to decrypt messages therefore, he cannot learn, at least not directly, location of the source node, but in order to get the worst case we considered the hunter, as in [3], non malicius, i.e. does not interfere with proper function of the network, device rich, i.e. he is equipped in such a way he can measure signal strenght and angle of arrival of any message, resource rich, i.e. he has unlimited amount of power, and informed, i.e. he knows location of the sink, and the network structure and protocols. Using his devices and resources the hunter can analyse messages at RF level, so he can try to capture panda by back-tracing the routing path used by messages until the source. As an example, consider the sensor network represented in Figure 1. There are N = 11 sensor nodes n0 , n1 , ..., n10 , with n0 representing the sink.

Fig. 1. Example of hunting activity

In Figure 1 we show the shortest path routing tree connecting each sensor node ni to the sink n0 , i.e., node n0 is the root of the tree. If the hunter is located near node n6 and detects radio activity, then a node in the set {n7 , n8 , n9 , n10 } is the source node. Instead, if no activity is detected, then the panda is near one of the remaining nodes, i.e., a node in the set {n1 , n2 , n3 , n4 , n5 } is the source node.

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Observe, that in any case the hunter splits the network and obtains information about the panda location. This leads to a strict connection between location privacy and routing protocol in a WSN. Routing protocols must be privacy-aware in order to save, or at least prolong, panda’s life.

Fig. 2. Example of random routing

A simple way to improve privacy is to introduce some randomness in the routing behavior. Indeed, in random routing the next relay is chosen randomly between all the neighbors of the current relay. As an example, in Figure 2 we show a path obtained applying random routing in the same WSN shown in Figure 1. In this case, the fact that node n3 is forwarding a packet does not mean that the source node is in the set {n3 , n4 }. However, the length of path followed by packets in random routing can increase significantly, which involves large energy consumption. In other words, the increase in privacy is achieved at the expenses of higher energy consumption. It follows that appropriate tradeoffs are needed. 2.2

System Model

Let us consider a WSN composed of M nodes denoted as n0 , n1 , ..., nM−1 . For any node ni , with i < M , we define Φ(ni ) the set of neighbors1 of ni and φ(ni ) the number of its neighbors, i.e., φ(ni ) = |Φ(ni )|. Now suppose that n0 is the sink and let us call d(ni ) the distance between node ni and the sink n0 . Obviously, d(n0 ) = 0 and d(ni ) = minn∈Φ(ni ) {d(n) + 1}. Observe that routing of packets towards the sink in a sensor networks can be modeled by means of a matrix Q ∈ (M−1)×(M−1) , the generic element of which, [Q]i,j , represents the probability that the next relay of a packet transmitted by ni is nj , with i and j ∈ [1, (M − 1)]. We define Q as the routing matrix. 1

We say that two nodes are neighbors if they are in the radio coverage of each other.

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In order to model random routing we define the best next relay Ψ (ni ) as the neighbour of ni which is closest to the sink, i.e., it is a node that satisfies the following relationship d (ψ(ni )) ≤ d(m), ∀m ∈ Φ(ni )

(1)

Let us stress that even if several nodes may satisfy the relationship in eq. (1), for each ni only one node ψ(ni ) is selected. Accordingly, if shortest path routing is utilized [Q][i,j] is equal to 1 if nj is the best next relay, i.e., if nj = ψ(ni ), and is equal to 0, otherwise. We define as p-random routing a routing algorithm which chooses the best next relay with probability p and any other neighbor node with equal probability. Accordingly, the routing matrix of a p-random routing protocol is ⎧ p if nj = ψ(ni ) and φ(ni ) > 1 ⎪ ⎪ ⎨ (1−p) if nj = ψ(ni ) and φ(ni ) > 1 (2) [Q]i,j = φ(ni )−1 ⎪ 1 if nj = ψ(ni ) and φ(ni ) = 1 ⎪ ⎩ 0 otherwise Note that if p is equal to 1, then random routing becomes a shortest path routing.

3

Performance Analysis

We define and derive the location privacy loss when p-random routing is applied in Sections 3.1. Then, in Section 3.2, we will derive the corresponding energy consumption. Such performance metrics will be evaluated as a function of the probability p. This allows us to evaluate appropriate tradeoffs between privacy loss and energy consumption. 3.1

Privacy Loss

A measure of privacy is crucial to the evaluation of privacy enhancement solutions. Accordingly, in the recent past some research effort has been devoted to the definition of an appropriate privacy metric. In [6] an overview of the most interesting solutions is provided. Here we extend the definition proposed in [1] to the location privacy case. Let S be the random variable representing the current position of the panda. We identify the location with the sensor node that detects the presence of the panda. Accordingly, at any time the random variable S can assume a value in the set {n0 , n1 , ..., nM−1 }. Now suppose that following an attack, the hunter can observe a variable X which is correlated to S and can assume one of the following N values: {x0 , x2 , · · · , xN −1 }. The loss of privacy is related to the amount of information gained by the hunter following the attack. Such information is given by the difference between

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the uncertainty on S before and after knowing X. In the context of the information theory the measure of uncertainty of a random variable can be evaluated as the entropy of such a variable, H(S). In [1] the loss of privacy is calculated as: ρ = 1 − 2−I(S,X)

(3)

where I(S, X) is the mutual information between S and X and is given by I(S, X) = H(S) − H(S|X). The uncertainty on S is (see [7] for example) defined as: H(S) = −

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where pS (nm ) represents the probability that the source node is nm , whereas the uncertainty on S given X is H(S|X) = −

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where pSX (nm , xn ) represents the joint probability that S assumes the value nm and X assumes the value xn , whereas pS (nm |xn ) represents the probability that S assumes the value nm given that X assumes the value xn . Obviously, the probability pS (nm |xn ) can be calculated as pS (nm |xn ) =

pSX (nm , xn ) pSX (nm , xn ) = M−1 pX (xn ) i=0 pSX (ni , xn )

(6)

Suppose that all locations are equiprobable, i.e., pS (nm ) = 1/M for any nm . Accordingly, the uncertainty on S given in eq. (4) can be calculated as H(S) = log2 M . Also, suppose that the hunter attacks the WSN at node n∗ . Following the attack, the hunter detects radio activity if the path between the source node and the sink passes through the node n∗ and viceversa. Accordingly, X can assume only two values:  0 if there is no radio activity at node n∗ X= (7) 1 if there is radio activity at node n∗ As a consequence, we can rewrite eq. (5) as H(S|X) =

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In eq. (8) we need to calculate the probability pSX (nm , x) which can also be used in eq. (6) to calculate pS (nm |xn ). The probability pS (nm |xn ) is given by pSX (nm , x) = pX (x|nm ) · pS (nm ) = pX (x|nm )/M

(9)

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Observe that pX (x|nm ) represents the probability that a packet generated by node nm does not pass through n∗ , if x = 0, and that such packet passes at least ones through n∗ , if x = 1. Now we will calculate pX (1|nm ); once this is known, pX (0|nm ) can be easily evaluated as pX (0|nm ) = 1 − pX (1|nm ) (10) Recall that pX (1|nm ) is the probability that a packet generated by node nm passes through node n∗ at least once before reaching the sink n0 . Let V be the random variable representing the hop at which the packet visits for the first time node n∗ . Applying the theorem of the total probability, the probability pX (1|nm ) can be calculated as the sum of probabilities that a packet generated by node nm visits at the V -th hop node n∗ , for any value of V , i.e., pX (1|nm ) =

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The probability in the sum in the right handside of eq. (11) is the probability that the packet generated by nm does not visit node n∗ and does not reach the sink until hop (v − 1), and, finally, at the v-th hop visits node n∗ . This can be calculated as: ∗

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where: – w(j) is an array of M − 1 elements, w(j) ∈ M−1 , all set equal to zero, with the exception of the j-th element which is equal to 1, i.e.,  0 if i = j and 1 ≤ i ≤ M − 1 (13) [w(j) ]i = 1 if i = j and 1 ≤ i ≤ M − 1 – G is an [M − 1] × [M − 1] matrix, G ∈ [M−1]×[M−1] , and its generic element [G][i,j] represents the probability that a packet received by node ni will be relayed to node nj , with nj = n∗ , and is not relayed by node n∗ . This can be obtained as follows:  [Q][i,j] if j = n∗ (14) [G][i,j] = 0 if j = n∗ – [w]T represents the transponse of the array w. Substituting eq. (12) in eq. (11) we can easily obtain:  pX (1|nm ) = w

(m)

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By applying the spectral decomposition to matrix G = D ·B ·D−1 , where B is a diagonal matrix containing the eigenvalues βi of G and D is the matrix whose columns are the corresponding eigenvectors, we can rewrite eq. (15) as follows: ∞  ∗ (m) v pX (1|nm ) = w ·D· B · D−1 · [w(n ) ]T (16) v=0

We call K the sum in the right hand side of eq. (16). We can easily obtain that K is a diagonal matrix whose generic element is  1/(1 − βi ) if i = j [K][i,j] = (17) 0 otherwise. Accordingly, eq. (16) can be rewritten as ∗

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(18)

where K has been calculated in eq. (17). Once pX (1|nm ) has been calculated we have all the parameters required for the calculation of the uncertainty on S given X, i.e., H(S|X). Note that the value of privacy loss ρ depends on n∗ . Since the hunter knows the structure and protocols of the network, the node n∗ which maximizes the privacy loss will be selected. As a consequence, the WSN gives a privacy loss γ given by γ = maxn∗ {ρ}. 3.2

Energy Consumption

The energy consumption for routing a packet from its source to the sink can be calculated as the product of the energy cost for a single hop transmission, c, and the number of hops between the source node and the sink2 . We call Z the random variable representing the number of hops needed for a packet to reach the sink. The average energy consumption, , needed to route a packet to the destination can be calculated as = c · E{Z} = c ·

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where E{Z} represents the average value of Z and pZ (z) represents the probability that the number of hops between the source and the destination is equal to z. The probability pZ (z) is the probability that a packet does not reach the sink in (z − 1) hops and finally arrives at the sink at the z-th hop. Therefore, it is easy to show that pZ (z) can be written in compact form as 

pZ (z) = π (S ) · P z−1 · ω T

(20)

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Observe that c can also take possible retransmissions into account. In this sense, analysis of c is simple and not reported in this paper for space constraints.

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– π (S ) is an array of (M − 1) elements, π (S ) ∈ M−1 . Its generic element is given by:

  π (S ) = pS (nm ) = 1/M with 1 ≤ m < M . (21) m

– P is an [M − 1] × [M − 1] matrix, i.e., P ∈ [M−1]×[M−1] . Its generic element [P ][i,j] represents the probability that a packet received by node ni is transmitted to node nj , with nj = n0 . Accordingly, the generic element of P is given by: if i and j ∈ {1, 2, ..., M − 1}

[P ][i,j] = [Q][i,j]

(22)

– ω is an array of M − 1 elements, i.e., ω ∈ M−1 . Its generic element [ω]m , with 1 ≤ m < M represents the probability that a packet is relayed by node nm to the destination. Accordingly, [ω]m = [Q][m,0]

(23)

Applying the spectral decomposition of P and following a procedure analogous to that presented in Section 3.1, we can rewrite eq. (19) as 

= c · π (S ) · T · H · T −1 · ω T

(24)

In eq. (24) the matrix H is a diagonal matrix and its generic element is  1/[(1 − λi )]2 if i = j [H][i,j] = (25) 0 otherwise where λi is the i-th eigenvalue of P and T is the matrix of the eigenvectors of P.

4

Numerical Examples

In this section we apply the proposed analytical framework to describe how this can be used to evaluate the tradeoffs between location privacy and energy efficiency in WSN. We consider a network of M sensor nodes uniformly distributed on a squared area of size 1 km × 1 km. We assume that all sensor nodes have coverage radius equal to R = 200 m. Once position of sensor nodes is set and the value of the parameter p, characterizing the random routing, is known, it is possibile to construct the routing matrix Q as given in eq. (2). Starting from the routing matrix Q it is possible to evaluate the privacy loss γ and the average energy consumption as reported in Section 3. All values in the following figures have been evaluated as the average of the results obtained in 20 cases. For each case, a new distribution of sensor nodes has been generated. Moreover, for each case individual routes are chosen considering the same sink node and a source node chosen in a random fashion based on uniform distribution.

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Fig. 3. Privacy loss, γ, versus the probability p for different values of the number of sensor nodes, i.e., M = 50 and M = 100

In Figure 3 we show the privacy loss, calculated as described in Section 3.1, versus the value of the probability p for two different values of the number of nodes, i.e., M = 50 and M = 100. In Figure 3 the privacy loss increases as the probability p becomes higher. This is an expected result. Indeed, using low values of p makes the routing behavior fuzzy and therefore, the hunter cannot obtain significant information attacking the network.

Fig. 4. Normalized energy consumption /c versus the probability p for different values of the number of nodes, i.e., M = 50 and M = 100

In Figure 4 we show the average energy consumption to deliver a packet to the sink, , versus the probability p for M = 50 and M = 100. More specifically, in Figure 4 we show the values of the ratio /c. We present normalized energy consumption values because c depends on the specific communication technology utilized, and not on the routing algorithm. As expected, the energy consumption decreases as the value of the probability p increases; furthermore, the higher the

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Fig. 5. (Upper plot:) Average energy consumption  versus privacy loss γ and (Bottom plot:) The value of the probability p versus the corresponding privacy loss γ

number of sensor nodes M , the lower the energy consumption. This is because, if there are more nodes, it is likely to find better next relays than in case there are few nodes. To highlight the tradeoff between privacy loss and energy efficiency, in Figure 5 we show two plots. In the upper plot we represent the normalized energy consumption, /c, versus the corresponding value of the location privacy loss, γ. As expected, the privacy loss increases as the energy consumption decreases. This figure has been obtained considering M = 100 nodes and can be utilized by the designer to select an appropriate tradeoff between energy efficiency and privacy. Once a point in the curve is chosen, the designer can use the bottom plot to obtain the corresponding value of p that gives the selected performance.

5

Conclusion

In this paper we have presented an analytical framework for the evaluation of the tradeoff between location privacy and energy efficiency in a WSN applying random routing to increase privacy protection. The proposed framework is based on a Markov-based modeling of the random routing behavior. The framework can be used by network designers to evaluate the most appropriate value of the probability p characterizing the random routing behavior in accordance with the application requirements.

Acknowledgments This paper has been partially supported by European Commission under contract DISCREET (FP6-2004-IST-4 contract no. 27679).

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References 1. D. Agrawal, C. Aggarwal. On the Design and Quantification of Privacy Preserving Data Mining Algorithms. Proc. of the Twentieth ACM SIGACT-SIGMODSIGART, Santa Barbara, California, USA. May 2001. 2. M. Anand, Z. G. Ives, I. Lee. Quantifying Eavesdropping Vulnerability In Sensor Networks. Department of Computer & Information Science, University of Pennsylvania, 2005. 3. P. Kamat, Y. Zhang, W. Trappe, C. Ozturk. Enhancing Source-Location Privacy in Sensor Network Routing. Proc. of International Conference on Distributed Computing Systems (ICDCS 2005), Columbus, OH, USA. June 2005. 4. C. Ozturk, Y. Zhang, W. Trappe, M. Ott. Source-Location Privacy for Networks of Energy-Constrained Sensors. In Proc. of IEEE IEEE Workshop on Software Technologies for Embedded and Ubiquitous Computing Systems (WSTFEUS), Vienna, Austria. May 2004. 5. Y. Xi, L. Schwiebert, W. Shi. Preserving Source Location Privacy in MonitoringBased Wireless Sensor Networks. Department of Computer Science, Wayne State University, 2006. 6. DISCREET Project, State of the art Deliverable. http://www.ist-discreet.org/ Deliverables/D2103.pdf 7. S. Haykin. Communication Systems, 4th edition.

Efficient Error Recovery Using Network Coding in Underwater Sensor Networks Zheng Guo, Bing Wang, and Jun-Hong Cui Computer Science & Engineering Department, University of Connecticut, Storrs, CT, 06269 {guozheng,bing,jcui}@engr.uconn.edu

Abstract. Before the wide deployment of underwater sensor networks becomes a reality, one of the challenges that needs to be resolved is efficient error recovery in the presence of high error rates, node mobility and long propagation delays. In this paper, we propose an efficient error-recovery scheme that carefully couples network coding and multipath routing. Through an analytical study, we provide guidance on how to choose parameters in our scheme and demonstrate that our scheme is efficient in both error recovery and energy consumption. We evaluate the performance of our scheme using simulation and our simulation confirms the results from the analytical study.

1 Introduction Underwater sensor networks are ideal vehicles for monitoring aqueous environments. However, before the wide deployment of underwater sensor networks becomes a reality, a range of challenges must be tackled [1,2,3]. One such challenge is efficient error recovery in the presence of high error rates, node mobility and long propagation delays (caused by fast fading acoustic channel, water currents and slow acoustic communication). Using common error-recovery techniques such as Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC) in underwater sensor networks has the following drawbacks. ARQ-based schemes require the receiver to detect losses and then request the sender to retransmit packets. This may lead to long delays. FEC-based schemes proactively add redundant packets to eliminate retransmission from the source. The FEC can be applied on an end-to-end or hop-by-hop basis (as in [4]). However, in either case, the proper amount of redundancy is hard to decide due to the difficulty of obtaining accurate error-rate estimates [3]. In our prior study [5], we demonstrate that network coding is a promising technique for error recovery in underwater sensor networks. The main idea of network coding [6,7] is that, instead of simply forwarding a packet, a node may code several incoming packets into one or multiple outgoing packets. Network coding is suitable for underwater sensor networks because (1) underwater sensor nodes are usually larger than land-based sensors and posses more computational capabilities [8]; (2) the broadcast property of acoustic channels naturally renders multiple highly interleaved routes 

This work is supported in part by the NSF CAREER Grant No. 0644190 and in part by the Uconn Large Grant FRS 449251.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 227–238, 2007. c IFIP International Federation for Information Processing 2007 

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from a source to a sink. The computational power at the sensor nodes coupled with the multiple routes provides ample opportunity to apply network coding. In this paper, building upon our preliminary work [5], we provide an in-depth study on using network coding in underwater sensor networks. Our main contributions are as follows. First, we propose an error-recovery scheme that carefully couples network coding and multipath routing. Second, we analytically study the performance of this scheme along with several other error-recovery schemes. Our analysis provides guidance on how to choose parameters in our scheme and demonstrates that, among the multiple schemes, our scheme is most efficient in terms of error recovery and energy consumption. Last, we evaluate the performance of our scheme using simulation and the simulation confirms the results from the analytical study. As related work, multipath routing schemes have been proposed for error resilience in sensor networks (e.g., [9,8]). Our scheme carefully combines network coding and multipath routing and provides much better error recovery than using multipath routing alone (see Sections 4 and 5). The study of [10] provides error resilience using multiple virtual sinks: a source forwards packets to multiple high-bandwidth virtual sinks, which then forward the packets to the final destination. This scheme requires a specialized delivery infrastructure while our scheme does not have such a requirement. The rest of the paper is organized as follows. Section 2 describes the problem setting. Section 3 describes our error-recovery scheme based on network coding. Sections 4 and 5 study the performance of our scheme along with several other schemes using analysis and simulation respectively. Finally, Section 6 concludes the paper and presents future work.

2 Problem Setting We now describe the problem setting. Consider a source-sink pair in an underwater sensor network. The path (or multipath) from the source to the sink is determined by a single-path (or multipath) routing algorithm. We refer to the intermediate nodes on the path(s) as relays. We consider several error-recovery schemes including single-path forwarding, endto-end FEC, hop-by-hop FEC, multipath forwarding and network coding. In single-path and multipath forwarding, packets are simply forwarded, without any coding. Singlepath forwarding is a baseline scheme since it does not exploit any extra mechanism for error recovery. Multipath forwarding recovers error through redundant packets over the multiple paths (a relay does not forward duplicate packets). FEC-based schemes use a single path from the source to the sink: end-to-end FEC encodes packets at the source and decodes them at the the sink; in hop-by-hop FEC, each relay on the path decodes incoming packets, encodes the recovered packets, and then forwards them to the next hop. Network coding requires multiple paths from the source to the sink; a node encodes incoming packets into one or multiple outgoing packets, as to be described in detail in Section 3. A packet successfully received (under single or multipath forwarding) or recovered (under FEC or network coding) is referred to as a successfully delivered packet. Since efficient error-recovery schemes for underwater sensor networks must achieve high

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error-recovery rate and conserve sensor node energy simultaneously, we consider the following two metrics. The first metric is the number of successfully delivered packets over the total number packets from the source, referred to as successful delivery ratio, denoted as R. The second metric is the total number of transmissions from the source to the sink (including transmissions from the source and relays) normalized by the successful delivery ratio. Since the number of transmissions roughly corresponds to the amount of energy consumed in the network, we refer to this metric as normalized energy consumption, denoted as T . This metric represents the average number of transmissions required for a successfully delivered packet. We next describe our network coding scheme for underwater sensor networks and then evaluate the various schemes using analysis and simulation.

3 Using Network Coding in Underwater Sensor Networks We now describe our error-recovery scheme based on network coding. This scheme carefully couples network coding and multipath routing to achieve a good balance between error recovery and energy consumption. In the following, we first describe how to apply network coding (we use random linear coding [11] due to its simplicity) given a set of paths from a source to a sink. We then describe how to adapt the multiple paths or the amount of redundancy to improve the efficiency of network coding. 3.1 Network Coding Scheme Packets from the source are divided into generations, each generation contains K packets. The source linearly combines K packets in a generation using randomly generated coefficients. More specifically, let X1 , . . . , XK denote the K packets in a generation. The source linearly combines these K packets to compute K  outgoing packets, deK noted as Y1 , Y2 , . . . , YK  where Yi = j=1 gij Xj . The coefficient gij is picked randomly from a finite field F2q . The set of coefficients (gi1 , . . . , giK ) is referred as the encoding vector for Yi [7] and are carried in a packet as overhead. We choose K  ≥ K since adding a small amount of redundancy at the source (e.g., K  = K + 2) reduces the impact of packet loss on the first hop (which cannot be recovered at later hops) and improves error recovery at the sink [5]. A relay in forwarding paths stores incoming packets from different routes in a local buffer for a certain period of time, then linearly combines the buffered packets belonging to the same generation. Suppose a relay, r, receives M incoming packr . Let (fi1 , . . . , fiK ) denote the encoding vector carried by Xir , i = ets, X1r , . . . , XM 1, . . . , M . Since transmitting dependent packets is not useful for decoding at the sink, relay r computes M  outgoing packets, where M  is the rank of the coefficient matrix r (fij ), i = 1, . . . , M , j = 1, . . . , K. Therefore, M  ≤ min(M, K). Let Y1r , . . . , YM   M r r r denote the outgoing packets, Yi = j=1 hij Xj , where hij is picked randomly from r r the finite field F2q . Let (gi1 , . . . , giK ) denote the encoding vector of Yir , i = 1, . . . , M  . M r r Then gij = l=1 hil flj . When the sink receives K packets with linearly independent encoding vectors, it recovers the original packets by matrix inversion [7]. The complexity is O(K 3 ).

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sin k

s o u rc e

s o u rc e /s in k m u lti-p a th re la y o th er n o d es

Fig. 1. Illustration of transmitting a packet along multiple paths from the source to the sink. Nodes in a dashed circle form a relay set.

3.2 Path or Redundancy Adaption for Network Coding The efficiency of network coding relies on the quality of the underlying paths determined by a multipath routing algorithm. We next describe a multipath property under which network coding is efficient (in terms of both error recovery and energy consumption). Fig. 1 illustrates the process of transmitting a packet along a multipath. The source broadcasts the packet to its downstream neighbors (nodes within its transmission range and in the forwarding paths), referred to as a relay set. Nodes in the relay set further forward the packet to their neighbors, forming another relay set. Intuitively, a multipath suitable for network coding should contain a similar number of nodes in each relay set. This is because, a relay set with too few nodes may not provide sufficient redundancy; a relay set with too many nodes wastes energy to provide more redundancy than what is necessary for error recovery. We develop two schemes to adjust the multipath or the amount of redundancy to improve the efficiency of network coding. In both schemes, a node uses the number of its downstream neighbors to approximate the size of its downstream relay set. This is because the former can be easily estimated through localization service (e.g., [12]) and localized communication between a node and its neighbors while the latter is difficult to estimate. The first scheme requires that sensor nodes have multiple levels of transmission power [13]. A node selects a transmission power so that the estimated number of downstream neighbors is between Nl and Nu , where Nl and Nu are lower and upper thresholds respectively. We refer to this scheme as transmission-range adaption. In the second scheme, each node has a fixed transmission range and a node adapts the amount of redundancy that it injects to the network. More specifically, a node with less than a Nl downstream neighbors encodes more outgoing packets to increase the amount of redundancy. Similarly, a node with more than Nu downstream neighbors encodes less outgoing packets to reduce the amount of redundancy (we only do this when the coefficient matrix at the node has a full rank of K). We refer to this scheme as redundancy adaption. Our analytical results in the next section provide guidance on how to choose parameters for the above two adaption schemes. Note that both schemes only require localized

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information and hence are easy to deploy. Furthermore, they can be applied to mobile underwater sensor networks when coupled with a multipath routing scheme that supports mobility (e.g., [8]).

4 Analytical Study We now analytically study the performance of the various error-recovery schemes in Section 2. Our goal is two-fold: (1) analytically compare the efficiency of the various schemes; (2) provide guidance on how to choose parameters in network coding. In the interest of space, we only present the results for multi-path forwarding and network coding; the results for other schemes can be found in [14]. Multi-path forwarding and network coding use the same multipath from the source to the sink. Assume that there are H relay sets from the source to the sink, indexed them from 1 to H (see Fig. 1). The sink is in the H-th relay set. Let Ni be the number of relays in the i-th relay set. For simplicity, we assume that the relay sets do not intersect. Furthermore, a node in a relay set can receive from all nodes in the previous relay set. Last, a node only uses packets forwarded from its previous relay set (i.e., packets received from nodes in the same relay set are discarded). For both schemes, we derive the normalized energy consumption, T , from the successful delivery ratio, R, as follows. Consider an arbitrary packet (regardless of being successfully delivered or not), let Ti denote the average number of times that it is transmitted from the nodes in the previous relay set (or the source) to those in the i-th relay set. Then H Ti (1) T = i=1 R We assume that the acoustic channels have the bit error rate of pb . Let p be the probability that a packet has bit error. Then p = 1 − (1 − pb )L for independent bit errors and a packet size of L bits. We next present the analysis for multipath forwarding and network coding. 4.1 Analysis of Multipath Forwarding Consider an arbitrary packet P . Let αi be the probability that a node in the i-th relay set receives packet P . Let αi,n be the probability that n nodes in the i-th relay set receive packet P , n = 0, . . . , Ni . Assume that packet losses are independent. Then  1−p i=1 (2) αi = Ni−1 n α (1 − p ), 2≤i≤H i−1,n n=0 This is because, for a node in the first relay set, the probability that it receives packet P from the source is 1 − p; when i ≥ 2, a node in the i-th relay set receives packet P when it receives at least one copy of this packet from the (i − 1)-th relay set. Assume that packet transmissions to nodes in a relay set are independent. Then   Ni n (3) αi,n = αi (1 − αi )Ni −n , n = 0, . . . , Ni n

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Since packet P is an arbitrary packet and the sink is in the H-th set, we have R = αH . The above results indicate that αH can be obtained in the following manner. We first obtain α1,n from α1 (of value 1 − p), and then obtain α2 using α1,n . This process continues until eventually αH is obtained. Since a node forwards packet P at most once, we have  1, i=1 Ti = (4) αi−1 Ni−1 , 2 ≤ i ≤ H After obtaining R and Ti , we calculate the normalized energy consumption T from (1). 4.2 Analysis of Network Coding Consider an arbitrary generation of K packets. Under linear random coding, when a sink receives at least K packets in the generation, the probability that it can recover the K original packets is high for a sufficiently large finite field [11]. Therefore, for simplicity, we assume that the sink recovers the K original packets as long as it receives at least K packets in the generation. We do not differentiate nodes in the same relay set. Let βi,k be the probability that a node in the i-th relay set receives k packets (when 0 ≤ k < K) or at least k packets (when k = K) from all nodes in the previous relay set, 1 ≤ i ≤ H. Since the sink is in the H-th relay set and the generation is arbitrary, we have R = βH,K . We next derive βi,k , 1 ≤ i ≤ H, 0 ≤ k ≤ K. The nodes in the first relay set receive packets from the source. Therefore     K (1 − p)k pK −k , 0 ≤ k < K k  (5) β1,k = 1 − K−1 k=K j=0 β1,j where K  ≥ K is the number of encoded packets from the source. For i ≥ 1, 0 ≤ k < K, we obtain βi+1,k as follows. We index the nodes in the i-th relay from 1 to Ni . Let γi,j,k denote the probability that a node in the i-th relay set receives k packets from the j-th node in the previous relay set, 1 ≤ i ≤ H, 1 ≤ j ≤ Ni−1 , 0 ≤ k < K. Since each relay transmits no more than K packets, we have γi,j,k =

K

βi−1,k

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(6)

For a node in the (i+1)-th set, let kj be the number of packets that it receives from the jth node in the previous relay set. To obtain βi+1,k , we need to consider all combinations Ni of kj ’s such that j=1 kj = k, kj = 0, . . . , k. That is, Ni



βi+1,k = kj =0,...,k

s.t.

 Ni

j=1 j=1 kj =k

γi+1,j,kj

(7)

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For a small generation size K, the above quantity is easy to compute. We use small K (e.g., K = 3) since our study [5] indicates that it is sufficient to achieve good performance using small K (also confirmed by simulation in the settings of Section 5). We obtain βi+1,K from βi+1,k , 0 ≤ k < K as βi+1,K = 1 −

K−1

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(8)

k=0

From the above, we calculate R = βH,K as follows. We first obtain β1,k , which is used to compute γ2,j,n and β2,k , 0 ≤ k ≤ K. This process continues until eventually βH,K is obtained. Since a relay transmits no more than K packets, we have   K /K, i=1 Ti = Ni−1 K (9) kβ , 2≤i≤H i−1,k k=0 K After obtaining R and Ti , we calculate the normalized energy consumption T from (1). 4.3 Numerical Results

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We next compare the various schemes based on our analytical results. The bit error rate is in the range of 10−4 to 1.5 × 10−3 to account for potential high loss rate in underwater sensor network (e.g., due to fast channel fading). For network coding, a generation contains 3 packets (e.g., K = 3). The source transmits K  = 5 packets. For multipath forwarding and network coding, we set the number of relay sets, H, to 7 or 9, and assume all relay sets contain the same number of nodes, i.e., Ni = N , i = 1, . . . , H. Similarly, for single-path forwarding and FEC, we set the number of hops from the source to the sink to 7 or 9. For FEC, each block contains 3 packets (same as the generation size in network coding) and the amount of redundancy is 3N − 3 since a relay set contains N nodes in multipath forwarding and network coding. 6

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Fig. 2 plots the successful delivery ratio and normalized energy consumption for various schemes when H = 9. We observe that network coding outperforms the other schemes: it achieves the highest successful delivery ratio and the lowest normalized energy consumption for the range of bit error rates when N = 3 (i.e., each relay set contains 3 nodes). Furthermore, network coding achieves similar performance when H = 7 (not plotted), indicating that it is insensitive to the length of the path (network size). We also observe that when the number of nodes in each relay set, N , is decreased from 3 to 2, the successful delivery ratio of network coding drops sharply. Based on the above results, we set Nl = Nl = 3 in our simulation 5. From Fig. 2, we also observe that multipath forwarding achieves a similar normalized energy consumption and a lower successful delivery ratio than network coding for the same value of N . The successful delivery ratio under hop-by-hop FEC is sensitive to both the bit error rate and the number of hops on the path (network size), indicating that the amount of redundancy needs to be carefully selected according to these two parameters. The successful delivery ratio under single-path forwarding and end-to-end FEC decreases significantly as the bit error rate increases, indicating that they are not suitable for high error-rate underwater sensor networks.

5 Simulation Study We now evaluate the performance of the various error-recovery schemes using simulation. The underwater sensor network is deployed in a cubic target area of 1km × 1km × 1km, which is a reasonable network size for underwater sensor networks. The source and sink are deployed respectively at bottom corner and surface corner, on the diagonal of the cube. The MAC layer supports broadcasting. The routes from the source to the sink is determined by Vector-based Forwarding (VBF) [8]. In VBF, a routing pipe is a pipe centered around the vector from the source to the sink. Nodes inside the routing pipe are responsible for routing packets from the source to the sink; nodes outside the routing pipe simply discard all incoming packets. The relay set formation depends on node density and routing protocol used, using VBF is easy to format relay sets and we will propose two techniques in 5.2 to adjust the relay sets. Each packet is 50 bytes. For network coding, each generation contains K = 3 packets; the source outputs K  = 5 packets for each generation and each relay outputs no more than 3 packets. We choose a finite field of F28 [11], leading to packets of 53 bytes (including 3-byte encoding vector). A relay has a memory to store 10 packets for each generation; packets transmitted from the node are removed from the memory. We look at two types of sensor deployment: grid random deployment and uniform random deployment. In grid random deployment, the target area is divided into grids; a number of nodes are randomly deployed in each grid. In uniform random deployment, nodes are uniformly randomly deployed in the area. Grid random deployment covers the area more evenly than uniform random deployment while uniform random deployment is easier to deploy. The comparative results of the various schemes from simulation are consistent with those from analytical study. We focus on the performance of network coding and multipath forwarding in the following.

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Fig. 3. Simulation results under grid random deployment

5.1 Performance Under Grid Random Deployment In grid random deployment, the target area is divided into 125 grids, each grid is 200m× 200m × 200m. Each grid contains 2 nodes, randomly distributed in the grid. Based on the analytical results in Section 4, we set the transmission power and pipe radius of a node to cover 3 to 4 downstream neighbors (with an average of 3.1). This is achieved when each node uses a transmission range of 300 m [15] and a pipe radius of 150 m. Figures 3 (a) and (b) plot the successful delivery ratio and normalized energy consumption for network coding and multipath forwarding. The confidence intervals (from 20 simulation runs) are tight and hence omitted. We also plot the analytical results when N = 3 (i.e., each relay set contains 3 nodes). For network coding, we observe that the simulation results are very close to those from the analysis, indicating that the analysis provides a good approximation and guidance on choosing parameters in network coding. For multipath forwarding, the analytical results are slightly (no more than 8%) higher than those from the simulation. This might be because we assume a node can hear from all nodes from its previous relay set in the analysis, which provides an overestimate of the successful delivery ratio. We observe that network coding provides significantly better error recoveries than multipath forwarding for high bit error rates. The normalized energy consumption under network coding is slightly higher than that under multipath forwarding because the source adds redundancy and more packets are forwarded at a relay in network coding (a relay discards duplicate packets in multipath forwarding). For the sake of comparison, we also plot the analytical result under hop-by-hop FEC in Fig. 3. When using this scheme, the number of hops (on the single path) from the source to the sink is 9, and a block contains 3 packets (to be consistent with the generation size in network coding). Each blocks adds 28/9 ∗ 3 − 3 = 7 redundant packets since the routing pipe used in network coding and multipath forwarding contains 28 nodes. Note that, although we purposely add a higher amount of redundancy for hopby-hop FEC, it still achieves much lower successful delivery ratio than network coding for relatively high bit error rates.

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We now demonstrate that it is indeed important for a node to have 3 to 4 downstream neighbors for efficient network coding, as indicated by the analytical results. For this purpose, we either fix the transmission range to 300 m and vary the pipe radius or fix the pipe radius to 150 m and vary the transmission range. The results are plotted in Figures 4(a) and (b) respectively, where the bit error rate is 1.5 × 10−3 . In both cases, we observe that a good balance between error recovery and energy consumption is achieved when the transmission range is 300 m and the pipe radius is 150 m (i.e., when a node has 3 to 4 downstream neighbors).

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Fig. 4. Successful delivery ratio and normalized energy consumption under grid random deployment: (a) Transmission range is 300 m, (b) Pipe radius is 150m

5.2 Performance Under Uniform Random Deployment We now present the results under uniform random deployment. Under this type of deployment, we find that using the same transmission range and pipe radius for all the nodes cannot ensure 3 to 4 downstream neighbors for each node. We therefore allow a node to adjust its transmission range or the amount of redundancy that it injects into the network. We first present the result under transmission-range adaptation. The pipe radius is set to 150 m. A node set its transmission range to have 3 to 4 downstream neighbors (with an average of 3.3). The resulting transmission ranges are from 100 to 400 m for all the nodes. Fig. 5 plots the successful delivery ratio under network coding. We observe that transmission-range adaption achieves a similar successful delivery ratio as that from the analytical result using N = 3. This indicates that transmission-range adaption is effective for error recovery. For comparison, we obtain the results when all nodes uses a transmission range of 300 m. We observe that it achieves significantly lower successful delivery ratio (see Fig. 5) and higher normalized energy consumption (not plotted) than those under transmission-range adaption. We next present the results when all nodes uses the same transmission range of 300 m and adjusts the amount of redundancy according to the number of its downstream neighbors. In Fig. 6, a node adds one more outgoing packet when it has less than 3

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downstream neighbors and removes an outgoing packet when it has more than 6 downstream neighbors. We observe that this adaption achieves a similar successful delivery ratio as that from the analysis using N = 3 with only slightly higher normalized energy consumption (not plotted). The above results demonstrate that adjusting redundancy is also helpful for efficient error recovery under network coding.

6 Conclusion and Future Work In this paper, we first proposed an efficient error-recovery scheme that carefully couples network coding and multipath routing for underwater sensor networks. We analytically studied the performance of our scheme along with several other error-recovery schemes. Our analysis provided guidance on how to choose parameters in our scheme and demonstrated that our scheme is most efficient among the multiple schemes. Finally, we evaluated the performance of our scheme using simulation. Our simulation results confirmed the analytical study that our scheme is efficient in both error recovery and energy consumption. As future work, we are pursuing in three directions: (1) analyzing the traffic congestion and delay for network coding; (2) using network coding in multicast applications

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in underwater sensor networks, e.g., command distribution or software update from one source to all other nodes; (3) using network coding in the architecture with multiple virtual sinks.

References 1. I. F. Akyildiz, D. Pompili, and T. Melodia, “Challenges for efficient communication in underwater acoustic sensor networks,” ACM SIGBED Review, vol. 1, July 2004. 2. J. Heidemann, W. Ye, J. Wills, A. Syed, and Y. Li, “Research challenges and applications for underwater sensor networking,” in Proceedings of the IEEE Wireless Communications and Networking Conference, Las Vegas, Nevada, USA. 3. J. H. Cui, J. Kong, M. Gerla, and S. Zhou, “Challenges: Building scalable mobile underwater wireless sensor networks for aquatic applications,” in IEEE Network, Special Issue on Wireless Sensor Networking, June 2006. 4. P. Xie and J. H. Cui, “SDRT: A reliable data transport protocol for underwater sensor networks,” tech. rep., University of Connecticut, Computer Science and Engineering Dept., February 2006. 5. Z. Guo, P. Xie, J.-H. Cui, and B. Wang, “On applying network coding to underwater sensor networks,” in Proceedings of ACM WUWNet’06, Los angeles, CA, September 2006. 6. R. Ahlswede, N. Cai, S. R. Li, and R. W. Yeung, “Network information flow,” IEEE Transactions on Information Theory, vol. 46, July 2000. 7. C. Fragouli, J.-Y. L. Boudec, and J. Widmer, “Network coding: An instant primer,” ACM SIGCOMM Computer Communication Review, January 2006. 8. P. Xie, J. H. Cui, and L. Lao, “VBF: Vector-based forwarding protocol for underwater sensor networks,” in Proceedings of IFIP Networking’06, Coimbra, Portugal, May 2006. 9. D. Ganesan, R. Govindan, S. Shenker, and D. Estrin, “Highly-resilient, energy-efficient multipath routing in wireless sensor networks,” ACM SIGMOBILE Mobile Computing and Communication Review, vol. 5, no. 4, 2001. 10. W. K. Seah and H. Tan, “Multipath virtual sink architecture for underwater sensor networks,” in Proceedings of the MTS/IEEE OCEANS2006 Asia Pacific Conference, May 2006. 11. T. Ho, R. Koetter, M. Medard, D. R. Karger, and M. Effros, “The benefits of coding over routing in a randomized setting,” in International Symposium on Information Theory (ISIT), 2003. 12. V. Chandrasekhar, W. K. Seah, Y. S. Choo, and H. V. Ee, “Localization in underwater sensor networks - survey and challenges,” in Proceedings of ACM WUWNet’06, Los angeles, CA. 13. J. Wills, W. Ye, and J. Heidemann, “Low-power acoustic modem for dense underwater sensor networks,” in Proceedings of ACM WUWNet’06, Los angeles, CA, September 2006. 14. Z. Guo, B. Wang, and J.-H. Cui, “Efficient error recovery using network coding in underwater sensor netowrks,” tech. rep., University of Connecticut, Computer Science and Engineering Dept., November 2006. 15. D. B. Kilfoyle and A. B. Baggeroer, “The state of the art in underwater acoustic telemetry,” IEEE Journal of Oceanic Engineering, vol. OE-25, no. 5, pp. 4–27, 2000.

Key Predistribution Schemes for Sensor Networks for Continuous Deployment Scenario* Abdülhakim Ünlü, Önsel Armağan, Albert Levi, Erkay Savaş, and Özgür Erçetin Sabanci University, Istanbul, Turkey {aunlu,onsel}@su.sabanciuniv.edu {levi,erkays,oercetin}@sabanciuniv.edu

Abstract. In sensor networks, secure communication among sensor nodes requires secure links and consequently secure key establishment. Due to resource constraints, achieving such key establishment is non-trivial. Recently some random key predistribution techniques have been proposed to establish pairwise keys. Some of these approaches assume certain deployment knowledge is available prior to deployment and nodes are deployed in groups/bundles. In this paper, we propose another practical deployment model where nodes are deployed over a line one by one in a continuous fashion. In this model, sensor nodes can also be deployed over multiple parallel lines to cover two-dimensional area. Based on this model, we develop two key predistribution schemes. Analysis and simulation results show that our key predistribution schemes make use of the deployment knowledge better than the existing schemes. Thus they perform better than other location-aware protocols using the metrics of connectivity, resiliency, memory usage and communication cost for key establishment.

1 Introduction In sensor networks [1], confidentiality, privacy and authenticity of communication between sensor nodes are important when nodes are deployed in an environment where there are adversaries. In order to fulfill these security requirements, cryptographic techniques are employed. Generally symmetric cryptography is used to provide security in sensor networks. In order to use symmetric key cryptography, communicating sensor nodes must share the same key. Distribution of keys to large amount of sensor nodes, so that they can establish secure links, is an active research area. Generally key predistribution schemes [2-8], where the keys are stored in sensor nodes before deployment, are used for this purpose. A naïve way of key predistribution is to generate a master key and install this master key to all nodes before the deployment. However in this scheme, when a node is captured, the master key is also captured and all secure links in the sensor network are compromised. *

Albert Levi and Abdülhakim Ünlü are supported by the Scientific and Technological Research Council of Turkey under project number 104E071. Erkay Savaş is supported by the Scientific and Technological Research Council of Turkey under project number 104E007.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 239–250, 2007. © IFIP International Federation for Information Processing 2007

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Another extreme key predistribution way is to assign unique link keys for each node. In this method, compromise of one node leads to compromise of only that node’s links. However, this method is not scalable since the total number keys to be predistributed per node should be as much as the number of nodes in the network in order to guarantee that after deployment each neighboring node pair shares a key. In order to overcome this scalability problem and effectively use the node memory, Eschenauer and Gligor proposed a probabilistic key predistribution scheme [5]. In this scheme, before sensor deployment, a key server creates a key ring for each node, by picking a limited amount of random keys from a large key pool. Then the key server loads the key ring to memory of each node. After deployment, sensor nodes in the field let their neighbors know which keys they have. If two neighboring nodes share one or more identical keys, then they can establish a secure link. After this shared key discovery with direct neighbors, neighboring node pairs that do not share keys can establish secure links in multiple hops. If the local connectivity (in terms of secure links) is above a certain threshold, then random graph theory [9] states that overall sensor network will be cryptographically connected with high probability. Du et al. utilized Blom’s key management scheme [12] in a key predistribution scheme for sensor networks [4]. This scheme shows a threshold property; until λ nodes are captured, the network is perfectly secure, but after λ nodes are compromised all secure links are compromised. Some recent papers on random key predistribution [3,7,10,11] utilized expected location information of sensor nodes in their models. In all these location-aware approaches, it is assumed that nodes are prepared in small groups and deployed as bundles, e.g. groups of nodes can be dropped from a plane, similar to parachuting troops or dropping cargo. The nodes in the same group have a very large chance to be in the range of each other. Moreover, the node groups that are dropped next to each other also have a chance to be close to each other on the ground. Using this deployment location knowledge, key pools and key rings are arranged and performance of key predistribution schemes can be improved substantially. In location aware schemes, the node deployment model is one of the most important design criteria that directly affects the performance of the scheme. As discussed above, a batch deployment strategy is assumed in the location aware random key predistribution schemes proposed in the literature. Such a deployment strategy may not be appropriate for scenarios like borderline or perimeter defense - if sensors are deployed in bundles, it is likely that there will be places on the border with a few or no sensor nodes. Moreover, there is still room to further improve the performance, in terms of connectivity, resiliency and memory usage, of location-aware key predistribution schemes with more realistic deployment models. 1.1 Our Contribution We introduce a new deployment model, called the continuous deployment model, and develop two key pre-distribution schemes on this model. The main idea behind the continuous deployment model is to drop the nodes one by one (i.e. not in batches) continuously from an aerial vehicle. The aerial vehicle may follow a continuous line for perimeter defense applications. In applications that need area coverage, the vehicle may follow a route with several parallel lines. We use the latter scenario, which is

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more complicated than the former one, in the development and the analysis of key pre-distribution schemes developed based on the continuous deployment model. In our first key pre-distribution scheme, it is assumed we know the order in which the nodes are dropped off for each line. For key predistribution in this scheme, we take a deterministic approach and assign pairwise keys to sensor nodes. In our second key predistribution scheme, we relaxed the order assumption such that the dropping order of the sensor nodes is not known, but the nodes to be dropped for each line are grouped. Here we use a probabilistic key predistribution mechanism; for each line, each node is assigned some keys from the key pools. We anticipate that the use of more deployment knowledge, as in the methods that we proposed, would improve the performance of the system. We performed analytical and simulation-based performance evaluation of the proposed schemes and show that the proposed approach actually improves key predistribution performance over Du et al.’s scheme [3], in which the nodes are deployed in groups, in terms of connectivity, resiliency against node capture and memory usage.

2 The Continuous Deployment Model In this section, we introduce a practical deployment model, where nodes are deployed sequentially but not in batches. In our deployment model the nodes are dropped one by one following a trajectory. This model can be easily realized by dropping nodes through a pipe in a plane as the plane flies over a known route. For example, if a rectangular area is to be covered with sensor nodes, the plane takes a route where it scans the rectangular area line by line. Figure 1 shows an example sensor network deployed in this model.

Fig. 1. A sample sensor network

The point where a node is dropped out of plane or helicopter is called its deployment point. However, due to several reasons its actual position drifts from deployment point. The actual position of a node on the field after deployment is named its resident point. Both deployment and resident points are defined in two-dimensional space. In the rest of the paper, the deployment area is assumed to be a rectangular one. In this area, there are L parallel lines and N nodes per line. Our deployment model assumes fixed intervals between the deployment points of two consecutive nodes of a line. The deployment point of ith node on jth line is denoted as dji, where j=1 ... L and i=1 ... N. Similarly the resident point of that node is denoted as rji.

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The resident point of a node may float away from its deployment point. Due to this fact, two nodes with deployment point dli and dlj, where dli < dlj, can be at resident points rli and rlj where rli > rlj. In our model, two nodes can be neighbors according to their deployment points, but they can be out of each others’ coverage after deployment. We call two nodes neighbors only if their resident points are close enough so that they can directly communicate over radio. The density of the lines and node dropping frequency are important system parameters to keep the resulting sensor network connected. Our model utilizes two-dimensional Gaussian distribution function to determine probability of a node being at a resident point based on its deployment point.

3 Continuous Key Predistribution Scheme Key distribution for our deployment model can be performed in two ways. In the first way, we assume that the deployment order of individual nodes is known. In this way, the neighboring relationships, in terms of the deployment points, are known. Such knowledge yields very efficient key distribution method that will be discussed later. However, in order to realize this, we have to transfer cryptographic materials to the nodes just before dropping them, so we need to have a complex setup inside the plane. Alternatively, we may transfer cryptographic materials before loading them to plane, but we have to preserve nodes’ order by, for example, keeping all the sensor nodes in pipes. In the second way, a line of nodes is treated as a single group. We do not assume knowledge of order of nodes; we just form groups of nodes and then store cryptographic material according to the key distribution scheme that will be explained later. Then, we deploy each group as a line in a random order. This approach is simpler to realize than the first method, but it has some performance deficiencies that will be discussed in this paper. We propose two different key predistribution schemes, Scheme I and Scheme II, for the above two ways. Both of them follow well-known three phase approach as in other key predistribution schemes proposed in the literature. First phase is the “predistribution” phase, where keys are stored in nodes according to a method proposed by the scheme. Second phase is the “direct key establishment” phase, where nodes discover their neighbors and find out if they share common keys with their neighbors to form secure link. Third phase is the “path key establishment phase”, where a node tries to find secure paths to its neighbors, with which it does not share common keys, in order to establish secure link. A secure link exists between two nodes if they both own at least one key in common and they are neighbors. We assume that all keys have unique IDs. 3.1 Key Predistribution Scheme I Parameters and the symbols used in this scheme are: N number of nodes on a line L number of lines that makes up the sensor network M number of keys shared with nodes on the same line

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number of keys shared with nodes on adjacent line distance between deployment points on a line radio range of a node ith line, where i=1 .. L deployment point of jth node on line Li, j=1 .. N the id of the sensor node with deployment point dij resident point of jth node on line Li, where j=1 .. N

Along with the deployment model examined in the previous section, sensor nodes, which are placed adjacent in the pipe, have high probability of being neighbors after deployment. Similarly, sensor nodes, which have similar locations in consecutive pipes, maintain the likelihood of being neighbors. As a result of this observation, we infer that a pairwise key predistribution method would work efficiently. Thus, we adopted such a strategy in our method. There are three phases in this scheme as described above: (i) Predistribution Phase, (ii) Direct Key Establishment Phase, and (iii) Path Key Establishment Phase. Predistribution phase. This phase is split into two: inline key predistribution and cross-line key predistribution. Inline key predistribution is for the nodes within the same line of deployment. Cross-line key predistribution is for the nodes in adjacent lines. Figure 2 depicts this phase. Q nodes Li+1 M nodes Li si1

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Inline Key Predistribution. The setup server creates and stores pairwise keys in sensor nodes such that each node shares keys with its M neighbors on the current line. More formally, for all i=1 .. L, and j=1 .. N, the setup server creates M keys to be stored in sij such that sij and its M neighboring sensor nodes, si(j-M/2),…,si(j1),si(j+1),….,si(j+M/2),share unique keys. Cross-Line Key Predistribution. Sensor nodes also share keys with their neighbors in neighboring lines. For all i=1 .. L, and j=1 .. N, the setup server creates 2*Q keys to be stored in sij such that this node shares unique pairwise keys with Q nodes from the lower line, s(i-1)(j-Q/2),…….,s(i-1)(j+Q/2), and also Q nodes from an upper line, s(i+1)(jQ/2),…….,s(i+1)(j+Q/2). After these two processes, a sensor node will have M+2Q keys before deployment.

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Direct Key Establishment Phase. After deployment, sensor nodes communicate with their neighbor nodes to discover shared keys in order to establish secure links Shared key discovery is trivial, since sensor nodes already have IDs of nodes with which they share pairwise keys. So a node only needs to know the IDs of its neighbors. When a node finds a matching node in its neighborhood, they can immediately start using their pairwise shared key. Unauthorized entities cannot know the IDs of keys used to secure links or IDs of keys in any node, since only node IDs are to be transmitted over unencrypted links,. This phase is indifferent for both nodes on the same line and nodes on the adjacent lines. Path Key Establishment Phase. After direct key establishment phase, a node may end up with a case where it has neighbors that it cannot find a shared key to establish a secure link. Thus, these two neighboring nodes without a secure link will have to find a secure path, which is a path of secure links, through their other neighbors. The process of establishing a secure link over a secure path is called path key establishment. The process works as follows. Assume node sij does not have a secure link with its neighbor node sik. Node sij asks its 1-hop neighbors, with which it has secure links, to see if they also have secure links with node sik. If any of the neighbors, say sin, has such a secure link, then sin generates a random key and sends the key to both node sij and sik over secure links. Then the nodes sij and sik use this key to establish a secure link. If none of the 1-hop neighbors have secure link with node sik, then node sij asks its 2-hop neighbors. If not found again, sij asks to next hop neighbors until it finds a node that shares key with sik. If the graph of secure links is a connected graph, a node eventually finds a secure path to any node in the sensor network. In our analysis, we will show that a node can reach all its neighbors with high probability in three hops of secure links. 3.2 Key Predistribution Scheme II Parameters and the symbols used in this scheme are: N L Li Si sI sc MI Mc K d A Aij dli rli sij kij

number of nodes on a line number of lines that makes up the sensor network ith line, where i=1 .. L key space of line i number of nodes a key in Si is distributed on Li number of nodes a key in Si is distributed on neighbors of Li memory space of nodes of Li for keys from Si memory space of nodes of neighbors of Li for keys from Si number of unique keys in a key space distance between deployment points on a line radio range of a node circular area around node sij, where sij can send and receive radio signals deployment point of node i on line l resident point of node i on line l the id of sensor node with deployment point dij the id of jth key in key space Si

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In this model, we do not assume a particular order in the deployment points of the nodes of a line. Thus we use less deployment knowledge as compared to predistribution scheme I. Although the scheme is still based on pairwise key distribution some redundancy should also be added in order to achieve a reasonable level of connectivity. Setup server generates groups of unique keys for each line. This keys form the key space, Si, of line i. There are K keys in each key space, and a node from line i gets keys from Si, Si-1 and Si+1 according to the key predistribution method. The duplication of each key is limited and determined parametrically. Similar to Scheme I, this scheme has three phases; predistribution, direct key establishment and path key establishment. Predistribution Phase. In key predistribution step, we describe the method how keys are distributed to nodes on various lines. Setup server generates key spaces for each line, Si, where i = 1 .. L, then distributes sI and sc copies of each key as explained below. Our aim here is to distribute the keys such that nodes that are expected to be near share more keys. Key predistribution method for each kij, where i = 1 .. L and j = 1.. N, is as follows: 1. 2. 3.

Key kij is randomly generated for key space of Si of line Li. sI nodes with sufficient space in their MI are randomly selected on Li and kij is installed in those sI nodes. sc nodes with sufficient space in their Mc are selected randomly from each neighboring lines of Li. So, 2sc nodes are selected from two neighboring lines. Then kij is installed in those sc nodes in each neighboring line.

At the end of key predistribution phase, each key from key space Si has a total of (sI + 2sc) copies on three lines; sI copies in Li and 2sc copies in Li-1 and Li+1. And each node has a total of MI + Mc keys installed. We can calculate K, the size of each key space Si, i = 1 .. L, by using sc, sI, MI, and Mc. Since there are N sensor nodes on line i, and since setup server loads exactly MI unique keys from Si into each node on line i, setup server will need NM I keys. Each key from Si will have sI copies on line i. Also, sc copies of keys from Si+1 and Si-1 will be loaded into nodes from line i, and each node has Mc memory for keys from neighboring key spaces. Then, number of unique keys in any key space, K, can be computed as follows:

K = NM I s I = NM c 2s c Direct Key Establishment Phase. After deployment, nodes have to find shared keys with its neighbors. This phase is similar to the basic scheme [5]. Here, each node needs to know which keys its neighbors have so that it can decide which keys they share. Each node broadcasts a message containing the indices of the keys it carries. Nodes can use these broadcast messages to find out if they share common keys with their neighbors. If a node can find a shared key with one of its neighbors, it can use that key to establish a secure link between itself and its neighbor. Path Key Establishment Phase. If two neighboring nodes cannot find a shared key directly, they have to reach a common key over a secure path. This method is identical to the path key establishment method in Scheme I.

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4 Performance Analysis In our analysis and simulation, we use the following configuration. Deployment area is 1000m x 1000m. There are 50 deployment lines, i.e. L=50, and the distance between lines is 20m. On each deployment line there are 200 nodes, i.e. N=200. Total number of sensor nodes, NxL, is 10000. Distance between two adjacent deployment points, d, is 5m. Communication range, R, for each node is 40m. Standard deviation of normal distribution, σ, is 10m. For scheme II, total number of unique keys is 50K = 100000. 4.1 Local and Global Connectivity In this section, we show our simulation results of the probability of a node sharing a key with its neighbors. This probability is called local connectivity, Plocal. The detailed formulation for Plocal could not be given here due to space limitations. Figure 3 shows local connectivity versus memory usage m. We compare results for our scheme I and scheme II with Du et al’s [3] scheme. Scheme II has higher connectivity than [3] for all values of m. For scheme II, different values of sI and sc results in different Plocal values even for the same memory usage. In our experiments for various m values, we obtained best results when sI and sc are equal. Scheme I outperforms both scheme II and Du’s scheme for low m values. In our simulations, scheme I reached a maximum local connectivity value of 0.8518 at M=28 and Q=26 that yields m=80. As the number of keys used increases after m=80, local connectivity stays the same. Increasing M and Q, and consequently m, values means that a node shares keys with distant nodes. This will not contribute to the local connectivity, because distant nodes have very small probability of falling within that node’s communication range. Simulation results in Figure 3 confirm our explanation. There are two factors that makes scheme II’s connectivity performance better than Du et al.’s scheme. Firstly, in our schemes we use more deployment knowledge such that in Du’s scheme there is a single deployment point for each bundle of nodes, whereas in scheme II there are deployment points for each node. Secondly, in scheme II, 1 0.9 0.8

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we distribute copies of a key homogeneously. We distribute copies of a key to both upper and lower neighboring lines, so a node can use keys in its Mc to establish secure links with nodes on the same line, on its direct neighbor lines and nodes on two lines away. In addition, by introducing sI and sc, we can have a fixed number of copies of all keys. Du’s scheme can have the same average number of copies of keys with same m values but a particular key can have a much higher or much lower number of copies. Fixing number of copies in scheme II contributes to homogeneity of key distribution. A high local connectivity value means that a node can communicate with most of its neighbors securely. However, a high local connectivity value does not guarantee that there will not be isolated parts in the network. Thus, we need to examine that whether our schemes can create too many isolated components or not. We measured, global connectivity, which is the ratio of size of largest isolated part to the size of whole network, through simulations. The results show that 100% global connectivity is reached when m is as low as 10 for Scheme I and 30 for Scheme II. Since we determine the deployment point of all nodes in Scheme I and fix the number of copies of a key in Scheme II, we minimize the possibility that network has more than one isolated part. Our simulation results support this idea. 4.2

Resiliency Against Node Capture

We investigate the effects of compromised nodes on direct key establishment. We assume that total c randomly chosen nodes are compromised. The fraction of additional communications that can be compromised based on the information from the compromised nodes defines the resiliency of our system. This section is focused on the resiliency of Scheme II against node capture attacks. Scheme I uses pairwise keys, therefore it is %100 resilient against compromising of sensor nodes. Let kaj denote a key generated for line La. Assume kaj is used for a link between two nodes that are not compromised. We know that there are Si copies of kaj on the nodes of La, Sc copies of kaj on the nodes of La-1 and Sc copies of kaj on the nodes of La+1. Thus, in order to compromise kaj, adversary should compromise nodes from La-1, La, and La+1. If there are j compromised nodes on La, the probability that kaj is not compromised on line La is given as: ⎛N − pcomp _ i ( j ) = ⎜⎜ ⎝ sI

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Thus, if there are x compromised nodes on La-1, y compromised nodes on La and z compromised nodes on La+1, the probability that kaj is not compromised becomes Pcomp_c(x)*Pcomp_i(y)*Pcomp_c(z) .The probability that there are x compromised nodes on line La-1, y compromised nodes on line La and z compromised nodes on line La+1 is calculated as: ⎛ c ⎞⎛ c − x ⎞⎛ c − x − ⎟⎟⎜⎜ pc _ xyz = ⎜⎜ ⎟⎟⎜⎜ ⎝ x ⎠⎝ y ⎠⎝ z

y ⎞⎛ 1 ⎞ x ⎛ 1 ⎞ y ⎛ 1 ⎞ z ⎛ L − 3 ⎞ c− x− y− z ⎟⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎠⎝ L ⎠ ⎝ L ⎠ ⎝ L ⎠ ⎝ L ⎠

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By using equations 1, 2, and 3, we calculate the probability that an adversary obtains a key, which is used for a link between two nodes that are not compromised, out of randomly compromised c nodes as given below: N N −2

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Comparison of our scheme II and Du et al.’s [3] scheme is shown in Figures 4 and 5. In both schemes probability of a link being compromised, Pcomp_all, is plotted against number of nodes captured. In Figure 4, number of keys in a node is taken as 60 and number of nodes is 10000 for both schemes. In Figure 5, we fix local connectivity to 0.86 for both schemes. Our scheme is outperforms Du’s scheme, because we can reach a local connectivity of 0.86 with only m=90 keys in a node, whereas Du’s scheme requires m=140 to reach the same local connectivity. Probability of a secure link being compromised when a number of nodes are captured is directly proportional to the number of copies of a key. In scheme II, number of copies of a key is a parameter determined by sI and sc. In Figure 5, for scheme II there are 3+2*3 = 9 copies of a key. In Du et al.’s scheme, a key has a random number of copies but we can find an average number of copies of a key by using |S|, number of unique keys in the sensor network, N, number of total nodes and m, number of keys in each node: k average = N ⋅ m S . Because we used the same m values for both scheme II and Du’s scheme in Figure 4, there were six copies of a key for both schemes and we got very similar results for both schemes as shown in the figure. 0.09 Our Scheme 2 Du's Scheme

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4.3 Path Key Establishment Overhead As the number of hops in path key establishment phase increases, a node can reach more of its neighbors and communication cost increases. We analyzed path key establishment through simulations for scheme II and depicted the results in Figure 6.

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The ratio of neighbors that a node can reach in i hops is defined as pl(i). Obviously, pl(1) gives local connectivity. Our scheme performs better than Du et al.’s scheme [3] such that our scheme needs less number of hops for small m values. It can be observed from Figure 6 that for m=60 or larger values of m, a node can reach all its neighbors in at most two hops. In [3], only 63% of the nodes reach their neighbors in at most two hops when m=60. Moreover, in [3], m should be 200 in order for a node to reach all of its neighbors in at most two hops. 0.18 Our Scheme 2 Du's Scheme

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5 Conclusions In this paper, we proposed a new deployment model and two novel key predistribution schemes based on the proposed model. In our deployment model, we proposed

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the nodes to be deployed in lines in a continuous fashion. This model is practical and can be realized easily. In the proposed scheme I, we assume to know the deployment points of each node and with that knowledge we distribute pairwise keys to each node to be used for communication between its neighbors. In scheme II, we loosen this assumption and assume that a node can be at any deployment point in a known line. We compared our schemes with Du et al.’s key predistribution scheme [3]. Performance evaluation showed that scheme I can reach high local connectivity values even with small memory usage. This is due to the assumption in scheme I that we can know the neighbors of each node according to their deployment points. However, there is an upper limit in local connectivity; other schemes can have better local connectivity with high memory usage, whereas local connectivity in scheme I stays the same at 0.85 after a certain point. On the other hand, scheme II achieves higher local connectivity values than Du’s scheme in all cases. Both scheme I and II show good performance in global connectivity and it is possible to reach 100% global connectivity with small memory usage. Moreover, scheme II has better node capture resiliency than Du et al.’s scheme with the same local connectivity value. Furthermore, communication cost of path key establishment overhead is smaller in our schemes.

References [1] F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, August 2002. [2] H. Chan, A. Perrig, and D. Song. Random key predistribution schemes for sensor networks. In IEEE Symposium on Research in Security and Privacy, pages 197–213, 2003. [3] W. Du, J. Deng, Y. S. Han, S. Chen, and P. Varshney. A key management scheme for wireless sensor networks using deployment knowledge. In Proceedings of IEEE INFOCOM’04, March 2004. [4] W. Du, J. Deng, Y. S. Han, and P. Varshney. A pairwise key predistribution scheme for wireless sensor networks. In Proceedings of ACM CCS’03, pages 42–51, October 2003. [5] L. Eschenauer and V. D. Gligor. A key-management scheme for distributed sensor networks. In Proceedings of the ACM CCS’02, pages 41–47, November 2002. [6] D. Liu and P. Ning. Establishing pairwise keys in distributed sensor networks. In Proceedings of ACM CCS’03, pages 52–61, October 2003. [7] D. Liu and P. Ning. Location-based pairwise key establishments for static sensor networks. In Proceedings of ACM SASN ’03, pages 72–82, October 2003. [8] S. Zhu, S. Setia, and S. Jajodia. LEAP: Efficient security mechanisms for large-scale distributed sensor networks. In Proceedings of ACM CCS’03, pages 62–72, October 2003. [9] J. Spencer, The Strange Logic of Random Graphs, Algorithms and Combinatorics 22, Springer-Verlag 2000. [10] D. Liu, P. Ning, and W. Du. Group-Based Key Pre-Distribution in Wireless Sensor Networks. In Proceedings of 2005 ACM Workshop on Wireless Security. [11] D. Huang, M. Mehta, D. Medhi, and L. Harn. Location aware Key Management Scheme for Wireless Sensor Networks. SASN’04, October 25, 2004, Washington, DC, USA. [12] R. Blom. An optimal class of symmetric key generation systems. In Proceedings of EUROCRYPT 84, 1985.

Using Auxiliary Sensors for Pairwise Key Establishment in WSN Qi Dong and Donggang Liu Department of Computer Science and Engineering The University of Texas at Arlington Box 19015, Arlington, Texas 76019-0015, USA {qi.dong,dliu}@uta.edu

Abstract. Many techniques have been developed recently for establishing pairwise keys in sensor networks. However, they are either vulnerable to a few number of compromised sensor nodes or involve expensive protocols for establishing keys. This paper introduces a much better alternative for achieving high resilience to node compromises and high efficiency in key establishment. The main idea is to deploy additional sensor nodes, called assisting nodes, to help the key establishment between sensor nodes. The proposed approach has many advantages over existing approaches. In this approach, a sensor node only needs to make a few local contacts and perform a few hash operations to setup a key with any other sensor node in the network at a very high probability. The majority of sensor nodes only need to store a single key in their memory space. Besides these benefits, it still provides high resilience to node compromises. The implementation of this approach on TelosB motes also demonstrates its feasibility for pairwise key establishment in sensor networks. Keywords: Key management, pairwise keys, sensor networks.

1

Introduction

Wireless sensor networks are ideal candidates for a wide range of applications in military and civilian operations such as health monitoring, data acquisition in hazardous environments, and target tracking. Security has been recognized as a critical requirement for many sensor applications, especially in military operations. Key management is the cornerstone to ensure the security of many network operations. As one of the most fundamental security services, pairwise key establishment enables secure node-to-node communication using cryptographic methods such as encryption and authentication. Many techniques have been developed recently to setup pairwise keys in sensor networks [1,2,3,4,5,6,7,8,9,10]. Perrig et al. developed the SNEP protocol to provide pairwise key establishment using a KDC [1]. This approach, however, introduces huge communication overhead and is vulnerable to single point failure. A number of key pre-distribution schemes were proposed to establish keys I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 251–262, 2007. c IFIP International Federation for Information Processing 2007 

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without the online KDC [2,3,4,5,6]. These approaches preload a small set of secrets into every sensor node before deployment to make sure that after deployment, every two sensor nodes can setup a shared key using their preloaded secrets. However, these approaches either require expensive protocols (e.g., path key establishment) to setup keys or are vulnerable to a small number of compromised sensor nodes. In addition, some techniques also use the sensors’ location information and assume static sensor nodes [7,8,11,10,9]. However, these two assumptions may not be true in practice. This paper presents a novel technique for pairwise key establishment in sensor networks. The main idea is to deploy additional sensor nodes, called assisting nodes, to help the key establishment between sensor nodes. Different from the nodes in traditional networks where they are mainly used for sensing and forwarding, the assisting nodes are only responsible for key management in the network, exploiting a novel dimension of using sensor nodes. The proposed approach has many advantages over existing approaches. First, it can achieve a very high probability of establishing a shared key between any two sensor nodes. Second, a sensor node only needs to make a few local contacts and perform a few hash operations to setup a key with any other sensor node in the network. Third, the majority of sensor nodes only need to store a single key in their memory space. Fourth, it does not depend on the sensors’ location information and can be used for the sensor networks with highly mobile sensor nodes. Besides these benefits, our approach still provides high resilience to node compromises. Finally, the implementation of this approach on TelosB motes [12] also demonstrates its feasibility for key establishment in sensor networks. The rest of the paper is organized as follows. Section 2 presents our pairwise key establishment protocol as well as the detailed analysis. Section 3 gives the implementation issues. Section 4 reviews related work. Section 5 concludes this paper and points out some future work.

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Pairwise Key Establishment

This section provides the technical detail as well as the analysis on how to establish pairwise keys using auxiliary sensors. In this paper, we consider the sensor networks consisting of a large number of tiny resource-constrained sensor nodes [13]. These sensor nodes can be static or highly mobile. We assume that the attacker can eavesdrop, modify, forge, replay or block any network traffic. We also assume that the attacker can compromise a few sensor nodes and learn all the secret information, including the keying materials, on those compromised nodes [14]. 2.1

Protocol Description

Typically, sensor nodes are deployed to sense the conditions in their local surroundings and report observations for various uses. However, in this paper, we exploit a new dimension of using sensor nodes and believe that it is important

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to deploy sensor nodes to facilitate certain network protocols such as key management. Hence, the main idea of our approach is to deploy additional sensor nodes, called assisting nodes, to help the pairwise key establishment between sensor nodes. The detailed protocol is presented below. Let n be the network size and m be the number of assisting sensor nodes. For convenience, we call the sensor nodes that are not assisting nodes as the regular sensor nodes. – Initialization: Before deployment, the base station generates a master key Ku for every sensor node u. The master key Ku is only known by the sensor node u and the base station. Every assisting node i will get preloaded with a hash H(Ku ||i) for every regular sensor node u, where H is a one-way hash function, and “||” denotes the concatenation operation. Hence, an assisting node will need to store n hash images. This clearly introduces considerable storage overhead at assisting sensor nodes. However, the only job of the assisting nodes is to help pairwise key establishment. As a result, they can use all their memory, including the flash memory, to store these values. Therefore, we believe that it will be feasible for an assisting node to store n hash images. For instance, the TelosB motes have 1MB flash memory and can store the hash images for a network of 128,000 sensor nodes if every hash is 8 bytes long. Additionally, research focusing on high-capacity and energyefficient storage subsystem on sensor network platforms has drawn a lot of attention, which will soon make it possible to equip a sensor node with a large flash memory [15] without increasing the cost significantly. Therefore, more and longer hash images can be stored in each assisting node for a very large sensor network. – Pairwise Key Establishment: After deployment, every regular sensor node discovers the assisting nodes in its neighborhood. When a sensor node u needs to establish a pairwise key with another node v, it will send a request to every neighbor assisting node i. The request message includes the IDs of both sensor nodes and will be protected by the key H(Ku ||i), which has been preloaded to the assisting node i. The assisting node i will serve as a KDC and generate a reply to u. This reply message includes two copies of a random key R, one is protected by H(Ku ||i) (for node u) and the other is protected by H(Kv ||i) (for node v). This procedure is similar to the Needham-Schroeder Symmetric Key Protocol[16]. After the request, u will get a random key from every neighbor assisting node. Let {R1 , ..., Rl } be the set of all these random keys. The final key Ku,v between u and v is simply the bit-wise XOR of all these keys, i.e., Ku,v = R1 ⊕ R2 ⊕ · · ·⊕ Rl . Obviously, as long as at least one random key is secure, the final key will be safe. Though our later analysis in Section 2.2 shows that even a small fraction of assisting nodes can guarantee a high probability of establishing pairwise keys using the above algorithm, it is still possible that a regular sensor node cannot find any assisting sensor node in its neighborhood since the accurate deployment of assisting nodes may not be guaranteed in some scenarios. To deal with this issue, we have supplemental key establishment, where a regular sensor node may

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discover the set of assisting nodes within a certain number of hops. This will certainly increase the chance of finding an assisting node to use. An additional benefit of doing this is to achieve better security performance. From the previous description, u derives the final key by applying XOR operations to all the random keys, which implies that the more random keys, the higher the security of the final key. – Supplemental Key Establishment: In this step, a sensor node u discovers the assisting sensor nodes that are no more than h hops away from itself. This can be easily achieved by having node u’s neighbors to help collecting the IDs of the assisting nodes around them. The neighbor nodes will broadcast the inquiry message on behalf of u, and forward u the replies from assisting nodes. Once such set is discovered, the remaining step will be similar to the pairwise key establishment discussed before. The discovery and usage of assisting nodes multiple hops away will introduce additional communication overhead since the intermediate nodes will be needed to relay the messages. However, this will only involve communication in a local area, which we believe will not be a big problem for the current generation of sensor networks. Also, the need for the supplemental key establishment will not likely be invoked frequently. 2.2

Analysis and Discussion

This subsection will present the performance analysis of the proposed scheme, focusing on the probability of establishing pairwise key, the resilience against node captures, and the overheads. For simplicity, we assume that all the n regular sensor nodes and the m assisting nodes are evenly deployed in the field. Probability of Establishing Keys: During the pairwise key establishment, a sensor node u is required to communicate with at least one assisting node in its neighborhood to setup a key with another sensor node. Let d denote the average number of one-hop neighbors of a sensor node. The probability that any assisting node i is not in the local area of the regular sensor node u can be estimated by 1 − d/(m + n). Thus, the probability that a regular sensor node fails to find any assisting node in its neighborhood can be estimated by (1 − d/(n + m))m . The probability P of establishing a pairwise key can then be estimated by P = 1 − (1 − d/(n + m))m . As d << n is usually satisfied in practice, P can be further approximated by 1 − e−dm/(n+m) . Figure 1 shows the relationship between the probability P of establishing key and the fraction of assisting nodes m n . From the figure, we can see that a small fraction (0.1) can guarantee a high probability (greater than 0.9) of establishing keys between sensor nodes. On the other hand, since P increases faster with larger number of d , the proposed scheme can achieve attractive performance in high-density networks. During the supplemental key establishment, the assisting nodes within h-hop range of u will be used. Obviously, the larger the range, the higher probability of finding the assisting nodes. Here we only analyze the situation when h = 2. From

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the above discussion, the probability that none of u’s neighbor nodes can find any assisting node can be estimated by ((1 − d/(n + m))m )d . Let P  denote the probability of establishing a pairwise key using the assisting nodes in two-hop range. Therefore, P  = P + (1 − P ) × (1 − (1 − d/(n + m))md). Figure 1 indicates that by using two-hop assisting nodes, even when the fraction of m n is as small as 0.003, the probability of establishing key is still greater than 0.9. Resilience against Node Captures: We assume that the base station will never be compromised. Thus, the master key of any non-compromised node will be always safe since the assisting nodes are only equipped with the hashes of the master key. Even if an assisting node is captured, it is computationally infeasible to get the original keys from the hashes due to the one-way property of hash functions. We also note that the sensor node derives the final key by applying XOR operation to all the random keys. The pairwise key will be secure unless all the related assisting nodes are compromised. This indicates an attractive property of our scheme: a benign assisting node can guarantee the security of the keys established in its neighborhood as long as it can communicate with the sensor nodes in its neighborhood. We then study the probability Pc of a key being compromised when a certain fraction of nodes are compromised. Assume the attacker will randomly compromise a fraction fc of sensor nodes. From our earlier analysis, Pc is equal to the probability that all the corresponding random keys are compromised. The number of assisting nodes that might provide those random keys can be estimated (m×d)/n by m×d . Figure 2 shows that our apn . Hence, Pc can be estimated by fc proach is highly resilient to the node compromise attack. It also implies that we can enhance the security of key management by deploying more assisting nodes.

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Overheads: The proposed scheme only requires a single key for every regular sensor node and n hash values for an assisting node. As we discussed before, the assisting node is only responsible for the pairwise key establishment and can use all its memory, including the flash memory, to store these hash images. The proposed scheme involves small computation overheads. A regular sensor node only need to apply a few symmetric key operations and hash operations to establish a key with any other sensor node. We note that in pairwise key establishment phase, the communication is in one-hop range. Although multi-hop communication will occur when supplemental key establishment is needed, the communication is still limited in a local area. Moreover, we have shown that in most cases, only one-hop communications are needed. As a result, the supplemental key establishment will not incur significant communication overhead for our protocol. 2.3

Comparison with Previous Schemes

This section will compare the proposed scheme with previous techniques for pairwise key establishment such as the basic probabilistic scheme [2], the q-composite scheme [3], the random pairwise keys scheme [3], the random subset assignment scheme [4], and the grid-based scheme [4]. Security Performance: We assume the network size n = 20, 000 and the average number of neighbors d = 50. For previous schemes, we assume each sensor can store 200 keys. Hence, for the grid-based scheme [4], the probability of two nodes sharing a direct key is 0.014. For the other schemes, we set P = 0.33 to make sure the network is well connected. However, from our previous analysis, our approach can always guarantee the establishment of pairwise keys at a very high probability using a small fraction of assisting nodes.

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Figure 3 shows the fraction of compromised links in the presence of compromised nodes for different schemes. The figure tells us that in terms of protecting the direct keys, our scheme can provide high resilience to node compromises, which is similar to the random subset assignment scheme and the grid-based scheme [4]. In addition, we must remember that our scheme can guarantee a much higher probability of establishing a pairwise key between two sensor nodes in a densely deployed sensor network. Note that the previous schemes need to employ expensive protocols for path key establishment when two sensor nodes cannot directly setup a pairwise key. As a result, the attacker might discover not only the direct keys but also the indirect (path) keys by compromising the intermediate nodes used in the establishment of the indirect (path) keys. On the contrary, our approach does not need to setup path keys. Even if the attacker has captured the nodes which relay the keying information, the key will not be disclosed. Figure 4 shows the fraction of compromised (direct or indirect) keys between non-compromised nodes in the presence of compromised nodes. The figure clearly shows that our scheme performs much better than other schemes. For example, when 70% sensor nodes are compromised, the fraction of compromised pairwise keys between non-compromised sensor nodes is only around 0.18. Contrarily, at least 88% keys have been exposed in the previous schemes. Additionally, our proposed scheme can guarantee that a single benign assisting node can protect the keys established in its neighborhood as long as this node can talk to the sensor nodes in its neighborhood. Overheads: In the proposed scheme, only a single master key is stored in every regular sensor node, while the previous schemes have considerable storage requirements for achieving high performance. For instance, In Figure 3 and Figure 4, the previous schemes require every node to store 200 entries to achieve

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the desire performance. In terms of the computation overhead, our proposed approach involves only a few number of symmetric key operations and hash operations. Hence, it will not incur much additional overhead. From the previous discussion, most of the communication overhead is introduced in the pairwise key establishment phase, which only requires direct communications in one-hop range. The multi-hop communication in the supplemental key establishment rarely occurs due to the high probability of finding a neighbor assisting node. On the other hand, for many previous schemes such as the grid-based scheme [4] and PIKE [6], a sensor node often needs to go through the path key establishment to setup keys with other sensor nodes. Such path key establishment could be very expensive in practice since the intermediate sensor node that can help establish the pairwise key may be located far away from the two sensor nodes that want to establish a shared key. According to the above discussion, we can clearly see that our proposed approach has significant advantages over existing schemes in terms of storage, computation and communication overheads. 2.4

Security Reinforcing Version

From our previous analysis, we note that once an assisting node is compromised, the attacker is able to discover all the random keys generated by this node. Although the actual pairwise key is combined from multiple random keys generated by different assisting nodes, it is still not desirable to let the attacker figure out the old random keys. In the following, we give a simple extension to fix this problem by updating the keys at the assisting nodes. The basic idea is to update the key at every assisting sensor node after the pairwise key establishment. In other words, the hash key at any assisting node will be changed immediately after it is used once. As a result, the attacker is not

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able to reveal any random key generated before even if the assisting sensor node is compromised at certain point. To achieve this goal, we will take the advantage of the one-way hash function H(·). We will also maintain a sequence number for every hash key shared between a regular sensor node and an assisting node. For example, initially, the hash key Hu = H(Ku ||i) and the sequence number Su = 0 will be stored in the assisting node i for the regular sensor node u before deployment. Once the assisting node i receives the request message to setup a pairwise key between u and v, i will send a random key R to one of the nodes along with the current sequence number Su and Sv via the secure links. At this time, the two copies of the random key R are protected by the hash values Hu and Hv respectively. After that, node i will replace Hu with H(Hu ) and Hv with H(Hv ), and also increase Su and Sv by 1. When the regular sensor node u receives the message from i, u will verify the authentication and confidentiality of the message using the same hash Hu , which can be computed based on Ku , i and Su . Node u can then derive the random key R for pairwise key establishment. In the protocol, node u may choose to keep track of the sequence number Su with each neighbor assisting node in its local area to reduce the overhead during the calculation of the hash key Hu . Such sequence number can certainly be used to deal with the replay attacks as well. Therefore, by employing one-way hash function, the improved approach can enhance the resilience against node captures, i.e., any compromised node will not reveal any secret about the pairwise keys established before. However, compromised assisting sensor nodes can still participate in the pairwise key establishment in the future when new nodes are added in the network. These malicious assisting nodes may disclose valuable information to adversaries. Fortunately, our scheme guarantees that as long as there are at least one benign assisting node in a given area, the final pairwise key will be still safe no matter how many sensor nodes are compromised. Based on this property, we may deploy new assisting sensor nodes to replace the old and untrustworthy ones to achieve better security during the pairwise key establishment.

3

Implementation Issues

Based on the previous analysis, we can see that our proposed pairwise key establishment approach is efficient for resource-constrained sensor nodes. In this section, we will present the implementation issues. We have implemented the prototype of the proposed scheme under TinyOS platform [17]. We use RC5 module [18] to implement the security primitives such as hash and MAC operations, assuming 8-byte long hash values and keys. This mechanism is transparent to the applications. In the protocol, the regular sensor node will first send request messages to its neighbor assisting nodes and wait for their responses. An assisting node generates the random key and send the reply message to every requesting sensor node. After the regular sensor node collects the random keys, it will combine these keys to derive the final pairwise key.

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Our scheme has been tested on the TelosB [12] motes. For the assisting nodes, the additional code space is 2598 bytes in the ROM, and the extra usage of data space in the RAM is 892 bytes. We also make use of the 1M flash memory on the chip to store the hash values of the regular nodes’ master keys. For the regular nodes that need to setup secure communication links with 50 neighbors, the additional code space in the ROM is 2102 bytes, and the extra data space in the RAM is 682 bytes. Clearly, the proposed scheme is practical for sensor networks in terms of the code size.

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Related Work

The closest related work to the techniques studied in this paper is pairwise key establishment. Many techniques have been proposed along this research direction, including the basic probabilistic scheme [2], the q-composite scheme [3], the random pairwise keys scheme [3], the two threshold-based schemes [4,5] and PIKE [6]. Additionally, the prior deployment and post deployment knowledge were also used to improve the performance of key pre-distribution in various situations [7,8,11,10]. This paper gives a better way for pairwise key establishment by exploiting a new dimension of using sensor nodes. There are many other studies on sensor network security, including broadcast authentication [1,19], tamper-resistant hardware [20], secure data aggregation [21], and vulnerabilities, attacks, and countermeasures [22]. We consider them complementary to our technique.

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Conclusion and Future Work

In this paper, we developed a novel scheme to establish the pairwise keys in sensor networks. This scheme takes advantage of special nodes (the assisting nodes) in the network for key management, representing a new dimension of using sensor nodes. The analysis indicates that our scheme has significant advantages over the existing approaches. By making use of these cheap assisting nodes, we ease the burdens of other regular sensor nodes and further extend the lifetime of the whole network. Several research directions are worth investigating. We are interested in conducting a thorough evaluation in a large scale sensor network. We are particularly interested in issues on how to tolerate the communication error and delay in bad channel conditions, how to withstand the high deployment failure rate where a lot of multi-hop communication may be needed. In addition, the additional assisting nodes in the proposed scheme are only deployed to establish the pairwise keys. However, we may make further use of those nodes to defend the network against various other attacks. Acknowledgment. The authors would like to thank the anonymous reviewers for their valuable comments.

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References 1. Weis, S., Sarma, S., Rivest, R., Engels, D.: Security and privacy aspects of low-cost radio frequency identification systems.: In SPC‘03, LNCS, Vol.2802. Springer-Verlag (2003) 454 V 469 2. Ohkubo, M., Suzuki, K., Kinoshita, S.: Cryptographic Approach to Privacy-friendly Tags. In RFID Privacy Workshop. MIT (2003)

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3. Menezes, A.J., van Oorschot, P.C., Vanstone, S. A.: Handbook of Applied Cryptography. CRC Press (1997) 4. Avoine, G., Dysli, E., Oechslin, P.: Reducing time complexity in RFID systems.: In SAC‘05. LNCS, Vol. 3897. Springer-Verlag (2006) 291 V 306 5. Gaubatz, G., Kaps, J.P., Öztürk, E., Sunar, B.: State of the Art in Ultra-Low Power Public Key Cryptography for Wireless Sensor Networks.: In PerSec‘05, Kauai Island, Hawaii (2005) 6. Hoffstein, J., Silverman, J., Whyte, W.: NTRU report 012, version 2. estimated breaking times for NTRU lattices, Technical Report 12, NTRU Cryptosystems, Inc. (2003) 7. National Institute of Standards and Technology (NIST). FIPS-197: Advanced Encryption Standard, November (2001). Available online at http://www.itl.nist.gov/fipspubs/ 8. Fujistsu web site, 2006. Referenced 2006 at http://www.fujitsu.com/us/services/edevices/ microelectronics/memory/fram/ 9. Feldhofer, M., Dominikus, S., Wolkerstorfer, J.: Strong authentication for RFID systems us-ing the AES algorithm.: In CHES‘04. LNCS, Vol. 3156. Springer-Verlag (2004) 357 V 370 10. Molnar, D., Wagner, D.: Privacy and security in library RFID: Issues, practices, and architectures.: In CCS‘04. ACM Press, Washington, DC, USA (2004) 210 V 219 11. Juels, A., Weis, S.: Authenticating pervasive devices with human protocols. In CRYPTO‘05. LNCS, Vol. 3621. Springer-Verlag (2005) 293 V 308. 12. NTRU RFID data sheet, 2006. http://www.ntru.com/products/NtruRFID.pdf 13. NTRU RFID white paper, 2006. www.ntru.com/products/RFID_White_paper_FNL.pdf 14. Hoffstein, J., Pipher, J., Silverman, J.H.: NTRU: A Ring-Based Public Key Cryptosystem.: Algorithmic Number Theory (ANTS III). LNCS, Vol. 1423. Springer-Verlag, Berlin (1998) 267-288 15. Graham, N.H., Nguyen, P., Pointcheval, D., Proos, J., Silverman, J. H., Singer, A., Whyte, W.: The Impact of Decryption Failures on the Security of NTRU Encryption. In CRYPTO‘03, Santa Barbara, USA (2003) 16. Dimitriou, T.: A Lightweight RFID Protocol to protect against traceability and cloning attacks. In SecureComm‘05 59 V 66 17. Capkun, S., Hubaux, J.P.: Secure positioning of wireless devices with application to sensor networks. In INFOCOM‘05 18. Avoine, G., Oeschlin, P.: A Scalable Protocol for RFID Pseudonyms. In Persec (2004) 19. Molnar, D., Soppera, A., Wagner, D.: A Scalable, Delegatable Pseudonym Protocol Enabling Ownership Transfer of RFID Tags. Workshop on RFID and Light-Weight Crypto, July 14-15, Graz, Austria (2005) 20. Dai W.: Crypto++, a Free C++ Library for Cryptography. http://www.eskimo.com/~weidai (2004) 21. O‘Rourke, C., Sunar, B.: Achieving NTRU with Montgomery Multiplication. In IEEE Trans. on Comp., vol. 52, No. 4, April (2003) 22. Kaya, S. V., Savas, E., Levi, A., Ercetin, O.: Privacy-Aware Multi-Context RFID Infrastructure using Public Key Cryptography http://students.sabanciuniv.edu/~selimvolkan / MultiContext_RFID_Framework.pdf (November 19, 2006)

Minimum Cost Configuration of Relay and Channel Infrastructure in Heterogeneous Wireless Mesh Networks Aaron So and Ben Liang Department of Electrical and Computer Engineering University of Toronto, Toronto, Ontario, Canada M5S 3G4 {aaronso,liang}@comm.utoronto.ca

Abstract. Fixed broadband wireless access is a promising technology allowing Internet service providers to expend their customer base in sparsely populated rural areas. Because the size of the target service area is humongous, relay infrastructure is essential. Installing and maintaining this relay infrastructure is the main cost associated with such networks. Thus, we develop an optimization framework which computes the minimum number of relay stations and their corresponding channel configurations such that a pre-specified subscribers’ traffic demand can be satisfied. Since the problem is a mixed-integer program, we propose an efficient optimization algorithm to compute the optimal solution in a reasonable amount of time. Our numerical results show that by using a few relay stations in a rural community, broadband Internet access can be established in a cost effective manner. Keywords: Fixed wireless broadband Internet access, relay stations, optimal placement and channel assignment.

1 Introduction Since high wiring cost is one of the biggest factors inhibiting wired broadband Internet access in sparsely populated rural areas, broadband wireless has long held the promise of delivering a wide range of data and information services to business and residential customers quickly and cost-effectively. With the publication of a comprehensive industry standard, namely IEEE 802.16, broadband wireless is ready to unleash its full potential. The IEEE 802.16 standard requires two separate physical layer specifications because the propagation characteristics of radio waves are so different in the lowerand upper-microwave regions. The WirelessMAN-OFDM and WirelessMAN-SC specifications utilize the 2-11 GHz and 10-66 GHz spectrum respectively. Lower frequency signals can penetrate walls and deflect from obstacles, while higher frequency transmissions must meet strict line-of-sight requirements. However, the advantage of using high frequency bands is an abundance of bandwidth. This intrinsic property of IEEE 802.16 technology makes it ideal for a heterogeneous architecture. 

This research was made possible thanks to Bell Canada’s support through its Bell University Laboratories R&D program.

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For the network under investigation, we assume that there is a base station wired to the ISP core network, and this base station is assigned to serve sedentary subscribers in a particular area. Because of the size of the coverage area, the base station usually cannot serve every subscriber by single-hop communication. As a result, several relay stations (RSs) are installed in the network, for example, on the subscribers’ rooftops, to relay traffic from and to the base station. If line-of-sight communications can be established among some RSs and the base station, the bandwidth abundant high frequency spectrum is used to form a backbone network. The lower spectrum, on the other hand, is used by the base station and RSs to communicate with the subscribers and form the corresponding local network. For this architecture, the cost of the network is dominated by the installation and maintenance cost of the RSs. Under this hypothetical heterogeneous mesh networking architecture, the focus of this work is to minimize the number of RSs used in the mesh network while maintaining the prespecified uplink and downlink demands of the subscribers. Note that the above IEEE 802.16 specifications are used only as an example; the analytical framework presented in this paper is general and can be applied to mesh networks based on other types of wireless technologies. To the best of our knowledge, this work is among the first solutions to address the problem of joint relay equipment placement and channel assignment in a heterogeneous wireless mesh network. In this work, we describe a heterogeneous wireless mesh network architecture with relay infrastructure and develop an analytical framework which determines whether a network with a particular relay station placement and channel assignment can satisfy the subscribers’ demands and interference constraints. Furthermore, we propose an optimization framework which combines a heuristic with Bender’s decomposition to calculate the minimum deployment and maintenance cost of a given heterogeneous wireless mesh network. The rest of this paper is organized as follows. In Section 2, we review the related work in multihop wireless networks. In Section 3, we describe the network infrastructure and equipment capabilities. In Section 4, we define our relay station placement and channel assignment problem mathematically, and describe an optimization solution. In Section 5, we discuss the convergence time and performance of the proposed optimization algorithm. Finally, concluding remarks are given in Section 6.

2 Related Works Motivated by recent advances in ad hoc networking [1][2], wireless multihop mesh networking is now considered as the next evolutionary step for wireless data networks. To bring wireless mesh networks closer to reality, in [3], Draves et al. conducted a detailed empirical evaluation of several link-quality metrics on route computation performance in wireless mesh networks. The issue of interference management in wireless mesh networks has been discussed in several contexts. In [4], Jain et al. considered the fundamental question of how much throughput a given wireless mesh network can achieve under different interference conditions. To address operational issues, an interferenceaware channel assignment algorithm for multi-radio wireless mesh networks was proposed in [5] by Ramachandran et al.

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The problem of wireless network equipment placement has also been addressed in several works. In [6], So and Liang proposed a Lagrangian approach to computes the optimal placement of a fixed number of relay node, which relay traffic in a two-hop fashion, to improve throughput in a WLAN. In the context of community mesh networks, innovative integration techniques were developed by Begerano in [7] to minimize the number of wired access points in a mesh network to reduce wiring cost, while maintaining users’ QoS constraints. To the best of our knowledge, there is no existing work that addresses the problem of joint relay equipment placement and channel assignment in community wireless mesh networks, which is what we investigate in this work.

3 Infrastructure and Equipment Capabilities To establish a network in a rural area, an operator needs to establish a site for the initial base station and the central office, which should have high capacity backhaul connection with the ISP core network. One cost effective backhaul solution is to lease dark fiber from electrical utilities or railroad companies. However, by using this approach, the network point of access is already fixed. Thus, the ISP does not have the freedom to choose the location of the central office and initial base station. In this work, we consider the case where the location of the base station and central office is given. Our goal is to place the minimum number of relay stations in the network such that the demands of the subscribers can be met. Since the subscriber locations are fixed and the high frequency spectrum is used, by using advanced antenna technologies and the three-dimensional space intelligently, we can effectively control interference in both the backbone and local networks. Adaptive array antenna technologies [8][9] have the ability to focus a beam very tightly toward a receiver, virtually eliminating the effects of interference. However, since such equipment is expensive, we assume that it is used only in the backbone network. For the local network, we assume a more affordable and common approach as follows [10]. Polarized directional antennas only disposes magnetic fields horizontally. When the antenna is tilted downward (or upward), beyond

Backbone Network Link Local Network Link

Base Station

Relay Station 2 Relay Station 1

High Capacity

Fiber Optic Link Relay Station 3

ISP Core Network

Fig. 1. Tilted polarized directional antenna systems

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a certain distance, the radiation will simply be absorbed into the ground (or outer space). As shown in Fig. 1, two relay stations, e.g., Relay Station 2 and 3, can use the same channel for the local network and do not interfere with each other as long as they are placed far apart from each other. However, since subscribers who use the same RS are located in the same multipath environment, they have to share the channel in a timemultiplexed fashion. In the next section, we define the problem mathematically.

4 System Model and Optimization Framework Suppose there are N subscribers and one base station in the system, and they are represented by the set V = {0, 1, ..., N }, where the base station is represented by the index 0. Let VR ⊆ V be the set of nodes where the installation of relay stations are feasible.1 We can use the set VR to form a directed-complete graph representing the backbone B network. The link weight from node i to node j, denoted Cij , represents the capacity in terms of bit per second from node i to node j using the backbone technology. The capacity between two nodes is zero if line-of-sight communication cannot be established. Similar to the backbone network, we can use the set V to form a directed-complete graph representing the local network. Within the local technology, the capacity from L . Since a link which handles local traffic has to be node i to node j is denoted by Cij L associated with the base station or a relay station, Cij = 0 if both i and j are not in the set VR . As mentioned above, two relay stations using the same channel would interfere each other’s local network operations if they are placed in each other’s interfering zones. Let N (i) be the set of nodes that interfere the operation of node i, where i ∈ VR . Moreover, when there are NC local channels available, let Λ = {1, 2, ..., NC } be the local channel set. Furthermore, for each subscriber i, there is a pre-specified uplink demand, ui , and downlink demand, di . Given the above as the input to our problem, we define the following decision variables. For discrete decision variables, let us set Xiλ = 1 if an RS which uses channel λ is installed in node i; otherwise we set Xiλ = 0. We term Xiλ the location-channel variables. Let us define the following continuous decision variables. Let fijd and fiju be the amount of downlink and uplink traffic flow from node i to node j by using the backbone technology respectively. Let hdij and huij be the amount of downlink and uplink traffic flow from node i to node j by using the local technology respectively. All input and decision variables are non-negative. Moreover, we define X01 = 1 since the base station is always present, and without loss of generality, we can also let the base station use channel 1 for its local network operation. Next, we formulate our problem as a mixed integer program. 4.1 Optimization Formulation Our goal is to find the minimum number of RSs in the system which satisfies all the demand and interference constraints. The optimization formulation is as follows: 1

Whether a subscriber site is in VR or not depends on the willingness of the subscriber and other physical conditions. Furthermore, the base station, which has index 0, is included in the set VR .

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min : X

s.t.



Xiλ

(1)

i∈VR ,λ∈Λ



i∈VR \{0}



d f0i +





hd0i =



ui

(2)

di

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i∈V \{0} u fji



hui0 =

i∈V \{0}

i∈VR \{0}

i∈V \{0}

∀ i ∈ VR \{0}

(4)

fijd

∀ i ∈ VR \{0}

(5)

huij ≥ ui

∀ i ∈ V \{0}

(6)

hdji ≥ di

∀ i ∈ V \{0}

(7)



huji



fiju

+

=

j∈V \{0}

j∈VR ,i=j



d fji −

j∈VR ,i=j

hdij =

j∈V \{0}

j∈VR ,i=j





u fi0 +



279



j∈VR ,i=j

j∈VR



j∈VR



hdij + huij

j∈V,j=i

L Cij



hdij + huji ≤ k

j∈V



+

hdji + huji



L Cji

Xiλ

≤ (1 − Xiλ )k + 1

∀ i ∈ VR , λ ∈ Λ(8)

∀ i ∈ VR

(9)

∀ i ∈ VR

(10)

λ∈Λ

Xiλ ≤ 1

λ∈Λ B fiju + fijd ≤ Cij



Xiλ

∀ i ∈ VR , j ∈ VR , i = j (11)

Xjλ

∀ i ∈ VR , j ∈ VR , i = j (12)

λ∈Λ B fiju + fijd ≤ Cij

Xiλ +





λ∈Λ

Xjλ ≤ 1

∀ i ∈ VR , λ ∈ Λ

(13)

j∈N (i)

The objective (1) minimizes the number of RSs to be installed in the network. Constraints (2) and (3) verify that the amount of traffic entering and exiting the base station equals the total uplink and downlink demands respectively. Constraints (4) and (5) verify that the amount of traffic entering each RS matches the amount of traffic exiting each RS (the conservation of flow at each RS). Constraints (6) and (7) verify that the uplink and downlink demands are met respectively. Constraints (8) and (9) work together with an arbitrary large number k. If an RS which uses channel λ is placed at node i, then Xiλ = 1, and the right hand side of constraint (8) is 1. Since local uplink and downlink traffic share the channel in a time-multiplexed fashion, constraint (8) verifies that the local traffic enters and exits through the ith RS does not exceed its capacity. If no RS is installed at node i, then Xiλ = 0 ∀λ ∈ Λ and the right hand side of constraint (8) is k + 1. Thus, constraint (8) does not impose any restriction on the traffic exiting and entering node i. However, the right hand side of constraint (9) is 0. This ensures that no

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local uplink traffic enters node i and no local downlink traffic exits node i. Constraint (10) verifies that at most one channel can be assigned to an RS. Constraints (11) and (12) work together to ensure that a positive backbone traffic between node i and j exists only if an RS is placed at node i and an RS is placed at node j. Finally, constraint (13) ensures that no two RSs which use the same channel are placed in each other’s interfering zone. 4.2 Problem Reformulation and Bender’s Decomposition Traditionally, any mixed integer problem can be solved by branch-and-bound. However, such approach is virtually intractable even for a small number of discrete variables because an exponential number of linear programs have to be solved. Given that we have a large number of continuous variables and a relatively small number of integer variables, Bender’s decomposition breaks down the problem to a sequence of small 0-1 integer problems [11] which can be solved efficiently by commercial optimization softwares such as CPLEX. In the following, we first reformulate the above analytical framework so that it can be decomposed by Bender’s method. Then, we describe the algorithm that we used to solve the RS placement and channel assignment problem. To apply Bender’s decomposition to a mixed integer program, the problem needs to be organized into the following form 2 min c1 y + c2 x

(14)

s.t. A1 y + A2 x ≥ b,

(15)

x,y

where x is a vector represents the location-channel variables Xiλ , y is a vector represents the set of continuous variables fiju , fijd , huij , hdij , c1 = 0t , c2 = 1t , and (·)t denotes vector transposition. For a fixed value of the location-channel variables x = x , problem (14) reduces to the following feasibility problem: min T (y| x)  c1 y

(16)

s.t. A1 y ≥ b − A2 x 

(17)

y

Obviously, given a particular RS placement and channel assignment, x , the resulting problem, (16)(17), may or may not be feasible. To make all location-channel variables feasible, let us introduce one positive continuous variable, v, and a very large infeasibility constant, P . We can then modify  (16)(17) by changing A1 to A1 = [A1 |1], c1 t   to c1 = [0 |P ], and y to y = y v. Then, the modified feasibility problem is the following: min T (y  | x) = c1 y  = P v 

(18)

 s.t. A1 y  ≥ b − A2 x

(19)

y

For any x  which makes (16)(17) infeasible, problem (18) (19) is still feasible, but it will suffer a very large infeasible penalty P v. 2

a = b is equivalent to a ≥ b and b ≥ a.

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Now, let us consider the dual of the modified feasibility problem (18)(19). Let u be the set of dual variables. The dual of the modified feasibility problem may now be formulated as follows: max D(u| x)  (b − A2 x )t u u

s.t. At1 u ≤ ct1 u≥0

(20) (21) (22)

Denote the optimal solutions to the linear programs (18) and (20) by y ∗ and u∗ respectively. Then, by duality theory, c1 y ∗ = (b − A2 x )t u∗

(23)

We now consider all the extreme points of the dual problem (20). Note that the extreme points are defined by the feasible region described by (21) and (22) which are independent of the location-channel variables x. Thus, the extreme points can be generated without any knowledge of the RS locations and channel assignments. Let us denote the ith extreme point by ui and total number of extreme points by p. We know from the theory of linear programming that at least one optimal solution to any linear problem occurs at an extreme point of the feasible region. Thus, the original problem can be reformulate as the following pure 0-1 problem: min c2 x + D

(24)

s.t. D ≥ (b − A2 x)t ui ∀i ∈ [1, p],

(25)

x

or equivalently, min D

(26)

x



s.t. D ≥ c2 x + (b − A2 x) u

t i

∀i ∈ [1, p].

(27)

The difficulty with problem (26) is that the number of extreme points of the dual problem is potentially very large. Thus, we do not want to enumerate all of the constraints in (27) explicitly. Also, at the optimal solution to (26), only a small subset of the constraints (27) are likely to be tight. Thus, even if we could enumerate all of them, many of them would prove to be unnecessary. On the other hand, if we solve (26) with only a subset of the constraints in (27), we will obtain a valid lower bound on the optimal value of the original objective function. Furthermore, if all of the constraints that are tight in the optimal solution to (26) happen to be in the subset of constraints that we include, then the value of the objective function (26) will exactly equal the optimal value. To generate the desired subset of extreme points, Bender’s method adds constraints to the constraint set (27) one by one [11]. When a new constraint is added, the optimal solution of (26) returns either a better (larger) lower bound value or the optimal solution to the original problem (14) if a feasible RS placement and channel assignment exists.3 3

If no feasible solution exists, the algorithm will return a very large number.

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4.3 Modified Bender’s Method By using the original Bender’s method, at each iteration, one needs to solve a small pure 0-1 minimization problem. Even though this approach makes the problem manageable, it could be time consuming and potentially require a large amount of time to compute. The purpose of finding the solution of (26) at each iteration is to find an appropriate extreme point to add to the constraint set (27). Instead of performing the minimization (26) at each iteration, we propose to use a heuristic to find a decent extreme point at each iteration, and only perform minimization (26) when the extreme point generated by the heuristic is invalid. To further reduce the run time of the Bender’s method, we propose to use the results generated by each iteration to reduce the solution space. If a feasible RS placement and channel assignment exists, the optimal solution must be an integer which equals the minimum number of required  relay stations. To take advantage of this observation, we add the constraint, Ll ≤ i∈VR ,λ∈Λ Xiλ ≤ Lu , to the problem, where the minimum number of required relay stations must be greater than or equal to Ll and smaller than or equal to Lu . We initialize Ll = −∞ and Lu = ∞. We update Lu whenever a new (smaller) upper bound is found, and we update Ll whenever the lower bound, which is computed by the minimization of (26), increases by more than 1. Fig. 2 presents a flow chart of the modified Bender’s decomposition approach to solve the RS placement and channel assignment problem.

Empty constraint set (27) and an arbitrary RS placement and channel assignment xˆ incumbent upper bound = ’

Solve the modified feasibility problem (20) (21) (22) and obtain extreme point u t and upper bound= c 2 xˆ  (b  A2 xˆ ) u STOP YES if upper bound < incumbent upper bound incumbent upper bound = upper bound

Lu

¬incumbent upper bound ¼

lower bound • incumbent upper bound?

NO

xˆ

xˆ '

Add new constraint

D' t c2 x  (b  A2 x)t u to constraint set (27)

Solve updated problem (26)(27) and obtain a potential lower bound and a new RS placement and channel assignment xˆ ' by heuristic.

NO

lower bound • incumbent upper bound?

Solve updated problem (26)(27) and obtain a lower bound and a new RS placement and channel assignment xˆ ' by optimization. YES

Ll

ªlower bound º

Fig. 2. Flowchart of the modified Bender’s decomposition approach for solving the RS placement and channel assignment problem

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We begin by using an empty constraint set (27), and we add new constraints to it iteratively. By using the RS locations and channel assignments, x , generated by the previous iteration4, we solve the modified feasibility problem (20). When this problem is solved, we obtain an extreme point u. From this, we obtain the minimum infeasibility penalty for the RS placement and channel assignment, x , suggested by the previous iteration. The sum of the infeasibility penalty and the cost of the RSs constitute an  + (b − A2 x )t u. upper bound of the problem. That is to say the upper bound = c2 x We keep the best (lowest) upper bound found so far and save it as the incumbent upper bound. The newly generated extreme point is then used to add a new constraint, D ≥ c2 x + (b − A2 x)t u, to the constraint set (27). In the original Bender’s method, one solves the updated problem (26) and obtain a lower bound value and a new RS placement and channel assignment x  . In this work, we propose to use a simple heuris5 tic to generate decent values of x  , and we only perform minimization if the lower bound value generated by the heuristic is higher than or equal to the incumbent upper bound. Otherwise we set x  = x  and generate another extreme point.6 If the lower bound generated by the minimization and the incumbent upper bound are equal, we stop. Otherwise, we set x =x  and go back to the beginning of the iterative phase.

5 Numerical Analysis In this section, we present numerical results based on a hypothetical IEEE 802.16 network. A link capacity model, similar to that in [6], is used to determine the operational bit rate between any pair of nodes. The optimal RS placement and channel assignment in a typical rural environment will be derived by the proposed modified Bender’s decomposition method. By using the proposed optimization framework, we evaluate the cost of deploying a heterogeneous wireless mesh network with relay stations in a sparse rural area. The cost of the network is the minimum number of RSs required. We set the infeasibility constant P to 1000. For the backbone network, a 20MHz spectrum is occupied, and the IEEE 802.16 WirelessMAN-SC technology is used. For the local network, we use the IEEE 802.16 WirelessMAN-OFDM technology and a 20MHz spectrum as well. As shown in Fig. 4, the subscribers are distributed in a 12km × 12km rural area, and the base station and central office are located at node 0, where they can be connected to the ISP core network via the fiber optic network of the railroad company, which is assumed to be underutilized. According to the IEEE 802.16 WirelessMAN-OFDM specifications, channel bandwidth can be adjusted dynamically. However, the bandwidth occupied by each channel is vendor specific. In this work, as an example of illustration, we assume that the local network spectrum is divided into three channels.7 Each RS or base station has a 4km 4 5 6

7

For the first iteration, we use an arbitrary RS placement and channel assignment. In our numerical result, we use a simple descent algorithm. Problem (26) is a small pure 0-1 minimization problem. This problem can be solved to optimality in a reasonable amount of time by any commercial optimization softwares if the number of integer variables is not too large (e.g. less than 1000). The link rate of one channel is one third of the original 20MHz channel.

A. So and B. Liang 5

x 10

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Fig. 4. Network configuration of a heterogeneous wireless mesh network

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interference zone. In other words, if a base station or RS using a particular channel is placed in one location, another RS which uses the same channel cannot be placed within a 4km radius of the former base station or RS. Among the 58 nodes, we assume the ISP has access to 50 of them for the installation of RSs. Furthermore, we set the uplink and downlink demand of each subscriber to 1Mbps, and 2Mbps respectively. As mentioned in Section 4.2, the original version of Bender’s decomposition method requires solving a small pure 0-1 optimization problem in each iteration, which potentially takes a very long time to perform. In the modified version as shown in Fig.2, we have integrated a classic descent algorithm [12] to reduce the runtime of the Bender’s method. The convergence of the original and modified Bender’s decomposition methods are shown in Fig. 3, and the resulting configuration of the network is shown in Fig. 4. It takes about 22 hours for the modified version of the Bender’s decomposition method to converge to the optimal value, while in the same amount of time, the gap between the upper bound and the lower bound generated by the original method is still very large. Even though the extreme points generated by the heuristic at some iterations are not the desired extreme points, the heuristic can rapidly generate a set of useful extreme points which leads to faster convergence than the original approach.

6 Conclusion In this work, we investigate the optimal placement and channel assignment of wireless relay stations to minimize the operational cost of a wireless mesh network. We have presented a heterogeneous wireless mesh network architecture which uses relay stations to form a backbone and a local network. Furthermore, we have developed an analytical model to investigate whether a particular RS placement and channel assignment can satisfy the user demands and interference constraints. We use Bender’s decomposition to compute the optimal number of RSs and their corresponding placement and channel assignment which minimize the operational cost of a heterogeneous wireless mesh network. Furthermore, we integrate heuristics in the algorithm to reduce the runtime of the Bender’s decomposition method. Given a set of network parameters, the proposed framework and optimization technique can offer significant run time advantages, when used by network designers to compute the optimal placement and channel assignment of relay stations and to provide design guidelines on the network setup and maintenance cost estimations.

References 1. Perkins, C.E., ed.: Ad Hoc Networking. Addison-Wesley Longman (2001) 2. Haas, Z., Deng, J., Liang, B., Papadimitratos, P., Sajama, S.: Wiley Encyclopedia of Telecommunications. John Wiley & Sons (2002) 3. Draves, R., Padhye, J., Zill, B.: Comparison of routing metrics for multi-hop wireless networks. In: Proc. of ACM SIGCOMM, Portland, OR, U.S.A. (Sept. 2004) 4. Jain, K., Padhye, J., Padmanabhan, V.N., Qiu, L.: Impact of interference on multi-hop wireless network performance. In: Proc. of ACM MOBICOM, San Diego, CA, U.S.A. (Sept. 2003) 66–80

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5. Ramachandran, K., Belding, E., Almeroth, K., Buddhikot, M.: Interference-aware channel assignment in multi-radio wireless mesh networks. In: Proc. of IEEE INFOCOM, Barcelona, Spain (April 2006) 6. So, A., Liang, B.: A Lagrangian approach for the optimal placement of wireless relay nodes in wireless local area networks. In: Proc. of the International IFIP-TC6 Networking Conference, Coimbra, Portugal (May 2006) 160–172 (extended version in press, to appear in the IEEE Transactions on Mobile Computing) 7. Bejerano, Y.: Efficient integration of multihop wireless and wired networks with QoS constraints. IEEE Transactions on Networking 12(6) (Dec. 2004) 1064– 1078 8. Glapa, S.: Navigating the harsh realities of broadband wireless network economics. Technical report, ArrayComm, Inc. (2004) 9. Beaton, M.: Unwiring broadband, technology white paper. Technical report, Navini Networks (2002) 10. D. Sweeney, ed.: WiMax Operatos’s Manual, Building 802.16 Wireless Networks. 2 edn. Apress (2005) 11. Martin, R.K.: Large Scale Linear and Integer Programming. 1 edn. Kluwer Academic Publishers (1999) 12. Wolsey, L.: Integer Programming. 1 edn. Wiley (1998)

Optimization Models for the Radio Planning of Wireless Mesh Networks Edoardo Amaldi, Antonio Capone, Matteo Cesana, and Federico Malucelli Politecnico di Milano, Dipartimento Elettronica ed Informazione, Milano, Italy {amaldi,capone,cesana,malucell}@elet.polimi.it

Abstract. In this paper we propose novel optimization models for the planning of Wireless Mesh Networks whose objective is to minimize the network installation cost, while providing full coverage to wireless mesh clients. Our mixed integer linear programming models aim at selecting the number and positions of mesh routers and access points, while taking into account in an accurate way traffic routing, interference, rate adaptation, and channel assignment. We provide the optimal solutions of the proposed problem formulations on a set of realistic-size instances and discuss the effect of different parameters on the characteristics of the planned networks.

1

Introduction

The network devices composing Wireless Mesh Networks (WMNs) [1] are of three types: Mesh Routers (MRs), Mesh Access Points (MAPs) and Mesh Clients (MCs). The functionality of both the MRs and the MAPs is twofold: they act as classical access points towards the MCs, whereas they have the capability to set up a Wireless Distribution System (WDS) by connecting to other mesh routers or access points through point to point wireless links. Both MRs and MAPs are often fixed and electrically powered devices. Furthermore, the MAPs are geared with some kind of broadband wired connectivity (LAN, ADSL, fiber, etc.) and act as gateways toward the wired backbone. MCs are users terminals connected to the network through MAPs or MRs. Several parameters concur to the determination of a general wireless mesh network effectiveness including the number of radio interfaces for each device, the number of available radio channels, the access mechanism, the routing strategies and the specific wireless technology used to implement the mesh paradigm. All these parameters are de facto degrees of freedom the network designer can exploit to deploy an effective WMN, thus optimization criteria are needed for the tuning of such parameters. To this end, many works have appeared in the literature with the purpose of providing optimized protocols for WMNs. So et al. propose in [2] a multichannel MAC protocol in the case single interface transceivers are used, whereas reference [3] analyzes those networks where even multiple radio interface per wireless node can be used adapting the channel access protocol. Das et al. propose two I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 287–298, 2007. c IFIP International Federation for Information Processing 2007 

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Integer-Linear programming models to solve the fixed channel assignment problem with multiple radio interfaces [4], whilst [5] and [6] address the problems of channel assignment and routing jointly, providing different formulations to the optimization problem. The most of the previously published work assumes a given network topology, i.e., the general approach tends to optimize the channel assignment and/or the routing assuming given positions for the MRs and the MAPs. On the other hand, the purpose of the present work is to model the radio planning problem of WMNs, providing quantitative methods to optimize number, positions and coordination of MRs and MAPs and the overall topology of the network. The problem of planning WMNs differs from that of planning other wireless access networks, such as cellular systems [7] or WLANs [8]. In the latter cases, network planning involves selecting the locations in which to install the base stations or access points, setting their configuration parameters (emission power, antenna height, tilt, azimuth, etc.), and assigning channels so as to cover the service area and to guarantee enough capacity to each cell [9]. In the case of WMNs, each candidate site can host either MAPs or MRs, which have different installation costs. Roughly speaking, MAPs are more expensive than MRs since they must be directly connected to the wired backbone and might be more powerful than MRs in terms of both processing and transmission capabilities. Moreover, the traffic to/from the wired backbone has to be routed on a path connecting the MR to one MAP at least. In this context, capacity limits of radio links among MRs and between MRs and MAPs play a key role since the traffic routed on a link must not exceed its capacity. The resulting network design problem must simultaneously consider the radio coverage of users, like in classical radio planning for wireless access networks [9], and the traffic routing, like in the design of wired networks [10]. Very few previous works consider the problem of planning WMNs or, more in general, fixed multi-hop wireless network. The main attempt to address the problem appears in [11] where the focus is on locating internet transit access points (MAPs in the terminology adopted in this paper) in wireless neighborhood network. Heuristic solutions and a lower bound are provided. Since the positions of all the other nodes (MRs) is given and such nodes are also the only traffic ending points in the network, the problem considered in [11] is actually a subproblem of that proposed in this paper since the coverage part is not included. Moreover, the interference model is based on the number of hops crossed by flows, while the model considered in this paper is a fluidic version [12] of the protocol interference model [14]. The problem in [11] was originally proposed in [13] where, on the other hand, a time division multiple access is assumed and the slot scheduling optimization is included in the model. In this work we propose an optimization model for the problem of planning WMNs based on mathematical programming which takes into account both the local and the multihop connectivity requirements. The problem is NP-hard but it can be solved to the optimum for realistic size instances. We provide

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optimal solutions for a set of synthetic instances and discuss the effect of different parameters on the characteristics of the solution. Our work is organized as follows: Section 2 gives the formulation of the proposed model and comments on its main features, whilst Section 3 reports numerical results. Concluding remarks are given in Section 4.

2

Wireless MESH Network Planning

For the sake of simplicity, we define a basic version of the model for the WMN planning problem neglecting interference, adaptive modulation and multiple channels, and then we extend it step-by-step. Let us consider the network description presented in Figure 1. Similarly to the coverage problems commonly considered for wireless access networks [9], let S = {1, . . . , m} denote the set of CSs and I = {1, . . . , n} the set of TPs. A special node N represents the wired backbone network. The cost associated to installing a MR in CS j is denoted by cj , while the additional cost required to install a MAP in CS j is denoted by pj , j ∈ S. The total cost for installing a MAP in CS j is therefore given by (cj + pj ). Traffic generated by TP i is given by parameter di , i ∈ I. The traffic capacity of the wireless link between CSs j and l is denoted by ujl , j, l ∈ S, while the capacity of the radio access interface of CS j is denoted by vj , j ∈ S. The sequence Si of CSs that can cover TP i is calculated for all TPs considering a non increasing order of (i) (i) (i) received signal strength, Si = {j1 , j2 , . . . , jLi }, i ∈ I. According to TPs and CSs location and propagation information the connectivity parameters can be calculated. Let aij , i ∈ I, j ∈ S, be the coverage parameters:  1 if a MAP or MR in CS j cover TP i aij = 0 otherwise, and bjl , j, l ∈ S, the wireless connectivity parameters:  1 if CS j and l can be connected with a link bjl = 0 otherwise.

N wjN zj

j yjl

Wired backbone

MAP MR TP

l xij i

Fig. 1. WMN planning problem description

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Decision variables of the problem include TP assignment variables xij , i ∈ I, j ∈ S:  1 if TP i is assigned to CS j xij = 0 otherwise, installation variables zj , j ∈ S:  1 if a MAP or a MR is installed in CS j zj = 0 otherwise, wired backbone connection variables wjN , j ∈ S (if zj = 1, wjN denote if j is connected to the wired network N , i.e. if it is a MAP or a MR):  1 if a MAP is installed in CS j wjN = 0 otherwise, wireless connection variables yjl , j, l ∈ S:  1 if there is a wireless link between CS j and l yjl = 0 otherwise, and finally flow variables fjl which denote the traffic flow routed on link (j, l), where the special variable fjN denotes the traffic flow on the wired link between MAP j and the backbone network. Given the above parameters and variables, the WMN planning problem can be stated as follows:  (cj zj + pj wjN ) (1) min j∈S



s.t.

xij = 1

∀i ∈ I

(2)

j∈S



xij ≤ zj aij ∀i ∈ I, ∀j ∈ S  di xij + (flj − fjl ) − fjN = 0

i∈I

(3) ∀j ∈ S

(4)

l∈S

flj + fjl ≤ ujl yjl  di xij ≤ vj

∀j, l ∈ S

(5)

∀j ∈ S

(6)

∀j ∈ S

(7)

∀j, l ∈ S

(8)

i∈I

fjN ≤ M wjN yjl ≤ zj , yjl ≤ zl yjl ≤ bjl zj (i) + 

Li  h=+1

xij (i) ≤ 1 h

∀j, l ∈ S ∀ = 1, . . . , Li − 1, ∀i ∈ I

xij , zj , yjl , wjN ∈ {0, 1}

∀i ∈ I, ∀j, l ∈ S

(9) (10) (11)

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The objective function (1) accounts for the total cost of the networks including installation costs cj and costs related to the connection of MAP to the wired backbone pj . If for some practical reason only a MR and not a MAP can be installed in CS j, the corresponding variable wjN is set to zero. Constraints (2) provide full coverage of all TPs, while constraints (3) are coherence constraints assuring respectively that a TP i can be assigned to CS j only if a device (MAP or MR) is installed in j and if i is within the coverage set of j. Constraints (4) define the flow balance in node j. These constraints are the same as those adopted for classical multicommodity flow problems. The term  assigned TPs, l∈S flj is the total traffic i∈I di xij is the total traffic related to received by j from neighboring nodes, l∈S fjl is the total traffic transmitted by j to neighboring nodes, and fjN is the traffic transmitted to the wired backbone. Even if these constraints assume that traffic from TPs is transmitted to the devices to which they are assigned and that this traffic is finally delivered by the network to the wired backbone, without loss of generality we can assume that di accounts for the sum of traffic in the uplink (from TPs to the WMN) and in the downlink (from WMN to the TPs) since radio resources are shared in the two directions. Constraints (5) impose that the total flow on the link between device j and l does not exceed the capacity of the link itself (ujl ). Constraints (6) impose for all the MCs’ traffic serviced by a network device (MAP or MR) not to exceed the capacity of the wireless link used for the access, whilst constraints (7) forces the flow between device j and the wired backbone to zero if device j is not a MAP. The parameter M is used to limit the capacity of the installed MAP. Constraints (8) and (9) defines the existence of a wireless link between CS j and CS l, depending on the installation of nodes in j and l and wireless connectivity parameters bjl . The constraints expressed by (10) force the assignment of a TP to the best CS in which a MAP or MR is installed according to a proper sorting criteria (such as the received signal strength), whilst constraints (11) defines the decision variables of the model to assume binary values only. Obviously, the above model is NP-hard since it includes the set covering and the multi-commodity flow problems as special cases. The model defined above considers fixed transmission rates for both the wireless access interface and for the wireless distribution system, and it will be referred to as Fixed Rate Model (FRM) throughout the paper. The FRM can be easily extended to endorse transmission rate adaptation. As to the wireless distribution system, the rate adaptation can be accounted directly in the variables ujl . Rate adaptation in the wireless access network can be accounted in the model with a slight modifications of constraints (6). We consider several concentric regions centered in each CS, assigning to each region a maximum rate value. All the TPs falling in one of these regions can communicate with the node in the CS using the specific rate of the region. Formally, we can define the set of regions for a given CS j Rjk = 1, ..., K and the set Ijk ⊂ I containing all the TPs falling in region k of CS j. Such sets can be determined for each CS j using the incidence variable akij which is equal to

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1 if TP i falls within region k of the CS j and to zero otherwise. Each of these regions of a given CS j is assigned a maximum capacity defined by variable vjk . Using such definitions, the FRM can be extended to the case of rate adaptation in the wireless access part of the network by substituting the constraints (6) with the following new constraints:   i∈Ijk di xij ≤ 1 ∀j ∈ S (12) vjk k∈R j

The new defined model with constraints (12) will be referred to as Rate Adaptation Model (RAM) throughout the paper. Both the FRM and the RAM do not consider the effect of the interference on the access capacity and on the capacity of wireless links connecting mesh nodes. However, in practical cases we must take into account interference effect. We focus here on the case of IEEE 802.11 and assume that all MAPs and MRs share the same radio channel for the access part and use another shared channel for the backbone links. Since now the access capacity is shared by all mesh nodes, we can take into account interference quite easily modifying constraints (12) and considering not only the TPs assigned to the node in CS j but all TPs in the coverage range:   i∈Ijk di ≤ 1 ∀j ∈ S (13) zj vjk k∈R j

The interference limiting effect on the wireless link capacities is more difficult to account for, since it depends on the network topology and the multiple access protocol. Considering the protocol interference model proposed in [14], we can define sets of links that cannot be active simultaneously. These sets depend on the specific multiple access protocol considered. In the case of CSMA/CA, adopted by IEEE 802.11, each set Cjl considers a link (j, l) and includes all links that are one and two hops away in the mesh-network graph (links connecting j and l to their neighbors and their neighbors to the neighbors of their neighbors). To each set we can associate a constraint on the flows crossing its links: yjl

 (k,h)∈Cjl

fkh ≤1 ukh

∀j, l ∈ S

(14)

Obviously, by describing the capacity limitation due to set Cjl with the constraint on flows crossing its links, we make an approximation since we pass from a discrete model to a fluidic one [12]. The effect of this approximation and the one due to traffic dynamics can be accounted by properly reducing capacity values ukh . We estimated through simulation of IEEE 802.11 multi-hop networks that a reduction of 5% is sufficient to achieve consistent results. Replacing constraints (5) with (14) and (6) with (13) we get a new model referred to in the paper as Interference Aware Model (IAM). Note that the nonlinear constraints (14) can be easily linearized.

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As a matter of fact, FRMT/RAM and IAM can be considered extreme cases with respect to interference, corresponding respectively to those scenarios where enough channels and radio interfaces are available in the mesh nodes so that interference can be neglected, and to those scenarios where only one channel is available for the mesh backbone. In all the other intermediate scenarios, channel assignment to mesh nodes must be included in the optimization model. The extension of the model to the multiple channels case is quite straightforward and, due to length constraints, here we just outline the modifications needed. Let us assume that a set F of Q channels is available and that each mesh node is equipped with B radio cards. New installation variables zjq (q ∈ F, j ∈ S) must be considered which are equal to one if a mesh node is installed in j and q are extended is assigned channel q, and to zero otherwise. Also link variables yjl q . In the to include the channel used by link (j, l), as well as flow variables fjl objective function we replace variable zj with new variable tj which is equal to 1 if a mesh node is installed in j. Constraints are easily modified to include the new dimension related to channels. In particular, constraints (3)-(5) are replaced with the following:  q zj aij ∀i ∈ I, ∀j ∈ S (15) xij ≤ q∈F



di xij +

i∈I

   q q flj − fjl − fjN = 0

∀j ∈ S

(16)

l∈S,q∈F q yjl

 (k,h)∈Cjl

q fkh ≤1 ukh

∀j, l ∈ S, ∀q ∈ F.

(17)

To limit to B the maximum number of channels assigned to a mesh node a new set of constraints must be added:  q zj ≤ B ∀j ∈ S, (18) q∈F

and to define new variables tj we add: tj ≤ zjq ∀j ∈ S, ∀q ∈ F.

(19)

The new model will be referred to as Multi-Channel Model (MCM).

3

Numerical Results

In this section we test the sensitivity of the models proposed in previous section to different parameters like the number of candidate sites, the traffic demands from the MCs and the installation costs. To this end, we have implemented a generator of WMN topologies which considers specific parameter settings and makes some assumptions on propagation and device features. Obviously, these assumptions do not affect the proposed model which is general and can be applied to any problem instance. The generator considers a square area with edge L = 1000 m,

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and it randomly draws the position of m CSs and of n = 100 TPs. The coverage area of a mesh node is assumed to be a circular coverage region with radius RA = 100 m. Only feasible instances where each TP is covered by at least one CS are considered. The wireless range of wireless backbone links is RB = 250 m, while the capacities of the access links,vj and backbone links, ujl , are both set to 54 Mb/s for all j and l. The capacity of links connecting MAPs to the wired network is M = 128 Mb/s, while the ratio between the cost of a MR and a MAP is β (β = 1/10 unless otherwise specified). All the results commented hereafter are the optimal solutions of the considered instances obtained formalizing the model in AMPL [16] and solving it with CPLEX [15] using workstations equipped with a AMD Athlon (TM) processor with CPUs operating at 1.2GHz, and with 1024Mbyte of RAM. 3.1

Fixed Rate Model

Once assigned the number and the positions of either CS and TP, the quality of the deployed WMN and consequently the overall installation cost depends on two parameters: the traffic demand d of the MCs and the ratio between the MR and MAP installation costs β. In this section we analyze the sensitivity of the proposed model to these parameters.

(a) d=600Kb/s

(b) d=3Mb/s

Fig. 2. Sample WMNs planned by the FRM with increasing traffic demands of the MCs and finite capacity of the installed MAPs (M = 128M b/s). Standard setting of the topological parameters.

Effect of the Traffic Demands. Figure 2 reports an example of the planned networks when applying the FRM to the same instance with two different requirements on the end user traffic, d = 600Kb/s and d = 3M b/s for all MCs. Table 1 analyze the characteristics of the solutions of the FRM when varying the number of candidate sites. The results presented are obtained averaging each point on 10 instances of network topology. For each couple (m, d) the tables

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Table 1. Solutions provided by the FRM d=600Kb/s

d=3Mb/s

MAP MR Links Time (s) MAP MR Links Time (s) m=30 2.25 23.75 21.50

0.4

23.65 20.20

0.63

m=40 1.45

22.55

1.43

3.40 23.75 21.00

10.93

m=50 1.25 24.15 22.9

4.69

3.25 23.95 21.55

32.88

24

4

report the number of installed MRs, the number of installed MAPs, the number of wireless links of the WDS and the processing time to get the optimal solution. Two main results come from the observation of the table: first, the very same effect of traffic increase observed in Figure 2 is evident also on averaged results, in fact the number of installed MAPs increases when increasing the traffic demands. Second, for a given traffic value, increasing the number of CS to 50 augments the probability for a MC to be connected to a MAP through a multi hop wireless path, therefore the model tends to install less MAPs and more MRs. On the other side, if the number of CS is lower (30), the model installs more MAPs since not all the MCs can be connected to the installed MAPs through multi hop wireless paths. In other words, with high m the solution space is bigger and the model favors those solutions providing connectivity which have a lower impact on the network cost, i.e., those installing more MRs than MAPs. Effect of the Cost Parameter. The number of installed MAPs and MRs intuitively depends on the installation cost ratio between a simple wireless router and a mesh access point. Table 2. Solutions provided by the FRM when varying the installation cost ratio β. Number of CS m = 30. d=600Kb/s β

d=3Mb/s

MAP MR Links MAP MR Links

1/10 2.10 23.40 21.30

3.80 23.30 20.20

1/7

2.40 24.00 21.60

4.10 23.80 20.10

1/5

2.40 24.30 22.70

4.10 23.80 20.10

1/3

2.40 24.30 21.60

4.10 23.80 20.10

1/2

2.80 23.60 20.80

4.20 23.60 19.60

Table 2 reports the characteristics of the solutions when varying the parameter β for different values of the offered traffic d. The results reported in the table shows that if the cost for installing a MAP decreases with respect to the cost of MRs, the proposed model tends to install more MAPs. However, the differences observed with different cost ratios are much smaller than what one could expect.

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d=600Kb/s

MAP MR Links Time (s) MAP MR Links Time (s) m=30 2.80 23.80 21.00

2.24

2.80 25.40 22.60

2.24

m=40 1.70 24.60 22.90

9.66

1.70 26.00 24.30

13.25

m=50 1.20 24.40 23.20

46.83

1.20 26.10 24.90

61.37

The reason is that in the considered scenario the optimization process is driven mainly by the capacity constraints. We obtained results (not shown here) with a bigger difference in the number of installed MAPs by letting higher capacity for the links connecting MAPs (setting high values to parameter M ). 3.2

Rate Adaptation Model

In real wireless networks, the capacity of a given wireless link depends on the distance between transmitter and receiver. The RAM endorses this fact by defining three capacity regions around a MR (and MAP) and assigning the link between MC and MR (or MAP) an increasing capacity when getting nearer to the MR (or MAP) location. The rate values vik = v k ∀j adopted to obtain numerical results emulates IEEE 802.11g transmission and depends on the distance r: 0m ≤ r ≤ 30m v k = 36M b/s, 30m < r ≤ 60m v k = 18M b/s, 60m < r ≤ 100m v k = 2M b/s. The behavior of the RAM is similar to the one of the FRM in terms of sensitivity to the model parameters. Table 3 summarizes the characteristics of the solutions of the RAM when varying the number of candidate sites. The traffic offered by the MCs is lower with respect to the one used to test the FRM since the link rates are in the average lower then those in the FTR case. As a result, we observed that higher values of d may lead to unfeasible instances. The results obtained highlight a behavior of the RAM very similar to the one already observed for the FRM in the same configuration. 3.3

Interference Aware Model

The IAM considers the impact of the interference on the access capacity, through constraints (14), and the capacities wireless links, through constraints (13). Table 4 reports the results obtained with the IAM for the special case in which rate adaptation is not included. The parameter settings are the same adopted for the FRM and therefore results in Table 4 can be directly compared with those reported in Table 1. Sets Cjl have been obtained considering the IEEE 802.11 multiple access scheme. We observe that the number of MAPs installed is remarkably higher and the number of links lower with respect to the FRM case. This is due to the capacity reduction of wireless links that favors solutions where MRs and MAPs are interconnected through paths with a small number of hops. In fact, with short paths between MRs and MAPs the effect of interference is weaker. Obviously, short paths

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Table 4. Solutions provided by the IAM without rate adaptation d=600Kb/s

d=3Mb/s

MAP MR Links Time (s) MAP MR Links Time (s) m=30 3.40 22.20 19.40

4.37

8.50 22.00 14.20

4.16

m=40 2.50 23.10 20.90

258.09

7.80 22.50 16.10

53.98

m=50 2.30 23.40 21.60 1,706.63

7.70 23.40 17.80 1,345.32

Table 5. Solutions provided by the MCM without rate adaptation d=600Kb/s

d=3Mb/s

MAP MR Links Time (s) MAP MR Links Time (s) m=30 2.25 23.75 21.60

2.99

4.00 23.65 20.55

9.79

m=40 1.45 24.00 22.60

73.43

3.40 23.75 21.30

141.18

m=50 1.25 24.05 22.85

393.38

3.30 23.65 21.35

673.52

require a higher number of MAPs. Another relevant difference between the FRM and the IAM results is the computation time which is much higher for IAM in most of the cases. As expected this is due to the structure of constraints (13) which involve several flow variables simultaneously. A similar behavior can be observed considering the results (not shown here) obtained with IAM and rate adaptation. 3.4

Multiple Channel Model

The MCM adds to the planning problem the channel assignment to multi-radio devices. Table 5 reports the numerical results obtained by the MCM when considering Q = 11 channels and B = 3 radio interfaces, which are typical values for the IEEE 802.11a technology. We observe that the results are not very different from those obtained with the FRM (see Table 1) in terms of installed MAPs, MRs and links. On the other side, the computation complexity of MCM is much higher than the one of the other models: considering a constraints of four hours on the computation time, we are able to obtain the optimum solution (reported in the table) only in the 80% of the instances. Therefore, the MCM should be adopted when planning multi-channel/multi-radio WMNs only in case the number of available channels and interfaces is very limited. In the other cases the FRM/RAM models can be safely used for planning the network and frequency assignment can be optimized in a second phase [4].

4

Conclusion

In this paper we proposed an optimization model based on mathematical programming whose objective function is the minimization of the overall network

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installation cost while taking into account the coverage of the end users, the wireless connectivity in the wireless distribution system and the management of the traffic flows. Technology dependent issues such as rate adaptation and interference effect have been considered. To test the quality of the solutions provided by the model, we generated synthetic instances of WMNs and solved them to the optimum using AMPL/CPLEX varying several network parameters. The numerical results we gathered show that the model is able to capture the effect on the network configuration of all these parameters, providing a promising framework for the planning of WMNs.

References 1. I.F. Akyildiz, X. Wang, I. Kiyon, A Survey on Wireless Mesh Networks, IEEE Com. Mag., Sept. 2005, Vol. 43, No. 9, pp. 23–30. 2. J. So, N. Vaidya, Multi-Channel MAC for ad hoc Networks: Handling MultiChannel Hidden Terminals using a Single Transceiver, ACM MOBIHOC 2004, pp. 222-233. 3. A. Adya et al., A Multi-Radio Unification Protocol for IEEE 802.11 Wireless Networks, IEEE BROADNETS 2004, pp. 344–354. 4. A.K. Das, H.M.K. Alazemi, R. Vijayakumar, S. Roy, Optimization models for fixed channel assignment in wireless mesh networks with multiple radios, IEEE SECON 2005, pp. 463–474. 5. M. Alicherry, R. Bhatia, L. E. Li, Multi-radio, multi-channel communication: Joint channel assignment and routing for throughput optimization in multi-radio wireless mesh networks, ACM MOBICOM 2005, pp. 58–72. 6. A. Raniwala, K. Gopalan, and T. C. Chiueh, Centralized channel assignment and routing algorithms for multichannel wireless mesh networks, ACM MC2R, Apr. 2004, Vol. 8, No. 2, pp. 50–65. 7. E. Amaldi, A. Capone, F. Malucelli, Planning UMTS Base Station Location: Optimization Models with Power Control and Algorithms, IEEE Trans. on Wireless Comm., Sept. 2003, Vol. 2, No. 5, pp. 939–952. 8. S. Bosio, A. Capone, M. Cesana, Radio Planning of Wireless Local Area Networks, ACM/IEEE Trans. on Net., to appear. 9. E. Amaldi, A. Capone, F. Malucelli, C. Mannino, Optimization Problems and Models for Planning Cellular Networks, in Handbook of Optimization in Telecommunications, Kluver Academic Publishers, 2006. 10. M. Pioro, D. Medhi, Routing, Flow, and Capacity Design in Communication and Computer Networks, Morgan Kaufmann Publishers, 2004. 11. R. Chandra, L. Qiu, K. Jain, M. Mahdian, Optimizing the placement of integration points in multi-hop wireless networks, IEEE ICNP 2004, pp. 271–282. 12. M. Kodialam, T. Nandagopal, Characterizing the Capacity Region in Multi-Radio Multi-Channel Wireless Mesh Networks, ACM Mobicom 2005, pp. 73–87. 13. Y. Bejerano, Efficient integration of multi-hop wireless and wired networks with QoS constraints, ACM MOBICOM 2002, pp. 215–226. 14. P. Gupta and P.R Kumar, The capacity of wireless networks, IEEE Trans. on Inf. Theory, Mar. 2000, Vol. 46, No. 2, pp. 388–404. 15. ILOG CPLEX 8.0 user’s manual, 2002. 16. R. Fourer, D. M. Gay, and B. W. Kernighan, AMPL, A modeling language for mathematical programming, 1993.

Interference-Aware Multicasting in Wireless Mesh Networks Sudheendra Murthy1 , Abhishek Goswami2 , and Arunabha Sen1 1

School of Computing and Informatics, Arizona State University, Tempe, AZ 85287 [email protected],[email protected] 2 Mobile Devices Software, Motorola [email protected]

Abstract. Multicasting is one of the most important applications in Wireless Ad hoc Networks and the currently emerging Wireless Mesh Networks. In such networks, interference due to the shared wireless medium is a prime factor in determining the data rate achievable by a multicast application. In this research work, we present an interferenceaware multicast routing algorithm that takes into account the effects of interference to determine the maximum bandwidth multicast structure. We characterize the problem of computing maximum bandwidth multicast structure as a graph problem of finding minimum degree weakly induced subgraph in a graph subject to the connectivity and interference constraints. We establish the intractability of the problem and provide efficient heuristic that performs close to the optimal in most of the cases. We also present the design of a more practical distributed algorithm. The simulation results demonstrate the benefits of our heuristic over Shortest Path Tree and Minimum Steiner Tree approximation algorithms. Keywords: Wireless Mesh Network, Minimum Interference Multicast, Weakly Induced Connected Subgraph, NP-Complete.

1

Introduction

Research in Wireless Mesh Networks (WMNs) has gained tremendous momentum recently as a result of its commercial deployment in many US cities including Seattle, Philadelphia, Tempe [1]. WMNs are increasingly being used to provide cost effective and reliable Internet connectivity to residents and businesses in these cities. These WMNs consist of a set of wireless routers (access points) to which the end users connect, a set of wireless routers that act as forwarding nodes and a set of gateway routers that provide connectivity to the Internet. Data from the end users is routed in the WMN towards the gateway routers and to the Internet. 

This research was supported in part by ARO grant W911NF-06-1-0354. The information reported here does not reflect the position or the policy of the federal government.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 299–310, 2007. c IFIP International Federation for Information Processing 2007 

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The widespread deployment of WMNs has fueled research work in providing better support to multimedia applications like real-time video transport and Voice over the Internet (VoIP) services. Common to many of these applications is the need for a multicast framework that facilitates efficient distribution of datagrams to a cohort of hosts. Multicasting results in bandwidth savings as compared to multiple unicast sessions. Early efforts in providing multicast support in WMNs ignored to consider the interference effects of the shared wireless medium in which the mesh routers operate. Interference is an important factor that dictates the bandwidth available for the multicast transmission. The mesh routers are usually equipped with IEEE 802.11 a/b/g interfaces. Interference in the context of this paper refers to the bandwidth sharing effect between nodes operating in close range caused due to the CSMA/CA nature of 802.11. In this paper, we present a novel multicast framework that considers the effects of interference and constructs a multicast structure that provides maximum bandwidth to the applications. Our objective is to identify multicast forwarding group nodes whose transmission induces least amount of interference. To the best of our knowledge, this is the first paper that proposes such a framework. The main contributions of this work are as follows. 1. We provide a novel formulation of the interference-aware multicasting problem as a graph problem of finding weakly induced subgraph in a graph representing the mesh routers. 2. We prove the NP-completeness of the problem, thereby establishing its intractable nature. 3. We provide an Integer Linear Program technique of optimally solving this problem. 4. We provide a centralized heuristic algorithm that performs close to the optimal and describe a distributed implementation of the algorithm. We compare the performance of the proposed heuristic with the optimal, shortest path solution and minimum Steiner tree solution. The road map for rest of the paper is as follows. In section 2, we introduce the required notation and give the formal problem definition. The complexity analysis of the problem is provided in section 3. The Integer Linear Program formulation to obtain the optimal solution is provided in section 4. The centralized heuristic and the design of its distributed extension are provided in section 5. Section 6 presents the evaluation of our heuristics. Section 7 reviews the related work in this area, while section 8 concludes the paper.

2 2.1

Problem Formulation Wireless Transmission and Interference Model

We assume uniform transmission range RT and interference range RI for all the routers in the WMN. We represent a WMN by a directed potential communication graph G(V, E) in which the vertices represent the mesh routers. Denote

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7 1

RT 2

7

2 3 4

6

1

RI 2

Fig. 1. A Potential Communication Graph G(V, E) with transmission radius RT

7

2

2 6 3

5

301

4

5

1

6 3 4

Fig. 2. Potential Interference Graph H(V, F ) with interference radius RI =1.5RT

5

Fig. 3. SH , the subgraph weakly induced by the vertex set S = {3, 6, 7} on the graph of figure 2

the Euclidean distance between routers u and v by dist(u, v). Two directed edges {(u, v), (v, u)} ∈ E if dist(u, v) ≤ RT and implies that mesh router u can communicate directly with mesh router v. We assume that each router is equipped with one radio and all routers operate on the same channel. Any of the mesh routers can be the source node in the multicast tree. However in many applications, the multicast source comes from the wired network and hence, the multicast source is a gateway mesh router. An access point router becomes a receiver of the multicast structure if there is at least one end-user connected to the access point who wants to receive this multicast stream. We assume a 802.11 CSMA/CA medium access control scheme1 . Thus, a transmission between two nodes may prevent all nodes within the transmission range of the sender from transmitting due to carrier sensing. We assume that the interference range RI is q × RT with q ≥ 1. We model the co-channel interference of the WMN with the help of a directed potential interference graph. A directed potential interference graph is represented by H(V, F ) in which the vertex set corresponds to the mesh routers. Two directed edges {(u, v), (v, u)} ∈ F if dist(u, v) ≤ RI . A directed edge (u, v) in the potential interference graph implies that the transmission of router u can cause interference at router v. Figure 1 shows a sample potential communication graph with 7 mesh routers. The circles around the routers are drawn with radius RT /2 and hence two intersecting circles implies that the two routers are within the transmission range of each other. Figure 2 shows the corresponding potential interference graph with RI =1.5RT . Since RT ≤ RI , the potential communication graph is always a subgraph of the potential interference graph. This interference model is different from the frequently used conflict graph representation of interference in a WMN. In the conflict graph representation, a vertex vij is in the vertex set of the conflict graph if (i, j) is an edge in the potential communication graph. There exists an edge between two vertices vij and vkl in the conflict graph if min (dist(i, k), dist(i, l), dist(j, k), dist(j, l)) 1

Although we consider the standard 802.11 protocol without RTS/CTS, our techniques can be applied to RTS/CTS 802.11 environments with some minor changes.

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≤ RI . In the context of the problem explored in this paper, our interference model offers some benefits over the conflict graph model. Firstly, our interference model is more intuitive in the sense that it captures the idea of co-channel interference occurring at the receiving nodes in a WMN. Secondly, it can easily be seen that the conflict graph and the potential interference graph can be derived from each other when the potential communication graph is known. Finally, the potential interference graph modeling makes our problem definition more elegant. 2.2

Graph Definitions and Notations

All graphs defined in this paper are directed graphs. We drop the prefix directed and henceforth, a graph implies a directed graph. In this section, we introduce the required graph terminology. – In-degree of a vertex v in a graph G is the number of arcs coming into v and the out-degree of v is the number of arcs going out of v. The in-degree and − + out-degree of a vertex are represented by δG (v) and δG (v) respectively. – The maximum in-degree of a graph G represented by Δ− (G) is the maxi− mum of the in-degrees of its vertices. That is, Δ− (G) = maxv∈V (G) δG (v). + Similarly, the maximum out-degree of a graph G represented by Δ (G) is the maximum of the out-degrees of its vertices. That is, Δ+ (G) = maxv∈V (G) + δG (v). – A vertex u is a neighbor of vertex v if there is a directed edge from v to u. The (closed) neighborhood of vertex v in graph G(V, E) denoted by N [v] is the set that includes v and the neighbors of v. The (closed) neighborhood of a subset S ⊆ V of graph G(V, E) denoted by  N [S] is the set that includes S and the neighbors of S. That is, N [S] = S ∪ v∈S N [v]. – The subgraph weakly induced by vertex set S ⊆ V in graph G(V, E) is defined as the graph with the vertex set N [S] and edge set E ∩ (S × N [S]). In other words, the edge set of the subgraph weakly induced by S consists of all the edges induced by the vertices of S along with the directed edges from set S to its neighbors in the graph. The weakly induced subgraph of graph G on the vertex set S is denoted by S G . An example of the subgraph weakly induced by the set of vertices S = {3, 6, 7} on the potential interference graph of figure 2 is shown in figure 3. Throughout this paper, we use the notation G(V, E) to represent the potential communication graph and H(V, F ) to represent the potential interference graph. 2.3

Problem Definition

The maximum data rate that can be achieved in a multicast structure is limited by the data rate of the bottleneck link. The data rate of the bottleneck link is determined by the amount of interference experienced by the receiver of the link. For instance, a mesh node present in the communication range of 5 transmissions

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experiences more interference and thus provides less throughput compared to a mesh node present in the communication range of 4 transmissions. In an effort towards maximizing the data rate of the multicast structure, we try to select the forwarding group nodes that induce least amount of interference. That is, given the locations of wireless mesh routers, a multicast source node and a set of multicast receivers, our goal is to find the group of forwarding nodes that induce the least interference on the forwarding nodes and the receivers. In terms of the potential communication graph and the potential interference graph, the problem is formally stated as follows. Given a potential communication graph G(V, E), its corresponding interference graph H(V, F ), a multicast source vertex s ∈ V and a set of receiver vertices R ⊆ V \ s, find vertex set S ⊂ V such that 1. the subgraph of G weakly induced by S, i.e., S G has directed paths connecting s to each ri ∈ R and 2. the maximum in-degree of the vertices of the subgraph of H weakly induced by S, i.e., Δ− (S H ) is minimized. We term this problem as the Minimum-Degree Weakly Induced Connected Subgraph (MDWICS) problem. The optimal subset S of the MDWICS problem contains the source vertex and the forwarding group vertices that result in the least interference. In the next section, we prove the hardness of the MDWICS problem.

3

Computational Complexity

To prove the NP-completeness of the MDWICS problem, we provide a polynomial time transformation from Exact Cover by 3 Sets (X3C) problem [2]. Consider the scenario in which the interference and transmission ranges of the transmitters are equal. In this case, the potential communication graph and the potential interference graph have the same set of vertices, edges and thus, can be represented by a single graph, say G(V, E). The decision version of the MDWICS problem is then stated as follows. INSTANCE: Directed graph G = (V, E), vertex s ∈ V designated as source, vertices R ⊆ V \ s designated as the set of receivers and a positive integer K. QUESTION: Is there a subset S ⊂ V such that the subgraph weakly induced by S in G denoted by S G has paths from s to each ri ∈ R and Δ− (S G ) is at most K. Theorem 1. MDWICS problem is NP-complete. Proof. Clearly MDWICS problem is in NP since a nondeterministic algorithm need only guess the vertex set S and check in polynomial time whether the subgraph weakly induced by S in G has paths from s to each ri ∈ R and the maximum in-degree of the subgraph is at most K. Suppose a finite set X = {x1 , x2 , . . . , x3q } and a collection C = {C1 , C2 , . . . , Cm } of 3-element subsets of X make up the instance of X3C. From this

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instance, we will construct an instance of the MDWICS problem using local replacement technique. Corresponding to every element xi ∈ X, 1 ≤ i ≤ 3q and subset Cj ∈ C, 1 ≤ j ≤ m, introduce vertices xi and ci respectively in graph G(V, E). These vertices together with an additional vertex s make up the vertex set of G. The edge set of G consists of two types of edges. The first set includes directed edges (cl , xi ), (cl , xj ) and (cl , xk ) for every subset Cl = {xi , xj , xk }. The second set includes directed edges from s to each ci . The graph constructed using this mechanism has m+3q+1 vertices and 4m edges. Designate vertex s as source and vertices R = ∪3q i=1 xi as the set of receivers. This completes the construction procedure of the proof. Suppose that K = 1. We claim that there exists a S ⊂ V such that S G has paths from s to each xi , 1 ≤ i ≤ 3q and Δ− (S G ) ≤ 1 if and only if the corresponding X3C instance contains an exact cover for X. s c1

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It is easy to verify that if X3C has an exact cover, then the vertices corresponding to the subsets in the X3C solution along with vertex s is a solution to the MDWICS problem. Conversely, suppose S ∈ V such that S G has paths from source s to each of the receivers x,i s and maximum in-degree of S G is at most 1. Consider the set S  = S ∩ {c1 , . . . , cm }. Note that each vertex ci ∈ V , 1 ≤ i ≤ m has exactly 3 outgoing edges. |S  | = q, since otherwise there would be either no path (|S  | < q) or multiple paths (|S  | > q) to some xi thereby violating maximum in-degree of 1. It follows that the subsets corresponding to the vertices in S  form an exact cover to the X3C problem.

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Optimal Solution

In this section, we provide an Integer Linear Program (ILP) formulation [3] to solve the MDWICS problem optimally. Given a potential communication graph G(V, E), potential interference graph H(V, F ), source s ∈ V and receivers R ⊆ V \s, the problem is to find subset S ∈ V that minimizes the maximum in-degree of S H subject to the constraint that S G has paths from s to each ri ∈ R. The ILP finds the weakly induced subgraph that achieves the above mentioned goal. From this subgraph, we can extract the desired node set S by removing leaf nodes, that is, nodes with zero out-degree.

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The indicator variables are defined as follows. xi,j = 1, if edge (i, j) ∈ E is p in the optimal solution, 0 otherwise. Define fi,j =1, if there is a flow from s to p receiver p through link (i, j) ∈ E in the optimal solution, 0 otherwise. fi,j is used to ensure connectivity from s to receiver r. Define yi,j =1 for edge (i, j) ∈ F if node i is the transmitter in the optimal solution, 0 otherwise. yi,j is used to capture the interference caused by i’s transmission on node j. The objective is to minimize D, the maximum in-degree of all the nodes in the interference subgraph. The following set of constraints define the problem accurately. – The degree constraint specifies that the maximum in-degree of the nodes in the optimal solution interference subgraph should be no larger than D. That is, (i,v)∈F yi,v ≤ D, ∀v ∈ V . – The broadcast constraint captures the broadcast characteristics of the wireless medium. When a node transmits, its transmission affects all nodes in its transmission range. This is represented by x(v,i) = x(v,j) , ∀v ∈ V, (v, i) and (v, j) ∈ E, i = j. – The interference constraint models the interference characteristic of the network. For every node in the potential communication graph, if there is a directed communication edge going out, then there is a directed interfering edge going out from the corresponding node in the potential interference graph. This is represented by yv,i ≥ xv,j , ∀(v, i) ∈ F, ∀(v, j) ∈ E. Connectivity from the source node to each receiver is ensured by the following flow conservation constraints. These constraints are similar to the multi-commodity flow constraints [3]. – The total incoming flow into an intermediate node is  equal to the total  p p = (v,j)∈E fv,j , ∀p ∈ outgoing flow from that node. That is, (i,v)∈E fi,v [1, k], ∀v ∈ {V \ {s ∪ R}}. – For each receiver and for each flow, the incoming flow to  outgoing  is equal p flow except for the flow destined for the receiver. (i,r)∈E fi,r = (r,j)∈E p fr,j , ∀p ∈ {R \ r}, ∀r ∈ {R}. – There is zero incoming flow and unit outgoing flow to the source node. That  |R|  p p = 0 and (s,i)∈E fs,i = 1, ∀p ∈ R is, p=1 (i,s)∈E f(i,s) – For each receiver, the outgoing flow from that receiver should be zero, if the  r flow is for the same receiver. (r,j)∈E fr,j = 0, ∀r ∈ R p – The dependence between fi,j and xi,j for edge (i, j) ∈ E is represented by |R| p |R| × xi,j ≥ p=1 fi,j , ∀(i, j) ∈ E

5

Proposed Algorithms

In this section, we first present a centralized heuristic for the MDWICS problem and then describe the design of a distributed version. 5.1

Centralized Heuristic

The greedy heuristic takes as input the potential communication graph G(V, E), the potential interference graph H(V, F ), multicast source vertex s and multicast

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receiver set R. The output of the heuristic is the set of multicast forwarding group nodes. In the algorithm, S G and S H are the weakly induced subgraphs of G and H respectively on vertices S as defined before. The algorithm maintains a feasible solution set S of vertices that contain paths from the source vertex to all the receivers and a set W of vertices that contains the set of visited vertices. Initially, all vertices in the graph are included in set S and set W contains the source vertex (Step 1). The algorithm then selects in each iteration a vertex in S \ s that has the maximum in-degree in the subgraph weakly induced by S in graph H. If there are multiple such vertices, any one of them is selected arbitrarily. This vertex will be a potential candidate for removal since it has the highest in-degree in S H . Step 5 checks if removal of this vertex from the graph G disconnects any of the receivers from s. If not, this vertex and all its incident edges are removed from both G and H. This vertex is added to the set of vertices W visited so far. The algorithm terminates when all vertices in the graph have been visited. Algorithm 1. MDWICS Heuristic Input: potential communication graph G(V, E), potential interference graph H(V, F ), source s ∈ V , receiver set R ⊆ V \ s Output: set S of multicast forwarding group vertices 1: S ← V , W ← {s} 2: while V \ W = ∅ do − (v)|v ∈ S \ s} 3: v ← arg max{δS H  4: S ←S\v 5: if ∃ directed paths from s to each ri ∈ R in the graph S  G then 6: S ← S 7: Remove vertex v and all its incident edges from both graph G and H 8: end if 9: W ← W ∪ {v} 10: end while 11: return S \ s

5.2

Distributed Protocol

The design of the distributed interference-aware multicast protocol is based on the Optimized Link State Routing (OLSR) protocol [4]. OLSR could be used in mesh networks to maintain the state and quality of the links. The distributed protocol takes full benefit of the topology knowledge obtained by the OLSR protocol with its Topology Control (TC) messages. The TC messages are efficiently dispersed in the network through multipoint relays (MPRs). Setting the parameter TC REDUNDANCY=2 at each node in the OLSR protocol ensures that each node gets information about every other node, link in the network (Section 15.1 [4]). Each node in the network is uniquely identified by its IP address. The source initiating the multicast session generates a unique ID for the session based on its IP address and a sequence number for the multicast group.

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The source with its topology information independently computes the set of multicast forwarding group nodes. It then disseminates through the MPRs, a MC FG message consisting of the IP addresses of the forwarding group nodes and a MC JOIN message to the multicast group members. A node upon receiving a MC FG message checks if its IP address is listed in the message. If so, it records the session ID in a forwarding group (FG) table. Only the first MC FG message received by a node is broadcasted with subsequent MC FG messages being discarded. The MC JOIN message is to inform the multicast group members about the initiation of the session. The session ID is included in every packet forwarded by the multicast source. If a node receiving this packet has the session ID listed in its FG table, it forwards the packet. Evaluation of this distributed version is the focus of current ongoing work.

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Simulation Environment, Results and Discussion

We conducted extensive experiments to evaluate our centralized heuristic. We compared the performance of our heuristic with the Shortest Path Tree (SPT) algorithm, Minimum Steiner Tree (MST) approximation algorithm [5] and the optimal solution obtained by solving the ILP formulation given in section 4. The Shortest Path Tree algorithm finds the set of edges connecting s to each receiver such that the length of the shortest path (measured as the number of hops) from s to each receiver is minimized. The MST algorithm presented in [5] is a O(log 2 k)-approximation algorithm (k is the number of receivers) for the Minimum Steiner Tree problem. We used Cplex 10 to solve the ILPs for the optimal solution. In all our experiments, the number of mesh routers in the network was fixed to be 70. The area for the deployment of the mesh nodes was a square area whose sides were computed based on the required node-density. For instance, for a required node-density of 100 nodes/km2 , the locations of the 70 nodes were randomly generated in a square of area of 0.7 km2 . The locations of the 70 nodes were generated randomly. The transmission radius of the mesh routers was fixed at 100m. The multicast source and the multicast receivers were selected randomly. The simulation scenarios were designed to measure the impact of three parameters namely, the number of receivers, the density of the network (nodes per unit-area) and the ratio of interference radius to transmission radius (RI /RT ) on the interference-degree of the multicasting structure produced by the four approaches. The interference-degree is the maximum number of forwarding group mesh nodes that affect any node in the multicast structure. As discussed before, this determines the maximum achievable data rate for the multicast structure. In the first set of experiments (figure 5), the number of receivers was fixed at 28 and the node-density at 100 nodes/km2 . RI was increased from 100m to 250m in steps of 50m resulting in RI /RT ratio to vary between 1 and 2.5. It is to be noted from figure 5 that as the RI /RT ratio, in turn the interference range of the mesh routers increase, the interference-degree increases rapidly. This is

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intuitive since an increase in the interference range introduces more interference on the mesh nodes. For all values of RI /RT ratio, our heuristic performs better than the SPT and MST algorithms.

Fig. 5. Interference-degree vs RI /RT ratio with # Receivers=28 and nodedensity=100 nodes/km2

Fig. 6. Interference-degree vs # receivers with RI /RT =2 and nodedensity=100 nodes/km2

In the second set of experiments (figures 6, 7), we fixed the RI /RT ratio at 2. For node densities 100 nodes/km2 and 200 nodes/km2 , we plotted the variation of the interference-degree with the number of receivers. It can be observed that as the number of receivers increases, the interference-degree for each algorithm increases. However, the interference-degree of the heuristic stays closer to the optimal compared with the SPT and MST algorithms.

Fig. 7. Interference-degree vs # re- Fig. 8. Interference-degree vs nodeceivers with RI /RT =2 and node- density with RI /RT =1 and # density=200 nodes/km2 receivers=28

In the next experiments (figure 8), we studied the effect of varying density on the interference-degree. The number of receivers was set at 28 and the RI /RT

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ratio at 1.5. The results may seem to be counter-intuitive at first glance, since increased density must lead to increased interference-degree and the first two points in the graph do not confirm this observation. However for each value of node-density, the area in which the nodes are deployed changes and thus, the locations of the nodes are recomputed for each node-density resulting in different communication and interference topologies. In all the experiments, the performance of our heuristic matches closely with that of the optimal. The performance SPT and MST algorithms in most of the cases are far from optimal. This is natural since these algorithms prefer shorter paths from source to the receivers and do not pay attention to the interference caused by these paths on the network nodes.

7

Related Work

Most of the related work in this area focusses on efficient network layer multicast and broadcast in multihop wireless networks and MANETs. Multicast schemes can be classified as either source-based or mesh-based. Source-based protocols construct shortest paths from source to the receivers. AMRIS, MAODV and MOLSR [6] are the well known source-based protocols. The mesh-based protocols consider multiple paths from source to the receivers. Examples of meshbased protocols are ODMRP, CAMP and FGMP [7]. In the presence of mobility, mesh-based protocols are advantageous as they maintain alternate paths for each receiver. Another scheme [8] based on on-demand routing and genetic algorithm executes faster than the conventional multicast schemes. Moreover, 802.11 QoS issues studied in [9] reveal that the design framework of multicast protocols should share flow characteristics across multiple layers and cooperate to meet the application’s requirement. Study done in [10] states that in multicasting there is no one size fits all protocol that can optimally serve the need of all types of application. Recently, a joint optimization approach in [11] emphasizes network coding technique for multicast routing and game theory approach for interference management. The closest work to ours is [12] in which the problem of computing multicast trees with minimal bandwidth consumption in mesh networks is considered. The authors show that this NP-complete problem is equivalent to minimizing the number of multicast transmissions rather than the edge cost or the total number of edges of the multicast tree. On the contrary, a tree with minimum number of transmissions may not provide minimum interference or maximum bandwidth.

8

Conclusion and Future work

In this paper, we categorized the interference-aware multicasting problem as a graph problem of finding a weakly induced subgraph of minimum degree. For this purpose, we introduced a new model of interference called the potential interference graph. We analyzed the intractable nature of this problem and presented an efficient greedy-based heuristic algorithm. We also presented a distributed

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extension of our heuristic. The simulation results provide substantial evidence of the superior performance of our heuristic compared to the Shortest Path Tree and Minimum Steiner Tree approximation algorithms. Future work in this area lies in analyzing the performance of the distributed algorithm and providing approximation bounds to the centralized algorithm.

References 1. Online: City-wide wi-fi projects. (http://www.seattlewireless.net, http:// www.wirelessphiladelphia.org, http://www.waztempe.com) 2. Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman Press (1979) 3. Bazaraa, M., Jarvis, J., Sherali, H.: Linear Programming and Networks Flows. John Wiley & Sons (2004) 4. Online: Rfc 3626 - optimized link state routing protocol (olsr). (http://www. ietf.org/rfc/rfc3626.txt) 5. Charikar, M., Chekuri, C., yat Cheung, T., Dai, Z., Goel, A., Guha, S.: Approximation algorithms for directed steiner problems. In: ACM-SIAM Symposium on Discrete Algorithms (SODA). (1998) 6. Kunz, T., Cheng, E.: Multicasting in ad-hoc networks: Comparing maodv and odmrp. In: International Conference on Distributed Computing Systems (ICDCS). (2002) 7. Madruga, E., Garcia-Luna-Aceves, J.: Multicasting along meshes in ad-hoc networks. In: International Conference on Communications. (1999) 8. Banerjee, N., Das, S.: Modern: Multicast on-demand qos-based routing in wireless networks. In: Vehicular Technology Conference. (2001) 9. Zhu, H., Li, M., Chlamtac, I., Prabhakaran, B.: A survey of quality of service in ieee 802.11 networks. IEEE Wireless Communications 11 (2004) 10. Gossain, H., Cordeiro, C., Agrawal, D.: Multicast: wired to wireless. IEEE Communications Magazine 40 (2002) 116–123 11. Yuan, J., Li, Z., Yu, W., Li, B.: Cross-layer optimization framework for multihop multicast in wireless mesh networks. IEEE Journal on Selected Areas in Communication 24 (2006) 12. Ruiz, P., Gomez-Skarmeta, A.: Heuristic algorithms for minimum bandwith consumption multicast routing in wireless mesh networks. In: ADHOC-NOW. (2005)

Characterizing the Capacity Gain of Stream Control Scheduling in MIMO Wireless Mesh Networks Yue Wang1 , Dah Ming Chiu2 , and John C.S. Lui1 1

Dept. of Computer Science & Engineering, The Chinese University of Hong Kong {ywang,cslui}@cse.cuhk.edu.hk 2 Dept. of Information Engineering, The Chinese University of Hong Kong [email protected]

Abstract. Stream control has recently attracted attentions in the research of MIMO wireless networks as a potential way to improve network capacity. However, inappropriate use of stream control may significantly degrade network capacity as well. In this paper, we provide the first formal study on stream control scheduling in MIMO wireless mesh networks. We derive the theoretical upper bound on network capacity gain of stream control scheduling. We also provide an efficient scheduling algorithm and show that its achieved network capacity gain is close to its theoretical upper bound. Moreover, we point out the poor performance of a previous stream control scheduling algorithm SCMA under the general settings of wireless mesh networks. This formal characterization provides a deeper understanding of steam control scheduling in MIMO wireless mesh networks. Keywords: MIMO, stream control, network capacity, scheduling.

1 Introduction Multiple-input multiple-output (MIMO) technology is regarded as one of the most significant breakthroughs in recent wireless communication[1]. By exploiting multi-paths in indoor or outdoor environment, MIMO is able to provide very high data rate by simultaneously transmitting multiple independent data streams on the same wireless channel. This kind of simultaneous transmissions, referred to as spatial multiplexing, is very desirable to wireless mesh networks (WMNs) where network capacity is the main concern.

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Fig. 1. MIMO Wireless nodes with three antennas

Generally speaking, MIMO can be represented as a multiple transmitters and multiple receivers system. As illustrated in Fig. 1, there are three antennas for each wireless I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 311–321, 2007. c IFIP International Federation for Information Processing 2007 

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link 1

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node. At the transmitting wireless node, a primary bit stream is splitted into three individual bit streams, which are then encoded and transmitted by the three antennas. The signals are mixed naturally in the wireless channel. At the receiver end, these three bit streams are separated and decoded, and then combined into the original primary bit stream. An analogy of the stream separation is similar to solving three unknowns in a group of three linear equations. In general, each MIMO node has K ≥ 1 antennas. Link capacity grows linearly with K since there are K independent streams. This link capacity gain is referred to as spatial multiplexing gain.1 Roughly speaking, for the receiver to successfully separate and decode incoming streams, the following conditions must be satisfied: (a) the number of successfully decoded streams is not greater than K and (b) the strength of additional streams (treated as noise by the receiver) is far weaker than that of the successfully decoded streams. Otherwise, it is not possible to decode any stream[3]. When a wireless link simultaneously transmits 0 or K streams on a channel, we call it Non-Stream Control (NSC) scheduling. Alternatively, one can use Stream Control (SC) scheduling to improve the capacity of MIMO systems. Under SC scheduling, a wireless link can simultaneously transmit k streams along a channel, where 0 ≤ k ≤ K. SC scheduling provides more flexibility than does NSC scheduling since it can choose an appropriate number of streams so as to maximize network capacity. Recent studies [4][5][6] show that SC can be applied to multiple interfering links to improve network capacity.

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An example to illustrate the improvement in network capacity by SC is shown in Fig. 2. There are four MIMO nodes with two flows of streams, one from node A to node B along link 1, and the other from node C to node D along link 2. The two links 1

Another important feature of MIMO is that it has diversity gain that provides an increase in SNR at the receiver by partially or fully redundant K streams. However, it was shown in [3] that we often achieve higher throughput by exploiting spatial multiplexing instead of diversity. For the rest of this paper, we only focus on the scheduling based on spatial multiplexing.

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are placed close to each other and thus get mutual interference. Assume there are four antennas on each node (K = 4). Under NSC scheduling, each wireless node transmits 4 streams at an aggregate rate of 100 Kbps. The maximum network capacity is 100 Kbps when the two links transmit in an alternate manner (Fig. 2(b)). Under SC scheduling, however, each link can choose to transmit k streams where k ∈ {0, 1, 2, 3, 4}. Specifically in MIMO, these streams have different transmission rate [2]. For example, the ratio of transmission rate for the 4 streams is 1.0 : 0.8 : 0.7 : 0.5. Each link can choose the two best streams to transmit with an aggregate rate of 1.0+0.8 1.0+0.8+0.7+0.5 · 100 = 60 Kbps (this is called stream selection), achieving a network capacity of 120 Kbps (Fig. 2(c)).

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Previous work [4][5][6] showed that MIMO with stream control can increase the network capacity by 20 − 65% for a set of mutually interfering links. However, it was noted in [3] that when stream control is not applied judiciously, one may encounter the problem of receiver overloading that will significantly degrade the overall network capacity. We illustrate this problem in Fig. 3. Here, each wireless node has K = 4 antennas. Link 1 interferes with link 2, 3 and 4 while the last three links do not interfere with each other. If link 1 transmits 4 streams in one time slot and the other three links each transmit 4 streams in the next time slot, it will result in an average of 8 streams per time slot. If all four links use SC, then each can only transmit one stream (upper bounded by 4 streams for link 1). In this case, only 4 streams per time slot can be achieved, which has a poorer performance than NSC scheduling. The reason is that the use of SC for link 1 suppresses full transmissions of the other three links. Sundaresan et al. proposed a heuristic algorithm called SCMA (Stream-Controlled Multiple Access) for using SC scheduling in multihop wireless networks [3]. The algorithm states that SC is used for multiple interfering links only when they belong to a single maximal clique (more discussions in later sections). In other cases, NSC scheduling should be used. SCMA can work well for dense link interference graph where links likely belong to a single maximal clique. In this paper, we provide the first formal characterization on the capacity gain of SC from the angle of scheduling. In particular, we

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address the fundamental question that how much network capacity gain can be expected from SC scheduling. In order to answer these questions, in Section 2, we derive the theoretical upper bound on the network capacity gain of SC scheduling to NSC scheduling. In Section 3, we present a greedy scheduling algorithm when NSC or SC is used. In Section 4, we discuss the simulation results and compare the network capacity of NSC and SC scheduling as well as that of SCMA. Finally, Section 5 discusses the limitations of our work and concludes the paper.

2 Upper Bound on Capacity Gain of Stream Control Scheduling In this section, we first define the terminologies used in wireless networks, then we propose a simplified model for MIMO WMNs and derive the theoretical upper bound on the network capacity gain of SC scheduling. We consider a multihop wireless network with n static nodes. Each node has multiple omnidirectional antennas that operate on one channel. We model it as a directed graph G = (V, E) where V represents the set of wireless nodes and E represents the set of wireless links. Each wireless node has the same transmission power and receiving sensitivity, resulting in the same maximum transmission range by a certain path loss model (e.g. two-ray ground model). Therefore, two nodes can form a link when the distance between them is not greater than the maximum transmission range. We use the range-based interference model[7] that is shown to be realistic by [8]. Let L1,2 (L3,4 ) be the link between node 1 and 2 (node 3 and 4). Let dij denote the distance between node i and node j. L1,2 is interfered by L3,4 when node 1 or node 2 is interfered by node 3 or node 4, precisely, min(d1,3 , d1,4 , d2,3 , d2,4 ) ≤ S · d1,2 . Here we call S the interference range factor that is determined by the SNR threshold for successful receiving and the underlying path loss model. Note that the above reception and interference models are actually implemented in ns-2 simulator.

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To represent the interference between links, we introduce the link interference graph GI = (V I , E I ) , also known as flow contention graph [9]. we rename it here to avoid confusion with end-to-end flows. V I represents the set of wireless links and E I the set of interfering link pairs. That is, if link i is interfered by link j or vice versa, we add two edges (i, j) and (j, i) into E I specifying that i and j can not transmit at the same

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time. Fig. 4 shows the link interference graphs for the networks in Fig. 2 and Fig. 3, respectively. To derive the upper bound on network capacity, we assume that the system operates in a synchronous time-slotted mode. Links transmit at the beginning of each time slot in a TDMA mode. They can transmit at the same time, provided that they do not interfere with each other. Besides, we do not consider packet losses caused by fading. This is a reasonable assumption since MIMO can significantly mitigate fading losses. Next, we assume that there are K > 1 antennas on each wireless node, and thus a link can simultaneously transmit at most K independent data streams. We introduce the stream control gain g, a factor for all wireless links. The total capacity of multiple mutually interfering links using SC is at most g times that using NSC. 1 < g < 2 because g ≥ 2 will violate the physical constraint (i.e. multiple interfering links can not transmit simultaneously at their full rate). Finally, we define network capacity gain using SC. Assume that the traffic pattern (i.e., the ratio of average link rate) is fixed. Let CN SC and CSC be the respective network capacity for NSC and SC scheduling. We have CSC = GSC · CN SC , where GSC is defined as the network capacity gain of SC scheduling. We now present the interference constraint in MIMO wireless networks. Without the loss of generality, we normalize the capacity of a MIMO link to 1 and define the link transmission rate as a fraction of 1. Let y(t) be the link transmission rate vector at time t (i.e., ye (t) is the transmission rate of link e), and N (e) the set of neighbors (i.e., interfering links) of e. If e transmits at time slot t, the sum of transmission rate in N (e) ∪ e must be less than or equal to g, otherwise, receivers can not decode the mixed signals correctly. Formally, we define the MIMO interference constraint as, ⎛ ⎞  1ye (t)>0 · ⎝ye (t) + ye (t)⎠ ≤ g, ∀e ∈ E and t ≥ 0. (1) e ∈N (e)

The function 1 is an indicator function, returning 1 when ye (t) > 0, and returning 0 otherwise. For NSC scheduling, g = 1 and ye (t) is 0 or 1. For SC scheduling, 1 < g < 2 and ye (t) is a real number between 0 and 1 since a MIMO link can properly choose the number of streams and their transmission rate. Thus, NSC scheduling can be seen as a special case of SC scheduling. We now state and prove the fundamental theorem of SC scheduling. Theorem 1. Assume that the traffic pattern (i.e., the ratio of average link rate) is fixed, the network capacity of SC scheduling is at most g times that of NSC scheduling. Proof: We prove it by two steps. First, we define the SC scheduling SC(1) when g = 1, and prove that the capacity of SC(1) is equal to that of NSC. Second, we prove that the capacity of SC scheduling is at most g times that of SC(1). Step 1: We only need to prove that the capacity of NSC scheduling is greater than or equal to that of SC(1) scheduling, since the capacity of SC(1) scheduling is at least equal to that of NSC scheduling. As we are interested in the average link rate, it is sufficient to prove that for any feasible link transmission rate vector y SC of SC(1) at some time slot, there is a sequence of link transmission rate vectors of NSC {yN SC (t)|t = 1, 2, ..., T }

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for T time slots, whose average is greater than or equal to y SC . In other words, we scale y SC by a large number of time slots T to make it an integer workload vector u = y SC · T (real number can be approximated by rational), and prove that u can be scheduled in T time slots using NSC. We prove this by induction for the number of links m in the network. When m = 1 (u and y SC are scalars), we can easily schedule u in T since y SC ≤ 1. Assume that we can schedule u for m links in T . We consider adding the (m + 1)th active link e. The neighbors of e consume at most e ∈N (e) ue  time slots, and we can use the remaining (T − e ∈N (e) ue ) time slots (which is greater than ue by Eq. (1) for g = 1) to schedule ue . So, we can schedule u for m+1 links in T . Step 2: We prove it by contradiction. Assume the capacity of SC is greater than g times that of SC(1), then we can get the capacity of SC (1) by dividing all link transmission rate by g, which is greater than itself. Remark: SC scheduling can not provide greater than g times network capacity gain although it provides a finer scheduling scheme. Therefore, if g is of small value, SC is not worthwhile to consider as the computational cost of SC scheduling is higher than that of NSC scheduling. Besides, the upper bound is not always achievable due to the potential problem of receiver overloading.

3 A Stream Control Scheduling Algorithm In the previous section, we derived the theoretical upper bound of network capacity gain of SC scheduling. However, this upper bound may not be achievable. In this section, we present a centralized SC scheduling algorithm and obtain the capacity gain in a practical way. It is important to point out that this algorithm provides a lower bound on the achievable network capacity. Given a wireless network G and a vector of link traffic workloads u (ue is the workload on link e), the scheduling algorithm will calculate the number of time slots needed to transmit all workload in u. Let T N SC and T SC be the number of time slots expended for NSC and SC scheduling respectively. So the network capacity of NSC and SC scheduling is:  ue CN SC = e∈E , (2) N SC T ue CSC = e∈E , (3) SC T and the network capacity gain of SC scheduling is, GSC =

CSC CN SC

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To schedule u, we design a greedy scheduling algorithm GreedySC. At each time slot, the algorithm finds schedulable links of maximal workloads greedily so as to minimize the total number of time slots used. Fig. 5 depicts the algorithm. The inputs to this algorithm are the link interference graph GI , the workload vector u and stream control

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GreedySC(GI , u, g) Ts := 0; A := E; While A = φ Ts := Ts + 1; y := 0; Sort A in the deceasing order of u; /* Step 1. using NSC*/ For each e ∈ A If ye = 0, ∀e ∈ N (e) ye := 1; ue := ue − 1; If ue ≤ 0 A := A − {e}; fi fi end for /* Step 2. using SC*/ For each e ∈ A and ye = 0 If yN(e) ≤ 1 and (yN(e ) ≤ 1, ∀e ∈ N (e) and ye > 0) ye := g − 1; ue := ue − (g − 1); If ue ≤ 0 A := A − {e}; fi fi end for end while

Fig. 5. Specification of GreedySC Scheduling Algorithm

gain g. Here, Ts is the number of time slots expended, A is the set of uncompleted links, and yN (e) is the sum of transmission rate for links in N (e). Note that GreedySC can also be applied to NSC scheduling by letting g = 1. The algorithm is described as follows. For each time slot, we first sort the links in decreasing order of the remaining workloads. In Step 1, we use the NSC mode to schedule links. That is, we iteratively pick a link e from the sorted list. If no neighboring links of e have been scheduled, we schedule e for transmission (ye := 1). Otherwise, we do not schedule it. Then we move on to the next link on the sorted list until we finish examining all the links. In Step 2, we use the SC mode to schedule links. Again, we iteratively pick a link e from the sorted list that was un-schedulable in step 1. If Eq. (1) can be satisfied for e and all its neighboring links, we schedule e for SC transmission (ye := g − 1). Otherwise, we do not schedule it. Then we move on to the next link on the sorted list until we finish examining all the links. At this point, we decide the scheduling for one time slot. The above process is repeated in the next time slot until all the traffic workload in u is completed.

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The algorithm naturally avoids the problem of receiver overloading as it schedules links using NSC first and thus guarantee the network capacity of SC scheduling is always greater than that of NSC scheduling. Note that, according to the scheduling sequence of links in GreedySC, a SC link e (ye = g − 1) must neighbor some NSC link e (ye = 1). Otherwise, e can be scheduled using NSC. And all neighboring links of e or e can not be scheduled to satisfy Eq. (1). Therefore, for stream selection, the two links can select streams of other rate to achieve ye + ye = g (e.g., ye = ye = g2 ). GreedySC achieves higher network capacity than SCMA. To understand this, let us describe SCMA first. In short, multiple interfering links can be scheduled simultaneously using SC, only when they all belong to a single maximal clique2 . SCMA can not cause receiver overloading, because links using SC do not affect the transmission of other maximal cliques. Besides, SCMA can be implemented distributedly [3]. For example in Fig. 4 (a), link 1 and 2 belong to the single maximal clique {1, 2}, so they can be scheduled simultaneously using SC. In Fig. 4 (b), there are no two interfering links both belonging to a single maximal clique, as link 1 belong to 3 maximal cliques {1, 2}, {1, 3} and {1, 4}. So SCMA can not use SC here. But in fact, links belonging to multiple maximal cliques can also use SC for transmission, which is considered by GreedySC. For example in Fig. 4 (b), GreedySC will schedule link 1 using NSC (y1 = 1) and link 2 using SC (y2 = g − 1). Formally, consider any two links a and b belonging to a single maximal clique. Suppose a is scheduled using NSC in step 1 of GreedySC. It is easy to see that b can be scheduled using SC in step 2 of GreedySC. In this way, GreedySC covers the case of SCMA and thus achieves higher network capacity.

4 Performance Evaluation This section shows the simulation results in general WMNs. We test the capacity gain of SC scheduling for GreedySC and SCMA, and compare it with the theoretical upper bound g. By general WMNs, we mean static multihop wireless networks with the general settings of (1) network density and (2) interference level in current practices. We measure network density by node degree, i.e., the number of links a wireless node has, and measure interference level by the interference range factor S. The node degree in a WMN is typically around 2 to 6, otherwise, there will be too much interference. For example, our simulation shows that a link is interfered by about 16 other links when the average node degree is 3. We vary the SNR threshold for successful receiving from 6 to 10dB [11] depending on the environment and coding scheme. And the resulting interference range factor S varies from 1.4 to 1.8 by the two-ray ground path loss model[10]. Note that higher SNR threshold implies more interfering links. We designed a TDMA simulator to generate WMNs and schedule link workloads. First, we randomly place 25 wireless nodes in an area of 1000m×1000m, with the maximum transmission range of 250m. The average node degree is 4.2. g = 1.5 for all wireless links. We generate 100 flows with sources and destinations randomly selected. 2

Here a maximal clique is a maximal fully connected subgraph of GI . All links of a maximal clique interfere (neighbor) with each other. A link may belong to multiple maximal cliques.

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Each flow has the same traffic demand of 1000. And we map the flow traffic demand x into link traffic workload u by u = Rx, where R is the routing matrix (Rij = 1, if flow j is on link i; Rij = 0, otherwise). We use the shortest path routing for these flows and run the simulation for tens of times to get the average. Fig. 6 shows the network capacity gain of SC scheduling GSC when GreedySC and SCMA are used. SCMA provides little capacity gain because there are few links belonging to a single maximal clique, while GreedySC provides 30−40% improvement on network capacity compared with NSC scheduling. An interesting observation is that there are still a small number of links belonging to single maximal cliques when we increase SNR threshold to make link interference graph denser (introduce more edges in GI ). We found the reason from the simulation traces. The average size of maximal cliques gets larger as the link interference graph becomes denser. As a result, most links are in the overlap of multiple maximal cliques. So we can not expect that many links belong to a single maximal clique until the link interference graph is sufficiently dense (the extreme is one clique). Next, we consider the effect of network density on the network capacity gain of SC scheduling. We keep all parameters of the previous experiment but reduce the area size to 800m×800m to increase the network density. The average node degree now is 6.1. Fig. 7 shows the network capacity gain of SC scheduling. The performance of GreedySC and SCMA is similar to that of 1000m×1000m. The reason is that both the number of links and interfering link pairs increases when the network density increases. As a consequence, the link interference graph is scaled up proportionally. We also did simulation in grid topologies. In this experiment, we place 25 wireless nodes in a 5 × 5 grid. There is a distance of 200m (and 160m later) between two horizontally or vertically neighboring nodes. The other parameters are the same as that in random topologies. SCMA does not work at all here because no links belong to a single maximal clique, while GreedySC provides the network capacity gain of 1.2 to 1.3 (Fig. 8 and Fig. 9). In short, SCMA performs poorly in general WMNs. Finally, simulation shows that the network capacity gain of GreedySC is a linearly increasing function of g and is closer to its theoretical upper bound (g) than SCMA. Fig. 10 shows the result for 25-node random topologies in an area of 1000m×1000m when SIR threshold is 10dB.

Y. Wang, D. Ming Chiu, and J.C.S. Lui 1.6

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5 Conclusion and Future Work In this paper, we characterized the network capacity of SC by formulating it as a mere scheduling problem. We proved that the network capacity of SC scheduling is at most g times that of NSC scheduling, where g is stream control gain. We then proposed an efficient algorithm GreedySC so as to realize NSC and SC scheduling. Intensive simulations showed that the capacity gain achieved by GreedySC is close to its theoretical upper bound. Therefore, we can use GreedySC as a benchmark for other SC algorithms. In particular, we pointed out the poor performance of SCMA under the general settings of WMNs, indicating that there are plenty of rooms for enhancement. There are some limitations in our work. First, our model assumes perfect packet scheduling. Actually, one can easily incorporate packet losses into our theorem and algorithm to get similar results. Second, we use a simplified physical layer model and do not account for time-varying fading channels. It remains an open problem to jointly consider packet scheduling and more detailed physical layer model in MIMO WMNs. Third, GreedySC currently is a centralized algorithm. The distributed implementation requires special supports from physical layer. For example, transmitters need to estimate accurately the number of streams that receivers can accommodate so as to transmit extra

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streams using SC. In sum, there is a lot of future work for researchers both in the area of network layer and physical layer before SC becomes a practical technique in MIMO WMNs.

References 1. D. Gesbert, M. Shafi, D. Shiu, P.J. Smith, and A. Naguib, “From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems,” IEEE J. Selcted Areas in Comm., vol. 21, pp. 281-301, 2003. 2. David Tse and Pramod Visvanath, “Fundamentals of Wireless Communication”, Cambridge University Press, 2005. 3. K. Sundaresan, R. Sivakumar, M.A. Ingram, and T.-Y. Chang, “Medium access control in ad hoc networks with MIMO links: optimization considerations and algorithms,” IEEE Trans. on Mobile Computing, vol. 3, pp. 350 - 365, 2004. 4. M.F. Demirkol and M.A. Ingram, “Control Using Capacity Constraints for Interfering MIMO Links,” Procs., Intl Symp. Personal, Indoor, and Mobile Radio Comm., vol. 3, pp. 1032-1036, 2002. 5. M.F. Demirkol and M.A. Ingram, “Stream Control in Networks with Interfering MIMO Links,” Procs., IEEE WCNC, vol. 1, pp. 343-348, 2003. 6. J.-S. Jiang, M.F. Demirkol, and M.A. Ingram, “Measured Capacities at 5.8 GHz of Indoor MIMO Systems with MIMO Interference,” Procs., IEEE VTC, vol. 1, pp. 388-393, 2003. 7. P. Gupta and P. R. Kumar, “The capacity of wireless networks,” IEEE Trans. on Info. Theory, vol. 46, pp. 388-404, 2000. 8. J. Padhye, S. Agarwal, V. N. Padmanabhan, Lili Qiu, A. Rao and Brian Zill, “Estimation of Link Interference in Static Multi-hop Wireless Networks,” Procs., USENIX IMC, pp. 305310, 2005. 9. H. Luo, S. Lu, V. Bharghavan, “A new model for packet scheduling in multihop wireless networks,” Procs., ACM MOBICOM, pp. 76-86, 2000. 10. K. Xu, M. Gerla, and S. Bae, “How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks,” Procs., IEEE GLOBECOM, vol. 1, pp. 72-76, 2002. 11. A. Kochut, A. Vasan, A. U. Shankar, A. Agrawala, “Sniffing out the correct physical layer capture model in 802.11b,” Procs., IEEE ICNP, pp. 252-261, 2004.

AP and MN-Centric Mobility Prediction: A Comparative Study Based on Wireless Traces Jean-Marc Fran¸cois and Guy Leduc Research Unit in Networking (RUN) Department of Electrical Engineering and Computer Science Institut Montefiore, B28 — Sart-Tilman University of Li`ege 4000 Li`ege, Belgium {francois,leduc}@run.montefiore.ulg.ac.be

Abstract. The mobility prediction problem is defined as guessing a mobile node’s next access point as it moves through a wireless network. Those predictions help take proactive measures in order to guarantee a given quality of service. Prediction agents can be divided into two main categories: agents related to a specific terminal (responsible for anticipating its own movements) and those related to an access point (which predict the next access point of all the mobiles connected through it). This paper aims at comparing those two schemes using real traces of a large WiFi network. Several observations are made, such as the difficulties encountered to get a reliable trace of mobiles motion, the unexpectedly small difference between both methods in terms of accuracy, and the inadequacy of commonly admitted hypotheses (such as the different motion behaviours between the week-end and the rest of the week).

1

Introduction

Wireless networks have experienced spectacular developments those last ten years. They are today facing two major changes: – The number of wireless users grows quickly, and those users always ask for more bandwidth. The current trend is thus to reduce the transmitters’ coverage which in turn increases the rate at which mobile hosts (MHs) switch from an antenna to the next (or handover rate). – Since voice, television, and data networks are now merging, it would be desirable to be able to guarantee various quality of service (QoS) levels. When a mobile terminal moves, one of the main causes of service degradation is switching between the network’s access points (APs): changing one’s current AP requires re-routing the received and sent data flows, a procedure which is likely to cause packet losses and delays. Predicting a MH’s next handover(s) 

This work has been supported by the Belgian Science Policy in the framework of the IAP program (Motion P5/11 project) and by the IST-FET ANA project.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 322–332, 2007. © IFIP International Federation for Information Processing 2007

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allows taking pro-active measures to reduce handovers impact, a problem known as mobility prediction. In the following, we study a wireless WiFi network and aim at predicting each mobile host’s next AP. Mobility prediction methods can be classified into two main families: – MH-centric (MHC): the agent performing predictions is bound to a MH; it builds a model of this particular MH’s movements (e.g. [1,2,3]); – AP-centric (APC): the prediction agent is bound to an AP; this AP builds a model using the motion of the MHs passing by (e.g. [4,5,6]). Those models can be built on-line, as the mobiles move, and can perform a prediction anytime. The pieces of information that allow deducing the likely motion of a mobile terminal are very varied (e.g. GPS coordinates). In what follows, we assume that the piece of information used is the sequence of most recently encountered APs. This assumption is not strong since it only requires that MHs record the last APs they have been associated with. The various prediction methods presented in the literature have rarely been validated using mobility traces extracted from a real network. [7] is a notable exception which shows that simple markovian models perform nearly as well as other, more complex methods. We thus focus on those models in this paper. In the following, we present a comparison between MH and AP-centric prediction methods using the mobility traces of a large-scale WiFi network. It is expected that the conclusions drawn here could be applied to other kinds of mobile networks. The rest of the paper is organised as follows. Section 2 defines the prediction models utilized in the article. Sections 3 and 4 give a description of the traces used, the way they have been processed, and basic performance results. Section 5 compares both types of prediction schemes (MHC and APC). Section 6 concludes.

2

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A next-AP predictor, or prediction agent, is the entity responsible for building a model of MH’s movements; this model can be used for predictive purposes. 2.1

Centralized and Decentralized Methods

In this paper, we study the differences between the two most popular prediction schemes. In the first method, APC, the prediction agents are the APs. Each AP builds a model of the movements of mobiles passing by. The MHs’ involvement is minimal, since they only send during each handover an identification of their previous APs. This architecture is particularly well suited to situations where predictions are mainly useful to the fixed network infrastructure (which could, for example, use it to reserve resources anticipatively).

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The second method, MHC, is more distributed: every MH builds a model using its own movements. It is expected to be more reliable since more specific: the behaviour of a particular mobile cannot be simplified to the mean behaviour of all the MHs moving in the same area. However, this scheme does not fit well the standard wireless network paradigm, where terminals are supposed to be small, memory and processing limited devices (such as a low-end GSM), and not suited to running a learning algorithm. Moreover, no prediction can be made when a MH visits APs for the first time. 2.2

Markovian Models

We model MHs’ motion habits thanks to their location history (or trace), i.e. the sequence of APs crossed during their journey. Considering each AP as a symbol of a (finite) alphabet, a MH’s trace is a sequence of symbols and prediction aims at guessing symbol i + 1 given the first i. Observing MHs’ motion allows a prediction agent to tune the model’s parameters so that prediction improves over time It has been shown ([7,8]) that in this context, simple Markov predictors perform as well as other, more complex methods1 (such as [9,10,11,12]). We thus only consider this class of predictors here. Let L = {L1 , L2 , L3 . . . } be the set of locations and L = L1 , L2 , L3 . . . a location history. The order n markovian hypothesis is: P (Li = l|L1 , . . . , Li−1 ) = P (Li = l|Li−n , . . . , Li−1 )

∀ l ∈ L, i > n

(1)

Less formally, this equation states that the stochastic variable that describes the next-AP probability follows a distribution that only depends on the last n symbols. We assume a stationary distribution2 . The next-AP distribution can easily be learnt on-line. We assume that the agent responsible for building the markovian model is regularly notified of MH(s) movements. If we denote Lm the location history of mobile m, the order-n model estimation rule is:  m m O(Lm i−n , . . . , Li−1 , l ; L ) (2) P (Li = l | Li−n , . . . , Li−1 ) = m∈M m m m m∈M O(Li−n , . . . , Li−1 ; L ) where the O(· ; ·) operator finds the number of occurrences of its first argument in its second, and M is the set of mobiles involved. In the case of MHC, each terminal only models its own motion, thus M is a singleton. When a model is used to perform a prediction, the most probable next AP (given the current context, i.e. the MH’s last n APs) is chosen. No prediction 1

2

To be fair, some of those not only aim at location prediction, but at other purposes such as mobile paging. This hypothesis is confirmed by the results presented in section 5.3.

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can be performed if the context has never been observed before. To limit the consequences of this possibility, we build together with each order-n model, n−1 other models of order n − 1, n − 2, . . . , 1. If a prediction cannot be performed because the current context is seen for the first time, we fallback on a lower-order model. We do not aim at predicting when a mobile will enter or leave the network; only the proper inter-AP movements are taken into account.

3

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The traces used have been collected by Dartmouth University in the context of the CRAWDAD project ([13]). It is a collection of events generated by the WiFi network of the campus. Syslog and SNMP data have been recorded for 2 years and cover 6202 MHs and 575 APs ([14]). This data have been analysed ([8]) to extract the actual movement traces (i.e. for each MH, a sequence of APs). A special AP, denoted OFF, indicates that a mobile has been ‘deauthenticated’ or that it has not generated any activity since at least 30 minutes; we then consider that it has been disconnected. Each MH is identified using its MAC address; we assume that each MAC address matches one (and only one) user. Apparatus with more than one interface and apparatus shared by more than one person are considered rare. In the following, the same data are exploited to perform MH and AP-based predictions. More formally, each time MH m moves, its last n movements m m Lm i−n , . . . , Li−1 and the next AP Li are used to learn the parameters of an order-n markovian model; those “context, next AP” couples are the elements

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Fig. 1. Overview of the learning process for order-2 markovian models. The mobiles’ motion generates location histories that allow agents to build a set of “context, nextAP” couples (here denoted between brackets). The special “OFF” AP is introduced when the MH leaves the network; since we do not aim at predicting this event, those APs do not appear in the learning sets.

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(or learning samples) of the learning set. With MHC, the prediction agent is bound to ‘m’; with APC, it is bound to Lm i−1 . Notice that the contexts fed to m Lm i−1 always end with Li−1 ; an order n model built with APC is thus as complex as an order n − 1 model built using MHC. Figure 1 depicts graphically the learning process. Each time a terminal moves, its new AP is appended to its mobility trace; a special ’OFF’ AP is added when the MH is disconnected from the network. This trace is then converted to a series of “context, next-AP” couples; the maximal context length depends on the order of the models built. The couples corresponding to MH m populate the learning set bound to m (left); the couples whose context ends with AP p are the learning samples that compose the learning set of p’s agent (right). MH-centric modelling

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Figure 2 compares APC and MHC in terms of the distributions of the learning sets’ cardinality (i.e. the number of “context, next AP” couples). A lot of MHs barely move: 12% perform less than 8 movements (plot on the left, sum of the percentages reported in the first 3 bars). On the contrary, few APs are unpopular: 15% are crossed by less than 256 MHs. The impact of under-learning should thus be more pronounced using the distributed MHC method. 3.1

Ping-Ponging

It is known (e.g. [15]) that such dataset exhibits the ping-ponging artefact (pingponging is defined as repeatedly changing one’s current association back and forth between two —or more— access points). Mobility prediction is concerned about the physical movements of mobile terminals, not about those quick artefacts. This does not mean that predicting ping-ponging is not an interesting topic, but that it is only marginally related to the question studied here. We thus try to remove this artefact. Considering the location history L1 , . . . , Ln of a MH, the movement to Ln is classified as ping-ponging if Ln−2 = Ln . This simple rule surely does trigger “false positive”: MHs physically moving back and forth from an AP to another are classified as ping-ponging. However, we notice that the proportion of movements classified as ping-ponging varies from one interface manufacturer to another. Our

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criterion thus looks reasonable since it filters handovers triggered by a technological cause. About 3 movements out of 10 are classified as ping-ponging.

4

Next-AP Predictions Accuracy

Table 1 shows the next-AP prediction accuracy using both APC and MHC, for various model orders. Table 1. Prediction accuracy. Each number corresponds to the ratio between the number of accurate predictions and the total number of predictions. The columns on the right show the performance one would obtain if ping-ponging were not removed.

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APC N/A3 28.7% 47.6% 53.0% 55.2% 55.4%

APC N/A 40.9% 64.5% 64.9% 65.1% 64.9%

MHC 24.0% 39.4% 58.4% 59.5% 59.8% 59.6%

MHC 38.7% 68.4% 72.9% 73.0% 73.0% 72.8%

We first observe that a simple, statistical approach4 gives unsatisfactory results. Models improve quickly: order-2 models are quasi-optimal. Beyond that, performance improves very slowly and reaches its apogee with order-4 models. Performance decreases with models of higher order, showing a slight over-learning. The columns on the right are those obtained if the ping-ponging movements are not removed; they show that ping-ponging is easy to predict, even with simple markovian models. Results related to MHC are in accordance with [7]. Since table 1 shows that ping-ponging has a major impact on the prediction results, it would be interesting to repeat the experiments presented in [7] once ping-ponging has been filtered out. The good prediction ratio is, overall, quite low. We can suppose that it would be higher for other kinds of networks where terminals are usually not switched off during their displacements (e.g. GSM), even if some of the terminals composing this dataset are WiFi phones ([14]). In this study, absolute accuracy performance is not our primary concern; we here emphasize the differences between APC and MHC schemes. The decentralized MHC scheme works better than APC. This result was expected, as different persons have their specific behaviour: averaging the movement patterns of the people crossing the same AP only gives a rough estimate of 3

4

An order n model is based on contexts of length n. With APC, the prediction agent is the same as the last element of the context, which is undefined in the case of a context of length 0. That is, an order 0 for MHC, or 1 for APC.

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the way they move. Surprisingly however, the accuracy difference between APC and MHC is small (from 55.4% to 59.8%); in practice, this means that getting decent prediction performance does not require to embed a prediction agent in each mobile: placing them in the fixed infrastructure can suffice. Section 5.2 gives hints on why the results of the two methods are so close.

5 5.1

Stressing the Differences Between APC and MHC Prediction Accuracy vs Learning Set Cardinality

0.8

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Prediction accuracy

The markovian models’ parameters learning process and the prediction process are interleaved: when a mobile is associated with an AP, this AP predicts where the terminal is going; as soon as the next AP is known, this piece of information is added to the learning set and allows it to improve the mobility model. The ratio of accurate predictions is thus a function of the elapsed learning time, and the way it evolves depends on the method used — APC or MHC.

10 0

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Learning set cardinality

Fig. 3. Prediction accuracy (strong lines, left axis) and proportion of prediction agents (thin lines, right axis) vs learning sets’ cardinality using order 3 models

Figure 3 (bold lines, left axis) draws the instantaneous prediction accuracy as a function of the learning sets cardinality. This plot shows that learning sets made of a few hundred elements lead to prediction ratios of more than 40%, regardless of whether prediction agents depend on APs or MHs. For bigger learning sets, both methods show different profiles. For APC, performance gets slowly better and stabilizes at 55% when the learning set is made of about 3000 samples. With MHC, the results increase much faster, reaching 60% when the learning set is composed of 1000 elements, and settling at about 73% with 2000 elements. This last percentage is astonishingly good and is thus studied more carefully below.

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On the same figure, thin lines (right axis) give the proportion of prediction agents which have a learning set cardinality higher than a given value. For example, for MHC, about 80% of the mobiles have a learning set composed of less than 500 elements. The curve associated with MHC decreases much faster than that of APC: nearly all the models have a learning set with a cardinality smaller than 3000. The APC curve has a very different shape: there are still some learning sets with cardinalities greater than 8000 elements. Only a small number of MHs are thus responsible for prediction ratios higher than 70%. A manual inspection of motion traces shows that ping-ponging between 3 APs or more seems to explain this anomaly. Fortunately, this situation is rare and should only marginally impact the average performance. This figure allows finding the steady-state (i.e. on the long run) good prediction ratio reached by each method. If APC clearly settles at about 55%, the case of MHC needs to be considered more carefully. As mentioned above, performance of 70% or more are not realistic. We thus remove the 10% of best performing MHs and measure the performance of the remaining mobiles once they have reached a learning set cardinality greater than 500 elements; the good prediction ratio obtained is then 60.8%. 5.2

Next-AP Distributions’ Entropy

Knowing the last APs encountered by a mobile terminal (or context) does not allow a perfect prediction of its next AP. This uncertainty can be formalized as a random distribution of next APs; this distribution is characterized by a given entropy. This entropy is commonly linked to the difficulty of predicting the motion of the mobile. Mean entropies of order 1, 2, and 3 models are given in table 2 for APC and MHC. Table 2. Next-cell distribution entropies (in bits). Standard deviations are given between parentheses. Method APC MHC

1 1.86 (0.95) 0.98 (0.50)

2 1.72 (1.18) 0.82 (0.66)

3 1.58 (0.49) 0.91 (0.37)

The two schemes exhibit strong differences. This runs counter to the results obtained in terms of prediction accuracy (see table 1) which showed a difference indeed, but as small as about 5%. From this experiment, we can conclude that MHC clearly predicts more precisely which APs might be next encountered by a MH, but this problem is different from the one generally studied, which is only concerned with finding the most probable next AP. Thus, even if entropy estimation allows us to get a quantitative measurement of the mobiles’ motion uncertainty given a model, directly linking entropy to prediction accuracy gives a biased picture. A more complete study of this point can be found in [16].

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Time Division

It is commonly supposed (e.g. [17]) that it is desirable to divide a learning set in homogeneous time slices: it seems for example sensible to expect different motion behaviours during the week-end and during the rest of the week, and it is thus reasonable to build different models for those periods of time. Table 3. Prediction accuracy when different order 4 models are built for various time divisions. The results given are the (weighted) mean performance of the models built. Granularity No time division Week/Week end Morning/Afternoon January 2003 Two hours periods Days of week

APC 55.2% 54.2% 54.1% 53.8% 53.1% 52.0%

MHC 59.8% 58.4% 58.2% 57.0% 53.4% 54.6%

Using such a method would however bring two drawbacks: (a) the start of the model’s learning curve could cause bad performance, and (b) short time periods could yield too small learning sets. Table 3 gives the results obtained using various time divisions. The results are surprising: in no case do the time slices improve the results. Two hypotheses can explain this fact: (a) the MHs’ behaviours are the same during all the time periods (movements are not cyclic and can be described as a stationary process) or (b) the motion context already captures those differences.

6

Conclusions and Future Works

Mobility prediction schemes can be divided into two main classes, here designated AP-centric (or centralized) and MH-centric (or decentralized). Quite surprisingly, they have never been directly quantitatively compared. This article partially fills this gap using a study based on markovian models. The parameters of those models are fit via the analysis of a database containing the real motion traces of the mobile hosts of a campus WiFi network. It allows us to draw a number of conclusions: – Contrary to one could expect, the measured accuracy difference between APC and MHC is only a few percents (typically 55% vs 59%). – In any case, the prediction accuracy is low (less than 60%); this is certainly a characteristic of WiFi users, and we expect other networks (e.g. GSM) to exhibit more predictable, regular motion patterns. This is not a real concern for this study as we are more interested in comparing APC and MHC rather than in absolute results.

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– Next-AP prediction uncertainty can be estimated by an entropy measurement, but this only partially reflects prediction accuracy and, in this case, does not provide an accurate comparison of APC and MHC. One should thus refrain from linking entropy to prediction accuracy as this can introduce a bias. – Quite surprisingly, we notice that building models specific to certain periods of time (e.g. week/week-end, morning/afternoon) does not bring any improvement. This comparison could be continued along several lines. For example, the relevance of an hybrid method combining APC and MHC schemes could be explored; the prediction of APC could, for example, be used when a MH reaches an AP it has never seen before. This situation barely arises in the dataset used here, hence such an experiment should be tried using traces extracted from a different network.

References 1. Misra, A., Roy, A., Das, S.: An information-theoretic framework for optimal location tracking in multi-system 4G networks. In: Proc. of INFOCOM’04. (2004) 2. Laasonen, K.: Clustering and prediction of mobile user routes from cellular data. PKDD 2005, LNAI 3721 (2005) 569–576 3. Samaa, N., Karmouch, A.: A mobility prediction architecture based on contextual knowledge and spatial conceptual maps. IEEE Trans. on mobile comp. 4 (November/December 2005) 4. Hadjiefthymiades, S., Merakos, L.: Using path prediction to improve TCP performance in wireless/mobile communications. IEEE Communications 40(8) (August 2002) 5. Soh, W.S., Kim, H.: Dynamic bandwidth reservation in cellular networks using road topology based mobility predictions. In: Proc. of IEEE INFOCOM’04. (Mars 2004) 6. Pandey, V., Ghosal, D., Mokherjee, B.: Exploiting user profile to support differentiated services in next-generation wireless networks. IEEE Network (September/October 2004) 7. Song, L., Kotz, D., Jain, R., He, X.: Evaluating location predictors with extensive Wi-Fi mobility data. Technical Report TR2004-491, Dartmouth College (February 2004) 8. Song, L., Kotz, D., Jain, R., He, X.: Evaluating location predictors with extensive Wi-Fi mobility data. In: Proc. of 23rd Annual Joint Conference of the IEEE Computer and Communications Societies. Volume 2. (March 2004) 1414–1424 9. Bhattacharya, A., Das, S.: LeZi-update: an information-theoretic approach to track mobile users in PCS networks. In: Proc. of MobiCom’99, Seattle (August 1999) 10. Yu, F., Leung, V.: Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks. Computer Networks 38(5) (2002) 11. Cleary, J.G., Witten, I.H.: Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications 32(4) (1984) 396–402 12. Jacquet, P., Szpankowski, W., Apostol, I. In: An universal predictor based on pattern matching, preliminary results. (2000) 75–85

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13. Kotz, D.: CRAWDAD, a Community Resource for Archiving Wireless Data At Dartmouth. http://crawdad.cs.dartmouth.edu/ (2005) 14. Kotz, D., Essien, K.: Analysis of a campus-wide wireless network. In: MobiCom. (September 2002) 107–118 Revised and corrected as Technical Report TR2002-432. 15. Conan, V., Leguay, J., Friedman, T.: The heterogeneity of inter-contact time distributions [. . . ]. arXiv.org, cs/0609068 (2006) 16. Fran¸cois, J.M.: Performing and Making use of Mobility Prediction. PhD thesis, University of Li`ege (2007) 17. Choi, S., Shin, K.: Predictive and adaptive bandwidth reservation for hand-offs in QoS-sensitive cellular networks. SIGCOMM CCR 28(4) (1998) 155–166

A Flexible and Distributed Home Agent Architecture for Mobile IPv6-Based Networks Albert Cabellos-Aparicio and Jordi Domingo-Pascual Technical University of Catalonia, Department of Computer Architecture, Advanced Broadband Communications Center, c/Jordi Girona, 1-3, 08034 Barcelona, Spain {acabello,jordid}@ac.upc.edu.com http://www.ccaba.upc.edu

Abstract. Home Agents (HA) represent a single point of failure for Mobile IPv6-based networks. To overcome this problem many solutions have been published providing reliable HA architectures. These solutions require deploying redundant HAs on each sub-network. Although these solutions effectively mitigate this problem, they do not take into account the requirements of large networks with dozens of sub-networks. Deploying several HAs on each sub-network may be too expensive to deploy and to manage. In this paper we present a novel HA architecture that only requires a set of HAs for the whole network. Our basic idea is that the Mobile Node’s location can be announced to exit routers, this way re-directing packets can be done without involving the HA. Our solution provides reliability and load balancing as the existing solutions. Finally, we validate our proposal through an analytical model and compare it against other proposals through a simulation. Keywords: Mobility, Mobile IPv6, Home Agent Architecture.

1

Introduction

Mobile IPv6 [1] is considered to be one of the key technologies to provide mobility to the Internet. With “mobility” a user can move and change his point of attachment to the Internet without losing his network connections. In Mobile IPv6 a Mobile Node (MN) has two IP addresses. The first one identifies the MN’s identity (Home Address) while the second one identifies the MN’s current location (Care-of Address). The MN will always be reachable through its Home Address while it will change its Care-of Address according to its movements. A special entity called a Home Agent (HA) placed at the MN’s home network maintains bindings between the MN’s Home and Care-of Addresses. 

This work was partially funded by IST under contract IST-2006-NoE-0384239 (ISTCONTENT), MEC (Spanish Ministry of Education and Science) under contract TSI 2005-07520-C03-02 and the CIRIT (Catalan Research Council) under contract 2005 SGR 00481.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 333–344, 2007. c IFIP International Federation for Information Processing 2007 

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In addition the communications between the MN and its peers (Correspondent Nodes) are routed through the HA. Thus, the MN relies on its HA for its connectivity. However, Mobile IPv6 incorporates a route optimization mechanism where the MN can communicate directly with its Correspondent Nodes (CN). This mechanism avoids triangle-routing through the HA reducing the HA’s load. However, this will not be used for short-term communications (e.g a MN accessing a web page). A HA may be responsible for multiple MNs on a Home Link. The failure of a single HA may then result in the loss of connectivity of numerous MNs. Thus, HAs represent the possibility of a single point of failure for a Mobile IPv6-based network. Moreover, MN’s communications through the HA may also lead to either the HA or the Home Link becoming the bottleneck of the system. In addition, the HA’s operations such as security check, packet interception and tunneling might not be as optimized in the HA software as plain packet forwarding. The Mobile IPv6 standard allows the deployment of multiple HAs on the Home Link to provide reliability and load balancing. This is done so that upon the failure of the serving HA another HA can take over the functions of the failed one. This provides continuous service to the MNs registered with the failed HA. However, the transfer of service is problematic [2]. The solution is MN-driven and forces the MN to detect the failure and select a new HA. This causes delayed failure detection, service interruption in the upper layer applications, increased workload on the MN, message overhead over the air interface and IPsec Security Associations re-establishment. Many research papers have been published that address these problems. The solutions presented in [4][5][6][7][8] increase HA reliability and load balancing by deploying several redundant HAs at the Home Link. In these solutions, all the HAs share the registration state and they define efficient mechanisms for HA recovery. These solutions reduce the service disruption time in front of Mobile IPv6. In addition, the MN’s traffic is balanced among the different HAs. The main difference among them is that some [4][5][6] are MN-driven solutions while others [7][8] are transparent to the MN. Unfortunately, these proposals are focused on providing HA reliability and load balancing on just a single Home Link but they do not take into account the global requirements of an Autonomous System (AS). An AS that hosts MNs may have dozens of sub-networks. Deploying reliable HAs requires several redundant HAs on each link. It is important to remark that the Mobile IPv6 protocol belongs to the IPv6 standard and, theoretically, any IPv6 node has mobility capabilities. Thus, these approaches are too expensive to deploy and to manage. A different proposal, which does not require deploying redundant HAs on each Home Link, is the Virtual Mobility Control Domain protocol (VMCD) [9][10]. The VMCD protocol allows multiple HAs to be placed at different domains. A MN may use multiple HAs simultaneously. The basic idea behind this proposal is that each HA advertises, through eBGP, the same home network prefix from multiple routing domains. Each MN then picks the best HA according to its

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topological position. The main drawback for this proposal is that the impact on the exterior BGP routing system scalability is unpredictable. In this paper, we present a novel flexible and distributed HA architecture that takes into account the mobility requirements of an AS and that does not impact the BGP routing system. We consider the HA as an entity that performs several differentiated operations. We have analyzed each operation and we have assigned each of them to an entity of the network. Our basic idea to distribute the operations is that a registration from a MN into a HA can be viewed as an internal route from the network’s point of view. That is, when a MN registers a new location into its HA it is actually installing a new route (Home Address → Care-of Address). We believe that this route can be announced throughout the network and thus, it is not necessary to deploy a HA on each link. As we will see, our solution only requires deploying one HA for the whole network. This HA should be reliable and our architecture allows deploying more than one HA to distribute the load. Moreover, our solution can use the redundant mechanisms presented in [4][5][6][7][8]. In addition, our solution reduces considerably the number of MN’s data packets transmitted into the network and is compatible with legacy MNs.

2 2.1

Introduction Design Rationale

In this subsection, we will analyze the different operations of a Home Agent (HA) and how they can be distributed from a network’s point of view. In the rest of the paper, we will use the following terminology: we define Home Network as the set of Home Links managed by our HA. We define Exit Routers (ER) as the routers that connect the Home Network with the rest of the Internet. These ERs may or may not be the AS’s border routers and an AS may have several Home Networks. Home Agents are responsible for maintaining bindings between the MN’s identity and its location. The HAs forward the MN’s signaling and the MN’s data packets as well. MNs send data packets through their HA when communicating with their Home Network or with CNs. Since MNs can communicate directly with its CNs it is expected that communications through the HA are mainly used for short-term connections. The Mobile IPv6 RFC states that packets sent through the HA may be secured through IPSec [3]. It should be taken into account that the MN can use IPSec with its peers regardless of the IPSec connection with their HA. We believe that it is not useful to secure MN to CN communications because the packets are only secured on half of the path (MN → HA) while the rest of the path (HA → CN) is not secured. Regarding the MN’s communications with the Home Network, protecting the path is useful. In this case the HA is acting like a Virtual Private Network (VPN) gateway. Under these assumptions and following the basic idea that a registration from a MN into a HA can be viewed as an internal route we can distribute the HA’s

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operations throughout the network. In our architecture, a single HA is required for the whole network; we call it a flexible Home Agent (fHA). This fHA will process (using IPSec) the MN’s signaling messages and will maintain registration information. It will also distribute this information throughout the network as internal routes. The network will directly process the MN’s communications with the CNs while the fHA will process the MN’s communications with the Home Network (using IPSec) in the same way as a VPN gateway. 2.2

Overview

Fig. 1 presents an overview of our architecture. Our proposal has only one HA (we call it a fHA) that will serve all of the MNs of the network. Take into account that our proposal allows more than one fHA (section 2.3) to be deployed to distribute the load. This fHA will be identified by an unicast address and the MNs will address its registration messages to it. Upon reception of a registration message, the fHA validates it and sends a routing message announcing the new route towards the MN. This information is then sent to each ER. In addition, the fHA advertises the route to the Home Link’s Access Router (AR). At this point, the network knows the location of the MN. When communicating with a CN through the HA, MNs do not address packets to the fHA but to an anycast [13] address owned by the ERs. For instance this anycast address can be configured on a Loopback interface. In this way, a given ER receives the MN’s data packets and de-capsulate, lookup and forward packets to the CN. Similarly, CNs send packets to the MN’s Home Address. Upon reception, the ER lookups the packet’s destination address (the MN’s Home Address). Since the fHA has previously installed a route at the ERs they know that the MN it is not at home. Therefore, the ERs encapsulate and forward the packet to the MN’s location. Our architecture manages efficiently MN to CN communications because some packets “bounce” at the ERs. This way the network’s internal traffic is reduced considerably. Regarding the communications from the MN to the Home Network, the MNs addresses its IPSec protected packets to the fHA that, in turn, de-capsulate and forward them to the MN’s peer. The MN’s peers address its data packets to the MN’s Home Address. Since the fHA has announced to the Home Link’s AR a route for the MN, the AR knows that the MN is away and it encapsulate the packet towards the fHA. The Home Link’s AR also multicasts Neighbor Advertisement messages on behalf the MN. This enables the AR to intercept communications from the Home Link to the MN and forwards them through the tunnel with the fHA. In the following subsections the detailed operations of our architecture are presented. 2.3

Dynamic fHA Address Discovery

This subsection specifies how the fHA announces their presence. In standard Mobile IPv6 HAs announce their presence through Router Advertisement messages.

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Fig. 1. Overview of our proposal

In this way, the MN’s can automatically select a HA. Our architecture implements this functionality in exactly the same way that Mobility Anchor Points (MAP) announce their presence in the Hierarchical Mobile IPv6 (HMIPv6) [12] protocol. Our mechanism is also compatible with legacy MNs. Each fHA sends Router Advertisement messages announcing its presence to the routers operating in the network. These messages include a preference value. In turn, the routers propagate the fHA’s announcements to ARs that then forward them to the Home Link. Each router will decrement the preference value. This way MNs can automatically discover their fHA’s address and select the best one according to the preference value. This mechanism has many benefits. On the one hand, it enables ARs to automatically discover the fHA thus avoiding manual configuration. On the other hand, it allows us to deploy more than one fHA on the network and distribute the load among them. The fHA’s Router Advertisement messages include the prefix(es) of the Home Network that it is serving and the anycast address owned by the ERs. Including the Home Network’s prefix enables the MNs to know if its peers are on the Home Network or not. Depending if the peer is on the Home Network or not MNs will address the data packets to the fHA or to the ERs. Finally, in order to provide compatibility with legacy Mobile IPv6 nodes, MNs may send its traffic to the fHA. 2.4

Signaling Processing

Each MN selects a given fHA through the above-mentioned mechanism. All the fHAs have pre-configured keys with the MNs as the Mobile IPv6 RFC states. Please note that ARs and ERs do not share any keys with the MNs. The fHAs receive registration messages from the MNs as stated by the Mobile IPv6 RFC. Upon reception of a successful registration message, the fHA has to announce this information (route) to the ERs, to the Home Link’s AR and to the rest of the

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fHAs. To distribute this type of information we use a routing protocol. Instead of designing a new routing protocol we use an already existing and deployed one. The routing protocol that best fulfills our requirements is the interior Border Gateway Protocol (IBGP) [11]. In our solution the fHAs, the ERs and Home Link’s ARs create an IBGP domain. It is very important to remark that this IBGP domain may be an already existing IBGP domain or a separate one. The routes announced through this IBGP domain always have the longest prefix (/128) and never affect regular BGP routes. It should be noted that the routes announced by the fHAs will never be distributed outside the network. Finally, the entities participating in the IBGP domain have pre-configured keys to provide confidentiality, integrity and authentication to the communications. For each successful received registration message, the fHAs send an IBGP UPDATE message to the ERs and to the AR responsible of the MN’s Home Link. The fHAs are able to determine the appropriate AR by inspecting the MN’s Home Address. We introduce new options in the IBGP UPDATE message. The UPDATE message sent to ERs includes the following information: (Home Address, Careof Address, Lifetime). Upon reception of this message, the ERs setup a tunnel endpoint with the MN. The tunnel source address is the anycast address while the destination address is the Care-of Address. In addition, each ER adds the following route to its routing table: (HomeAddress/128 → Tunnel). The tunnel and the route are automatically deleted after “Lifetime” seconds. The UPDATE message sent to the AR includes the following information: (Home Address, Lifetime). Upon reception of this message, the AR knows that the MN is away from home (note that the AR does not know the location of the MN). Next, the AR setups a tunnel endpoint towards the fHA that announced the route and adds the following route to its routing table: (HomeAddress/128 → Tunnel). The AR also starts sending multicast Neighbor Advertisement messages on behalf of the MN at the Home Link. If a node of the Home Network (or Home Link) sends a packet to the MN, the AR intercepts it and encapsulates it towards the fHA. Once again, the tunnel and the route are automatically deleted after “Lifetime” seconds. Once the MN returns home it sends a registration message to the fHA. Upon reception, the fHA sends an IBGP WITHDRAWAL message to the ERs and to the corresponding AR to immediately remove all the routes and tunnels related to the MN’s Home Address. 2.5

Data Packets Processing

This subsection presents how packets are routed from/to the MNs. MNs communicating with CNs encapsulate their data packets to the anycast address owned by the ERs (Fig. 2). The packets are received by the “nearest” ER that will de-capsulate and forward them towards the packet’s destination address (the CN’s address). If the exit point of the CN’s address is another ER then the packet traverses the network as a transit packet. It is important to remark that our solution does not require anycast routing. Packets addressed to

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Fig. 2. MNs to CNs communications

Fig. 3. MNs to Home Network communications

the anycast address are routed normally (like unicast) and delivered to a given ER. We use anycast addresses because it is the standard procedure to assign the same address to different network interfaces. MNs communicating with nodes located into their Home Network (Fig. 3) encapsulate their packets towards the fHA. However, packets sent by MN’s peers are addressed to the MN’s Home Address. The MN’s AR intercepts those packets. Since the AR knows that the MN is away from home, it encapsulates the packet towards the fHA. Since the Mobile IPv6 RFC states that the packets are tunneled through the HA encapsulating the packet from the AR to the fHA does not affect the path’s MTU [1]. As has already been mentioned, the MN’s communications with the Home Networks are protected with IPSec. 2.6

Flexible Home Agent Location

This subsection discusses the possible locations of the fHAs. Each fHA can be placed anywhere in the network, as a separate server, co-located with an ER/border router or even with a BGP Route Reflector.

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Fig. 4. fHA location example

One of the major benefits of our proposal is its flexibility. On the one hand, our architecture can serve all the MNs of a network with one or more fHAs. If more than one fHA is deployed MNs will select the nearest one based on the preference value. This way the load is distributed among them. Each fHA thus only process signaling messages and communications from/to the Home Network (like a VPN gateway). MNs to CNs communications are then processed by ERs. On the other hand, our architecture is transparent to MNs running with legacy Home Agents and both technologies may co-exist on the same network. Fig. 4 shows an example of the flexibility of our architecture. This AS has three networks and each one can independently select which approach it deploys. For instance, the “A” network can deploy both technologies. The fHA could serve MNs belonging to the “A.1” sub-network while MNs belonging to the “A.2” sub-network could be served by a legacy HA. The “B” network can deploy only legacy Home Agents on each sub-network. Finally, the “C” network can deploy two fHAs and all the MNs from “C.1” and “C.2” could be served by them. Only routers labeled in black must belong to the IBGP domain with the fHAs of their network. There will be a separate IBGP domain for each Home Network. MNs served by an fHA send its data packets to an anycast address owned by the ERs. Since the prefix of the anycast address belongs to the Home Network’s prefix, the AS’s border routers knows how to forward the packets and do not need to be aware of our protocol.

3

Evaluation

This section presents an analytical evaluation of our proposed scheme and a comparison with a network running Mobile IPv6 enhanced with existing solutions

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[4][5][6][7][8]. We do not consider solutions based on eBGP [9][10] because their impact on the exterior BGP routing system scalability is unpredictable. 3.1

Signaling Overhead

Let N be the number of ERs of a network that is running our proposal, let M be the number of deployed fHAs and let H be the total number of received registration messages per second (including foreign and home network registrations). Then our proposal requires sending H(N +M ) IBGP messages per second. 3.2

Transit Traffic Reduction

As has been commented previously, in our proposal some data packets will “bounce” at the network’s ER without being transmitted through the network. However in existing solutions [4-8] each packet sent through the HA has to be transmitted twice. One from the ER to the HA and another one in the opposite direction. In this subsection we compare this amount of transit traffic. We only consider the traffic exchanged between MNs and CNs that is routed through the HA.

Fig. 5. Transit Traffic in our proposal

Let I be the Kbps of traffic exchanged between all the MNs and its CNs through the HA. Then, existing solutions [4-8] have 2I Kbps of transit traffic. If we assume that each ER of the network has the same probability of being the exit point of a given packet then, our proposal has (1-1/N )I Kbps of transit traffic (Fig. 5). In addition, transit traffic in existing solutions [4-8] may follow a longer path than in our proposal. While in existing solutions [4-8] transit traffic must be transmitted to the HA in our proposal some transit traffic “bounces” at the ERs and the rest is transmitted from one ER to another. Home Links are usually deployed far away from the ERs while ERs may be close to one another (in terms of number of hops).

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Stored State at the Routers

In this subsection we will analyze the size of the routing tables and the number of tunnels configured at the ERs and ARs of a network running our proposal. Each ER has 1 tunnel and 1 route for each MN of its Home Network that is away from home. Each route and tunnel requires the Home Address, the Care-of Address and a lifetime, in total 34 bytes. Likewise, each AR will have just 1 tunnel with each fHA and 1 route for each of its nodes away from home.

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Simulation

In order to validate our proposal we have run a simulation. The simulation is intended to provide realistic values for the equations presented in section 3 and to compare our proposal with existing solutions [4-8]. In order to provide realistic values, we have configured a highly mobile environment by using a Random Trip mobility model [14]. Specifically, we have used the Random Waypoint on Generalized Domain model with a set of 8 domains. Each domain represents a layer-2 network where a MN can move without changing its point of attachment (i.e default router). Only when the MN changes from one domain to another it must register its new location. Please, refer to [14] for further information. The first domain is considered to be the Home Network while the rest of the domains are foreign networks. The Home Network has 1000 MNs, 2 ERs and 5 sub-networks. When running our proposal the Home Network has 2 fHAs while when running existing solutions [4-8] the Home Network has a set of reliable HAs on each sub-network (5 sets in total). In addition, each MN sends 64Kbps (VoIP) of unidirectional traffic towards its Home Network and 128 Kbps (Data) towards a CN. It should be taken into account that when a MN is at home traffic is sent directly and thus we do not consider it. Similarly, we do not take into account route optimized traffic. Finally, we have simulated this environment during 10000 seconds (roughly 2.7 hours). Our mobility model produces a mean of 4.68 foreign network registration messages per second (messages/s) and 0.80 Home Network registration messages/s. This means that our proposal requires sending 18.72 IBGP UPDATES messages/s and 3.2 IBGP WITHDRAWAL messages/s. Summarizing, our proposal introduces 21.92 signaling messages/s where each ER must process 5.48 messages/s. Regarding the transit traffic table 1 presents the results. In our proposal, fHAs have to process 465.04 Mbps. Our simulated network has two fHAs and each one processes 232.52 Mbps of data traffic. In [4-8] HAs process 1412.9 Mbps of traffic, our simulated network has 5 sets of HAs, this means that each set of HA processes 282.58 Mbps. In our proposal, the data traffic destined towards CNs is directly processed by ERs (947.86 Mbps). Regarding the transit traffic, our proposal reduces it by 75% compared to existing solutions [4-8]. It should be taken into account that existing solutions [4-8] must send each data packet twice, one from the ER to the HA and another one in the opposite direction.

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Table 1. Simulation Results (Values in Mbps) Existing Solutions [4-8] Our Proposal Traffic sent by MNs 1412.9 (465.04 to the HN, 947.86 to CNs) through the HA/fHA Traffic processed by 1412.9 465.04 HAs/fHAs Traffic processed by ERs N/A 947.86 Transit Traffic 1895.72 473.93

Our solution has to forward transit traffic into the network only if the receiving ER is not the exit point of the packet’s destination address Finally, during the simulation a maximum of 900 nodes were away from home at the same time (average 717, minimum 685). This means that the maximum stored state on each ER is 29.9KB. This simulation shows that our proposal is viable, and that among other benefits, it can reduce the transit traffic considerably.

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Conclusion

In this paper, we have presented a flexible and distributed HA architecture. Existing proposals [4-8] provide a reliable HA architecture by deploying redundant HAs on each Home Link. Our proposal has the same benefits but with just one set of fHA for the whole network. Our solution is reliable: a failure on the MN’s ARs will not disconnect the MN. In this case the MN will still be able to communicate with the Home Network (except with the Home Link) and with the rest of the Internet. Since our solution allows deploying several fHA for each network, a failure of a fHA does not disconnect the MN. In this case, our solution can benefit from the proposed efficient failure recovery mechanisms presented in [7][8] because it is fully compatible with them. This way we can minimize the service interruption time. Finally, a failure on a ER does not disconnect the MN. In this case, the network announces the failure of the ER through the exterior routing protocol and the packets will be re-routed. Our solution also provides load balancing because the MN’s data packets are processed by ERs or by a set of fHAs. Moreover, it reduces considerably the transit traffic through the network (75% according to our simulation). Distributing HA’s operations requires adding some extra load at the ERs and at the ARs. The ERs have to setup tunnels and configure new routes towards the MNs while the ARs have to configure a tunnel with each fHA and intercept packets destined to its MNs. We believe that routers are hardware machines optimized to perform exactly this type of operations. It is important to remark that signaling and IPSec data packets are processed by fHAs, not by routers. In addition, our simulation of a highly mobile environment shows that each ER would require processing only an average of 5.48 signaling messages per second.

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Finally, as future work, we plan to implement Traffic Engineering for MN’s traffic in case that the exit routers are the AS’s border routers. We also plan to extend our solution to correspondent networks.

References 1. D. Johnson et al: Mobility Support in IPv6. RFC 3775, 2004 2. Jahanzeb Faizan et al: Problem Statement: Home Agent Reliability. IETF Draft (Work in Progress), 2004 3. S. Kent et al: Security Architecture for the Internet Protocol. RFC 2401, 1998 4. F. Heissenhuber, W. Fritsche and A. Riedl: Home Agent Redundancy and Load Balancing in Mobile IPv6. in Proc. 5th Int. Conf. Broadband Communications, 1999 5. H. Deng et al: Load Balance for Distributed Home Agents in Mobile IPv6. in Proc. 14th IEEE PIMRIC 2003 6. H. Deng et al: Load Balance for Distributed Home Agents in Mobile IPv6. IETF Draft (Work in Progress), 2003 7. J. Faizan et al: Efficient Dynamic Load Balancing for Multiple Home Agents in Mobile IPv6 based Networks. in Proc. 5th Int. Conf. Pervasive Services, 2005 8. R. Wakikawa et al: Home Agent Reliability Protocol. IETF Draft (Work in Progress), 2006 9. R. Wakikawa et al: Inter Home Agents Protocol Specifications. IETF Draft (Work in Progress), 2006 10. R. Wakikawa et al: Virtual mobility control domain for enhancements of mobility protocols. INFOCOM 2005 11. Y. Rekhter et al: A Border Gateway Protocol 4 (BGP-4) RFC 1771, 1995 12. H. Soliman et al: Hierarchichal Mobile IPv6 Mobility Management (HMIPv6) RFC 4140, 2005 13. S. Deering et al: Internet Protocol, Version 6 Specification RFC 2460, 1998 14. PalChaudhuri, S. et al: Perfect Simulations for Random Trip Mobility Models 38th Simulation Symposium, 2005

Using PANA for Mobile IPv6 Bootstrapping Julien Bournelle1 , Jean-Michel Combes2 , Maryline Laurent-Maknavicius1, and Sondes Larafa1 GET/INT, 9 rue Charles Fourier, 91011 Evry, France [email protected] [email protected] [email protected] 2 France Telecom R&D, 38/40 rue du General Leclerc, 92784 Issy-Les-Moulineaux, France [email protected]

1

Abstract. One of the current challenge of the Mobile IPv6 Working Group at the IETF is to dynamically assign to a Mobile Node its Home Agent, Home Address and to setup necessary security associations. If the Mobile Node is authenticated for network access, the current IETF approach is to use DHCPv6 to deliver the Home Agent and then to use IKEv2. In this article, we assume that the PANA protocol is used for network access authentication. We propose to add some functionalities to this protocol to support Mobile IPv6 Bootstrapping. The Home Agent is directly delivered to the Mobile Node and DHCPv6 is no more necessary. Moreover, it allows a better management of Home Address allocation by the AAA infrastructure. This proposal has been submitted to the IETF and implemented on a testbed at GET/INT research laboratory.

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Introduction

In the Mobile IPv6 protocol, a Mobile Node needs a Home Agent, a Home Address and IPsec Security Associations with its Home Agent to secure signaling messages. To ease the deployment of this mobility protocol, Internet Service Providers need scalable mechanisms to dynamically assign these pieces of information to their customers. This is known as the Mobile IPv6 Bootstrapping Problem. In this paper, we propose a solution based on the PANA protocol to deliver the Home Agent Information to the Mobile Node during the network access authentication phase. In section 2, the Mobile IPv6 protocol is presented. Section 3 describes the Mobile IPv6 Bootstrapping problem and the IETF bootstrapping solution based on the DHCPv6 protocol. Our solution which relies on the PANA protocol is detailed in section 4. Finally, this proposal has been implemented and a description of our testbed is given in section 5. I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 345–355, 2007. c IFIP International Federation for Information Processing 2007 

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Mobile IPv6 Overview

As it stands in [1], an IPv6 Mobile Node (MN) is uniquely identified by its Home Address (HoA), and is maintained reachable, whatever its position in the IPv6 network, thanks to a registration mechanism to its Home Agent (HA). Each time the MN attaches to a new network after some moves or reboots, the mobile is assigned a new local temporary IPv6 address either from a DHCPv6 server or through address autoconfiguration. The MN then has to inform its Home Agent of its temporary address also known as its Care-of Address (CoA). This operation of binding its HoA to its CoA is operated by the MN sending a Binding Update (BU) and the HA acknowledging the BU message with a Binding Acknowledgement (BA) (cf. Fig. 1). After the registration is completed, the HA intercepts all the data traffic directed to the HoA, and tunnels them to the current position of the MN (i.e. CoA). Thus any Correspondent Node (CN) only needs to know the HoA to transmit data to MN via HA. For Routing Optimization (RO) purpose, the Mobile IPv6 protocol enables CN and MN to directly communicate rather than going through HA.

Fig. 1. Mobile IPv6 architecture

Binding operations are highly sensitive to malicious traffic redirections, and do require strong protection. For binding updates operated by MN to HA, protection is guaranteed by the IPsec sub protocol ESP (Encapsulating Security Payload) in transport mode [2]. HA is thus enabled to authenticate the BU’s originator, and to prove the integrity of the BU content, especially the CoA which is located within the Alternate Care-of Address mobility option. However, this ESP mechanism assumes that a Security Association (SA) is pre-established between HA and MN (MIPv6 SA). For the binding updates operated by MN to CN, the mechanism is less secure as no strong assumption on a preshared Security Association might be imposed. The selected mechanism known as Return Routability [1] proposes that MN sends two messages to CN, one directly to CN and another one through HA, and that the two replies from CN follow the reverse path. The idea is that intercepting two messages instead of one to perform one possible traffic redirection is much

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more difficult. Moreover, because of ESP protection between HA and MN, HA brings a guarantee to CN about the authenticity of the request.

3

The Mobile IPv6 Bootstrapping Problem

3.1

Problem Description

As explained in section 2, the Mobile IPv6 service needs for activation that the Mobile Node be pre-configured with a HA, a HoA and MIPv6 IPsec SA with the Home Agent to protect Mobile IPv6 signaling. Note that another mechanism exists based on an authentication option to secure this signaling [3] but it is not considered in this article. These parameters may be statically configured on each Mobile Node. However, considering an operator willing to deploy the Mobile IPv6 protocol inside its IP networks, static configuration of millions of electronic devices is a burden and clearly not scalable. Moreover, as explained in [4], other reasons state for a dynamic bootstrapping mechanism: – Dynamic Home Agent assignment: to offer the Mobile IPv6 service, an operator will deploy multiple Home Agents. For load balancing between HAs, the less loaded HA should be allocated to MNs. Moreover, if a HA becomes unavailable for maintenance, network renumbering or failure, with a dynamic assignment, the operator is still able to provide the service. – Dynamic Home Address assignment: the operator may want do dynamically assign Home Address to its clients. Indeed, this is preferred for a better management of address allocation and to ease administrators’ tasks. The current trend at the Internet Engineering Task Force (IETF) is to first attribute the Home Agent to the Mobile Node and then to proceed to IKEv2 [5] exchanges between the MN and the HA. IKEv2 enables the MN to query a Home Address and, in the same time, to dynamically setup the MIPv6 SAs with its HA, as required by Mobile IPv6. Moreover, IKEv2 supports the EAP protocol [6] for the HA to authenticate the MN. Use of EAP permits the MN to use the same credentials that have been used for network access authentication (if any). Two generic scenarios are possible [4]: in the first one, the Mobile Node has a free network access (e.g. hotspots in a coffee shop) while in the second one, the MN is authenticated before getting network access. For the first scenario, the IETF proposed delivering the HA information through DNS infrastructure [7]. In the second scenario, the Home Agent information is delivered as part of the network access authentication procedure [8]. In both scenarios, the MN then uses IKEv2 with the delivered Home Agent. In this article, we assume that the Mobile Node is in the second scenario. As described in [8], the IETF approach is to use DHCPv6 [9]. In our proposed mechanism, we can bypass DHCPv6 exchanges by delivering the HA information during the network access.

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DHCPv6 Approach

One way to attribute the Home Agent for MN bootstrapping, relies on the re-use of the network access control architecture. At the same time the MN asks for the IP access to the operator’s network, it recovers the Home Agent. The IETF is working on a solution described in [8] using the DHCPv6 protocol [9]. Figure 2 shows the entities involved in this solution.

Fig. 2. MIP6 Bootstrapping with DHCPv6: architecture

Fig. 3. MIP6 Bootstrapping with DHCPv6: exchanges

Figure 3 presents the different steps of this solution. At first, the MN authenticates itself to the Network Access Server (NAS) with a protocol like PANA or 802.11i/802.1X (Step A). The NAS asks the AAA server in the Home Network (AAAH) about the MN’s rights (Step B). The AAAH checks in the same time whether the MN subscribed to the IPv6 mobility service. If this is the case, the

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AAAH replies to the NAS that the MN is allowed to access the network and which HA to be used (Step C). Then the NAS informs the MN it can access the network and stores the information about the HA assigned by the AAAH (Step D). To obtain the HA, the MN then sends a DHCP request (Step E). The NAS, which must also act as a DHCPv6 Relay, intercepts the request and forwards it to the DHCP server, adding the information about the HA assigned by the AAAH (Step F). If the MN required a HA in the local network, the DHCP server assigns a HA. If the MN required a HA in the Home Network, it is the one assigned by the AAAH. The DHCP sends the information about the HA to the NAS (Step G) which forwards the reply to the MN (Step H).

4 4.1

PANA Approach PANA Overview

PANA (Protocol for Carrying Authentication for Network Access) [10] is currently under standardization by the IETF and appears as a good candidate for handling MNs authentication at the network access as it is layer 2 agnostic, and it supports any EAP methods. Based on the client server model, the classical PANA architecture (cf. Fig. 4) utilizes a PANA client (PaC) located in the MN and a PANA Authentication Agent (PAA) located in the network access for instance in the access router (or NAS). For controlling MNs access to the network, an EP (Enforcement Point) should apply security and filtering policies at levels 2 and/or 3, so only authenticated and authorized MNs are permitted to send data traffic to the access network; EP is located at the access network and possibly on the same equipment than the PAA.

Fig. 4. PANA framework

Several phases are necessary for the MN to connect to the access network. First, the PaC needs to discover the local PAA, and negotiate services. This first phase includes classically three messages. Second for PAA to proceed to authentication of MN, some PANA messages are exchanged encapsulating EAP payloads. This authentication/authorization phase might rely on a AAA architecture handling authentication thanks to AAA servers and a AAA client located on the same equipment than PAA. Third, the MN is granted access to some Internet resources through the EP, but PANA messages are still exchanged for session

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keep alive reasons. From time to time, reauthentication of MN is required by the PAA or the AAA servers. All these PANA messages and AAA messages are in the form of AVPs (Attribute Value Pair). 4.2

Using PANA for MIP6 Bootstrapping

Without any modifications, in the case of a Mobile Node using PANA for network access authentication, exchanges would be first the PANA exchanges, then DHCPv6 to get the HA and then IKEv2. Our idea is to modify PANA in order to negotiate and to deliver Mobile IPv6 information to the Mobile Node. For this purpose, we introduced new AVPs in PANA messages. We defined a new Attribute Value Pair (AVP) called Mobility-Capability AVP. This AVP is used by the PANA Authentication Agent (PAA) to indicate to the MN/PaC that it supports Mobile IPv6 Bootstrapping. This AVP may be used to indicate other IP mobility capabilities such as Mobile IPv4 [11] or HIP [12]. This AVP is sent in the first PANAStart-Request message (PSR) as shown on Fig. 5. If the Mobile Node supports our proposal and wants to bootstrap IPv6 Mobility service, it includes the Mobility Capability AVP indicating its willingness in the PANA-Start-Answer message (PSA). This AVP may also be used to indicate whether the MN wants a local HA or a HA in its home domain. Compared to the DHCPv6 approach, this permits to the AAAH server to decide whether the MN is allowed to use a local HA.

Fig. 5. MIP6 Bootstrapping with PANA: exchanges

After this negotiation phase, we enter the authentication phase. The PAA relies on a AAA protocol to authenticate the MN. As described in [13] or in [14], the AAA signaling may be used by the PAA to indicate that it supports Mobile IPv6 Bootstrapping. Moreover, the visited domain may also indicate that it can locally allocate a HA. At the end of the authentication phase, the PAA obtains the HA information in the AAA message containing the result of the authentication. Note that if the authentication failed, it is not necessary to provide the HA information to the NAS (e.g. PAA). To deliver the HA to the MN, the PAA creates an AVP Home-Address (HoA) and puts it in the PANA-BindRequest message (PBR). Thus, at the end of the authentication, the MN has its Home-Agent and can use IKEv2 to get its HomeAddress and to setup necessary IPsec SAs.

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Pros and Cons

The main advantage regarding our solution is that it keeps the ”end-to-end” Internet philosophy. Indeed, in our solution, the MN knows that it will receive a HA and it might be guaranteed that this HA assignment is done by its AAAH (e.g. thanks to encryption mechanisms). In the DHCP based solution, the information about the HA is stored in the DHCP server and the MN cannot be totally sure that the information about the assigned HA comes from the Home Network. Another advantage is that the AAAH server can decide if the Mobile Node can use a local HA. Indeed, in our approach, this is managed at the beginning of the authentication phase. In the DHCPv6 approach, only the MN can decide if a local HA is required. Regarding the cost of our solution, this one does not require setting-up a DHCP infrastructure (i.e. client in the MN, relay in the NAS and server in the access network), so this reduces the cost of the Mobile IPv6 deployment. Moreover our solution may support privacy because it may be possible for the AAAH to cipher the information about the assigned HA up to the MN. Finally, our solution is naturally secured regarding the authentication and the integrity of the data because the security relies on PANA. This may not be the case for DHCPv6. Now, even if our solution reduces the allocation of the HA in one roundtrip time and provides hereabove advantages, the IETF is against the fact that network access control protocols support configuration.

5 5.1

Implementation Report Required Adaptation for the Implementation

Our proposal has been submitted to the IETF in [15] and can be integrated in the overall architecture of the proposed IETF solution. However, due to some external constraints, the implementation is different from the original proposal. The differences are explained in this section. At the time of implementation, no IKEv2 with EAP and Mobile IPv6 support was available. For this reason, we used IKEv1 [16]. However, IKEv1 does not permit to remotely configure the IPv6 address of its MN clients. For this reason, in our platform, the AAA server handles the Home Address Allocation and sends it to the PAA. Then the Home Address is carried to the MN using PANA messages. Normally, IKEv2 allows use of EAP to authenticate the MN. In IKEv1, only certificates or pre-shared keys permit to authenticate the IKEv1 exchanges. For this reason, we decided to bootstrap and distribute keying material for IKEv1 authentication between MN and HA. Our solution is to derive the PSK from the Master Session Key (MSK). This MSK is derived as part of the EAP authentication method and is available at both EAP client (MN) and EAP server (AAAH). Due to the colocation of HA and AAAH, the resulting PSK for IKEv1 is easily configured by modifying IKEv1 configuration file.

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Platform

The hosts A, B and C use FreeBSD 5.41 as operating system. As shown on Fig. 6, the MN is installed on host A, the host B acts as an Access Router and implements PAA and AAA client functionalities, and finally the AAA and EAP servers, colocated with the HA, are installed on host C. Details are given in next subsections.

Fig. 6. PANA - Mobile IPv6 testbed

5.3

Mobile Node

The host A was installed with SHISA2 to support Mobile IPv6, an EAP client, a PANA client and IPsec components (IKE, AH and ESP). The EAP client was extracted from xsupplicant3 while the PANA client has been implemented from scratch. The EAP-TLS method was selected as an authentication method between the EAP client and the EAP server. IPsec Security Associations to secure Mobile IPv6 signaling are established thanks to racoon application implementing IKEv1. We used a patched version of racoon (by Francis Dupont (Point6/CELAR)). The reason is that the original version of the IPsec-tools project is unable to setup correct IPsec SAs for Mobile IPv6 when MN is not in its home domain. The PANA client receives from the PAA the MN’s HoA and the HA address in corresponding AVPs in the PANA-Bind-Request message. After receiving the PANA-Bind-Request message, the PaC launches a shell script that first configures racoon, IPsec security policy database, and Mobile IPv6 with those HoA and HA addresses, and second executes racoon and Mobile IPv6 daemons. 5.4

Access Router/PAA/AAA Client

The PAA located in host B receives EAP messages from A and forwards them to the AAA client using a UNIX socket. The AAA client is a Diameter EAP client that relies on the WIDEDiameter library. The latter library was developed during the Nautilus6 project and implements the Diameter Base Protocol [17]. 1 2 3

www.FreeBSD.org http://www.mobileip.jp http://open1x.sourceforge.net

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AAA Server/EAP Server/Home Agent

Host C supports an EAP server, a Diameter EAP server, IPsec components (IKE, AH and ESP), and a HA. SHISA for Mobile IPv6 and HA support was installed as well as the racoon version from Francis Dupont. The Diameter EAP server relies on the WIDEDiameter library. It communicates with the EAP server thanks to a local UNIX socket in order to authenticate the EAP client within the MN. As soon as the authentication is successful, the Diameter EAP server sends the HA address and the HoA to the MN. The assignment is done according to the identity of the MN and a predefined users’ database. A shell script is then launched in order to configure racoon and IPsec policy and to execute racoon. 5.6

Tests and Results

The implementation of this proposal succeeds. It was carried out in several steps. First of all we tested separately each one of the protocols to make sure that they worked well. Then a modular approach was employed to test if the MN receives correctly the HoA and the HA addresses: on the one hand the AAA server must deliver them correctly to the AAA client and so to the PAA, on the other hand the PAA must deliver them correctly to the MN. Besides we also made sure that the PSK calculated by the AAA server and the MN were identical and correct. Once all these tests were positive, racoon and MIPv6 were launched automatically by PaC and AAAH server using Shell scripts. The MN and HA were checked to configure their SA and start their MIPv6 exchanges correctly. Due to some limitations of the racoon implementation, it was not possible to lead some tests with multiple Mobile Nodes. However tests done with only one Mobile Node have shown that the average time necessary to the MN to start using Mobile IPv6 is about 8.4 s: 0.04 s for PANA procedure, 3.8 s between the end of PANA procedure and the beginning of IKE procedure, 2.2 s for IKE procedure, 1.3 s between the end of IKE procedure and the beginning of BU/BA procedure, and 0.8 s for BU/BA procedure. These measurements were realized on a platform composed of three computers with processors’speed of about 1 GHz, memory of 512 Mo, and 100 Mbit/s links. It would have been interesting to perform similar measurements on the same platform using DHCPv6 approach. Unfortunately no implementation of this approach is currently available. By the way, it is important to notice, as the bootstrapping is done when the MN is switch on, the bootstrapping time is not critical as well as mobility mechanisms for handovers.

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Conclusion

Deploying a new protocol is a challenging task for Internet Service Providers. In the Mobile IPv6 case, ISPs must deploy Home Agents inside their networks and

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customers need equipments implementing the protocol. Moreover, as explained in this article, it is necessary to have a scalable way to dynamically assign some Mobile IPv6 specific parameters to customers’ devices. Our proposal provides a way to achieve that goal by slightly modifying the PANA protocol which is under standardization process at the IETF. This approach avoids additional DHCPv6 exchanges and is well optimized with a close integration to the network access authentication.

Acknowledgements We would like to thank the Nautilus6 project4 which provides us the Diameter Base Protocol implementation and Francis Dupont for his patch to racoon. We also would like to thank Fabien Allard (France Telecom R&D) for having installed our implementation and performed some tests on a different platform.

References 1. D. Johnson, C. Perkins, and J. Arkko. Mobility Support in IPv6. RFC 3775, June 2004. 2. J. Arkko, V. Devarapalli, and F. Dupont. Using IPsec to Protect Mobile IPv6 Signaling between Mobile Nodes and Home Agents. RFC 3776, July 2003. 3. A. Patel, K. Leung, M. Khalil, H. Akhtar, and K. Chowdhury. Authentication Protocol for Mobile IPv6. RFC 4285, January 2006. 4. A. Patel and G. Giaretta. Problem Statement for Bootstrapping Mobile IPv6. RFC 4640, September 2006. 5. C. Kaufman. Internet Key Exchange (IKEv2) Protocol. RFC 4306, December 2005. 6. B. Aboba, L. Blunk, J. Vollbrecht, J. Carlson, and H. Levkowetz. Extensible Authentication Protocol (EAP). RFC 3748, June 2004. 7. G. Giaretta, J. Kempf, and V. Devarapalli. Mobile IPv6 bootstrapping in split scenario. draft-ietf-mip6-bootstrapping-split-03, October 2006. 8. K. Chowdhury and A. Yegin. Mip6-bootstrapping via DHCPv6 for the Integrated Scenario. draft-ietf-mip6-bootstrapping-integrated-dhc-01, June 2006. 9. R. Droms, J. Bound, B. Volz, T. Lemon, C. Perkins, and M. Carney. Dynamic Host Configuration Protocol for IPv6 (DHCPv6). RFC 3315, July 2003. 10. D. Forsberg, Y. Ohba, B. Patil, H. Tschofenig, and A. Yegin. Protocol for Carrying Authentication for Network Access. draft-ietf-pana-pana-12, August 2006. Work in progress. 11. C. Perkins. IP Mobility Support for IPv4. RFC 3344, August 2002. 12. R. Moskowitz and P. Nikander. Host Identity Protocol (HIP) Architecture. RFC 4423, May 2006. 13. J. Korhonen, J. Bournelle, H. Tschofenig, C. Perkins, and K. Chowdhury. The NAS - HAAA Interface for MIPv6 Bootstrapping. draft-ietf-dime-mip6-integrated-01, June 2006. 4

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14. K. Chowdbury and A. Liora. RADIUS Attributes for Mobile IPv6 bootstrapping. draft-chowdbury-mip6-bootstrapp-radius-01.txt, October 2004. 15. J. Bournelle, M. Laurent-Maknavicius, and J-M. Combes. Using PANA in the Mobile IPv6 Integrated Case. draft-bournelle-pana-mip6-01, June 2006. 16. D. Harkins and D. Carrel. The Internet Key Exchange (IKE). RFC 2409, November 1998. 17. P. Calhoun, J. Arrko, E. Guttman, G. Zorn, and J. Loughney. Diameter Base Protocol. RFC 3588, September 2003.

Detecting 802.11 Wireless Hosts from Remote Passive Observations Valeria Baiamonte1 , Konstantina Papagiannaki2, and Gianluca Iannaccone2 Politecnico di Torino Intel Research Cambridge 1

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Abstract. The wide deployment of 802.11 WLANs has led to the coexistence of wired and wireless clients in a network environment. This paper presents a robust technique to detect 802.11 wireless hosts through passive observation of client traffic streams at the edge of the network. It is based on the estimation of entropy of packet interarrival times and on the analysis of variation in the measured entropy values across individual end host connections. With the aim of generating a physical layer “signature” that can be easily extracted from packet traces, we first perform controlled experiments and analyse them through Spectral Analysis and Entropy evaluation. Based on the gained insight we design a methodology for the identification of 802.11 wireless clients and test it on two data sets of packet-level traces collected in different networks. Our results demonstrate that wireless identification is highly precise in the presence of a sufficient traffic sample.

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Introduction

The proliferation of 802.11 WLANs has led to the emergence of hybrid local area networks where wireless and wired clients can seamlessly access the network’s resources. However, network access through 802.11 imposes a different kind of constraints on the network design. Wireless clients are likely to feature increased mobility, whereby the client point of attachment to the network may differ from one point in time to the next, affecting different parts of the infrastructure. Wireless client requirements with respect to throughput and latency may be harder to meet given the unpredictable nature of the wireless medium. Security solutions implemented in the core of the network may need to incorporate knowledge on the type of client to enforce appropriate solutions. Lastly, even the provisioning of services addressed to network clients may significantly benefit from knowledge regarding the capabilities of such clients. Recent research work has focused on generally characterizing and studying wireless traffic with the aim of highlighting essential differences and analogies with wireline traffic [1,2]. While particular network environments make the identification of wireless and wired clients trivial, say if clients obtain IP addresses from different address blocks, there exist network environments where such a task is much harder to perform [3,4,5]. For such environments previous approaches have advocated the use of active measurements [3] or passive inspection of TCP I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 356–367, 2007. c IFIP International Federation for Information Processing 2007 

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traffic [4,5]. The fundamental issue with the first approach is that it requires client cooperation for the collection of the appropriate information. The last two approaches, furthermore, precludes the identification of clients that may use protocols other than TCP. In this work we look into the problem of passive identification of wireless clients as observed at a location inside a network where a diverse set of client traffic is aggregated. We put forth the idea that a primary difference between wired and wireless systems is uncertainty which manifests itself at the physical and MAC layers. Uncertainty is naturally quantified using information theoretic measures such as that of entropy [6]. Techniques based on uncertainty estimation have lately been largely applied to traffic analysis. In [7,8,9], entropy of packet arrivals is used to detect traffic anomalies in wired networks. In this work, we exploit the concept of entropy to measure the “uncertainty” of wireless traffic. Variation of link quality and channel conditions together with the channel access mechanism employed by the 802.11 protocol, “brands” client traffic with a unique signature that can distinguish wireless from wired traffic. We look into the fundamental operations of the 802.11 protocol and the way it can perturb client traffic due to the random access mechanism employed. More practically, we first use active measurements in controlled experiments and deterministic traffic patterns. Our preliminary study draws on spectral analysis applied to packet interarrival times in such controlled environments. The inspection of the spectral density, in the signal built from the packet arrival process, lets us observe how intrinsic periodicity of sourced traffic can be either preserved or distorted depending on the physical medium traversed. Furthermore, spectral analysis allows us to define the signal bandwidth that needs to be filtered to extract information from a traffic flow, i.e. to define the time scales of interest for our purposes. Correctly filtering the traces, we then perform entropy evaluation of packet interarrival times, observing the differences in the content information of wired and wireless traffic flows. Based on these observations, we design an algorithm that can identify wireless clients through passive observations at an aggregation point. Using two different data sets collected from an enteprise and a campus environment we demonstrate that our algorithm achieves high accuracy in host detection when provided with a sufficient traffic sample.

2

Extracting a Wireless Signature

Our goal is to capture the physical layer “signature” in a traffic flow through the inspection of the packet interarrival times. We exploit two main features that a-priori differentiate wired from wireless access: (i) the unpredictability of the wireless medium, and (ii) the impact of the 802.11 medium access control (MAC) mechanism. Our conjecture is that both these mechanisms are bound to introduce random delays in the transmission of packets from a wireless host. Due to the unpredictability of propagation conditions on the shared radio medium, packets sent

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inside a wireless network may not be correctly decoded at the receiver. Packet loss causes wireless stations to retransmit, delaying the packet delivery at the receiver. Furthermore, every time a wireless station has a packet to send, it needs to contend for channel access. When many wireless clients co-exist in a WLAN, collisions are highly likely to happen, causing retransmission and increased backoff delays. Consequently, delay and increased jitter in packet reception is one fundamental feature of wireless traffic. If a host were to transmit the same constant bit rate (CBR) flow using the wired and wireless medium, we would expect to see increased variation in interarrival times in the wireless transmission compared to a purely periodic stream if access were wired. However, even in the case of a purely periodic data source inference of the access technology may still become complicated due to the behavior of the host and the location of the observation point. The transmission of a purely periodic traffic stream from a highly loaded host is unlikely to maintain its periodicity when observed at a remote location. Moreover, network congestion and multiplexing with other traffic may distort the original signature in the traffic. Such a task becomes even more challenging when the original traffic stream is not periodic. If the application used by the client does not generate periodic traffic (like VoIP for instance) we need to focus our attention to those parts in the traffic stream that relate to back to back packets. If packets are transmitted back to back, we would expect that the wireless medium will be able to distort their time of arrival at the receiver. Particular transport layers are bound to generate such back to back packets, for instance when TCP is transmitting a window of packets. However, if the transport layer protocol itself does not allow the observation of interarrival times of packets that have been generated closely spaced to each other, then the wireless signature will be very hard to recover. In what follows we use controlled experiments to study the behavior of periodic traffic when transmitted through the wireless medium. This section allows us to identify ways in which we can capture “clean” physical layer signatures that do not suffer from the above limitations. Different wireless scenarios are tested to investigate whether this signature is kept and how it varies across different conditions. The results of these experiments can clarify and better motivate the design of the methodology presented in Section 3. Experimental scenario. We generate traffic flows from wired and wireless clients inside a private LAN to destinations outside the LAN (several Planet Lab nodes. We use the tg traffic generator to send UDP and TCP traffic streams. The UDP streams are at constant bit rate of 1.6 Mbps, with 1000 byte packets sent every 5 ms. The TCP connections consist of the bulk transfer of large files (23Mbytes). All experiments are repeated both when clients attach to the network using a ethernet interface and when they use an 802.11b wireless NIC. All experiments are accompanied by sniffers at all clients, the AP (running HostAP), as well as a collection point at the edge of the private LAN that could represent a typical aggregation point where our algorithms would be deployed.

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More specifically, when it comes to wireless experiments we test the following different scenarios that may have a significant impact on the wireless signature captured: 1. Good: a topology where one client is placed in a good location and there is no contention (1 wireless client close to the AP, signal level: -43 dBm); 2. G-Cong: 3 clients are placed in good locations relative to the AP and contend for channel access (client-AP signal level: -43 dBm); 3. Bad: a topology where 1 client is placed in a bad location and there is no channel contention (1 wireless client far from the AP, signal level: -65 dBm); 4. Bad-Cong: a topology where 3 clients are placed in a bad location and contend for channel access (client-AP signal level: -65 dBm). Spectral Analysis The main purpose of this preliminary analysis is to investigate how the physical layer characteristics influence the explicit (e.g. CBR traffic) or implicit (e.g. TCP window of packets) periodicity of a traffic flow. Spectral analysis is carried out through the Discrete Fourier Transform DFT and specifically applied to the process of packet interarrival times. It aims at estimating the harmonic content of a traffic stream, such as to point out the frequency bandwidth whose power spectral density can be related to the MAC and physical layer behavior. The power spectrum of packet interarrivals not only keeps statistical information of a traffic flow, as a Probability Mass Function (PMF) would do, but it also describes how the “energy content” is spread over the frequency domain and how the “information content” is distributed over the signal bandwidth. More in detail, the signal to be processed is a discrete sequence defined on the time domain and describes the process of packet arrivals:  1 nT0 ≤ tarr < (n + 1)T0 (1) x[n] = 0 elsewhere x[n] is built as a sequence of pulses of amplitude 0 or 1 spaced by T0 . Pulses of amplitude 1 at nT0 correspond to packet arrivals occurring at times tarr , with nT0 ≤ tarr < (n+1)T0 . The time granularity T0 has to be precise enough to keep trace of all packet arrivals, with no overlapping of two or more packet arrivals in the discrete time interval [nT0 , (n + 1)T0 ]. To this end, since packet arrivals are never spaced by less than 20μs1 , we have chosen T0 = 20μs, thus the resolution on the frequency domain will be f0 = 1/T0 , equal to 50KHz. The observation window is N T0 wide, i.e. the total number of observed samples is equal to N . The Fourier transformed signal, defined on the discrete frequency domain is then: N −1 nk 1  x[nT0 ]e−j2π N X[kf0 ] = N n=0 1

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Further, before treating x[n] with spectral analysis, the discrete sequence is subtracted of its mean value. The mean value results in a large DC component in the spectrum that does not provide any useful information for our classification study. The DFT power spectrum P [n] is expressed by P [kf0 ] =| X[kf0 ] |2

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Power Spectrum results presented here are plotted as a function of time, instead of frequency, so as to relate the periodicity content information to the time domain and achieve an intuitive meaning. This is simply obtained rescaling the x-axis. In Figure 1(a), CPS of UDP wired and wireless CBR flows is computed. Interestingly, the distortion caused by the physical layer is clearly visible and deeply affects the periodicity of CBR flows. Packets have been generated as one every 5ms. If their transmission were not delayed, we would expect the ideal frequency spectrum of a pulse train with a pulse every N/T , N = 1, 2... and T = 5ms. The CPS, hence, would appear as a step function with a step every T .

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Focusing on the “udp wired” curve, indeed, we notice a step behavior due to bumps of power corresponding to the main harmonic components at N/T . This confirms that periodicity is actually preserved on the wire, whereas it is significantly reduced for wireless streams. The “wireless good and not congested” case still presents a step behavior, even though the amplitude of bumps decreases, while, when congested and bad located flows are analysed, the periodicity of CBR traffic completely disappears. In order to observe a similar behavior for TCP streams, we first need to observe how the signal power is distributed over the frequencies of a TCP signal. For this reason, Figures 1(c) and 1(d) are reported. There, the DFT Power Spectrum of a TCP wireless and a TCP wired connection are respectively compared. In the first, clear high-powered spectrum components are concentrated at the low time scale. These harmonic components refer to those packets sent inside the TCP congestion window, whose transmission is not delayed by the reception of TCP acknowledgements. A large amount of power is then concentrated at the higher time scales and expresses the effect of Round Trip Times RTTs. In the second power spectrum, shown in Figure 1(d) the transmission over the wireless medium has the effect of removing most of the periodicity, by reducing the signal power and distributing it over the whole bandwidth. Dominant frequencies are no more discernible. When the original traffic stream is not periodic, in order to catch the periodicity distortion, we need to focus our analysis on those parts of the traffic streams where packets are sent in a back to back fashion, thus where the access network effects are more visible. In the case of TCP traffic, this means taking into account the packets transmitted within a TCP window: we need to apply a filter and preserve only the high frequency bandwidth (i.e. the low time scales) where these phenomena can be caught. The filter can be easily applied over the sequence of packet interarrivals, dropping all the big interarrival times exceeding a certain threshold, TRT T . In this work TRT T has been choosen to be set to 10ms, so as to exclude big RTTs but include all the delays introduced by consecutive retransmissions in highly congested wireless networks. Thanks to this observation, we are able to isolate TCP wired flow from all the TCP wireless ones through the CPS, as in the UDP/CBR case (Figure 1(b)). Sample Entropy The DFT methodology, is computationally expensive and is difficult to be incorporated within an automated algorithm. A natural way to measure the “uncertainty’ is to use the concept of Entropy. We describe here the concept of empirical entropy, since we deal with empirical distributions. Let the random variable X denote the value of interarrival times, i.e. the time between back to back packets. X is randomly sampled or observed for m times inducing the empirical probability distribution on X, p(xi ) = mi /m, xi ∈ X and mi is the frequency of times X is observed to take the valuexi . The empirical entropy of interarrival time distribution is then H(x) := − xi ∈X p(xi ) log2 p(xi ). Empirical entropy values computed on the same distribution can be different, depending on the

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time scales considered to discretize the samples. For our computations, a bin size of 100μs has been chosen to compute the PMF. We compute the entropy of the PMF of packet interarrivals across time. Interarrivals are filtered as in the case of spectral analysis and each single point is computed acquiring the PMF of interarrival packet times every 20 seconds at the remote monitoring point. Figure 2 presents variance and entropy for UDP and TCP streams as a function of time. Entropy evaluation, differently from standard metrics like average value, median and variance, provides indeed a faithful estimation of the information content of a set of outcomes. Variance, for instance, is a measure of the average distance of the outcomes from the mean and quantifies how spread a distribution is around its mean value. This is not enough, though, to quantify the amount of observational variety and randomness retained in a set of observations. As a matter of fact, distributions that present low values of variance may assume high entropy values and vice versa. In the case of our experiments, the variance evaluation for UDP experiments, shown in Figure 2(a), is already able to differentiate wired flows from wireless ones. It also consistently increases in presence of congestion and low signal level within wireless environments. Delays and retransmissions result in a higher variability in packet arrivals, that affects the innate periodicity of CBR traffic. This does not hold for TCP flows though, whose variance is reported in Figure 2(b). Values range from 10−4 to 10−3 without a clear distinction among the different cases.

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In Figure 2(c) and Figure 2(d), instead, entropy values are clearly separated for all the cases, no matter which transport protocol is used. The information content is very low (around 1 bit) for the wired interarrivals, while it grows significantly for wireless packet flows. The difference between wired and wireless is still evident even when just one wireless client is transmitting in the WLAN.

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Detection Algorithm

From the lessons learnt during the controlled experiments, we can define a generic algorithm to classify end hosts based on their access media. The input of our scheme is a packet-level trace collected at a monitoring point. In the first stage, packets are aggregated on the basis of the IP address source. Within each IP-source set, 5-tuple flows are then isolated. Interarrival times between consecutive packets are computed and then filtered: only those, falling below TRT T , with TRT T = 10ms, are kept. The algorithm then computes two values: 1) the empirical entropy HIP , evaluated on the whole IP-source aggregated traces; 2) the empirical entropy of the largest (in terms of number of interarrivals) 5-tuple flow of each IP-source trace, HIP,5 . We then define Variation of Entropy the difference ΔH = HIP − HIP,5 . The two values HIP and ΔH are used to classify hosts. The pseudo-code of the proposed methodology is reported below: If HIP ≤ Hlower then the host is wired else if HIP ≥ Hupper then the host is wireless else if Hlower < HIP < Hupper if ΔH ≥ ΔHT HR then the host is wired else if ΔH ≤ ΔHT HR then the host is wireless

The thresholds are trained using a small set of passive traces and chosen as Hlower = 3.5bits, Hupper = 5bits and ΔHT HR = 0.5. Figure 3 shows the PMF of entropy computed over the training dataset and helps explaining the selection of thresholds. The mass of entropy for wireless flows is concentrated for HIP ≥ Hupper (the vertical dotted line on the right), while wired entropy values have higher probability for HIP ≤ Hlower (vertical line on the left). The PMF exhibits an overlapping area in the range [Hlower , Hupper ] bits, where the two distributions are superimposed. For the flows that fall into that region we use the variation of entropy as discriminator. With wireless hosts, the uncertainty measured by HIP,5 , i.e. on the largest 5-tuple flow, already accounts for the effects introduced by the wireless transmission. As a consequence, adding other smaller 5-tuple flows has a marginal impact on the value of the aggregate entropy resulting in a low ΔH.

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In the case of wired hosts, instead, the variation of entropy is driven by different factors. When HIP is large, ΔH is measuring the impact of aggregating different flows. By adding more outcomes the distribution defined over a limited interval becomes more informative, i.e., the aggregate entropy grows [6].

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Evaluation

Performance evaluation is carried out on two different datasets, namely Intel and Dartmouth traces. In order to evaluate the algorithm accuracy, results are compared with the ground truth, i.e. we know which IP addresses have been assigned to Ethernet and WLAN hosts form predefined blocks. Intel traces are collected on the access link of the Cambridge laboratory using a CoMo monitoring system [10]. The traffic contains a mix of connections sourced from 93 different hosts, out of which 27 use the wireless LAN. The Dartmouth traces are publicly available and refer to wide area wireless measurements taken at different APs in the university campus of Dartmouth college. We analysed connections from 162 distinct wireless IP addresses. All results from both the datasets are summarised in Figure 4(a). Hosts with at least two interarrival times are scattered on the basis of the aggregated entropy (x-axis) and variation of entropy (y-axis). Vertical lines at 3.5 and 5 bits delimit the interval [Hlower , Hupper ], while the horizontal line at 0.5bits reports the threshold ΔHT HR , used to select on the basis of the variation of entropy. Most of the IP addresses map unambigously in the wireless or wired regions. A dense cloud of wireless hosts can be noticed where the aggregate entropy is above Hupper and the variation of entropy below ΔHT HR . Most of the wired hosts, on the other hand, are either spread in the region below Hlower , or in the overlapping zone where the variation of entropy is higher. However, several hosts fall in the region where the classification is not certain. This is mainly due to the very small number of observations (i.e. few packets belonging to few connections) that we see in the traces from those end systems. Indeed, if we consider just those IP address for which we can measure at least 200 interarrival

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times, the accuracy improves significantly. Figure 4(b) shows the scatter plot for those end systems. We have also compared the accuracy of our method with the accuracy of alternative (and simpler) approaches. Figures 4(c) and 4(d) compare the accuracy of three methods as a function of the number of observed packets per source. The curve labelled as “Entr + Var Entr” refers to our proposal. “Minimum Interarr” refers to an approach that just looks at the minimum interarrival between all packets from the same source. This approach is based on the fact that on the wireless media two back-to-back packets still need to contend for the medium. Therefore, if the interarrival is above 100μs (i.e., the minimum interarrival of two 802.11a/g packets with no payload) the source is considered a wireless host. Interestingly, the minimum interarrival method performs very well on the Dartmouth dataset (Figure 4c) while it performs poorly on the Intel dataset (Figure 4d). The reason behind this is that some packets are queued on the wired interface right before the monitoring point leading to very small interarrival times. By picking always the minimum interarrival, this method leads therefore to large errors. The curve “Minimum Entr” computes the minimum Entropy among all 5tuple flows of a given source IP address. An Entropy below Hlower indicates that the source is wired. This approach always underperforms our proposed mechanism even when a large number of samples is considered.

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Related Works

Recent research work has focused on characterizing and studying wireless traffic with the aim of highlighting essential differences and analogies with wireline traffic [1] [2] [11]. The results from these studies were found informative to develop the proposed detection scheme. Studies closer to that presented in this paper are in [3,4,5]. In the first, the authors propose an access network type classification scheme, where a host interested in determining the connection type of a remote user, requests to send a known sequence of packets. Interarrival times of received packets are then recorded. At this stage, the host is able to infer the user’s connection type based on the median and entropy of the sequence of packet interarrival times. The scheme proves to be strongly reliable, but, unlike our solution, it requires the remote user cooperation. In the second, the classification scheme is also strongly related to our solution. A Bayesian inference algorithm is developed to estimate and classify whether TCP flows have traversed an 802.11 WLAN. The methodology relies on the time intervals occurring between TCP-ACK pairs and infers the connection type of a user based on this observations. Unlike the previous classification scheme, detection is performed through passive measurements, but the identification is possible only for clients that are employing the TCP protocol. The output of this algorithm is the fraction of wireless flows with a certain degree of belief. The error, estimated as the difference between the inferred fraction of wireless flows and the actual one, is bounded within +/- 0.05. The output of our algorithm, instead, extends this information by providing the identification of each single host within a certain traffic trace. The accuracy error, computed as the number of mistaken detections over all considered hosts, reaches 1% and 6% in the two data sets. Moreover, the methodology in [4] makes an inefficient use of traces because the detection is carried out only using TCP ACK-pairs with an inter-ACK time of 400us. (Due to TCP self clocking, wireless flows show a low number of such ACK-pairs, thus often 90% of wireless flows contain less than 10 ACK-pairs.) Differently our algorithm converges faster because it considers inter-packet pairs belonging to both UDP and TCP traffic connections whose time distance is below 10ms. The TCP-ACK pairs technique is also employed in [5]. In this work, some of the aforementioned limitations, e.g. the efficiency in the trace analysis, the promptness and accuracy of results, are overcome by using two effective sequential hypothesis tests, with and without training data, respectively. However, the detection is still limited to hosts sourcing TCP connections only. Valid references to information-theoretic techniques applied to traffic analysis can be found in [7], [8], [9], where entropy of packet arrivals is used to detect traffic anomalies in wired networks. Research work in [12] discusses the use of information theory and uncertainty to characterize wireless networks. Finally, Spectral Analysis of traffic flows is applied in [13], with the purpose of detecting and classifying denial of service attacks.

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Conclusion

We have presented a method to classify wired and wireless hosts only based on passive traffic observations at a remote location. Our method does not require any cooperation from the end-systems and is protocol agnostic. The accuracy is comparable with previously proposed approaches and outperforms na¨ıve methods. Our future work is focused on testing the methodologies over larger datasets in an attempt to isolate the specific sources of errors in our classification.

References 1. Hernandez-Campos, F., et al.: Assessing the real impact of 802.11 WLANs: A large scale comparison of wired and wireless traffic. In: LANMAN. (September 2005) 2. Balachandran, A., et al.: Characterizing user behavior and network performance in a public wireless LAN. ACM PER 30(1) (2002) 195–205 3. Wei, W., et al.: Classification of access network types: LAN, wireless LAN, ADSL, cable or dialup? In: Proceedings of IEEE Infocom. (March 2005) 4. Wei, W., et al.: Identifying 802.11 traffic from passive measurements using iterative bayesian inference. In: Proceedings of IEEE Infocom. (April 2006) 5. Wei, W., et al.: Passive online rougue access point detection using sequential hypothesis testing with tcp ack-pairs. Technical report, University of Massachussets Computer Science (November 2006) 6. Cover, T., Thomas, J.: Elements of Information Theory. John Wiley (1991) 7. Adya, A., et al.: Architecture and techniques for diagnosing faults in IEEE 802.11 infrastructure networks. In: Proceedings of ACM Mobicom. (September 2004) 8. Lakhina, A., et al.: Mining anomalies using traffic feauture distributions. In: Proceedings of ACM Sigcomm. (August 2005) 9. Xu, K., et al.: Profiling Internet backbone traffic: Behavior models and applications. In: Proceedings of ACM Sigcomm. (August 2005) 10. Iannaccone, G.: Fast prototyping of network data mining applications. In: Proc. of PAM. (March 2006) 11. Ridoux, J., Nucci, A., Veitch, D.: Seeing the difference in IP traffic: Wireless versus wireline. In: Proceedings of IEEE Infocom. (April 2006) 12. Das, S., Rose, C.: Coping with uncertainty in mobile wireless networks. In: PIMRC. (September 2004) 13. Hussein, A., Heidemannan, J., Papadopoulos, C.: A framework for classifying denial of service attacks. In: IEEE Globecom. (December 2004)

A Scheme for Enhancing TCP Fairness and Throughput in IEEE 802.11 WLANs Eun-Jong Lee1 , Hyung-Taig Lim1 , Seung-Joon Seok2 , and Chul-Hee Kang1 Department of Electronics Engineering, Korea University 5-ga, Anam-dong, Sungbuk-gu, Seoul 136-701 Korea {lej,limht,chkang}@widecomm.korea.ac.kr Department of Computer Science and Engineering, Kyungnam University 449, wolyong-dong, Masan, 631-701 Korea [email protected] 1

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Abstract. In this paper, we consider two fairness problems that occur in the infrastructure network (fairness between TCP uplink/downlink flows and fairness between competing TCP uplink flows). A large number of existing works have studied the TCP fairness issues and greatly solved the TCP unfairness problems. However, these solutions suffer from the drawback of TCP throughput degradation even though the TCP unfairness problem can be solved. In order to solve these problems effectively, we propose a scheme that modifies the receiver window size on the basis of maximum window size which is able to maximize link utilization. We have evaluated our scheme with ns-2 simulator and the results demonstrate that our scheme greatly improves both the TCP fairness and total throughput.

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Wireless local area networks (WLAN) based on the IEEE 802.11 standard are rapidly being deployed in hot spots such as hotels, libraries, and airports to provide the nomadic users with open access to the Internet. This WLAN is divided into two networks: infrastructure and Ad-hoc WLAN. The former is a network where all mobile stations only communicate through an access point (AP) in order to access to wired network and the latter is a network where mobile stations communicate directly with each other without access to the wired network. The IEEE 802.11 standard provides two channel access functions: Distributed Coordination Function (DCF) and Point Coordination Function (PCF). DCF is contention-based channel access method using carrier sense multiple access with collision avoidance (CSMA/CA). The CSMA/CA guarantees equal medium access opportunity to all stations. PCF is a contention free channel access method employing a polling technique by the Point Coordinator (PC) which determines the station that is going to transmit data. In this paper, our target network is infrastructure WLAN under DCF and we assume that the bottleneck between TCP end nodes is only on the wireless link. I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 368–379, 2007. c IFIP International Federation for Information Processing 2007 

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Currently, most of WLANs support the DCF with CSMA/CA mechanism. However, in the infrastructure network in the presence of both mobile senders and receivers, this mechanism leads to significant unfairness problems between uplink and downlink flows. In this paper, two TCP unfairness problems that occur in infrastructure WLAN are considered. Many existing works have studied the TCP fairness issues and greatly solved the TCP unfairness problems. However, these solutions suffer from the drawback of TCP throughput degradation even though the TCP unfairness problem can be solved. In order to solve these problems, we propose a scheme that modifies the advertised receiver window size on the basis of maximum window size which is able to maximize the channel utilization. Our scheme is only deployed on AP and we do not conduct any changes to the mobile station. The rest of the paper is organized as follows. Section 2 describes two TCP unfairness problems in infrastructure WLANs in detail. Section 3 presents related works. Section 4 investigates the source of the TCP throughput degradation in an existing solution. In Section 5, we describe the proposed scheme. In Section 6, we evaluate the proposed scheme based on simulation results. Finally, in Section 7, we present our conclusions.

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The scenario we consider is shown in Figure 1, where mobile stations in the WLAN communicate with a fixed server in a high-speed wired network through an AP. The mobile stations consist of one TCP sender and n TCP receivers. The TCP sender forwards TCP data to the AP through the uplink and the AP forwards TCP data to the mobile receivers through the downlink. All TCP data that will be transmitted to the mobile receivers are queued in the downlink buffer of AP and all TCP data that will transmit from the mobile sender are queued in a buffer of the each mobile station.

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Fig. 2. TCP throughput for multiple flows; (a) 4 downlink flows and 1 uplink flows, (b) 4 downlink flows and 4 uplink flows

Fig. 3. TCP received bytes for 14 uplink flows in IEEE 802.11b WLAN

Since the WLAN under DCF provides equal access opportunity to the media for all mobile stations, the AP and TCP sender equally compete to obtain channel access opportunity. The AP and one mobile sender are provided with equal access opportunities to the media. Hence, the mobile sender gets half of the channel bandwidth and the remaining half is equally shared by all the mobile receivers through the AP. Therefore, the data to be sent from the TCP sender has n times more opportunities than the data to be sent to the TCP receiver. The more the number of mobile sender increases, the more serious becomes the unfairness between uplink and downlink flow. Since the wired network is assumed to be a fast Ethernet at 100Mbps, the downlink access to the IEEE 802.11b at 11Mbps is the bottleneck. Moreover, the AP buffer suffers from heavy traffic load since the TCP data and ACKs share the AP downlink buffer. Consequently, overflow occurs at the AP and TCP senders of downlink flow recognize the packet drop as network congestion and reduce their data sending rate accordingly. However, the uplink flow still arrives at the maximum window size while the downlink flow struggles with the small window size of packets [1]. In order to confirm this problem, a simulation is conducted using the ns-2 simulator. Without loss of wireless channel, multiple mobile stations are set separately as senders and receivers to communicate with a fixed server. The AP queue limit is set to 100 packets [1]. The propagation delay of the wired network is set at 50ms. The TCP version is Reno and the size of TCP/IP packets is fixed at 1040bytes. The

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default receiver window size is set to 60 packets and we run the simulation for 190 seconds. The simulation results for received bytes for each receiver of multiple uplink and downlink flow are presented in Figure 2. As shown in Figure 2 (a), it is obvious that whenever there is an uplink flow passing through the AP, the throughput of all downlink TCP flows is decreased greatly. Moreover, as shown in Figure 2 (b), when the number of uplink flows increases, the unfairness between uplink/downlink flows becomes much more serious. 2.2

Unfairness Between TCP Uplink Flows

The greedy closed loop control nature of TCP leads to the unfair sharing of the channel bandwidth among each TCP, even when all mobile senders are in the WLAN. Consider the scenario of WLANs where all mobile stations operate as the TCP sender. When TCP senders transmit TCP data, the wireless stations queue TCP data to be sent to the destination and the returning TCP ACK packets are queued at the downlink buffer of the AP to access the wireless channel. For large numbers of stations, the AP downlink buffer is overwhelmed with the TCP ACKs, and then the TCP ACK may be dropped by buffer overflow. The dropping of TCP ACKs can disturb congestion window growth and invoke repeated timeouts. TCP flows with a small number of packets in flight are liable to invoke timeouts since the loss of a small number of data or ACK packets is sufficient to induce a timeout. Hence, TCP ACK losses at the AP buffer can easily occur in a newly started TCP flow [2]. Therefore, these stations cannot escape from this adverse condition. We confirmed the unfairness between competing uplink flows using ns-2 simulator. Figure 3 presents the received bytes for each TCP receiver. As shown in Figure 3, some stations experience starvation as a result of repeated timeouts.

3

Related Works

Many existing works have studied the TCP fairness issues in the cellular network, infrastructure WLANs and ad-hoc WLANs. In this section, we focus on the existing solutions related to infrastructure WLANs. These solutions are largely divided into two methods - a method for increasing the sending rate of the downlink flows and decreasing the sending rate of the uplink flows. The former method is illustrated in [2]. The authors ensure fairness between competing TCP uploads and downloads by prioritizing to TCP downlink traffic using the 802.11e AIFS, TXOP and CWmin parameters. The authors also indicated the unfairness between competing uplink flows and for this problem, proposed the ACK prioritization scheme. However, this solution suffers from the weak point that is possible to adapt only to the network that supports IEEE 802.11e MAC. The latter method is illustrated in [1], [3] and [4]. The authors in [3] and [4] proposed a scheme that suitably drops the incoming data to AP uplink buffer. The solution in [3] is based on rate control mechanisms using the token bucket

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filter and the scheme proposed in [4] ensures the uplink/downlink fairness using a virtual queue management, named VQ-RED. However, these solutions suffer from the drawback that they waste wireless channel utilization. Finally, the solution proposed in [1] is a TCP receiver window size manipulation scheme. The authors indicated that TCP uplink/downlink unfairness is influenced by AP buffer size and then proposed a scheme that modifies the receiver window size in TCP ACK into min (rwnd, buffer size / flow number) so that any loss of the packet does not occur in the downlink buffer. This solution is the most efficient among existing solutions because the TCP sender can reduce the data sending rate by itself and also dose not waste the wireless channel utilization owing to no loss of data at AP. Moreover, this method can be supported irrespective of IEEE 802.11 MAC type. These explain why our scheme is based on TCP receiver window size manipulation.

4

TCP Throughput Limits in an Existing Solution

Existing solutions suffer from the drawback of TCP throughput degradation while the TCP unfairness problem can be solved. We investigate the cause of the TCP throughput degradation based on TCP receiver window size manipulation that is most efficient among existing solutions.

Fig. 4. TCP throughput ratio (uplink/downlink) for RTT

Fig. 5. TCP received bytes for RTT

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We confirmed TCP throughput degradation by ns-2 simulation. Figure 4 shows the TCP uplink/downlink throughput ratio for Round Trip Time (RTT) in the case of the IEEE 802.11b standard applying the TCP receiver window size manipulation scheme (denoted in the following as “B/n”, where B is the AP buffer size and n is the number of TCP flows) and without applying any scheme (denoted in the following as “default”). As shown in Figure 4, the B/n scheme ensures the TCP uplink/downlink fairness irrespective of RTT while the default case suffers from significant unfairness during a small RTT. However, Figure 5 displays that the B/n scheme greatly decreases TCP total throughput in the case of an increase in RTT. The B/n scheme never reaches the maximum window size due to the limitation on the TCP congestion window growth. The TCP sender chooses the minimum value between congestion window and receiver window, this solution restricts the receiver window size to B/n. In other words, the TCP throughput is as follows. T CP throughput =

W indow size RT T

(1)

Then, we recognize that the B/n scheme limits the window size only to AP buffer size regardless of the increase or decrease in RTT. Thereby, TCP throughput with constant window size is reduced according to the increase in RTT. In order to solve this problem, we investigate the maximum window size that is able to be accommodated in the link without loss of the channel. The maximum window size considers a single bottleneck link with a capacity of μ packets per second and a FIFO buffer of size of B packets. For each connection, T denotes the propagation delay plus the service time in the bottleneck, namely RTT. The maximum window size is as follows. Wmax = B + μT

(2)

In this case, the bottleneck buffer is always full of packets and there are μT packets in flight [5]. The scheme that modifies receiver window size into min (rwnd, B/n) limits the window size only to AP buffer size (B) regardless of the factor titled T. Based on these causes, if the sending rate of all senders is limited by the standard of the maximum window size, not only TCP fairness will be guaranteed, but also link utilization will be maximized.

5

Proposed Solution

In this section, we illustrate the proposed solution which improves both TCP fairness and total throughput through a simple correction based on TCP receiver window size manipulation scheme. 5.1

Wmax -Based TCP Receiver Window Size Manipulation Scheme

We define proposed solution as Wmax -based TCP receiver window manipulation scheme. This scheme is to use the receiver window size field in the acknowledgment

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Value

Tslot τ Tp TPHY TDIFS TSIFS CWmin CWmax LMAC H LMAC ACK LRTS LCTS LTCP data LTCP ACK Rdata Rcontrol

20 μs 1 μs 144 μs 48 μs 50 μs 10 μs 31 μs 1024 μs 68 bytes 38 bytes 44 bytes 38 bytes 1040 bytes 40 bytes 11 Mbps 2 Mbps

slot time Propagation delay Transmission time of the physical Preamble Transmission time of the PHY header DIFS time SIFS time Minimum backoff window size Maximum backoff window size MAC overhead ACK size RTS size CTS size TCP data size TCP ACK size Data rate Control rate

packet. This field is originally used to match sending rate of TCP sender to processing rate of TCP receiver. Since the TCP sender chooses the minimum value between congestion window and receiver window, we can naturally reduce the sending rate of TCP using the receiver window field. Thus by manipulating the receiver window size at the AP, we can ensure that the TCP sending rate is limited to the value we calculate. A similar approach was used in [1] for TCP uplink/downlink fairness. However, this solution, as shown in the Section 4, suffers from TCP throughput degradation. Our solution is to modify the receiver window size on the basis of the maximum window size which is able to maximize the link utilization instead of the AP buffer size. We modify the receiver window size in all TCP acknowledgment packets passing through AP into not min (rwnd, B/n), but min (rwnd, Wmax /n). In our solution, the maximum window size is calculated by AP when TCP connection opens. This value is originally for a single connection, but we have to consider a network where there are multiple TCP connections. However, this is not a problem since our solution calculates the value for a single connection at the AP and then divides the maximum window size among all TCP connections. 5.2

Calculating Maximum Window Size

We consider a particular scenario of WLANs where a single mobile station communicates with a fixed server. In this case, we refer to the maximum window size shown in expression (2) that can be accommodated in steady state in the link. The bottleneck link corresponds to the downlink of AP in our scenario.

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Therefore, B is equal to AP buffer size and μ is the same as AP service rate. Therefore, we can newly present the expression (2). Wmax = AP Buf f er size + AP Service rate · RT T

(3)

In this case, the AP can easily find the AP buffer size and the RTT (we assume that we can measure RTT using ICMP packet, e.g. ping). Then we need to find the AP service rate (μ) In table 1, we define the IEEE 802.11b parameter value that will be used in our simulation and then we calculate the AP service rate based on the defined parameter value. The AP service rate (μ), TCP data transmission time (TTCP data ), and TCP ACK transmission time (TTCP ACK ) are as follows [6]. μ=

TT CP

data

Ttcp

1 data + Ttcp

(packet/seconds)

(4)

ack

= CW + TDIF S + TP + TP HY +

LRT S Rcontrol

LCT S + TSIF S + TP Rcontrol LM AC H + LT CP /IP H + LT CP data +TP HY + Rdata LM AC ACK +TSIF S + TP + TP HY + Rcontrol +TSIF S + TP + TP HY +

TT CP

ACK

= CW + TDIF S + TP + TP HY +

(5)

LRT S Rcontrol

LCT S Rcontrol LM AC H + LT CP /IP + Rdata LM AC ACK + Rcontrol

+TSIF S + TP + TP HY + +TSIF S + TP + TP HY +TSIF S + TP + TP HY

H

(6)

In the above, CW is the average backoff time [7] given by CW =

CWmin Tslot 2

(7)

We can derive from expression (5), (6) the summation of TCP data transmission time and TCP ACK transmission time and the value is 3868 μs. Then, we can derive the AP service rate using expression (4) and the value is approximately 2.15Mbps. Hence, we can again define the maximum window size as follows. Wmax = AP buf f er size + (2.15M bps ∗ RT T ) We will use this expression for our scheme.

(8)

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Measuring TCP Flow Number

In order to implement our solution, we need to measure the number of current TCP flows in the link. This measurement is similar to the method in [1]. Practically, it is very difficult to know the exact number of active flows, especially in the case of flows in an open connection. In addition, determining the direction of data is very hard (uplink flow or downlink flow). However, these are not problems for our solution since we count only the number of active flows regardless of the direction and consider a network where only TCP flows are present. In order to count the number of active TCP flows, we keep monitoring a pair of IP address and port number in the header of TCP packets passing through the AP. If a new flow is observed, the variable storing the number of active flows is increased by 1 and if a particular flow is not observed during a regular period, the variable is decreased by 1.

6

Simulation Results

In this section, we evaluate the performance of the proposed scheme by ns-2 simulator. The simulation scenario is depicted in Figure 1 and runs on equal terms with topology used in previous simulation with the exception of the number of TCP sender. This scenario assumes a network where the number of TCP sender is equal to the number of TCP receivers. In order to evaluate the performance, we have two important performance metrics: TCP fairness and total throughput.

Fig. 6. TCP throughput ratio for RTT

Fig. 7. TCP received bytes for RTT

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Fig. 8. TCP throughput ratio for number of stations

Fig. 9. TCP received bytes for number of stations

TCP fairness is the ratio of the TCP uplink throughput is the summation of uplink throughput and downlink throughput. The simulations run with various parameter functions such as RTT, number of stations and error rate. Further, we compare the performance metrics for three reference models: default, B/n and our solution (denoted in following as “Wmax /n”) Figure 6 and 7 show the throughput ratio and total throughput for varying RTT when the mobile stations are organized into four TCP sender and four TCP receivers respectively. As shown in Figure 6 and 7, our solution ensures uplink/downlink fairness similar to that of the B/n scheme and total throughput reaches as much as the default and provides a throughput enhancement of approximately 180% higher than that of B/n scheme. Figure 8 and 9 present results for the increase of the number of stations. When the number of stations is above 10, the ratio is suddenly increased and TCP receivers in WLAN cannot receive packets because of dropping at AP. However, our scheme ensures fairness as in the B/n scheme as well as shows a throughput enhancement of 2 times than B/n as shown in Figure 9. Figure 10 and 11 show fairness and total throughput for the error rate of the wireless channel. When error rate is 0.2, both fairness and throughput of three reference models show no great difference since the TCP of uplink flow cannot arrive at the desired window size due to the high error rate. When the error rate is 0.2, our scheme is no different than other schemes, however, with a proper

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Fig. 10. TCP throughput ratio for error rate

Fig. 11. TCP received bytes for error rate

Fig. 12. TCP received bytes for each uplink flow ID

error rate not only the fairness is ensured like in the B/n scheme, but also the total throughput always reaches that of the default. Therefore, the unfairness issues in infrastructure WLAN consider only when the channel error rate is 0.2 and below. Finally, we confirm the fairness and throughput between competing uplink flows. As shown Figure 3 in section 2.2, the unfairness problem in IEEE 802.11b WLAN without applying any scheme is very serious. However, B/n and Wmax /n schemes fairly ensure the fairness among all mobile stations as shown Figure 12. Further, the received bytes for each TCP receiver in the case of using the Wmax /n scheme nearly show a throughput enhancement of 2 times than that of the B/n scheme.

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Conclusion

In this paper, we proposed a new scheme that modifies the receiver window size on the basis of the maximum window size which is able to maximize the link utilization. Simulation results showed the fairness and total throughput compared with standard IEEE 802.11b and the case of applying a previous scheme. In the result, we confirmed that our scheme greatly improves not only the fairness between uplink and downlink flows, but also between competing uplink flows like that of the previous scheme, tagged B/n. In addition, we showed that the total throughput of our proposed scheme can reach as much as the default in all cases and approximately 2 times that of the B/n scheme. However, our scheme suffers from the disadvantage that it has to modify the TCP packet at AP. This packet modification would violate the TCP end-to-end semantics. Therefore, this point must be considered in future research. Acknowledgments. This research was supported by the MIC (Ministry of Information and Communication), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment).

References 1. Pilosof S, Ramjee R, Raz D, Shavitt Y and Sinhan P: Understanding TCP fairness over wireless LAN, In Proceedings of IEEE INFOCOM 2003, Volume 2, (April 2003) pp.863-872. 2. D.J. Leith, P. Clifford, D.W. Malone and A Ng: TCP fairness in 802.11e WLANs, IEEE Communication Letters, Volume 9, Issue 11, (Nov. 2005) pp.964- 966. 3. Blefari-Melazzi, N., Detti, A., Ordine, A. and Salsano, S.: Controlling TCP Fairness in WLAN access networks using a Rate Limiter approach, 2nd International Symposium on Wireless Communication Systems, (Sept. 2005) pp.375-379. 4. Xiaoyang Lin, Xiaolin CKaveg hang and Muppala,J.K: VQ-RED: An Efficient Virtual Queue Management Approach to Improve Fairness in Infrastructure WLAN, The IEEE Conference on Local Computer Networks, (Nov. 2005) pp. 632-638. 5. T.V. Lakshman and U. Madhow: The performance of TCP/IP for networks with high bandwidth-delay products and random loss, IEEE/ACM Transaction on Networking, volume 5, Issue 3, (June 1997) pp.336-350. 6. Daniele Miorandi, Arzad A. Kherani and Eitan Altman: A Queueing Model for HTTP Traffic over IEEE 802.11 WLANs, Computer Networks, Volume 50, Issue 1, (Jan 2006) pp. 63-79. 7. Y. Xiao and J. Rosdahl: Throughput and delay limits of IEEE 802.11, IEEE Communications Letters, Volume 6, Number 8, (Aug. 2002) pp.355-357. 8. A. Detti, E. Graziosi, V. Minichiello, S. Salsano and V. Sangregorio: TCP Fairness Issues in IEEE 802.11 Based Access Networks, submitted paper.

TCP NJ+: Packet Loss Differentiated Transmission Mechanism Robust to High BER Environments Jungrae Kim1 , Jahwan Koo2 , and Hyunseung Choo1, 1

School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea {witjung,choo}@ece.skku.ac.kr 2 Intelligent HCI Convergence Research Center Sungkyunkwan University 440-746, Suwon, Korea [email protected]

Abstract. Transmission mechanisms that include an available bandwidth estimation algorithm and a packet loss differentiation scheme, in general, exhibit higher TCP performance in wireless networks. TCP New Jersey, known as the best existing scheme in terms of goodput, improves wireless TCP performance using the available bandwidth estimation at the sender and the congestion warning at intermediate routers. Although TCP New Jersey achieves 17% and 85% improvements in goodput over TCP Westwood and TCP Reno, respectively, we further improve TCP New Jersey by exploring improved available bandwidth estimation, retransmission timeout, and recovery mechanisms. Hence, we propose TCP New Jersey PLUS (shortly TCP NJ+), showing that under 5% packet loss rate, a characteristic of high bit-error-rate wireless network, it outperforms other TCP variants by 19% to 104% in terms of goodput even when the network is in bi-directional congestion.

1

Introduction

Transmission control protocol (TCP) designed for wired networks is a connectionoriented transport protocol that provides reliable data communications [1], [2]. However, wireless infrastructures such as cellular networks, wireless LANs, and mobile computing have such characteristics as high bit-error-rate (BER), limited bandwidth, fading, and handoff, which severe performance degradation [3]. The main reason is that the congestion control mechanism in TCP cannot distinguish between the packet loss caused by wireless link error and that caused by network congestion, thus, reacting to the loss by reducing its congestion window (cwnd). Therefore, these inappropriate reductions of the cwnd lead to unnecessary throughput degradation for TCP applications [4]. 

Corresponding author.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 380–390, 2007. c IFIP International Federation for Information Processing 2007 

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Over the last decade, a considerable number of studies have been conducted on improving wireless TCP performance with the advances in wireless infrastructure technologies. According to the operations of TCP connection, wireless TCP algorithms can be divided into split or end-to-end approach [5]. Split approach attempts to prevent the wireless portion from the traditional network by separating TCP connection at the intermediate router (or a base station). The intermediate router behaves as a terminal (or a proxy) in both the wired and wireless portions. Both end hosts communicate with the intermediate router independently without knowledge of the other end. The drawbacks of split approach will be described in [5]. On the other hand, the end-to-end approach, such as TCP New Reno [6], Westwood [7], Jersey [8], and New Jersey [9], treats the route from the sender to the receiver as an end-to-end path, and the sender is acknowledged directly by the receiver. The receiver provides feedback reflecting the network condition, and the sender makes decisions for congestion control. TCP Westwood modifies addictive increase multiplicative decrease (AIMD) mechanism [2] which is the common congestion window control strategy of the wired TCP and intends to improve TCP performance by effectively adjusting its transmission rates on the basis of the available bandwidth estimation (ABE) algorithm at the sender. On the other hand, TCP Jersey and New Jersey are based on the integration of the sender side ABE algorithm and the receiver side packet loss differentiation scheme, thus, resulting in higher throughput and goodput than any other TCP variants. Even though TCP New Jersey achieves 17% and 85% improvements in goodput over TCP Westwood and TCP Reno, respectively, we further improve TCP New Jersey’s performance by exploring improved available bandwidth estimation, retransmission timeout, and recovery mechanisms. Hence, we propose TCP New Jersey PLUS (shortly TCP NJ+), showing that under 5% packet loss rate, a characteristic of high BER wireless network, it outperforms other TCP variants by 19% to 104% in terms of goodput regardless of background traffic when the network is in bi-directional congestion. The rest of the paper is organized as follows. Section 2 reviews the related works of the existing wired and wireless TCP schemes. Section 3 describes in detail the improved mechanisms of TCP NJ+. Section 4 presents performance evaluation via NS-2 [11] network simulator under various network conditions. The final section offers some concluding remarks.

2 2.1

Related Works TCP New Reno

TCP New Reno improves standard TCP Reno’s fast recovery [2]. The fast recovery is performed when packet loss is detected by the sender and then it enters the congestion avoidance phase after performing fast retransmit [2]. The multiple packet losses force the TCP Reno to invoke a slow down in the recovery of the dropped transmission rate. In TCP New Reno, the fast recovery does not terminate until receipt of full ACK. If the sender receives 3-dupack, which is a

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partial ACK, it will simply retransmit the lost packet and will not terminate the fast recovery until the all dropped packets are recovered. Hence transmission rates is maintained because it reduces the sending rate after all lost packets are retransmitted. Namely, TCP New Reno fast recovery takes care of the multiple packet drop from one cwnd. However, the limitation of TCP New Reno is that because it cannot distinguish the cause of packet loss more effective fast recovery cannot be performed. In addition, the reduction of sending rates to use the AIMD mechanism is to invoke the dropping of throughput in wireless networks where the multiple packet loss is usually occurred. 2.2

TCP Westwood

TCP Westwood is wireless TCP using end-to-end proactive congestion control. TCP Westwood estimates the current network bandwidth at the sender side. The sender estimates network bandwidth by exploiting the rate and pattern of returning ACK through the reverse links. However, TCP Westwood does not distinguish the cause of packet loss. It will adjust the transmission rates constantly, upon experiencing the packet loss. Therefore, it decreases the throughput in high BER wireless networks. It is a problem that the accuracy of estimated available bandwidth depends on network condition which is changed by network traffic in links. 2.3

TCP New Jersey

TCP New Jersey improves the available bandwidth estimation algorithm using the TCP Jersey. It also adjusts the slow start threshold(ssthresh) based on the current estimation. TCP Jersey and New Jersey consist of two key components, the available bandwidth estimation algorithm and the congestion warning mechanism that helps the sender to effectively differentiate the cause of packet loss at an intermediate router. In New Jersey, the TCP sender estimates current available bandwidth based on the packet interarrival time on the receiver. TCP New Jersey handles reverse links with background traffic. However, TCP New Jersey may experience the decreasing of throughput depending on background traffic in forward link to bring unexpected available bandwidth estimation. In addition, it cannot increase the reduced sending rates due to the packet loss effectively according to the cause of packet loss which contains both the packet loss by BER and by network congestion. Consequently, TCP New Jersey may degrade performance in wireless networks where packets are lost consistently by the high BER or the network congestion.

3

TCP NJ+

In TCP New Jersey, throughput may be reduced depending on the background traffic pattern. In addition, when the sender detects the packet loss or the retransmission timeout (RTO ) is expired, it may not recover the dropped sending rate effectively according to the cause of packet loss.

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We propose TCP NJ+ which improves the available bandwidth estimation algorithm and the recovery mechanism of TCP New Jersey in this paper. In the improved available bandwidth algorithm, the sender selects the maximum estimation to overcome the problem of available bandwidth estimation algorithm depending on background traffic pattern in comparison of two factors, the interarrival time of ACK packets at the sender and the interarrival time of data segments at the receiver. And also TCP NJ+ guarantees high throughput using the improved recovery mechanism which increases the reduced cwnd more effectively when the sender detects the packet loss or RTO expiration. 3.1

Improved Available Bandwidth Estimation

Both TCP Jersey and TCP New Jersey estimate the current available bandwidth based on Eq. (1). Rn−1 × RT T + Ln (1) Rn = (tn − tn−1 ) + RT T Here Rn is the estimated bandwidth when ACK packet n arrives at time tn at the sender, tn−1 is the previous ACK packet arrival time at the sender, Ln is the size of data packet n, and RT T is the round trip time at time tn in TCP Jersey. Meanwhile the data segment arrival time at the receiver is found using the timestamps [10] option in the TCP header instead of using the ACK packet arrival time at the sender. Hence tn and tn−1 in Eq. (1) are the data segment arrival time of the nth data packet and its previous data packet arrival at the receiver, respectively, in TCP New Jersey. Because the available bandwidth estimation algorithm on TCP New Jersey is computed by the packet transmission time for the receiver, it can explore accurate estimation, if the network condition is degraded by background traffic in reverse links, which returns the ACK packet. On the other hand, it cannot calculate the accurate available bandwidth estimation, if the network condition is deteriorated from the background traffic in forward links that transmits the data packet. Accordingly, both TCP Jersey and TCP New Jersey suffer from the problem that the available bandwidth estimation algorithm depends on the background traffic pattern which brings the degradation of performance. Hence, TCP NJ+ estimates the available bandwidth to compare two estimations in Fig. 1. The Rsn is the estimated bandwidth when the ACK packet n arrives at time tsn at the sender, and tsn−1 is its previous ACK packet arrival time at the sender. The Rrn is the estimated bandwidth, by using timestamps option, when the data segment n arrives at time trn at the receiver and trn−1 is the previous data segment arrival time at the receiver. Ln is the size of data packet n, and RT T is the round trip time. As the Fig. 1 presents, TCP NJ+ selects the maximum value over Rsn and Rrn to guarantee the appropriate sending rate. In conclusion, TCP NJ+ resolves the problem that the available bandwidth estimation algorithm on TCP New Jersey depends on the background traffic pattern. It achieves higher throughput regardless of the direction of background traffic as you see in section 4.

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J. Kim, J. Koo, and H. Choo Initialization : n ←1 R , R ,t ,t ← 0 s0 r 0 s 0 r 0 Procedure: ACK packet arrived at the sender if(timestamp ) Rsn ← ( RTT × Rsn −1 + Ln ) /((t sn − t sn −1 ) + RTT ) /* ABE based on ACK packet inter arrival time */ Rrn ← ( RTT × Rrn −1 + Ln ) /((t rn − t rn −1 ) + RTT )

/* ABE based on data packet inter arrival time */ R n ← max( R sn , R rn )

/* maximum value of two estimations */ n ← n +1

end if

Fig. 1. Improved available bandwidth estimation

3.2

Improved RTO Mechanism

If the ‘timeout’ is expired, the TCP sender concludes that the network is congested and reduces the ssthresh to the half of the current cwnd and the cwnd to one. In TCP Reno and TCP New Reno, RTO mechanism is operated by the AIMD algorithm [2]. Hence TCP Reno and TCP New Reno decrease the throughput because the RTO mechanism, which induces to drop the sending rate, is frequently occurred due to the high probability of packet loss in high BER wireless networks. An optimized window (ownd) in TCP New Jersey is computed by Eq. (2) shown below. Rn × RT T (2) owndn = segment size Here Rn is the value of available bandwidth estimation. If the RTO is expired by the nth packet, TCP New Jersey decides whether the packet loss is caused by either BER or the network congestion. If packet loss is caused by network congestion, the sender sets the cwnd to one and the ssthresh to owndn . Otherwise, it adjusts the cwnd and ssthresh to owndn . Since RTO is caused by BER instead of network congestion and the network condition becomes poor incidently, we can utilize the remainder of network bandwidth due to cwnd sets to one. Therefore it is inappropriate to set the cwnd to owndn which is computed by the minimum estimation when the link condition is degraded temporarily. In addition, when the network condition is dropped temporarily, it will decrease the throughput because the cwnd may be set to unexpected values. For this reason, as TCP NJ+ experiences RTO, it distinguishes the cause of network congestion and the BER. Depending on the cause, with RTO due to especially network congestion, TCP NJ+ sets the cwnd to one and the ssthresh to owndn as in the TCP New Jersey behavior. And for RTO due to the BER, TCP NJ+ sets the ssthresh to owndn and the cwnd to the value according to the algorithm given in Fig. 2 which is to employ owndn−1 as well as owndn . In

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if (RTO expired) if (Congestion Warning) /* if RTO due to congestion */ cwnd = 1; ssthresh = owndn; else /* if RTO due to BER */ cwnd = (owndn + owndn-1 ) / 2; ssthresh = owndn; end if end if Fig. 2. Improved RTO mechanism

TCP NJ+, owndn−1 value is higher than owndn because it is computed when the network condition is in a good state. TCP NJ+ achieves higher ownd, average of owndn−1 and owndn , and results in higher cwnd than TCP New Jersey, because the average of owndn−1 and owndn are always higher than owndn . Hence the throughput is guaranteed to reduce the recovery time of the dropped cwnd in order to avoid the network congestion and to utilize link bandwidth effectively in TCP NJ+. In addition, because RTO frequently occurs in high BER wireless networks, it shows higher performance over other wireless TCP schemes. 3.3

Improved Recovery Mechanism

TCP New Jersey differentiates the packet loss caused by the BER from that caused by network congestion using CW mechanism [8]. When the TCP New Jersey receives 3-dupack, the error recovery mechanism executes as follows. First, if the packet loss is caused by the network congestion, ssthresh is set to owndn , then if cwnd is lower than ssthresh, it is set to the current cwnd. But if the cwnd is higher than the ssthresh, it is set to owndn . Second, if the cause of packet loss is the BER, TCP New Jersey maintains the current ssthresh and cwnd. Therefore, there is no way to adjust cwnd when packet loss is caused by the BER. However the sender increases cwnd for every ACK it receives. But, when the sender receives the 3rd duplicated ACK, it does not increase the cwnd. This means that the relative loss of cwnd is increased according to growing packet loss. TCP NJ+ handles such problem in TCP New Jersey. The improved error recovery mechanism in TCP NJ+ is shown in Fig. 3. When TCP NJ+ receives 3-dupack, the improved error recovery mechanism is performed as follows. First, if packet loss is caused by network congestion, TCP NJ+ operates the same as TCP New Jersey. Second, when packet loss caused by BER occurs, ssthresh is set to owndn and cwnd is increased by 1 maximum segment size (MSS). The reason why the cwnd is increased by 1 MSS is to compensate the lost cwnd by the 3rd duplicated ACK, because the cwnd is not increased by fast recovery algorithm. However, the packet loss caused by the BER may utilize the

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if (3 dupack received by sender) if (Congestion Warning) /* if packet loss due to congestion*/ ssthresh = owndn; if (ssthresh < cwnd) cwnd = owndn ; end if else /* if packet loss due to BER */ ssthresh = owndn cwnd = cwnd + 1; end if end if Fig. 3. Improved recovery mechanism

remaining bandwidth. Hence the adjustment of the cwnd effectively is to invoke the improvement in performance. In TCP NJ+, the new recovery mechanism, which achieves the higher cwnd, ensures the remarkable performance improvement comparing to other wireless TCP schemes in high BER links where the packet loss occurs more frequently.

4

Simulation Results

We evaluate the goodput and fairness performance of TCP NJ+ using the NS-2 network simulator. One is a simple network topology and the other is a more realistic network topology with background traffic, as shown in Fig. 4 and Fig. 6, respectively. Various simulation parameters we use are presented in Table 1 [12]. Table 1. Simulation parameters Bandwidth

Wired : 100MB / Wireless : 2MB

Packet Size

762byte

Propagation Delay Wired : 10 - 20ms / Wireless : 1ms Queue Size

4.1

20 - 200 packets

Goodput Performance as Simple Topology

Goodput is the effective amount of data delivered through the network. It is a direct indicator of network performance. We evaluate the goodput of TCP NJ+, New Jersey, Westwood, and Reno on various wireless link error rates using the simple topology shown in Fig. 4.

TCP NJ+: Packet Loss Differentiated Transmission Mechanism

100MB, 10ms

387

2MB, 1ms

S

BS

D

Fig. 4. Simple network topology

The source (Node S) connects to Node BS via a 100MB wired link with 10ms propagation delay. Node BS is linked to the destination (Node D) via a 2MB wireless link with 1ms propagation delay. The queue size of the wired link is set to 150 and the wireless link queue size is set to 20 respectively. The goodput result is shown in Fig. 5(a). TCP Goodput vs. Wireless Link Error Rate

TCP Goodput vs. Wireless Link Error Rate in Forw ard Link Background Traffic 2000

1600

Goodput (Kbyte/sec)

Goodput (kbyte/sec)

2000

1200 800 NJ+ NJ WW Reno

400 0

NJ+ NJ WW Reno

1600 1200 800 400 0

0.1

1

2

3

4

5

10

0.1

1

Wireless Link Error Rate (% packet loss)

(a) Goodput vs. wireless link error rate

3

4

5

10

(b) Forward link background traffic

TCP Goodput vs. Wireless Link Error Rate in Reverse Link Background Traffic

TCP Goodput vs. Wireless Link Error Rate in Bi-directional Link Background Traffic

2000

2000 NJ+ NJ WW Reno

1600

Goodput (Kbyte/sec)

Goodput (Kbyte/sec)

2

Wireless Link Error Rate (% packet loss)

1200 800 400

NJ+ NJ WW Reno

1600 1200 800 400 0

0 0.1

1

2 3 4 Wireless Link Error Rate (% packet loss)

5

(c) Reverse link background traffic

10

0.1

1

2 3 4 Wireless Link Error Rate (% packet loss)

5

10

(d) Bi-directional link background traffic

Fig. 5. TCP NJ+ goodput performance

TCP NJ+ shows a higher goodput performance, when wireless link error rate increases. Especially, in a 5% wireless link error rate, TCP NJ+ outperforms TCP New Jersey by 19% and TCP Westwood by 54%. 4.2

Goodput Performance with Background Traffic

In TCP NJ+, the available bandwidth estimation algorithm guarantees considerable throughput regardless of the background traffic pattern. As illustrated in Fig. 6, we measure the goodput of TCP NJ+, TCP New Jersey, TCP Westwood, and TCP Reno on various wireless link error rates under forward where

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C1

C2

100MB, 10ms

100MB, 10ms FTP Cross-Traffic 2MB, 1ms

100MB, 10ms

S

R1

100MB, 20ms

BS

D 1MB, 1ms

FTP Cross-Traffic

100MB, 10ms

100MB, 10ms

C3

C4

Fig. 6. Simulation topology with background traffic pattern

data packets are transferred, reverse where ACK packets are traversed, or bidirectional background traffic. The source (Node S) connects to Node R1 via a 100MB wired link with 10ms propagation delay. R1 is linked to Node BS via a 100MB wired link with 20ms propagation delay. The asymmetric wireless link from BS to the destination(Node D) is represented by the differing bandwidth on the downlink (2MB) and uplink (1MB) with 1ms propagation delay. The crosstraffic flows, Node C1 to Node C2 (forward direction) and Node C4 to Node C3 (reverse direction), and in both direction are FTP background traffic via a 100MB wired link with 10ms propagation delay. The queue size of the wired link is set to 200 and the wireless link queue size is set to 20. The goodput result of the FTP forward background traffic is illustrated in Fig. 5(b). In a 5% wireless link error rate, TCP NJ+ outperforms TCP New Jersey by 30% and TCP Westwood by 55%. The goodput result of the FTP reverse background traffic is presented in Fig. 5(c). In 5% wireless link error rate, TCP NJ+ outperforms New Jersey by 20% and Westwood by 45%. The goodput result of the FTP bi-directional background traffic is presented in Fig. 5(d). TCP NJ+ outperforms New Jersey by 31% and Westwood by 56% in a 5% wireless link error rate. The simulation result shows that TCP NJ+ achieves higher goodput than New Jersey regardless of the background traffic pattern when the wireless link error rate increases. 4.3

Fairness

Fairness is also an important metric of TCP performance evaluation. It is the bandwidth allocation measure for the multiple connections of the same TCP. We use the Jain’s fairness index proposed in [13] in order to show the fairness of TCP NJ+, New Jersey, Westwood, and Reno on various link error rates using the topology in Fig. 7. The fairness results are summarized in Table 2. In conclusion, TCP NJ+ satisfies good fairness like the other TCP variants.

TCP NJ+: Packet Loss Differentiated Transmission Mechanism

S1

D1

S2 10

.. . .

389

D2

R1

BS

100MB, 45ms

R2 10MB, 1ms

S10

.. . .

10

D10

Fig. 7. Simulation topology for fairness Table 2. Fairness of TCP schemes vs. link error rate Error Rate(%) 0.0 0.1 0.5 1.0 5.0 10

5

NJ+

NJ

WW

Reno

0.9999 0.9999 0.9999 0.9999 0.9994 0.9903

0.9999 0.9999 0.9999 0.9999 0.9994 0.9904

1.0000 0.9999 0.9999 0.9998 0.9964 0.9811

1.0000 0.9998 0.9986 0.9989 0.9980 0.9875

Conclusion

We have proposed TCP NJ+, to improve the performance of TCP New Jersey. Three enhanced mechanisms are proposed in TCP NJ+. First, the improved ABE guarantees higher throughput regardless of the background traffic pattern because it estimates the optimal available bandwidth. Second, when RTO due to BER occurs, the improved RTO mechanism inflates by reducing the cwnd more quickly. Third, when the packet loss caused by BER occurs, the improved recovery mechanism makes the reduced cwnd to be increased quickly. Results from the simulations demonstrate that TCP NJ+ improves the performance even when wireless link error rates increase. Particularly, TCP NJ+ outperforms New Jersey by 19% and Westwood by 54% in a 5% wireless link error rate with no background traffic. Under a 5% wireless link error rate with background traffic, TCP NJ+ outperforms New Jersey by 27% and Westwood by 52%. In addition, the fairness is also satisfied. In conclusion, TCP NJ+ with the improved ABE, RTO, and recovery mechanisms are robust to high BER environments, showing significant performance improvements. Acknowledgments. This research was supported by the MIC(Ministry of Information and Communication), Korea, under the ITRC(Information Technology Research Center) support program supervised by the IITA(Institute of Information Technology Assessment), IITA-2006-(C1090-0603-0046).

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References 1. Postel, J.: Transmission control protocol. RFC 793, (1981) 2. Allman, M., Paxson, V., and Stevens, W.: TCP Congestion control. RFC 2581, (1999) 3. Xylomenos, G., Polyzos, G.C., Mahonen, P., and Saaranen, M.: TCP performance issues over wireless links. IEEE Communications Magazine, Vol. 39, (2001) 52–58 4. Lakshman, T. V. and Madhow, U.: The performance of TCP/IP for networks with high bandwidth-delay products and random loss. IEEE/ACM Transactions on Networking, Vol. 5, (1997) 336–350 5. Tian, Y., Xu, K., and Ansari, N.: TCP in wireless environments: problems and solutions. IEEE Radio Communications, Vol. 43, (2005) 27–32 6. Floyd, S. and Henderson, T.: The New Reno modification to TCP’S fast recovery algorithm. RFC 2582, (1999) 7. Casetti, C., Gerla, M., Mascolo, S., Sanadidi, M.Y., and Wang, R.: TCP Westwood: bandwidth estimation for enhanced transport over wireless links. ACM/IEEE MobiCom, (2001) 287–297 8. Xu, K., Tian, Y., and Ansari, N.: TCP-Jersey for wireless IP communications. IEEE Journal of Selected Areas in Communications, Vol. 22, (2004) 747–756 9. Xu, K., Tian, Y., and Ansari, N.: Improving TCP performance in integrated wireless communications networks, Computer Networks, Vol. 47, (2005) 219–237 10. Jacobson, V., Braden, R., and Borman, D.: TCP extensions for high performance, RFC 1323, (1992) 11. UCB/LBNL/VINT Network Simulator Online Avilable: http://www.isi.edu/ nsnam/ns 12. Song, C., Cosman, P.C., and Voelker, G.M.: End-to-end differentiation of congestion and wireless losses, IEEE/ACM Transactions on Networking, Vol. 11 (2003) 703–717 13. Jain, R., Chiu, D., and Hawe, W.: A quantitative measure of fairness and discrimination for resource allocation in shared computer systems, Research Report TR-301, (1984)

TCP WestwoodVT: A Novel Technique for Discriminating the Cause of Packet Loss in Wireless Networks Jahwan Koo, Sung-Gon Mun, and Hyunseung Choo School of Information and Communication Engineering, Sungkyunkwan University Chunchun-dong 300, Jangan-gu, Suwon 440-746, South Korea [email protected], {msgon,choo}@ece.skku.ac.kr

Abstract. Conventional TCP in wireless environment cannot differentiate packet losses caused by network congestion from those caused by wireless link errors, thus, resulting in severe performance degradation. Accordingly, efficient operation of TCP in wireless networks is a critical issue in the context of differentiation between packet loss. Towards this issue, we proposes a novel technique, WestwoodVT (WestwoodNR based on TCP Vegas buffer Thresholds), which is a sender-based TCP congestion control mechanism for discriminating the cause of packet loss to enhance the performance of TCP in wireless environment. Simulation results show that, under various wireless link error rates, it achieves a maximum of 41% and 118% improvements in goodput over WestwoodNR and TCP Reno, respectively. WestwoodVT only requires changes at the send-side. Thus, it eliminates any changes to the intermediate routers unlike TCP New Jersey. Therefore, it is cost effective to implement in already deployed networks. Moreover, its fairness and friendliness are also satisfied. Keywords: Transmission control protocol, wireless networks, congestion control.

1

Introduction

Conventional transmission control protocol (TCP) is a reliable transport protocol that is designed to perform well in wired networks. However, it may suffer from severe performance degradation in wireless networks due to atmospheric conditions, high transmission errors, temporal disconnections, and signal fading [1]. The main reason for the TCP performance degradation over wireless networks is that the conventional TCP (such as TCP Reno [2]) cannot distinguish between packet losses caused by transmission errors and those caused by network congestion, thus, blindly reacting to these losses by reducing its congestion window. Consequently, these inappropriate reductions of the congestion window lead to unnecessary throughput degradation for TCP applications [3]. 

Corresponding author.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 391–402, 2007. c IFIP International Federation for Information Processing 2007 

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To overcome such limitation and degradation, several schemes have been proposed and are classified in [4] and [5]. Generally, current research work has been classified into two directions: (1) end-to-end TCP modifications such as congestion control mechanism and (2) link layer approaches that include intermediate router mechanisms. However, End-to-end TCP modifications do not distinguish the cause of packet loss whether it is due to network congestion or wireless link error. Link layer approaches require more time and cost to be deployed in real wired and wireless networks. Therefore, without supporting any intermediate router mechanisms, the discrimination of the cause of packet loss using the modified end-to-end TCP mechanism is a critical issue in wireless environment. In this paper, we propose a novel technique, WestwoodVT (WestwoodNR based on TCP Vegas buffer Thresholds), which is a sender-based TCP congestion control mechanism for discriminating the cause of packet loss to enhance the performance of TCP in wireless environment. WestwoodVT uses the flow control concept of TCP Vegas [6] in order to discriminate the cause of packet loss. If the cause is due to network congestion, WestwoodVT retransmits packets using the available bandwidth estimator of WestwoodNR [7] [8], which is a very efficient TCP scheme in wired and wireless networks. Meanwhile, if the cause is due to wireless link errors, WestwoodVT ignores the loss and retransmits the packet as if the packet loss has never occurred. The result from the simulation using NS-2 demonstrates that, under various wired and wireless link error rates, WestwoodVT achieves a maximum of 41% and 118% improvements in goodput over WestwoodNR and TCP Reno, respectively. Furthermore, WestwoodVT and TCP New Jersey [9] show very similar performance. Since TCP New Jersey is supported by an intermediate router mechanism, WestwoodVT, which is required only a sender-side modification, outperforms WestwoodNR, TCP Reno, and TCP New Jersey in terms of efficiency, adaptation, deployment, simplicity, and cost in wired and wireless networks.

2 2.1

Related Works TCP Reno

TCP Reno [2] is a well-known standard TCP scheme. It has four transmission phases: slow start, congestion avoidance, fast recovery, and fast retransmit. At the beginning of a TCP connection, the sender enters the slow start phase, in which the congestion window size (cwnd ) is increased by one for every acknowledgment (ACK) received. When cwnd reaches the slow start threshold (ssthresh), the TCP sender enters the congestion avoidance phase, in which it increases its cwnd by the reciprocal of the current window size every time it receives an ACK. This increases the window by one in each round-trip time (RTT). When packet loss occurs at a congested link due to buffer overflow at the intermediate router, either the sender receives duplicate ACKs (DUPACKs), or the sender’s retransmission timeout (RTO) timer expires. These events activate TCP’s fast retransmit and recovery, by which the sender reduces the size of its cwnd to half and linearly increases cwnd, resulting in a lower transmission rate.

TCP WestwoodVT

2.2

393

TCP Vegas

Congestion detection in TCP Reno assumes the loss of packets as a signal of congestion. Since there is no mechanism in TCP Reno to early detect the congestion before the loss occurs, it is reactive rather than proactive. On the other hand, TCP Vegas [6] measures and controls the amount of extra data that a connection carries, where extra data means data that would not have been sent if the bandwidth used by the connection exactly matches the available bandwidth of the link. The goal of TCP Vegas is to maintain the proper amount of extra data in the network. Obviously, if a connection has too much extra data, it will result in congestion. If it has too little extra data, it is not able to respond fast enough to transient increase in the available bandwidth. TCP Vegas estimates the maximum expected transmission rate and measures actual transmission rate when received every ACK. It sets a variable, diff, which is calculated by the difference in actual from expected transmission rate. It also defines two thresholds, α and β, roughly corresponding to having too little and too much extra data in networks. When diff < α, TCP Vegas increases the congestion window linearly during the next RTT, and when diff > β, TCP Vegas decreases the congestion window linearly during the next RTT. TCP Vegas leaves the congestion window unchanged when α < diff < β. 2.3

WestwoodNR

WestwoodNR [7] [8] is a sender-side modification based on TCP New Reno, which is intended to better handle large bandwidth delay product (BDP) with potential packet loss due to network congestion or wireless link errors, and with dynamic load. It is a rate based end-to-end proactive approach, in which the sender estimates the available network bandwidth dynamically, by measuring and averaging the rate of returning ACKs. It relies on returning the ACK stream, and this information helps it better set the congestion control parameters: ssthresh and cwnd. In WestwoodNR, the transmission rate using the available bandwidth estimator is evaluated and used by the sender to update ssthresh and cwnd upon loss indication for efficient throughput in wired and wireless networks. However, WestwoodNR does not discriminate the cause of packet loss. It will adjust retransmission rates constantly upon experiencing packet loss. Therefore, it decreases the throughput in high wireless link error rate. This is also a problem that the accuracy of the estimated available bandwidth depends on network conditions, which is changed by network traffic in links. 2.4

TCP New Jersey

TCP New Jersey [9], known as the best existing scheme in terms of throughput and goodput, aims to improve wireless TCP performance using the available bandwidth estimator (ABE) at the sender and the congestion warning (CW) at the intermediate router. ABE estimates the bandwidth available to the TCP connection and guides the sender to properly adjust its transmission rate. CW is

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a congestion notification implemented at the intermediate routers that helps the sender to effectively distinguish packet losses caused by network congestion from those caused by transmission errors. Based on the bandwidth estimation from ABE and the congestion indication implied by CW, TCP New Jersey calculates the size of the congestion window. After that, the actual size of transmission window is determined as the minimum of the receiver-advertised window size and the sender-calculated congestion window size. Consequently, the joint approach with the combination of ABE and CW allows to improve TCP performance in high bit-error-rate (BER) wireless networks. However, TCP New Jersey may experience the decreasing of throughput depending on background traffic pattern. It cannot increase the reduced sending rates effectively according to the cause of packet loss. Consequently, if packet loss consistently occurs due to the high wireless link error, TCP New Jersey will reduce its throughput. In addition, CW is based on intermediate router mechanism. It is not easy to deploy TCP New Jersey into real networks because it requires the addition and modification of both the sender-side and the intermediate router-side modules. Thus, it consists of implementation, deployment, and management complexity.

3 3.1

WestwoodVT Motivation

WestwoodNR has more efficient ABE than other TCP schemes. However, it has no operation to discriminate the cause of packet loss. On the other hand, even if TCP New Jersey is known as the best existing scheme in terms of throughput and goodput, it requires the addition and modification of both the sender-side and the intermediate router-side modules. Thus, it consists of implementation, deployment, and management complexity. A novel TCP scheme for discriminating the cause of packet loss while introducing minimum additional complexity is needed. Specifically, our goal is to develop a solution that, from an implementation complexity perspective, requires little more than a sender-side modification. As we shall see, the only addition we consider is the discriminating of the cause and the effective retransmission mechanism in a sender-side only. Concretely, the discrimination mechanism uses the buffer state of network nodes based on the flow control concept of TCP Vegas. According to the cause, the retransmission mechanism uses the ABE of WestwoodNR. 3.2

Discriminating the Cause of Packet Loss

WestwoodVT investigates the buffer state of network nodes between sender and receiver in order to discriminate the cause of packet loss. The discrimination inherits from the concept of TCP Vegas. WestwoodVT estimates the maximum Expected transmission rate and Actual transmission rate when received every ACK. The Expected transmission rate is given by:

TCP WestwoodVT

395

W indowSize (1) BaseRT T where WindowSize is the size of the current congestion window, equal to the number of bytes in transit, BaseRTT is the minimum value of all measured RTT. The Actual transmission rate is given by: Expected =

W indowSize (2) RT T where WindowSize is equal to WindowSize of Eq. 1, RTT is the current calculated RTT. Additionally, WestwoodVT defines  is calculated by the difference of Actual from Expected transmission rate. The value of  presents the amount of current existing data in buffer of network nodes. Namely,  indicates the state of the current network. This is given by: Actual =

W indowSize W indowSize − ) × BaseRT T (3) BaseRT T RT T In addition, WestwoodVT defines buffer thresholds, α and β. These values indicate the lower and upper bound of network nodes’ buffer. The discrimination of the cause of packet loss in WestwoodVT is operated in congestion avoidance phase. In this phase, the sender estimates  using Eq. 1, 2, and 3 when it receives 3-DUPACK. WestwoodVT then compares  to buffer thresholds α and β. =(

12 12

Network Congestion

Buffer Buffer(byte) (byte)

10 10

△

>β

β88 Postpone the decision

66

α<△

<β

4

4 α

Wireless Link Error

22

00

△ 11

22

33

44

55

66

77

<

88

α 99

10 10

11 11

12 12

13 13

14 14

15 15

Time (second) (second) Time

Fig. 1. Discriminating of the cause of packet loss in WestwoodVT

Figure 1 presents main concept of the mechanism, which discriminates the cause of packet loss when the sender receives 3-DUPACK. If  is smaller than threshold α, WestwoodVT assumes that the current buffer state of network nodes is loose, and decides that the packet loss is due to wireless link error. If  is greater than threshold β, it assumes that the current buffer state of network nodes is tight. At this point, WestwoodVT considers the packet loss as that caused by network congestion. If  is greater than α and smaller than β, WestwoodVT does not decide the cause of the packet loss, maintains the current state, and retransmits lost packets. Therefore, WestwoodVT postpones the decision of cause until it receives 3-DUPACK for more accurate discrimination.

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J. Koo, S.-G. Mun, and H. Choo

The Operation of WestwoodVT

WestwoodVT classifies the cause of packet loss using the buffer state of network nodes. When the cause is due to network congestion, WestwoodVT operates congestion control mechanism using the ABE of WestwoodNR, which is a very efficient TCP scheme in wired and wireless networks. However, when the cause is due to wireless link error, there is no requirement to use congestion control mechanism because there is no network congestion. In this case, WestwoodVT increases cwnd by one, and then retransmits lost packets as if transmitting a standard packet.

/* In Congestion Avoidance */ if (3-DUPACK received) if (△ > β) Available bandwidth estimation of WestwoodNR; /* Due to congestion */ else if (△ < α) cwnd = cwnd + 1; /* Due to wireless link errors */ else cwnd = cwnd; /* Postpone the decision */ end if end if

Fig. 2. Pseudo code of WestwoodVT

The reason of increasing the congestion window is that the sender generally increases cwnd by one for every ACK received. At the packet loss is caused by wireless link error, when the sender receives third duplicated ACK, it does not increase the congestion window because of fast recovery mechanism; normally the sender increases the congestion window by one when it receives first or second duplicated ACK. This results in the loss of congestion window. In this situation, WestwoodVT modifies fast recovery mechanism using increasing the congestion window by one. Figure 2 shows the pseudo code of WestwoodVT.

4

Simulation Results

In this section, WestwoodVT is compared to WestwoodNR, TCP New Jersey, and TCP Reno using NS-2 simulator in terms of goodput, fairness, and friendliness. These are metrics of TCP performance evaluation on wired and wireless networks. Table 1 presents parameters for simulations.

TCP WestwoodVT

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Table 1. Simulation parameters

4.1

Packet Size

Queue Size

726 B

20 MSS

Bandwidth

Delay

wired

wireless

wired

wireless

100 MB

2 MB

10 ~ 20 ms

1 ms

Goodput Performance

Goodput is the effective amount of data delivered through the network. It is a direct indicator of network performance. The simulation environment is depicted in Fig. 3.

Sender

100 MB, 10 ms

Base Station

2 MB, 1 ms

Receiver

Fig. 3. Simulation topology for obtaining goodput performance

A single TCP connection running a long-live FTP application delivers data from sender to receiver, under various wireless link errors: 0.1%, 1%, 2%, 3%, 4%, and 5%, respectively. In Fig. 3, all network nodes maintain the queue size of 20 MSS. In TCP New Jersey, CW makes a decision the cause of packet loss, either network congestion or wireless link error. When amount of existing data over router’s buffer is greater than 3/4 of amount of the buffer, if packet loss occurs, CW assumes that the cause of packet loss is due to network congestion. Otherwise, CW makes a decision that the cause is due to wireless link error. Similarly, the buffer thresholds of WestwoodNR are configured to roughly three quarters of the queue size of network nodes. We setup the lower bound of WestwoodVT, α to 14. if  < 14, the cause is due to wireless link error. The upper bound, β is fixed to 16, if  > 16, the cause is due to network congestion. If  is equal to 15, WestwoodVT defers the decision of the cause. Finally, we run the simulation for TCP WestwoodVT, -WestwoodNR, -New Jersey, and -Reno, respectively. Figure 4 demonstrates the result of simulation, an average of 10 times, over 200 seconds. As shown in the simulation results, WestwoodVT is superior over WestwoodNR and TCP Reno in all wireless link error rates. With wireless link error rate of 1%, WestwoodVT outperforms WestwoodNR by 3% and TCP Reno by 21%. WestwoodVT outperforms WestwoodNR by 11% and TCP Reno by 54% with wireless link error rate of 2%. Furthermore, WestwoodVT is almost identical to TCP New Jersey, until the wireless link error rate reaches 4%. WestwoodVT achieves a maximum of 41% and 118% improvements in goodput over WestwoodNR and TCP Reno, respectively.

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Fig. 4. Comparison of goodput of TCP WestwoodVT, -WestwoodNR, -New Jersey, and -Reno

The performance of TCP New Jersey is overwhelming other TCP schemes beyond wireless link error rate of 4%. However, because real wireless link error rate is 1% ∼ 2% and the performance of WestwoodVT is similar to TCP New Jersey. Therefore, WestwoodVT, a sender-side scheme, is generally better than TCP New Jersey and the other TCP schemes in terms of efficiency, deployment, adaptation, and cost. 4.2

Goodput Performance with Background Traffic

In order to simulate the performance of TCP WestwoodVT, -WestwoodNR, New Jersey, and -Reno with background traffic of both forward and backward FTP traffic, we consider a more complex topology such as Fig. 5. Node 2

Node 1

100 MB, 10 ms

Sender

100 MB, 10 ms

Router 1

100 MB, 10 ms

Node 4

FTP Traffic

100 MB, 20 ms

FTP Traffic

100 MB, 10 ms

Base Station

2 MB, 1 ms

Receiver

100 MB, 10 ms

Node 3

Fig. 5. Complex simulation topology with background traffic

The queue size of all nodes are set to 20 MSS. A single TCP connection running a long-live FTP application delivers data from the sender to the receiver

TCP WestwoodVT

399

Fig. 6. Comparison of goodput of TCP WestwoodVT, -WestwoodNR, -New Jersey, and -Reno with background traffic

D

Router1 100 MB, 45 ms

Base Station

Router2 10 MB, 1 ms

...

10

...

S

10

Fig. 7. Simulation topology for obtaining fairness index

under various wireless link errors: 0.1%, 1%, 2%, 3%, 4%, and 5%, respectively. At the same time, cross-traffic flows are generated: forward traffic flows from node1 to node2 via router1 and base station, and backward traffic flows from node3 to node4 via base station and router1, respectively. Figure 6 depicts simulation results over 200 seconds, an average of 10 times. The buffer thresholds, α and β of WestwoodVT are configured to 14 and 16, respectively. These values are based on the queue size of network nodes. The performance of TCP Reno is worst. WestwoodVT achieves 6% ∼ 24% improvements in goodput over WestwoodNR, and it is overwhelming TCP New Jersey, under all wireless error rates. In this simulation, TCP New Jersey experiences the decreasing of throughput with background traffic. However, WestwoodVT keeps its performance constantly, regardless of wireless link error rates or background traffic. 4.3

Fairness of WestwoodVT

Another important issue of TCP is fairness. Multiple connection of the same TCP scheme must interoperate nicely and converge to their fair shares. We employ Jain’s fairness index [10] function to justify the fairness of TCP schemes. The perfectly fair bandwidth allocation would result in a fairness index of 1.

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J. Koo, S.-G. Mun, and H. Choo Table 2. Fairness comparison Wireless Link Error Rate

WestwoodVT

WestwoodNR

TCP New Jersey

TCP Reno

0.0%

0.999

0.999

0.999

0.999

0.1%

0.999

0.999

0.999

0.999

0.5%

0.999

0.999

0.999

0.999

1.0%

0.999

0.999

0.999

0.998

5.0%

0.992

0.991

0.997

0.994

We setup the simulation environment as shown in Fig. 7. We run the simulation for different TCP schemes and compare their fairness index. The results of fairness are summarized in Table 2. All TCP schemes including WestwoodVT achieve a fairly satisfactory fairness index under various wireless link error rates. 4.4

Friendliness of WestwoodVT

Friendliness is another important property of TCP. A friendly TCP scheme should be able to coexist with other TCP schemes and not result in starvation. S

D m

Router1

10 - m

100 MB, 45 ms

Base Station

Router2 10 MB, 1 ms

...

...

m

10-m

Fig. 8. Simulation topology for obtaining friendliness

Fig. 9. Friendliness under 1% of wireless link error rate

TCP WestwoodVT

401

To verify the friendliness of WestwoodVT, we setup the simulation environment, as shown in Fig. 8, where WestwoodVT coexists with TCP Reno. There are 10 pairs of TCP connections, of which m are TCP Reno connections and 10-m are WestwoodVT connections. All 10 connections are expected to share the 10MB link equally. We measure the throughput of each connection. The mean throughput of TCP Reno and WestwoodVT is calculated by summing up the throughput of the same TCP schemes and is divided by the number of connections, respectively. The results are presented in Fig. 9. WestwoodVT behaves more aggressively than nonwireless TCP such as TCP Reno. However, this behavior is anticipated since WestwoodVT is designed to perform better in lossy wireless environments.

5

Conclusion

In this paper, we have proposed WestwoodVT which utilizes the sender-based transmission window control mechanism for discriminating the cause of packet loss. It checks the buffer state of network nodes between sender and receiver by using TCP Vegas operating mechanism and discriminates the cause of packet loss based on the buffer state. When the packet loss is due to network congestion, WestwoodVT uses the ABE of WestwoodNR for packet retransmissions. Otherwise, it retransmits packets without congestion control mechanism. For performance evaluation, we setup WestwoodVT’s buffer thresholds α and β to 14 and 16, respectively. The result from the simulation demonstrates that, under 1% of realistic wireless link error rate, WestwoodVT achieves a maximum of 3% and 21% improvements in goodput over WestwoodNR and TCP Reno, respectively. WestwoodVT and TCP New Jersey show very similar performance in wired and wireless networks. In our opinion, WestwoodVT is better than other TCP schemes such as WestwoodNR, TCP New Jersey, and TCP Reno in terms of efficiency, adaptation, deployment, simplicity, and cost in wired and wireless networks.

Acknowledgments This research was supported by the MIC, Korea, under the ITRC support program supervised by the IITA, IITA-2006-(C1090-0603-0046).

References 1. Xylomenos, G., Polyzos, G. C.: TCP Performance Issues over Wireless Links. IEEE Communications Magazine, Vol. 39. (2001) 52–58 2. Allman, M., Paxson, V., Stevens, W.: TCP Congestion Control. RFC 2581, (1981) 3. Lakshman, T. V., Madhow, U.: The Performance of TCP/IP for Networks with High Bandwidth-Delay Products and Random Loss. IEEE/ACM Trans. Networking, Vol. 5, No. 3. (1997) 336–350

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4. Balakrishnan, H., Padmanabhan, V. N., Seshan, S., Katz, R. H.: A Comparison of Mechanisms for Improving TCP Performance over Wireless Links. IEEE/ACM Trans. Networking, Vol. 5, No. 6. (1997) 759–769 5. Tian, Y., Xu, K., Ansari, N.: TCP in Wireless Environments: Problems and Solutions. IEEE Radio Communications, Vol. 43. (2005) S27–S32 6. Brakmo, L. S., O’Malley, S. W., Peterson, L. L.: TCP Vegas: New Techniques for Congestion Detection and Avoidance. ACM/SIGCOMM Computer Communication Review, Vol. 24. (1994) 24–35 7. Casetti, C., Gerla, M., Mascolo, S., Sanadidi, M. Y., Wang, R.: TCP Westwood: Bandwidth Estimation for Enhanced Transport over Wireless Links. ACM/Mobicom. (2001) 287–297 8. Casetti, C., Gerla, M., Mascolo, S., Sansadidi, M. Y., Wang, R.: Westwood: End-toEnd Congestion Control for Wired/Wireless Networks. Wireless Networks Journal, Vol. 8. (2002) 467–479 9. Xu, K., Tian, Y., Ansari, N.: Improving TCP Performance in Integrated Wireless Communications Networks. Computer Networks, Vol. 47. (2005) 219–237 10. Jain, R., Chiu, D., Hawe, W.: A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems. Research Report TR-301, (1984)

Modeling TCP in a Multi-rate Multi-user CDMA System Majid Ghaderi1,, , Ashwin Sridharan2 , Hui Zang2 , Don Towsley1 , and Rene Cruz3 University of Massachusetts Amherst {mghaderi,towsley}@cs.umass.edu 2 Sprint Advanced Technology Labs {ashwin.sridharan,hui.zang}@sprint.com 3 University of California San Diego [email protected] 1

Abstract. Modern CDMA wireless channels support multiple transmission rates, which can be dynamically assigned to users based on traffic demand. However, in practice, assignment of high rate channels comes with the penalty of increased power as well as smaller orthogonal codes, which constrains their assignment to only a subset of active users. This motivates the need to carefully control high rate channel assignments so as to minimize power and achieve fairness among users. In this work, we propose a simple class of channel allocation policies to achieve this goal for TCP sessions. We develop an analytical model that explicitly captures both TCP dynamics and the impact of multiple users contending for a shared resource to evaluate the performance of the allocation policy. The model is shown to be accurate by comparing against ns-2 simulations and its utility demonstrated in computing the minimum number of required high rate channels to minimize contention.

1

Introduction

Modern CDMA networks incorporate powerful mobile processors and digital communication techniques that allow wireless channel schedulers to rapidly allocate (or de-allocate) high data rate channels in response to channel conditions or user traffic demands. Although an eminently desirable feature, in practice, high data rate channels are scarce resources in a CDMA wireless system that 



This research is continuing through participation in the International Technology Alliance sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory, the U.S. Government, the UK Ministry of Defense, or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Corresponding author.

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cannot be simply assigned to all users. This is due to the increased power requirements as well as reduced codeword set making such an allocation infeasible. Consequently, the high rate channels must be carefully utilized so as to fairly service users as well as minimize power. While the existing mechanisms in commercial CDMA networks based on buffer occupancy are reasonable for inelastic applications, it is not clear as to how to select high data rate channels or share them among multiple TCP sessions. In particular, TCP, which is the dominant transport protocol in the Internet, incorporates adaptive rate mechanisms and is quite sensitive to the available channel rates and channel errors. Hence, it can be expected to demonstrate complex behavior to dynamic rate allocations by the wireless scheduler. While rate allocation in CDMA networks for elastic applications in general has been previously studied [1, 2], they do not incorporate TCP dynamics. Our work takes the first step in addressing this issue by modeling a class of simple allocation policies in such a system while explicitly incorporating TCP dynamics of each session. Recently we demonstrated in [3] that for a single TCP session, significant throughput gains are possible if the scheduler selects and allocates channel rates as a function of the TCP transmission rate. In this paper,we extend this framework to include the presence of multiple users along with practical constraints on assignment of high rate wireless channels. Furthermore, we propose a class of allocation policies for sharing the high rate data channels among concurrent TCP sessions, whose performance is explored both analytically and through simulations. The utility of the model is demonstrated by evaluating the impact of various design features like the number of high-rate channels and their data rates on TCP throughput, which is useful for planning purposes. The rest of this paper is organized as follows. Section 2 describes the high rate contention problem in CDMA wireless systems. Section 3, presents our system model. The TCP-aware allocation policy is presented in Section 4 and analyzed in Section 5. Section 6 validates the accuracy of the model against ns-2 simulations. Our conclusions as well as future work are discussed in Section 7.

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CDMA Wireless System

In this section, we describe the wireless framework considered in the paper as well as motivate the problem. For ease of exposition, we use the CDMA2000 1xRTT [4] system as a reference example of a practical system although our analysis applies equally to other wireless systems that dynamically adapt their wireless channel rate in response to user traffic. The focus of this work is on mobile sessions in a single cell that are concurrently involved in TCP bulk transfers on the downlink, i.e., from the access network (or Base Station Controller (BSC)) to the mobile device. The throughput experienced by such sessions on the downlink is strongly influenced by the wireless channel scheduler residing at the BSC because it controls the channel rate allocated to each session. For example, CDMA2000 standards [4] specify up to six different channel rates that

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can be dynamically assigned to a user by the BSC. The lowest channel rate, called the fundamental channel can be simultaneously assigned to all users and hence defines the capacity in terms of the number of users. The higher channel rates are called supplemental channels and dynamically assigned to only a subset of users at any given instant in order to boost data rates. Given that the scheduler can choose from up to six rates, the obvious question arises as to why the scheduler does not always assign the highest channel rate to all users? Indeed, this issue forms the crux of the resource allocation problem investigated in this paper. As was briefly alluded to in Section 1, allocation of a high rate channel incurs penalties in two ways. First, in order to maintain a reasonably low frame error rate (FER), a high rate channel requires the wireless antenna to transmit at higher power which can introduce excess interference in neighboring cells and shrink the available power budget at the cell itself for transmission to other users. Doing so for all users would further exacerbate the problem. Second, and perhaps more importantly, in practice, higher rate channels in a CDMA system are usually achieved by reducing the orthogonal code length (called Walsh codes). This in turn implies that not all active users can simultaneously transmit at a higher rate, simply because fewer orthogonal codewords are available at that rate, making it inherently infeasible. Furthermore, as a direct corollary of the above argument, the higher the supplemental channel rate, the shorter the code length, and hence smaller the subset of concurrently assignable users. For both these reasons, it is desirable that the wireless channel scheduler judiciously assigns high rate channels such that a) it is utilized only when required so as to minimize power and b) cycled among all users desiring the high rate channel so as to achieve fair resource allocation. Since TCP incorporates an adaptive feedback mechanism that is sensitive to channel errors and available channel rate, its behavior to dynamic rate adaptation by the wireless scheduler can be quite different from that of inelastic applications. This immediately raises the issue of how to allocate supplemental channels to TCP sessions in a CDMA system and its impact on achieved throughput. We address this issue in this paper.

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System Model

We assume a single cell with N continuously backlogged TCP sessions transferring data over the downlink. Hence, there are N fundamental channels that can be simultaneously used by all users. 1. The cell is assumed to have K high rate supplemental channels, such that at any instant in time up to K ≤ N users can transmit at the higher rate. 2. We assume that the fundamental channel has rate C0 and the supplemental channel a rate of C1 . Clearly C0 ≤ C1 . At each point in time, the scheduler decides which type of channel is to be assigned to each TCP session. While the fundamental channel is always available, the allocation of high rate channels is arbitrated by the wireless scheduler because K ≤ N .

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3. The packet error probability is implicitly assumed to be a function of the assigned channel and denoted by p0 (p1 ) when the assigned channel rate is C0 (C1 ). Note that typically p0 ≤ p1 , reflecting the impact of smaller codewords to achieve higher channel rates. 4. We assume the presence of power control to primarily combat fast fading and interference effects. This is true in current systems where fast closedloop power control tracks a specified target SINR (or equivalently target FER). 5. We assume no (or a very small) buffer at the base station. Hence, TCP experiences congestion if its sending rate exceeds the maximum allocated channel rate. We note that modern cellular systems can support up to four or more different supplemental channel rates. However, obtaining succinct analytical expressions for TCP throughput even for three rates is quite difficult. Moreover, our simulations show that there is only a small improvement in system performance by considering more than two rates [5]. Finally, we emphasize that no specific assumptions have been made regarding how the two channel rates are achieved nor how they result in the specific channel error probabilities.

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TCP-Aware Rate Allocation

We now present our TCP-aware channel allocation scheme. It essentially comprises of a simple preemptive allocation discipline with parameter α that utilizes the TCP transmission rate to make preemption decisions. Specifically, we assume that the wireless scheduler can monitor the TCP rate of each session and, based upon it, decides what channel to allocate. The mechanism works in the following fashion: 1. Upon starting, a TCP session i is assigned a low-rate channel with rate C0 . 2. Since the TCP rate increases additively (in the absence of loss), when the transmission rate of the session reaches C0 , the capacity of the fundamental channel, the scheduler executes the following decision mechanism: (a) If a supplemental channel C1 is free i.e., less that K supplemental channels are occupied, then the session is always assigned the supplemental channel. (b) Otherwise, it is allocated a supplemental channel with probability α. Since at most K users can utilize the high rate channel, this means that with probability α a session already utilizing the high-rate channel is preempted to accommodate the requesting session. The session to be preempted is chosen randomly with probability 1/K. We note that the preempted session faces a congestion loss with probability one since its current transmission rate must have been greater than C0 and hence immediately halves its sending rate. (c) If the supplemental channel request is denied, which happens with probability 1−α, then the session experiences a congestion loss with probability one and immediately halves its sending rate, in this case to C0 /2.

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3. Finally, if a session occupying the supplemental channel drops its sending rate to less than that of the supplemental channel rate, either due to congestion or channel errors, its supplemental channel is de-allocated and it is assigned a fundamental channel1 . This makes sense, since it frees up resources that would not be utilized by the TCP session. The preemption probability α is a system parameter that needs to be optimized in order to maximize TCP throughput. The model we present in the next section explicitly captures the impact of α on TCP throughput and hence, can be utilized to find the optimal value of α.

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TCP Throughput Model

In the previous section, we proposed a simple scheme for TCP-aware channel allocation. We now present an analytical model to compute the throughput in such a system. Rather than list all the details of the analysis, due to lack of space, we highlight the salient features of the system as well as our approach to model it. The reader is referred to the technical report [6] for further information. From the perspective of a TCP session, such a multi-rate system results in the session being in either of two distinct states corresponding to whether it is allocated a fundamental or supplemental channel. Each type of channel has different channel capacities, round trip times and packet error probabilities (C0 , R0 , p0 ) and (C1 , R1 , p1 ) respectively. Given the operation of the proposed scheduler, the state of a session, is decided by two factors: a) its own sending rate or instantaneous throughput, and b) the supplemental channel occupancy, both of which the scheduler utilizes in channel allocation. The latter is critical since it reflects the impact of the multi-user resource sharing aspect of the system. Specifically, whether there are K or less supplemental channel allocations influences both the probability that the session receives a supplemental channel, as well as the rate at which it itself is preempted from a supplemental channel. Now, the supplemental channel occupancy in turn depends on the rate at which sessions in the fundamental channel state request the supplemental channel, which is actually a function of the time a session spends in each state (fundamental or supplemental). This essentially closes the loop in the sense that the state of the TCP session is a function of the supplemental channel occupancy, which itself depends on the state of each session. A natural approach to analyze such a system with coupled variables is the fixed-point method, which in this case, works as follows. We first derive the instantaneous throughput distribution equations for a TCP session given the supplemental channel occupancy statistics. Note that there is a one-to-one relation between the instantaneous TCP throughput and session state. Next,we utilize the newly computed throughput equations to update the supplemental channel occupancy statistics. These iterations are repeated till a desired convergence criteria is met. Both components are described in the next two sub-sections. 1

This implies that C1 ≤ 2C0 which is true in practical systems. In practice, most wireless rates follow the relation C1 = 2C0 .

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TCP Session Throughput

This sub-section computes the throughput of a single TCP session given the supplemental channel occupancy statistics. In order to capture the evolution of TCP dynamics in the dynamic two-rate environment, we utilize the model developed in [3] as a starting point and incorporate preemption from a supplemental channel as well as denial of the supplemental channel. The reader is referred to the technical report [6] for further information. We first state some assumptions regarding TCP dynamics as well as our notation: 1. Let i = 0(1) denote that the session is allocated a fundamental (supplemental) channel. 2. All TCP sessions are assumed to be homogeneous in the sense that they have the same propagation delay a. Hence, their round trip time in state i is given by Ri = a + L/Ci where L is the packet size. We note that the assumption of homogeneity is made in this work only for purposes of illustration of the model. As discussed in Section 5.2 our model can readily tackle heterogeneity as well. 3. In congestion avoidance mode, the TCP sending rate of a session, when in state i, grows at a linear rate of L/Ri2 bits/sec2 in the absence of loss. 4. We assume that the channel losses can be modeled by an inhomogeneous Poisson process with rate pi X(t), i = 0, 1 at time t, where pi is the packet loss probability. 5. Let fi (x, t) denote the density function of the instantaneous rate X(t) in state i. Then f (x, t) = f0 (x, t) + f1 (x, t), where f (x, t) denotes the density function of X(t). 6. Let q denote the probability that the session is allowed to transition from i = 0 to i = 1, i.e., its request for a supplemental channel is honored. Let ν denote the rate at which a session is preempted from a supplemental channel, i.e., transitions from i = 1 to i = 0. We note that these quantities are computed based on the supplemental channel occupancy. pi R2

R0 L We further define the terms φij = L2 j and δi = R 2 for i = 0, 1 and g = R . 1 i Using techniques from fluid analysis we can write differential equations for the evolution of the steady-state distribution f (x) of the instantaneous throughput X(t). Then, by applying Mellin transforms [7, 8], which is defined as  ∞ fˆi (u) = fi (x)xu−1 dx, (1) 2

0

we can solve the differential equations (see [6]) to obtain the steady state probability (fˆi (1)) and throughput (fˆi (2)) in each state i. We may then solve for the mean TCP throughput given by:  k  + Δ(2) k≥0 (φ00 ) Πk (2)ψ(2 + 2k) ˆ ˆ X = f0 (2) + f1 (2) = (2)  k  + Δ(1) k≥0 (φ00 ) Πk (1)ψ(1 + 2k) where,

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 g2  g2 1 − q  u ) (C ( ) Δ(gC ) − − 1 (C1 )u Δ(C1 ) 0 0 q 2u q (2g)u  φ  1  1 10  + 2),  Δ(u + 1) − − uΔ(u) −ν − − φ11 Δ(u u u δ0 (2g) δ1 (2g)  g2

− gu −

and, φ11

ν

Δ(x) = e−( 2 )x −( δ1 )x ,  C1  Δ(u) = Δ(u)xu−1 du . 2

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Supplemental Channel Occupancy

In the previous sub-section, we computed the TCP throughput of a single session given supplemental channel assignment probability q and preemption rate ν. Our goal in this sub-section is to show how these values may be computed. ( N  i  1)O

( N  i )O i

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Fig. 1. Number of users in high-rate region

We model the supplemental channel occupancy process as a Markov chain where the state, i, is equal to the number of concurrently active supplemental channels. Clearly, 0 ≤ i ≤ K. Now, let λ and μ denote, respectively, the rates at which a single user requests and releases a supplemental channel conditioned upon there being one available. We assume that both these processes are exponentially distributed, which is a reasonable assumption for large N and K. The transition rates for the Markov chain are then given as shown in Fig. 1. Denoting the stationary distribution of the Markov chain by π, it is easily seen that πi =

   i N λ π0 , i μ

(3)

K where i=0 πi = 1. It now remains to compute λ and μ. Since λ is the rate at which a session requests a high-rate channel, it is essentially equal to the rate at which the TCP rate crosses the low channel rate boundary X(t) = C0 conditioned on a

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supplemental channel being available. This is simply equal to λΔt, Δt → 0 and can be computed as follows:

 λΔt = P (C0 − δ0 Δt ≤ X(t) ≤ C0 ) ∧ (no loss in [t, t + Δt]) X(t) ≤ C0 P {(C0 − δ0 Δt ≤ X(t) ≤ C0 ) ∧ (no loss in [t, t + Δt])} P {X(t) ≤ C0 } (1 − p0 C0 Δt)f0 (C0 − δ0 Δt, t)(δ0 Δt) = f0 (1) =

(4)

In the limit, as Δt → 0 and t → ∞, we obtain λ=

δ1 f1 ((gC0 )+ ) . q f0 (1)

(5)

Please refer to [6] for further details. To compute μ, we note that from the Mellin transform analysis in previous subsection, we readily have expressions for f0 (1) and f1 (1), the steady-state probabilities of being in low rate and high rate regions respectively. It is then straightforward to see that f0 (1) μ=λ . f1 (1) The relevant supplemental channel occupancy statistics can now be computed in a straightforward fashion as follows: 1. The probability q that a session in state i = 0 requesting the supplemental channel, will be assigned one is given by: 0 0 0 ) + απK = 1 − (1 − α)πK , q = (1 − πK

(6)

0 where, α is the scheduler parameter and πK is the conditional probability that all K supplemental channels are occupied given that a user is about to request a supplemental channel. In [6], we have shown that

(N − i)πi πi0 = K . j=0 (N − j)πj

(7)

2. The rate at which a user in state i = 1 is preempted from the supplemental channel is given by: α 1 ν = λ(N − K)πK , (8) K 1 where, πK is the conditional probability that all K supplemental channels are occupied given that a user has a supplemental channel and is given by [6]

iπi πi1 = K j=0

jπj

.

(9)

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Finally, using a fixed-point iteration technique we can compute mean TCP throughput in multi-user systems as follows. We start with (q = 1, ν = 0) and compute (X, f1 (gC0 ), f0 (1), f1 (1)). Using these values, we then compute (λ, πK ) from (5) and (3). By substituting in (6) and (8), we compute new values for (q, ν). The iteration continues until computed TCP throughput X converges with some desired precision. In passing, we emphasize that the model can easily accommodate heterogeneous TCP sessions. Specifically, sessions can be classified into (say) L classes of users, based on similarity of propagation delay, error characteristics, etc. for each of which we can compute the TCP throughput following the approach in Section 5.1. The high-rate channel occupancy process is then readily seen to be an L-dimensional birth-death Markov chain with transition rates λl , μl . It is well known that such a chain has a product type form for the stationary distribution which can be easily solved to obtain the occupancy probabilities.

6

Numerical Results

This section is devoted to evaluating the accuracy of our model by comparing against ns-2 simulations as well as demonstrating its utility in design issues. We consider a CDMA downlink scenario with a fundamental channel rate of C0 = 76.8 Kbps and supplemental channel rate of C1 = 102.4 Kbps resulting in packet error probabilities of p0 = 7.7 × 10−4 and p1 = 7.9 × 10−4 respectively2 . This scenario is reflective of the fact that higher rate channels typically incur higher frame errors. We begin by determining the accuracy of the model. Fig. 2 plots the per-user TCP throughput as a function of α for N = 10 users with K = 2, 5 and 8 supplemental channels for both the model and simulation. We have also plotted 95% confidence intervals where each simulation point is averaged over 20 runs with different seeds. We note that the model closely tracks the simulation results for all values of K. More importantly, both the model and simulations indicate the same throughput behavior as a function of α. Specifically, for K = 2, there is an α∗ ∈ (0, 1) that maximizes the TCP throughput, while larger values of K results in the optimum α∗ = 1. However, as K increases, the throughput becomes less sensitive to α, e.g., for K = 8 in Fig. 2. 6.1

Impact of Number of Supplemental Channels

Next, we study the impact of the number of supplemental channels K on user throughput. As shown previously, the model gives reasonable estimates and hence we use analytical results from the model only. Fig. 3 plots the TCP throughput as a function of K for N = 5, 10 and 20 users. For each K, the maximum throughput achieved at the optimal α∗ was chosen. 2

The rate values were chosen to reflect those of a CDMA2000 1xRTT system. The packet error probabilities were computed based on BER of 10−5 and 1/5 error coding. The reader is referred to [3] for more details.

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The curves in Fig. 3 indicate that the throughput gain by adding additional supplemental channels follows a law of diminishing returns, i.e., beyond a certain K, addition of extra supplemental channels does not yield significant gains. For example, for N = 20, allocation of only K = 10 supplemental channels achieves nearly the same throughput as that with a dedicated high rate channel per user, i.e., K = 20. This stands to reason, since TCP increases its rate only additively. Thus not all users require high rate channels simultaneously and the resulting

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statistical multiplexing gain can be exploited to utilize less than N high rate channels while achieving almost the same effect as dedicating a supplemental channel to each user. 15

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N=5 N=10 N=20 10

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Fig. 4. Max preemption rate (ν) as function of K

Importantly, the preemption rate ν computed by the model from (8), which is plotted in Fig. 4 as a function of K at α = 1, captures this phenomenon. Specifically, beyond a certain K (as a function of N ) the contention rate goes to zero which indicates that additional supplemental channels have very little impact. This is an important feature since it allows for computation of the minimum number of supplemental channels to allocate in a cell as a function of number of active TCP sessions, preventing resource wastage.

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Conclusion

In this work, we addressed the issue of allocation of CDMA wireless channels to concurrently active TCP sessions. Due to inherent system constraints, high data rate channels cannot be assigned simultaneously to all sessions. This creates an interesting resource contention problem. We proposed a simple channel allocation scheduler for this problem in the context of TCP sessions and constructed an analytical model to study its performance. Through comparison with simulations the model was shown to provide reasonable estimates of TCP throughput and characterize interesting design parameters like the minimum required number of high data rate channels for a set of TCP sessions. There are many different future directions possible from our current work. Some of avenues that we are exploring include incorporation of more sophisticated schedulers as well as utilizing the model to address resource planning

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issues like selection of both the number and rates of supplemental channels and impact of heterogeneous users.

References 1. Fodor, G., Telek, M., Badia, L.: On the Tradeoff Between Blocking and Dropping Probabilities in CDMA Networks Supporting Elastic Services. In: Proc. IFIP/NETWORKING, Coimbra, Portugal (May 2006) 2. Altman, E.: Capacity of a Multi-Service Cellular Network with Transmission Rate Control : A Queueing Analysis. In: Proc. ACM MOBICOM, Atlanta, GA (September 2002) 3. Ghaderi, M., Sridharan, A., Zang, H., Towsley, D., Cruz, R.: TCP-Aware Resource Allocation in CDMA Networks. In: Proc. ACM MOBICOM, Los Angeles (September 2006) 4. Association, T.I.: TIA EIA IS-2000. Available at www.tiaonline.org/standards/sfg/ imt2k/cdma2000/ (March 2000) 5. Ghaderi, M., Sridharan, A., Zang, H., Towsley, D., Cruz, R.: Modeling and Optimization TCP Aware Cross-Layer of Resource Allocation in CDMA Networks. Technical Report RR06-ATL-030566, Sprint Advanced Technology Labs (March 2006) 6. Ghaderi, M., Sridharan, A., Zang, H., Towsley, D., Cruz, R.: Performance Modelling of TCP in a Multi-Rate Multi-User CDMA System. Technical Report RR06ATL120729, Sprint ATL (December 2006) Available at http://research.sprint.com/ publications/uploads/RR06-ATL-120729.pdf. 7. Baccelli, F., Cruz, R., Nucci, A.: CDMA channel parameters maximizing TCP throughput. In: Proc. Workshop on Information Theory and its Applications, La Jolla, CA, USA (February 2006) 8. Baccelli, F., McDonald, D.: Mellin transforms for TCP throughput with applications to cross layer optimization. In: CISS, Princeton (2006)

IEEE 802.11b Cooperative Protocols: A Performance Study Niraj Agarwal, Divya ChanneGowda, Lakshmi Narasimhan Kannan, Marco Tacca, and Andrea Fumagalli The OpNeAR Laboratory Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas {nxa041000,dhc042000,lnk051000,mtacca,andreaf}@utdallas.edu

Abstract. This paper investigates the use of cooperative communications in the context of IEEE 802.11b to combat radio signal degradation. The performance gain of both an existing cooperative protocol and the one proposed in the paper is discussed. It is quantitatively shown how much the two cooperative protocols increase throughput, lower delivery latency, and extend transmission span, when compared to the conventional IEEE 802.11b protocol. These features may help improve connectivity and network performance in ad hoc applications.

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WLAN’s (wireless local area networks) have experienced tremendous growth and become the prevailing technology in providing wireless access to data users. The family of IEEE 802.11 protocols is perhaps the most widely adopted solution [10]. It must be noted that wireless links do not have well defined coverage areas. Propagation and channel characteristics are dynamic and unpredictable. Small changes in the node position or direction of mobility may result in significant differences in the signal strength. Adaptation to such conditions is a key issue in today and future wireless communications. One of the characteristics of the radio medium is its inherent broadcast nature. Besides the intended destination, a signal transmitted by a source may be received by other neighboring nodes that are within earshot. This broadcast nature of the radio medium can be used to improve the system throughput by having a node, other than the source and the destination, actively help deliver the data frame correctly. The cooperating node is referred to as the relay. The essence of the idea is that, the destination benefits from data frames arriving via two statistically independent paths, i.e., spatial diversity. The advantages of cooperative communications include the ability to increase the radio channel capacity [6,7,14] and reduce the latency of automatic retransmission request protocols [8,9,15]. An IEEE 802.11b cooperative protocol was 

This research is supported in part by NSF Grants No. ECS-0225528 and CNS0435429.

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introduced to improve both throughput and latency of the medium access control (MAC) [3]. Data frames transmitted by the source are received by the relay, which in turn forwards them to the destination. The destination acknowledges the received data frame directly to the source. Other protocols which exploit the broadcast nature of wireless medium to achieve potential gains have been proposed in [12,13]. In [13], the source attempts to transmit the data to destination directly and when the direct transmission fails, the partner nodes help in retransmitting the same frame after a backoff process. In [12], the proposed protocol (ExOR), deals with routing a packet from the source to the destination using the help of intermediate nodes in a special way as compared to traditional routing. In this paper, cooperative communications in the context of IEEE 802.11b is further investigated. With the studied protocol, attempts to receive the data frame transmitted by the source are simultaneously made at both the relay and the destination. It is only when the destination is not successful in the reception attempt, that the relay re-sends the data frame again. The advantage of this approach is to limit the relay’s intervention to those cases when the source transmission attempt is not successful in reaching the destination. As discussed in the paper cooperative MAC protocols help cope with radio signal degradation. They provide higher throughput and lower latency when compared to the conventional IEEE 802.11b protocol. For a given throughput target, they achieve a maximum transmission span between the source and the destination that is up to 50% greater than one of the conventional IEEE 802.11b protocol. These features combined may help achieve improved connectivity and performance.

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This section describes the cooperative protocol proposed in the paper to enhance the performance of IEEE 802.11b. For simplicity, the protocol is described ignoring some control frames, e.g., the request to send (RTS), clear to send (CTS). The extension of the protocol description to include these additional control frames is straightforward. Assume that three nodes have agreed to cooperate1 , i.e., source S, destination D, and relay R. The proposed cooperative MAC protocol is based on the distributed coordination function (DCF) defined for the ad hoc mode of the IEEE 802.11b standard. As shown in Fig. 1, when transmitting a data frame, S makes a direct attempt to reach D. While transmission takes place, R receives and stores a copy of the data frame temporarily. Four cases are possible2 . The time diagrams of the transmitted frames are shown in Figs. 2-5, respectively. 1

2

The protocol required to reach a consensus among the three nodes willing to cooperate is beyond the scope of this paper. Routing protocols available in the literature can be extended and adapted to perform relay selection [11]. In the four cases it is assumed that the acknowledgment is always received correctly by S. The extension to account for acknowledgment loss is straightforward.

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Relay Data Destination

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Fig. 1. Cooperation of three nodes

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Fig. 2. Case 1: successful delivery of data and acknowledgement frames

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Fig. 3. Case 2: cooperation by R in retransmitting the data frame

TIMEOUT

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Fig. 4. Case 3: both S and R are unsuccessful

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1. Fig. 2: S transmitted frame is successfully received at D. D responds with a positive acknowledgment (ACK). 2. Fig. 3: S transmitted frame is successfully received at R, but not at D. D does not acknowledge the received data frame. Not receiving the ACK from D, R assumes that S’s attempt to reach D has failed, and proceeds with the transmission of the data frame copy. R transmitted frame is successfully received at D. D responds to S with a positive ACK. 3. Fig. 4: Same as case 2, but D does not receive the frame transmitted by R. 4. Fig. 5: S transmitted frame is neither received successfully at R nor at D.

TIMEOUT

Data

BACKOFF TIME

SIFS

Data

ACK

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ACK

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Fig. 5. Case 4: both D and R do not receive the data frame

For the cooperation protocol to work as described, time intervals between transmission attempts must be chosen carefully. Specifically, for the transmission of a data frame, S must sense the channel idle and wait for a time interval denoted as distributed inter-frame space (DIFS)3 . For ACK transmission, D does not need to wait. ACK is then received at S and R no later than a time interval denoted as short inter-frame space (SIFS). SIFS takes into account various latency factors, e.g., MAC software, transceiver hardware, and radio signal propagation. Both DIFS and SIFS are defined in IEEE 802.11b. For transmission of the data frame copy, R must wait a time interval denoted as relay inter-frame space (RIFS). RIFS is specifically introduced as a component of the cooperative protocol and is not defined in IEEE 802.11b. RIFS must be chosen to both allow the detection at R of the ACK transmitted by D (RIFS > SIFS), and prevent frame transmission of other nodes while the cooperation is taking place (RIFS < DIFS). A possible value for RIFS is the point (coordination function) inter-frame space (PIFS). PIFS is defined in IEEE 802.11b to allow the point coordination function to have collision-free access to the channel for coordinating data frame transmissions in the infrastructure mode. Choosing RIFS=PIFS is a possible option when operating the cooperative protocol in the ad hoc mode, as the point coordination function is not present. This choice is advantageous as the relay node will not need any special scheduling mechanism on its queues. The backoff procedure at S is same as in IEEE 802.11b. When the predetermined maximum number of transmission attempts is reached, the data frame is 3

Exception to this rule is when multiple frames containing the fragments of the same packet are sequentially transmitted by the same sender.

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discarded. Special attention is required to handle the transmission sequence of case 2 (Fig. 3). In this case, R senses the channel after SIFS. If the channel is idle, it indicates that the ACK frame is not being transmitted by D. Then, R begins the transmission of the data frame it received from S at RIFS. Due to the backoff procedure, S cannot start retransmission unless it senses the idle channel for at least DIFS > RIFS. As explained above, RIFS is chosen carefully so that S finds the channel busy after SIFS if R is trying to help the transmission between S and D. If D receives the frame transmitted by R, D sends ACK to S. On receiving ACK, S cancels its backoff procedure for retransmission and start the transmission procedure for the next data frame. If S does not receive the ACK, it goes ahead with the backoff procedure as defined in the IEEE 802.11 standard.When R fails in its attempt to transmit the packet to D, S will continue its backoff process (which is frozen when R is transmitting) and when the backoff ends transmits the packet to D.Thus, when the transmission from R is not successful, the backoff procedure at S does not get affected. As already mentioned, the proposed protocol does not change when RTS/CTS frames are considered. When R receives the RTS and/or CTS from S and/or D, it does not attempt transmission of its own data frames. However, it keeps listening and helps deliver the data frame from S to D whenever required. Source (After packet transmission) 1 [Case 1] Yes Reset Contention window and start backoff for next frame

Stop

At timeout, is ACK received?

No Start backoff

[Case 2] Yes

ACK received before DIFS?

[Case 3, 4] No

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Fig. 6. Source’s flowchart

The flowcharts of the cooperative protocol for S and R are shown in Figs. 6 and 7, respectively. As the flowcharts indicate, some changes are required in the MAC protocol for data transmission when compared to the IEEE 802.11b standard. No changes are required at D for data reception. R must know the addresses of both S and D in order to relay data frames between the two nodes. Note that if traffic is bidirectional, R can help relay data frames in both directions. Conversely, S and D can function with or without R, and need not know the address of R. Thus, the protocol and the data flow between S and D can smoothly adapt to changing channel conditions and relative

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ACK

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Ignore frame

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[Case 1] Yes

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No

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Stop

Fig. 7. Relay’s flowchart

locations of the three nodes. As already mentioned, the main difference between the protocol proposed in this section and the one in [3] is the attempt made by S to reach both D and R with the same frame transmission.

3 3.1

Results Channel Model

The path loss model used in the simulator is as follows: Esr = Est ·

GT × GR × λ2 (4π)2 (d)β

(1)

where, – – – – –

Esr , Est : energy per symbol at the receiver and transmitter, respectively, GT , GR : transmitter and receiver antenna gain, respectively, d: transmitter-receiver distance, λ: wavelength at the channel center frequency in m, β: path loss exponent, β = 2 in free space, typically 2 ≤ β ≤ 4 for environments with structures and obstacles [2,16].

Fading is assumed to be Rayleigh slow and flat, i.e., the fading coefficients are considered constant over a single frame transmission. The fading experienced by any given frame transmission is statistically independent of the fading experienced by any other frame transmission. The instantaneous signal to noise ratio at receiver j given a transmission from transmitter i is given by: F

2 )/10 10 γ(i,j) = ((Esr × P G/No ) × ri,j

(2)

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where, – Esr : energy per symbol at the receiver, – P G: processing gain due to spreading, – No : noise spectral density of the additive white Gaussian noise (AWGN) channel No = K B × T (3) – KB : Boltzmann constant, – ri,j : Rayleigh distributed random variable to model the Rayleigh fading magnitude from node i to j, – F : noise figure of the receiver (10 dB). 3.2

Simulation Results

In this section, simulation generated results are discussed to assess the performance gain in IEEE 802.11b when using cooperative protocols. In the study, three protocols are considered, i.e., the conventional IEEE 802.11b [1], MAC II in [3] (Poly MAC II), and the MAC protocol proposed in Section 2 (UTD MAC). The assumptions made and values chosen for the protocol parameters are shown in Table 1. Three nodes are used, i.e., S, R, and D. Data flow is either from S to D only (one-way traffic), or bidirectional between S and D (two-way traffic). R does not generate any own traffic. It is assumed that the three nodes have agreed to cooperate. They can freely use any of the four transmission rates provided by IEEE 802.11b, i.e., 1, 2, 5.5, and 11 Mbps. However, ACK frames are always transmitted at 1 Mbps to provide maximum reliability. Table 1. Parameters used in simulation Path Loss Exponent β

4

Flat Rayleigh Fading Average Transmitter Power

constant across frame 100 mW

PHY Header

192 bits

SIFS

10 μs

RIFS

30 μs

DIFS

50 μs

Slot Time

20 μs

Vulnerable Period

20 μs

Max Retrans. Attempts

6

Frame Size

1023 bytes

Min Contention Window

31 slots

Max Contention Window 255 slots Arrival Rate 1200 frames/s (saturation) MAC Header

34 bytes

MAC ACK

14 bytes

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Fading is assumed independent of the destination, e.g., when S transmits, the fading experienced at R is independent of the one at D. Frame error rates are computed using [5]. Multiple concurrent transmission attempts always result in collision. Propagation delay is assumed negligible. The DCF mode of operation is used. Neither the virtual carrier sense (RTS/CTS) mechanism, nor fragmentation are used. The maximum number of transmission attempts per data frame is 6. Simulation results are obtained using a C++ custom simulator and have 5% confidence interval at 95% confidence level. Simulation results are validated against the analytical model presented in [4]. Saturation load condition is obtained by choosing data frame arrival rates that exceed the network capacity. Data frames in excess are dropped and not counted. Throughput is defined as the number of MAC payload bits that are successfully delivered and acknowledged by D normalized to time. The MAC and PHY header bits do not contribute to throughput. Access delay is the time taken for a data frame from the instant it reaches the head of the transmission queue at S till its first bit of the successful transmission attempt is aired by S. When obtaining the curves for the Poly MAC II protocol, the relay node is chosen based on the transmission time gain that can be achieved if the packet goes through the relay [3]. The transmission rate for S (R) is chosen based on the distance of S (R) from R (D), as indicated in [3]. Once a relay is chosen, all the packets from S to D go through the relay R only, i.e., S never attempts to transmit directly to D. Upon correct reception, D directly transmits the ACK to S. The UTD MAC curves are obtained by selecting the transmission rates for S and R, respectively, that jointly yield the maximal throughput for each experiment. Cooperation in the UTD MAC is always invoked, regardless of the location of the three nodes. Fig. 8(a) shows throughput under saturation load for the three protocols as a function of the distance between S and D. Traffic is one-way. Four curves are reported for IEEE 802.11b, one for each transmission rate. R is always placed half way between S and D to provide good condition for cooperation. Under this condition, the two cooperative protocols offer increased throughput when compared to IEEE 802.11b for distances of 40 m and above. Poly MAC II best contribution is reached at 70 m and above. Fig. 8(b) is similar to Fig. 8(a) except that fading is absent in the former. The cooperative protocols perform better than the IEEE 802.11b after a distance of 60 m, indicating that the performance gain is still there, irrespective of whether or not the channel is affected by fading. The sudden transitions in the throughput are due to the change in the transmission rates used. Fading smoothens the transition area, as clearly visible in Fig. 8(a). Figs. 9(a) and 9(b) show throughput and expected access delay, respectively, under saturation load when the S-D distance is 100 m. R position varies along the S-D axis. S and D coordinates are (0, 0) and (100, 0), respectively. R coordinates are (X, 0), where X is the value on the horizontal axis in both figures. Traffic is one-way. The throughput of the cooperative protocols is significantly affected by the position of R. Poly MAC II does not invoke cooperation when

IEEE 802.11b Cooperative Protocols: A Performance Study 5

5 UTD MAC

UTD MAC Poly MAC II 802.11b − 11 Mbps 802.11b − 5.5 Mbps 802.11b − 2 Mbps 802.11b − 1 Mbps

4 3.5

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Distance between source and destination (m)

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(b) Without Fading

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802.11b

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Relay X Position (m)

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Fig. 9. R’s position along the S-D axis, S-D distance is 100 m Table 2. Bit rate pairs for UTD MAC in Figs. 9(a) and 9(b) S-R distance (m) 0-10 15-35 40-45 50-55 60 65-100 S Rate (Mbps) 1 11 11 5.5 5.5 2 R Rate (Mbps)

1

2

5.5

5.5 11

11

X ≤ 20 and X ≥ 80 m. The UTD MAC curves consist of a sequence of segments, each segment being obtained with a specific pair of transmission rates for S and R, respectively. The rate pairs are reported in Table 2 and help explain the UTD MAC plots. Sudden changes in the plots occur when the optimal transmission rate of either S or R changes. In the 0 ≤ X ≤ 10 m region the transmission rate of both S and R is 1 Mbps, as both nodes attempt to reach D from approximately the same distance. In the 15 ≤ X ≤ 35 m region, however, R increases its rate to 2 Mbps, thus providing a faster frame transmission time. In turn, S

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0.18

0.17 UTD MAC 802.11b

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802.11b

UTD MAC

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Fig. 10. R’s position orthogonal to the S-D axis, S-D distance is 150 m Table 3. Bit rate pairs for UTD MAC in Figs. 10(a) and 10(b) R’s Y position from S-D axis (m) 0-20 25-30 35-75 S Rate (Mbps)

2

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changes to 11 Mbps as it provides the fastest solution to send the frame to R. In the 65 ≤ X ≤ 100 m region R increasingly approaches D. S rate goes down to 2 Mbps, which is a suitable rate to reach both R and D. When only R is reached successfully by the frame, R rate of 11 Mbps delivers the frame to D at full speed, taking advantage of the reduced distance to D. Figs. 10(a) and 10(b) shows throughput and expected access delay, respectively, under saturation load when the S-D distance is 150 m. R position varies orthogonal to the S-D axis. S and D coordinates are (0, 0) and (150, 0), respectively. R coordinates are (75, Y ), where Y is the value on the horizontal axis in both figures. Traffic is two-way. In this scenario, Poly MAC II never invokes cooperation. Only IEEE 802.11b and UTD MAC are shown then. Even when R is 75 m away from the S-D axis, the cooperative protocol yields a noticeable throughput gain over IEEE 802.11b. The behavior of the access delay curve for UTD MAC as Y increases can be explained by inspecting the transmission rates used by S and R (Table 3). The step like delay increase in the 20 ≤ Y ≤ 30 m region occurs due to the rate reduction from 1 to 2 Mbps performed by R first, then by S. It must be noted that R rate is decreased before S rate is, as R must ensure reliable delivery to D, whereas S can be more aggressive given that R can provide a backup transmission attempt. In the 35 ≤ Y ≤ 75 m region the access delay increases slightly and it exceeds the delay of IEEE 802.11. This is because all nodes use 1 Mbps and the transmission via R takes longer time than the direct transmission from S to D. At Y = 0 m, UTD MAC performs three

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times better than IEEE 802.11b and when Y = 75 m UTD MAC performs two times better than IEEE 802.11b. Overall, both cooperative protocols offer tangible performance gains when compared to IEEE 802.11b if R is conveniently located between S and D. UTD MAC appears to be somewhat more flexible in accommodating the various positions of R.

4

Conclusion

The paper investigated the use of cooperative communications techniques to enhance the IEEE 802.11b MAC protocol ability to cope with radio signal degradation with and without fading channel. Two cooperative MAC protocols were compared, i.e., the one in [3] and the one presented in the paper. Both cooperative protocols have the potential to yield higher throughput and lower latency when compared to the conventional IEEE 802.11b protocol. Alternatively, the maximum transmission span between the source and destination for a desired throughput target can be increased by up to 50% when using the cooperative protocols. All these features may help achieve improved connectivity and network performance in ad hoc applications, where nodes’ relative locations are difficult to control and predict. However, as indicated in this study, to fully harness cooperative communications in IEEE 802.11b, the cooperating nodes must be able to carefully select their transmission rates. This subject will be addressed in a future work on this topic.

References 1. Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification: High speed physical layer extension in the 2.4 GHz band. sept 1999. 2. T. S. Rappaport (2001)Wireless Communications: Principles and Practice (2nd Edition), Prentice Hall PTR. 3. P. Liu and Z. Tao and S. Panwar (2005) A Co-operative MAC Protocol for Wireless Local Area Networks. Proc. IEEE International Conference on Communications (ICC) Seoul, Korea 4. Giuseppe Bianchi (march 2000) Performance Analysis of the IEEE 802.11 Distributed Coordination Function, IEEE Journal on Selected Areas in Communications, pages 535-547. 5. B. Kim and Y. Fang and T. Wong (2005) Throughput Enhancement Through Dynamic Fragmentation in Wireless LANs,Proc. IEEE Transactions on Vehicular Technology. 6. A. Sendonaris and E. Erkip and B. Aazhang (2003) User cooperation diversity Part I: System description IEEE Trans. Commun vol. 51 no. 11, pages 1927-1938 7. M. Janani and A. Hedyat and T. Hunter and A. Nosratinia (2004) Coded cooperation in wireless communications: Space-time transmission and iterative decoding, IEEE Trans. on Signal Processing, vol. 52, no. 2. pages 362-371.

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8. E. Zimmermann and P. Herhold and G. Fettweis (2004) The impact of cooperation on diversity-exploiting protocols, Proc. of 59th IEEE Vehicular Technology Conference (VTC Spring). 9. P. Gupta and I. Cerutti and A. Fumagalli (2004) Three transmission scheduling policies for a cooperative ARQ protocol in radio networks, Proc. WNCG conference. 10. Namgi Kim (2005) IEEE 802.11 MAC Performance with Variable Transmission Rates, IEICE Transaction on Communications”, 2005, vol. E88-B, no. 9. pages 3524-3531 11. Aggelos Bletsas and Andrew Lippman and David.P.Reed (2005). A simple distributed method for relay selection in cooperative diversity wireless networks, based on reciprocity and channel measurements. Vehicular Technology Conference, 2005. VTC 2005-Spring. 2005 IEEE 61st. Vol. 3 pages 1484- 1488 12. Sanjit Biswas and Robert Morris (2005) ExOR: opportunistic multi-hop routing for wireless networks, Vehicular Technology Conference, 2005. VTC 2005-Spring. 2005 IEEE 61st, pages 133-144 13. Sai Shankar N and Chun-Ting Chou and Ghosh M (2005) Cooperative communication MAC (CMAC) - a new MAC protocol for next generation wireless LANs, International Conference on Wireless Networks, Communications and Mobile Computing, pages 133-144 . 14. J.N. Laneman, G.W. Wornell, and D.N.C. Tse (2001), ”An efficient Protocol for realizing cooperative diversity in wireless networks,” in Proc. IEEE ISIT, Washington, p.294. 15. B. Zhao and M.C. Valenti (2005), Practical relay networks: a generalization of hybrid-ARQ, IEEE Journal on Selected Areas in Communications, vol. 23, no. 1, pages 7-18. 16. J. Wall and J.Y. Khan (2003), An Advanced ARQ Mechanism for the 802.11 MAC Protocol, Proceedings of Australian Telecommunications, Networks and Applications Conference(ATNAC).

It Is Better to Give Than to Receive – Implications of Cooperation in a Real Environment Thanasis Korakis1 , Zhifeng Tao2 , Salik Makda1 , Boris Gitelman1 , and Shivendra Panwar1 1

Department of Electrical and Computer Engineering, Polytechnic University, Brooklyn, NY 11201 2 Mitsubishi Electric Research Laboratories, Cambridge, MA 02139 [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. Thanks to the immense potential cooperative communications displays, extensive investigations have been directed to examine its performance by means of both analysis and simulation. In this paper1 , an implementation approach has for the first time been pursued to demonstrate the viability of realizing cooperation at the MAC layer in a real environment. The paper further describes the technical challenges encountered, details the corresponding solution proposed, and shares the experience gained. The experimental measurements in a medium size (i.e., 10 stations) testbed are then reported, which not only helps develop a deeper understanding of the protocol behavior, but also confirms that a cooperative MAC protocol delivers superior performance.

1 Introduction Cooperative communications, which refer to the collaborative processing and retransmission of overheard information at stations surrounding a source, has recently gained momentum in the research community [1]. The notion of cooperation takes full advantage of the broadcast nature of the wireless channel and creates spatial diversity, thereby achieving improvement in system robustness, capacity, delay, coverage range, and interference reduction. The innovation of cooperative communications is not confined only to the physical layer. It is available in various forms at higher protocol layers [2]. A MAC protocol called CoopMAC [3] illustrates how the legacy IEEE 802.11 distributed coordination function (DCF) can be enhanced with minimal modifications to maximize the benefit of cooperative diversity. As experimentation gradually becomes one of the de facto approaches for benchmarking [4] [5], a preliminary performance evaluation for cooperative MAC protocol was attempted in [6] in a relatively rudimentary experimental setting, for proof of concept purposes. Only 3 stations with dedicated roles as source, destination and helper 1

This work is supported in part by National Science Foundation (NSF) under award 0520054, and the New York State Center for Advanced Technology in Telecommunications (CATT). The work is also supported by Wireless Internet Center for Advanced Technology (WICAT), an NSF Industry/University Research Center at Polytechnic University.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 427–438, 2007. c IFIP International Federation for Information Processing 2007 

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were involved therein, and throughput for only one TCP session was collected. In this paper, the scope and scale of effort have been significantly expanded, where all major protocol functionalities are implemented in an open source driver for 802.11 devices, and a comprehensive set of experiments are conducted in a testbed consisting of up to 10 stations. As highlighted below, many different aspects of the protocol performance have been scrutinized, and new results obtained thereby reported. – – – –

Delay performance (e.g., average end-to-end delay, jitter) Impact of cooperation on the helper station Impact of the ”Hello Packet” interval Impact of buffer overflow on system performance

To familiarize the reader with CoopMAC, the basic idea of the protocol is first summarized in Section 2. The implementation effort is then elaborated in Section 3, and the primary configurations of the experiments specified in Section 4. A rich set of measurement results along with the insights revealed therein are reported in Section 5. Section 6 completes the paper with final conclusions and possible future work. For ease of explanation, the term relay and helper will be used interchangeably in the following discussion.

2 Cooperation at MAC Layer In order to deliver an acceptable frame error rate (FER), packets in IEEE 802.11 can be transmitted at different bit rates, which are adaptive to the channel quality. For IEEE 802.11b, in particular, four different rates are supported over the corresponding typical ranges, as depicted in Figure 1. Source

Destination

11

sh

M R bps

STAh

5.5 R

hd

Mb ps

R sd ps 1 Mb

STAd

R11

R5.5

R2

R1

STAs

Fig. 1. Illustration of the Cooperative MAC Protocol

One key observation conveyed by Figure 1 is that a source station ST As that is far away from the destination ST Ad may persistently experience a poor wireless channel, resulting in a rate as low as Rsd (e.g., 1M bps) for direct transmission over an extended period of time. If there exists some neighbor ST Ah who in the meantime can sustain higher transmission rates Rsh and Rhd (e.g., 11M bps and 5.5M bps in Figure 1) between ST Ah and ST As , and between ST Ah and ST Ad , respectively, station ST As can enlist the neighbor ST Ah to cooperate and forward the traffic on its behalf to the

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destination, yielding a much higher effective rate. More specifically, upon the transmission of a packet, station ST As should access all the rate information in a cooperation table (a.k.a. CoopTable), and compare an estimation of the equivalent two-hop rate (Rsh Rhd )/(Rsh + Rhd ) with the direct rate Rsd to determine whether the two-hop communication via the relay yields a better performance than a direct transmission. If cooperative forwarding is invoked, CoopMAC engages the selected relay station ST Ah to receive the traffic from the source ST As at rate Rsh and then forward it to the corresponding destination ST Ad at rate Rhd after a short interframe spacing (SIFS) time. In the end, destination ST Ad indicates its successful reception of the packet by issuing an acknowledgment packet (i.e., ACK) directly back to ST As . As an option, the RTS/CTS signaling defined in IEEE 802.11 can be extended to a 3-way handshake in CoopMAC to further facilitate the ensuing cooperative data exchange. To identify station that has been selected as a helper, the Address 4 field in the MAC header of data packet from ST As to ST Ah in CoopMAC should hold the MAC address of the final destination ST Ad, while the Address 1 field contains the MAC address of the selected helper ST Ah . When the packet is further forwarded by ST Ah to ST Ad , the helper will place the address of ST Ad in field Address 1, and leave the Address 4 unused. The key enhancement in the control plane at each station is the establishment and maintenance of the CoopTable, which contains essential information (e.g., Rhd , Rsh ) related to all the potential helpers. For ST As to acquire the value of Rhd and Rsh , a passive eavesdropping approach is followed, so that the overhead of additional control message exchange can be kept at minimum level. It is worthwhile to note that although CoopMAC seemingly bears some resemblance to the ad hoc routing protocols that adopt either minimal hop or other innovative metric for path selection [7], they are in essence fundamentally different. First and foremost, the objective of CoopMAC is to exploit spatial diversity and rate adaptation, not to increase the geographical extent of the network as in ad hoc routing. Secondly, all the associated operations occur in the MAC layer, which enjoys a shorter response time and more convenient access to the physical layer information, as compared to traditional network layer routing. Interested audiences are encouraged to refer to [3] for more detailed protocol specifications and technical discussions.

3 Implementation of Cooperation Key challenges encountered in the driver implementation and the corresponding solution will be summarized in this section. Nevertheless, due to the constraints of space, certain implementation details cannot be covered. Interested readers can access the official project website [8] for more technical information and free downloading of the CoopMAC driver. 3.1 Inaccessibility to Firmware When it comes to system design, all the features specified in IEEE 802.11 MAC protocol are logically partitioned into two modules, according to the time-criticality of each task. The lower module, which usually operates on the wireless card as a part

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of firmware, fulfills the time-critical functions such as the generation and exchange of RTS/CTS control messages, transmission of acknowledgment (ACK) packets, execution of random backoff, etc. The other module, which normally assumes the form of the system driver, is responsible for more delay-tolerant control plane functions such as the management of MAC layer queue(s), the formation of the MAC layer header, fragmentation, association, etc. As the cooperative MAC protocol requires changes to both the time-critical and delay-tolerant logic, the inaccessibility to firmware unfortunately causes additional complexity in implementation. Indeed, compromises had to be made and alternative approaches had to be pursued, due to this constraint. For illustrative purposes, three main circumventions that become necessary are outlined below. – Suspension of 3-way Handshake As mentioned in Section 2, a 3-way handshake option has been defined in the cooperative MAC protocol, which requires the selected helper to transmit a new control message called “Helper ready To Send” (HTS) between the RTS and CTS messages. Since the strict sequence of RTS and CTS packet has been hardwired in the firmware, an insertion of HTS becomes impossible at the driver level. It was therefore not possible to implement this option. – Unnecessary Channel Contention for Relayed Packet Once the channel access has been allocated to the source station, the helper should relay the packet a SIF S time after its reception, without any additional channel contention. Since the SIF S time is set as 10μs in IEEE 802.11b, any function demanding such a short delay must be implemented in the firmware. As a result, a compromise has been made in the implementation, where channel contention for the relayed packet on the second hop has to be attempted. – Duplicate ACK Each successful data exchange in the original cooperative MAC protocol involves only one acknowledgment message, which is sent from the destination to the source directly. Since the acknowledgment mechanism is an integral function of firmware, it is impossible to suppress the unnecessary ACK message generated by the relay station for the packet it will forward on behalf of the source. Therefore, an unwanted ACK from the relay had to be tolerated. As an implication of the circumventions described above, a faithful implementation of cooperative MAC is anticipated to outperform the one demonstrated in this paper. 3.2 Maintenance of the CoopTable As described in Section 2, the CoopTable is critical to enabling the cooperative operation. The passive approach for rate learning defined in the original CoopMAC protocol [3], however, has not been realized in our implementation due to the following reasons: – Unwanted Packet Filtering All the packets with a destination address different from the local MAC address are filtered out by the firmware. Hence, the driver is unable to retrieve any rate information from them.

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– Controllability of the Experiment Environment Even if the driver had access to such packets (e.g., by periodically switching the wireless card to the promiscuous mode), the additional random delay incurred by frequent mode switches, and traffic load and pattern at each station may complicate the collection of data in the experiments. Therefore, for sake of controllability of the experimental environment, an active information distribution approach has been followed instead. More specifically, a Hello packet is broadcast by each station in a periodic manner, to notify the neighbors about its existence as well as the sustainable transmission rate on the respective link. Upon the reception of the Hello packet, a station either inserts a new entry or updates an existing one in its CoopTable.

4 Experiment Configuration and Measurement Methodology 4.1 Testbed Configuration The testbed used in the experiment consists of 10 IBM T23 laptops, each of which contains an Intel Pentium III processor of 800 MHz and 384 MB memory. Redhat Linux 9.0 with kernel version 2.4.20 is installed as the operating system. In the ensuing experimental study, three different network topologies will be used. In each topology, one station is a dedicated destination, which mimics the functionality of an access point. The rest of the stations are either traffic sources, or helpers or both. To calibrate the testbed, the positions of stations have been adjusted until the throughputs achieved by all stations are roughly equal. 4.2 Measurement Methodology The majority of the statistics generated in the experiment, including throughput, packet loss and jitter, are measured by using Iperf [9], which is a powerful tool for traffic generation and measurement. A typical experimental setup could be to run an Iperf client at a handful of stations to generate UDP or TCP traffic streams, while an Iperf server residing on the dedicated destination receives the traffic and collects the statistics. To remove any random effect and short-term fluctuation, we run each experiment 5 times and each run lasts 10 minutes. Then, we get the average results. The measurement of average delay was non-trivial, since no mean end-to-end delay statistics are provided by Iperf or other off-the-shelf traffic measurement tools. As further explained in [5], tight synchronization between the transmitter and receiver is mandated, if the delay is to be measured directly. 4.3 Baseline Scenario To circumvent the synchronization requirement, which is notoriously difficult to meet, the end-to-end delay is therefore derived based upon a round trip delay that can be measured more easily. More specifically, a new testing function has been implemented in the driver, which lets the transmitter periodically broadcast a packet. Once the receiver

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successfully decodes the packet, it immediately sends another broadcast packet back to the transmitter. Since the delay incurred in each direction can be considered to be identical, the one-way end-to-end delay experienced by a data packet is approximately equal to half of the round-trip delay observed at the transmitter. The delay statistics derived thereof essentially is the time from the moment that the wireless MAC driver pushes the packet into the MAC transmission queue, until the time the packet is passed from the physical layer to the MAC buffer at the receiver. A closer examination of this delay value reveals that it consists of several major components, namely the delay incurred at the transmitter (e.g., kernel interrupt delay in the driver, random backoff time, DIFS), transmission time, and delay experienced at the receiver (e.g., delay associated with kernel interrupt that signals to the MAC layer the arrival of a new packet, etc.). Note that no time will be spent on transmitting an ACK packet, because a broadcast transmission does not require any acknowledgment.

5 Performance Evaluation Based upon the testbed described in Section 4, numerous experiments have been conducted, and the results obtained are reported and analyzed in this section. A baseline scenario, which only consists of 1 transmitter, 1 helper and 1 receiver, is first used to develop a basic understanding of the implication of cooperation, and establish a benchmark for performance study of more sophisticated settings. Thanks to its simplicity, this scenario isolates interfering factors such as collisions, and creates an ideal environment that gives rise to several crucial insights related to the behavior of CoopMAC. Throughput Improvement. In this experiment, source station ST As generates traffic using an Iperf client, while the corresponding Iperf server running at the destination ST Ad collects the end-to-end throughput statistics. The Iperf client at ST Ah has been switched on, so that it not only relays traffic on behalf of ST As , but also transmit its own packets to ST Ad. As readily demonstrated in Figure 2, CoopMAC enables ST As to deliver substantially higher throughput. More importantly, Figure 2 confirms that CoopMAC protocol creates a win-win situation, instead of a zero-sum game. That is, ST Ah benefits by helping forward the packets for the slow source station. At first glance counterintuitive, this observation can be explained by the fact that if ST Ah participates in forwarding, ST As can finish its packet transmission much earlier, thereby enabling both ST As and ST Ah to transmit more bits in unit time. Interaction with Transport Protocol. In Figure 2, we can see the throughput comparison in a scenario of a source, an active helper and a destination. Direct transmission between source station ST As and destination ST Ad always occurs at 1 Mbps, and helper station ST Ah can sustain 11 Mbps for communication with both ST As and ST Ad . An important trend displayed in Figure 2(a) is that bandwidth in the IEEE 802.11 network is equally shared by the two UDP sources ST As and ST Ah , respectively, in spite of the fact that physical layer bit rate supported by ST Ah is over 10 times higher than that at ST As . Indeed, this notion of fairness that 802.11 strives to maintain has been known

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Fig. 2. Throughput Comparison: Active Traffic from Helper

to be at the cost of serious network-wide throughput degradation [10]. The CoopMAC protocol preserves this fairness; no significant disparity in the throughput of ST Ah and ST As can be seen in Figure 2(a). For TCP traffic in the 802.11 network, however, Figure 2(b) indicates that the slow source station ST As surprisingly grabs even more bandwidth than the fast helper station ST Ah , which seems to defy conventional wisdom. It has long been known that the cross-layer interaction between the random access wireless MAC protocol and TCP congestion control mechanism is problematic [11]. We will conduct further investigation regarding the cause of this counter-intuitive phenomenon. 5.1 Hello Packet Interval It is known that the frequency at which the Hello packet is broadcast exerts crucial influence on the system performance. A new experimental scenario that contains 1 source, 2 helpers and 1 destination has been setup to investigate this impact. Packets are only generated at source station ST As in this experiment, and the rates supported on all related links are listed in Table 1. The second relay ST Ah2 remains available all the time, while the first one ST Ah1 alternates between awake and dormant state every 15 seconds to mimic user mobility and dynamic channel conditions. Note that since relay ST Ah1 maintains fast links to both the source and destination, it will be chosen as the helper as long as the source thinks that ST Ah1 is still located in close physical proximity. Of course, if the Hello packets from ST Ah1 disappear after it becomes dormant, ST As eventually would realize that ST Ah1 is unavailable, and therefore turns to ST Ah2 for help. The Hello packet interval is varied in the experiment, and the resultant UDP throughput is collected and plotted in Figure 3. A small value of this interval lets the source

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T. Korakis et al. Table 1. Settings for Study of Hello Packet Interval Rsd Rs h1 Rh1 d Rs h2 Rh2 d 1 Mbps 11 Mbps 11 Mbps 11 Mbps 5.5 Mbps

2 1.8

UDP Throughput (Mbps)

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

0.01

0.1 1 Hello Packet Interval (second)

10

Fig. 3. Impact of Hello Packet Interval

ST As be constantly updated on the current state of relay ST Ah1 , but causes more overhead. On the other, overhead can be reduced, but the information about the status of ST Ah1 may become stale at the source, as the interval grows excessively large. When the interval falls between the range of 0.1 to 0.2 seconds, a balance can be struck and the maximum throughput can be achieved, given that ST Ah1 goes off every 15 seconds. However, a general optimal operating region of the Hello interval value is far more complicated to predict, as the availability and suitability of a relay in reality depend on such highly random factors as channel fading, mobility and usage pattern. 5.2 End-to-End Delay Another key dimension of performance for any MAC protocol is the delay, which in fact plays a more critical role than throughput in determining the network’s capability of supporting QoS-sensitive applications. The scenario configured to measure the average end-to-end delay has been summarized in Table 2. The delay measurement methodology described in Section 4.2 has been applied, and the average delay is obtained based upon the experimental results for over 106 broadcast packets. As portrayed in Figure 4, it is evident that cooperative forwarding significantly lowers the average delay for all the cases studied, provided the MSDU size is larger than about 200 bytes. But once the MSDU size drops below 200 bytes, IEEE 802.11b seems to perform better, since it avoids the overhead associated with CoopMAC. Nonetheless, note that this drawback can be avoided, if CoopMAC adopts a dynamic relay selection algorithm, in which the source ST As would simply fall back to legacy 802.11 for small frames.

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Table 2. Settings for the Study on End-to-End Delay Case Rsd Rsh Rhd 1 1 Mbps 11 Mbps 11 Mbps 2 1 Mbps 11 Mbps 5.5 Mbps 3 1 Mbps 5.5 Mbps 5.5 Mbps

11

Mean End−to−End Delay (ms)

10 9

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400 500 600 MSDU Size (Bytes)

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Fig. 4. Mean End-to-End Delay

5.3 Protocol Dynamics To study the dynamic behavior of the protocol, a medium size testbed has been constructed, where 4 sources, 4 helpers and 1 dedicated destination are involved in the experiment. The UDP traffic is originated from both the source and the helper station, which implies that the channel access opportunities seized by each helper somehow have to be shared by both the locally generated traffic and the forwarded traffic. Table 3 lists the rate information related to the experiment. For both 802.11 and CoopMAC network, Figure 5 illustrates how the throughput achieved by each station changes with respect to the load applied. 1. Saturation Point The 802.11 network passes the critical tipping point as early as 0.2M bps/station, while CoopMAC does not experience saturation until a load of 0.5M bps/station. Thus, the maximum throughput achieved by CoopMAC is approximately 2.5 times higher than that for 802.11. 2. Post-Saturation Regime Once entering their respective saturation regions, all stations in 802.11 invariably start to witness significant packet drop and throughput deterioration. For helper stations in cooperative MAC, however, the decrease ends after an initial dip, and then stabilizes at a plateau of about 0.28M bps/station. The throughput of source stations in CoopMAC more or less follows the same trend of monotonic decline as observed in 802.11, but its absolute value is still notably higher.

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T. Korakis et al. Table 3. Settings for Study of Network Dynamics Rsi d , ∀i ∈ [1, 4] Rsi hj , ∀i, j ∈ [1, 4] Rhi d , ∀j ∈ [1, 4] 1 Mbps 11 Mbps 11 Mbps

550

Throughput per station (Kbps)

450 400 350

550 Helper 1 Helper 2 Helper 3 Helper 4 Source 1 Source 2 Source 3 Source 4

500 450 Throughput per station (Kbps)

500

300 Start to see significant packet loss

250 200 150

400 350

200 150 100 50

0.2

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250

50 0.15

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0 0.1

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0 0

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0.7

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1

(b) CoopMAC Fig. 5. Throughput Comparison

A closer scrutiny further suggests that this performance disparity between the helper stations and source stations in a CoopMAC network is an artifact resulting from our present implementation approach, and is expected to disappear once the access to firmware becomes available. More specifically, as explained in Section 3, the cooperative MAC protocol is currently realized at the driver level, which forces the helper stations to pass the received foreign packets into the driver space and queue them together with the native traffic in the same buffer. When the local load at the helpers grows high enough, the arrival rate of the indigenous packets at the buffer far surpasses that of the packets received from the source stations. Therefore, the rate at which the packets can be received at the helpers places a bottleneck on the end-to-end throughput of the forwarded traffic, which essentially gives local helper traffic preferential treatment. 5.4 Jitter To gain a more profound view of the delay performance, the jitter statistics for UDP traffic are collected and depicted in Figure 6. Direct transmission between source stations ST As and destination ST Ad always occur at 1 Mbps, and helper stations ST Ah can sustain 11 Mbps for communication with both ST As and ST Ad . Figure 6 depicts the maximum jitter for source and helper station, which is collected by Iperf for each traffic stream. Both Figures 6(a) and 6(b) indicate that jitter is sensitive to network size. Moreover, although helper stations support higher transmission rates than source stations, they suffer higher variance in end-to-end delay (jitter) in an 802.11

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network. A similar phenomenon was previously identified and an explanation offered in Section 5, where the interaction with the TCP layer was first investigated. Once cooperative MAC is adopted, the jitter performance for both source and helper stations can be improved. In addition, the fast helper stations now perceive lower jitter than the slow source stations, implying that the issue of unfairly high jitter for fast stations has been successfully resolved by CoopMAC.

6 Conclusions and Future Work This paper represents one of the first attempts that relies on an experimental approach to develop an understanding of cooperation at the MAC layer. The measurement results obtained confirm that cooperative MAC can substantially improve the performance (e.g., throughput, mean end-to-end delay, jitter) for not only the stations being helped, but also the ones who offer the cooperation. Furthermore, the paper sheds light on several critical issues particular to cooperation, such as the impact of MAC cooperation on the TCP protocol, the dynamics of protocol behavior, etc., which to the best of our knowledge have been presented for the first time. As for possible future work, user mobility would be incorporated into the experiment to examine its impact on the protocol performance. It is also worthwhile to develop further understanding about the implication of cooperation on power consumption, as energy efficiency is always a major design constraint for mobile devices. Moreover, investigation of the possible interference reduction effect of cooperative MAC would be attempted in a larger testbed that can emulate a multicell environment. In addition, access to the firmware codebase will be actively pursued, so that all the artifacts and constraints imposed by the current driver can be mitigated or completely eliminated, and the entirety of the cooperative protocol can finally be implemented in a faithful manner.

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References 1. J. N. Laneman, D. Tse, and G. W. Wornell, “Cooperative Diversity in Wireless Networks: Efficient Protocols and outage Behavior,” IEEE Transactions on Information Theory, vol. 50, pp. 3062–3080, December 2004. 2. G. Jakllari, S. V. Krishnamurthy, M. Faloutsos, P. V. Krishnamurthy, and O. Ercetin, “A Framework for Distributed Spatio-Temporal Communications in Mobile Ad hoc Networks,” in Proceedings of IEEE INFOCOM’06, (Barcelona, Spain), April 2006. 3. P. Liu, Z. Tao, and S. Panwar, “A Cooperative MAC Protocol for Wireless Local Area Networks,” in Proceedings of IEEE ICC’05, (Seoul, Korea), June 2005. 4. A. Raniwala and T. Chiueh, “Architecture and Algorithms for an IEEE 802.11-based Multichannel Wireless Mesh Network,” in Proceedings of IEEE INFOCOM’05, (Miami, FL), March 2005. 5. I. Dangerfield, D. Malone, and D. Leith, “Experimental Evaluation of 802.11e EDCA for Enhanced Voice over WLAN Performance,” in Proceedings of Second International Workshop On Wireless Network Measurement (WiNMee 2006), (Boston, USA), April 2006. 6. T. Korakis, S. Narayanan, A. Bagri, and S. Panwar, “Implementing a Cooperative MAC Protocol for Wireless LANs,” in Proceedings of IEEE ICC’06, (Istanbul, Turkey), June 2006. 7. S. Biswas and R. Morris, “Opportunistic Routing in Multi-Hop Wireless Networks,” in Proceedings of ACM SIGCOMM’05, (Philadelphia, PA), August 2005. 8. “Official Website of Cooperative MAC Implementation,” http://eeweb.poly.edu/ coopmac/. 9. “Iperf: The TCP/UDP Bandwidth Measurement Tool,” http://dast.nlanr.net/ Projects/Iperf/. 10. M. Heusse, F. Rousseau, G. Berger-Sabbatel, and A. Duda, “Performance Anomaly of 802.11b,” in Proceedings of IEEE INFOCOM’03, (San Francisco, CA), April 2003. 11. S. Xu and T. Saadawi, “Revealing the Problems with 802.11 Medium Access Control Protocol in Multi-hop Wireless Ad Hoc Networks,” Computer Networks: The International Journal of Computer and Telecommunications Networking, vol. 38, pp. 531 – 548, March 2002.

Modeling Approximations for an IEEE 802.11 WLAN Under Poisson MAC-Level Arrivals Ioannis Koukoutsidis1 and Vasilios A. Siris1,2 FORTH-ICS, P.O. Box 1385, 71110 Heraklion, Crete, Greece Computer Science Department, University of Crete, P.O. Box 2208, 71409 Heraklion, Crete, Greece {jkoukou,vsiris}@ics.forth.gr 1

2

Abstract. We examine two approximative models for the behavior of an IEEE 802.11 WLAN under Poisson MAC-level arrivals. Both models extend, in a simple manner, the analysis for saturated stations with the decoupling approximation. The first follows a well-known approach considering a constant busy station probability at each idle-sensed slot, equal to the load of the envisaged single-server queue. We extend the analysis of this model to calculate more accurately the throughput seen by a station in non-saturation conditions, by considering alternating ON/OFF periods. The second model uses this ON/OFF process structure for calculating attempt and collision probabilities, and subsequently performance measures, based on regenerative process theory. By comparison with simulation results, this model is shown to be more precise for low load conditions. The accuracy of the modeling approximations is also studied for a range of values of the minimum contention window, which is the most influential protocol parameter. Keywords: WLAN, 802.11, mathematical modeling, non-saturation.

1

Introduction

Tractable mathematical models of the IEEE 802.11 WLAN protocol operation with saturated stations (i.e., that always have queued packets to send) consider a decoupling approximation, in which the (re)-transmission processes of the different stations are mutually independent, and the probability of an attempt at each idle-sensed slot is the same throughout the time evolution of the system. The corresponding attempt and collision probabilities are then easily retrieved by the solution of a nonlinear system of equations, as shown in [1, 2, 3]. The consideration of saturated wireless stations is more appropriate for data traffic, as compared to voice traffic (where a codec transmits a fixed payload periodically), or more generally real-time traffic. Therein it is important to model 

This work has been supported by the General Secretariat for Research and Technology of Greece, through the project 05-AKMON-80 within Action 4.2 of the Operational Programme “Competitiveness” — 3rd Community Support Programme.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 439–449, 2007. c IFIP International Federation for Information Processing 2007 

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the behavior of an unsaturated network in the whole range from low load to nearsaturation conditions, under a certain arrival pattern. The performance of the network would certainly be different, but also the protocol parameters for which performance is optimal might change. In this scope, a fair amount of research has been devoted to the modeling of the 802.11 protocol in more realistic, non-saturation conditions, e.g., [4, 5, 6, 7]. The bulk of research papers consider Poisson MAC-level arrivals – both for mathematical simplicity and general applicability – and also use the decoupling approximation, assuming additionally a constant busy-station probability at each idle-sensed slot. This is taken equal to the load of the station regarded as a single-server queue. In [5, 6] the load is set as an additional parameter to the problem, that should be measured or estimated. However, a better model should avoid this need and include the characteristics of the traffic arrival distribution in its parameters. This is done in [7] where the authors, while studying voice traffic, extend the set of equations used in saturation conditions to also solve for the load. Then performance measures such as the mean channel access delay and throughput of a station are extracted. In this paper we revisit the analytical modeling of 802.11 in non-saturation conditions, under Poisson arrivals. We view the aforementioned load model as the predominant modeling approach, and further explore its accuracy. A critical remark is that previous research has considered throughput as seen by the system and not as seen by a station. We show that these two quantities may differ substantially in non-saturation. We then propose an approximative calculation of the throughput seen by a station, by considering alternating ON/OFF periods. Further, we construct a new model for calculating attempt and collision probabilities, and subsequently performance measures, based on a regenerative process with such periods. This model is shown to be more accurate for low load conditions, which are especially important when considering real-time traffic. We shall present and compare these modeling approaches, further examining their accuracy in a range of values of the minimum contention window, which is the most influential protocol parameter. We study a homogeneous network with the same parameters for all stations, but the models are readily extendable to multiple service classes, as suggested in the 802.11e standard.

2 2.1

Preliminaries Protocol Description

The IEEE 802.11 protocol gives specifications for the interconnection of telecommunications equipment in a WLAN, using a CSMA/CA medium sharing mechanism, and exponential backoff upon collisions [8]. Main elements of the mechanism are the method of random slot selection and the backoff algorithm. The number of idle-sensed slots that a station must wait prior to a packet transmission attempt is selected uniformly in the interval [0, 2(k∧m) ·CWmin −1], termed as the contention window; k is the current backoff stage, initialized to zero for the transmission of a new packet and incremented by one each time a collision

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occurs, up to a maximum value m. There is no collision detection in the wireless system and a successful transmission is understood by the proper reception of an acknowledgement (ACK) by the sender. All stations in range of each other are assumed to be synchronized with a perfect carrier sense mechanism so that they all perceive the same sequence of idle and busy slots. 2.2

Analysis in Saturation Conditions

The analysis in saturation conditions is the basis of our investigation, and shall be extended for non-saturation. Consider a number of contending stations N . We assume there does not exist a limit on the number of attempts by a station, after which a packet would be discarded, or that this limit is sufficiently high so that its effect is negligible. Denote the attempt and collision probabilities of a station by p, c, respectively, which are the same for all stations in a homogeneous network. Since the collision probability is dependent on the attempt rate and vice versa, appropriate expressions can construct a system of equations to solve for these values. From a stochastic analysis (either a Markov chain analysis [1], or a renewal theory analysis [2]), the attempt rate can be expressed as a function of the collision probability and the backoff parameters CWmin , m, as: p=

2(1 − 2c) . (CWmin − 1)(1 − 2c) + CWmin c(1 − (2c)m )

(1)

The collision probability at an attempt by a station is then given by c = 1 − (1 − p)N −1 .

(2)

We next proceed to derive the main performance measures of interest, which are the mean channel access delay and throughput. The channel access delay is defined as the delay a frame experiences from the time it arrives at the head of the transmission queue until it is successfully transmitted. The following derivation is simpler compared to other approaches, e.g., [7]. The probability of a successful transmission attempt by a station at an idlesensed slot is P succ = p(1 − p)N −1 , while the probability of a collision is P coll = p(1 − (1 − p)N −1 ). The probability of no attempt is 1 − p. When the number of elapsed idle-sensed slots until (and including) a success is ω, the mean number of experienced collisions is (ω − 1)P coll . Therefore the mean number of collisions experienced by a station until a successful transmission is ∞ 

(ω − 1)P coll P succ (1 − P succ )ω−1 =

ω=1

P coll (1 − P succ ) . P succ

Similarly, the mean number of “no-attempt slots” until a success is (1 − p)(1 − P succ )/P succ . Therefore the mean channel access delay is given by E[Dacc ] = T succ +

P coll (1 − P succ ) coll (1 − p)(1 − P succ ) T + E[S (N −1) ] , (3) P succ P succ

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where T succ , T coll are the durations of a successful transmission (including the reception of the acknowledgement), and a transmission which resulted in a collision, respectively. S (N −1) is the duration of a slot as perceived by a station which does not attempt in that slot; it is an i.i.d r.v standing for the duration of a generic slot (i.e., either an idle slot, a successful transmission or a collision), when N − 1 stations are considered in the system. Hence E[S (N −1) ] =(1 − p)N −1 T slot + (N − 1)p(1 − p)N −2 T succ + [1 − (1 − p)N −1 − (N − 1)p(1 − p)N −2 ]T coll .

(4)

The throughput seen by a station equals the rate of successfully transmitted MAC-level information per unit of time. We calculate it by considering that each end of a successful transmission is a renewal epoch for the system. Assuming a fixed MAC packet size σ and denoting the throughput by γ, we have γ=

σ . E[Dacc ]

(5)

This is opposed to γ = σP succ /E[S (N ) ], mostly derived as a measure in the literature (e.g., [1,5,6,7]), which can only be stated as the individual throughput seen by the system.

3

Modeling Approximations in Non-saturation

Our setting consists of contending stations transmitting to an access point, whose only purpose is to send back ACKs. We consider the same arrival pattern for all stations. No characterizations are made about the stability of the system, as the memory-full nature of the stochastic model makes this extremely difficult. Instead we assume that there is a range of arrival rates for which the system will be stable, in the sense that the queue size of every station remains finite. This assumption is in accordance with our simulation results, as well as numerous other experimental results in the literature which confirm stability for a range of arrival rates (e.g., [4, 5]). 3.1

Load Model

The first model assumes a constant instead of a time-dependent busy station probability at each idle-sensed slot. This is taken equal to the load of an envisaged single server queue, which generally models the arrival and departure sequence at the station.1 Denoting the load by , the probability of a collision at an attempt by a station becomes c = 1 − (1 − p)N −1 , (6) 1

It is worth noting that this modeling would be correct if stations were fed by a stationary (not only possessing stationary increments) arrival process and their queues were stable.

Modeling Approximations for an IEEE 802.11 WLAN

where

 = λE[Dacc ]

443

(7)

for an arrival rate λ. The probability of an attempt p (given the station has a packet to transmit) depends only on the collisions, and hence is given again by (1). The mean channel access time can then be derived by following the analysis in Sect. 2.2, where in place of p one uses the unconditional probability of an attempt, p. Thus we have a system of equations to solve for p, c, . To calculate the throughput experienced by a station, we would then have to consider a sequence of alternating ON/OFF periods, where the OFF period is geometrically distributed with parameter  (and may take the value 0). Treating this as a regenerative process, we would have γ = σ/(E[Dacc ] + (1 − )/ · E[S (N −1) ]). This turns out to be extremely inaccurate, yielding throughput results of about 1 order of magnitude greater. To tackle this, we shall make use of the throughput calculation in the more involved ON/OFF model of the next subsection. 3.2

Regenerative ON/OFF Model

Consider a probabilistic ON/OFF model, where durations of the ON and OFF periods are geometrically distributed. After a frame transmission in the ON period, a station “rests” with probability rON or has another frame to send and contends again with probability 1 − rON . The OFF period consists of a random number of generic slots, i.e., idle slots, successful transmission or collision times. At the end of each such slot, a station may keep resting with probability rOFF , or have a frame to send and become busy with probability 1 − rOFF . 1st collision 2nd collision success 1st collision

OFF

ON

11100 000 11 1st frame

success

OFF

111100 0000 11 2nd frame

regeneration cycle

Fig. 1. Sample path evolution of the system with ON and OFF periods. The end of an OFF period is a regeneration epoch for the system.

Fig. 1 illustrates a sample path of the system evolution. Clearly we have described a regenerative process and the time between the ends of successive OFF periods is a regeneration cycle. Then, as in the renewal case [2], we can derive time averages. The attempt probability for a station will be p=

E[R] , E[X]

where E[R] is the mean number of attempts in the regeneration cycle and E[X] the mean number of attempt opportunities in this cycle. It is obvious that the

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mean number of attempts until a frame is transmitted has the same expression as in the saturation case. For a collision probability c, we have E[R] =

1 . rON (1 − c)

Now denote the mean OFF period by θ, measured in attempt opportunities. The mean number of attempt opportunities in a regeneration cycle is E[X] = θ +

¯ E[X] , rON

¯ being the number of elapsed backoff slots until a frame is transmitted, with X which also has the same expression as in saturation. Recovering the appropriate expression and substituting θ = 1/(1 − rOFF ), we finally find p=

2(1 − 2c) (8) rON m 2 1−r (1 − c)(1 − 2c) + (CW min − 1)(1 − 2c) + CWmin c(1 − (2c) ) OFF

The inherent deficiency of the model is the assumption of a constant busy station probability after each successful transmission of a frame, while in reality it increases for each consecutive transmission. Here we take rON to be the probability that no packet arrives in the interval [0, Dacc ). The error in this approximation is smaller in low loads, where consecutive frame transmissions are less frequent. The distribution of Dacc is difficult to derive, and does not result in a simple expression. Instead we replace it with a constant value, equal to its mean. Considering Poisson arrivals with rate λ, we have rON = e−λE[D

acc

]

.

(9)

The value of rOFF is the probability that no packet arrives in [0, S (N −1) ). It can easily be calculated exactly, since S (N −1) takes discrete values: rOFF =(1 − p)N −1 e−λT

slot

+ (N − 1)p(1 − p)N −2 e−λT

+ [1 − (1 − p)N −1 − (N − 1)p(1 − p)N −2 ]e−λT

succ coll

(10) .

Equations (8), (9), (10), along with c = 1 − (1 − p)N −1 can be used to solve for the unknowns p, c, rON , rOFF . Based on the derived attempt and collision probabilities, the mean channel access delay is calculated following the same approach as in Sect. 2.2. The throughput is calculated as the mean MAC-level information transmitted in a regeneration cycle, over the mean duration of this cycle: γ=

σ/rON . (N −1) ]/(1 − r ON + E[S OFF )

E[Dacc ]/r

(11)

We shall employ the same formula for calculating the throughput in the load model of Sect. 3.1, using the derived quantities E[Dacc ], E[S (N −1) ] from the model, and replacing p with p.

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Remark 1. It should be clear that the ON/OFF periods here are just a workaround and do not refer to the arrival pattern. We briefly explain how such a pattern can be modeled in the Appendix.

4

Results

We note that it is very difficult to show existence and uniqueness of the solution of the nonlinear system of equations in the above models, despite their relatively simple form. The mapping f in the constructed multidimensional equation x = f (x) does not possess attractive monotonicity and concativity properties. We solve numerically for the unknowns using MATLAB’s fsolve function. Test cases are taken for basic channel access, noting that the method of RTS/CTS messages [8] that mitigates the hidden terminal problem becomes less efficient for unsaturated stations. According to protocol timings, the duration the medium is busy because of a successful transmission is T succ = DIF S + δ + TP LCP +

σ ack + δ + TP LCP + SIF S + , R Rb

(12)

while the duration of a collision is T coll = DIF S + δ + TP LCP +

σ . R

(13)

In the above formulae, DIF S (Distributed Inter-Frame Space) is the idle-sensed time that must elapse prior to a transmission attempt, δ the propagation delay, TP LCP the time to transmit the PLCP preamble and header (adjoined by the physical layer) and ack the size of a MAC level acknowledgement. ACK packets are always transmitted at a (usually lower) basic service rate Rb , whereas the TP LCP is fixed for each physical layer configuration. In addition, a SIF S (Short Inter-Frame Space) interval is used between the transmission of a frame and the sending of an acknowledgement, to allow the MAC layer to receive the packet and subsequently the transceiver to turn around. We consider an 802.11a implementation, for R, Rb equal to 6 Mbps and MAC packet size 160 bytes. Default values for MAC and physical layer parameters are taken from [9] and shown in Table 1. We compare the numerical results with those of a discrete-event simulator written in C++, which closely follows the modeled setup. The load model is also referred to as Model 1, and the regenerative ON/OFF model as Model 2. We first present in Fig. 2 a comparison of different throughput approximations in a range of Poisson arrival rates until saturation, based on Model 1. We have calculated the corresponding individual throughput “seen by the system”, which is shown to be highly inaccurate for non-saturation conditions. On the other hand, what is also shown on the graph is that the approximation by λσ is extremely accurate for a very large range of loads, reflecting the simple fact that when collisions are fewer, the throughput is almost equal to the information arrival rate.

446

I. Koukoutsidis and V.A. Siris Table 1. 802.11a MAC and physical layer parameters Parameter Value Tslot SIF S DIF S TP LCP ack

9 μs 16 μs 34 μs 20 μs 14 bytes

400

Throughput (kbps)

350 300

Eq. (11) ’Seen by system’ Simulation λ⋅σ

250 200 150 100 50 0 10

100 λ (packets/sec)

1000

Fig. 2. Comparison of different throughput approximations based on Model 1 in a case with CWmin = 32, m = 5 and N = 10 stations, for different Poisson arrival rates until saturation

Results on the comparison of the two methods are presented in Table 2. For cases where the load obtained by the numerical evaluation is smaller than 1 and thus the system appears stable, we calculate the associated queueing delay. The total packet transmission delay is, for an M/G/1 queue, E[Dtotal ] = E[Dacc ] +

λE[(Dacc )2 ] . 2(1 − )

(14)

For the calculation of the second moment of the channel access delay, see e.g. [7]. If the load turns out greater than 1, results are shown for saturation. Inaccuracies manifest themselves more explicitly in the estimation of mean delay, whose units are on a finer scale. We see that Model 1 overestimates delay for low or moderate arrival rates, and underestimates it for high loads. This reveals the nature of the average approximation. On the other hand, Model 2 is more accurate for lower loads but performs significantly poorer for higher load values, and fails to predict instability cases well. The two models are generally more precise in the estimation of the throughput. It should be stressed that – in contrast to delay measurements – the throughput measurement scale of kbps is a coarse scale when protocol timings are in μs. Notice, however, that the throughput expression used in Model 1 yields a greater error for highly loaded but not saturated stations.

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Table 2. Comparison of the two models (a) CWmin = 32, m = 5, N = 5 λ (pkts/s) Load 102 2·102 3·102 4·102 5·102 6·102

0.04 0.11 0.22 0.46 1.09 1.31

E[Dacc ] (ms) E[Dtotal ] (ms) Throughput (kbps) Analysis Simulation Analysis Simulation Analysis Simulation Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 0.537 0.485 0.441 0.553 0.498 0.452 127.74 127.79 127.80 0.644 0.519 0.542 0.699 0.552 0.591 253.56 254.39 255.58 0.811 0.576 0.734 0.968 0.643 0.934 372.62 377.94 383.70 1.121 0.680 1.152 1.678 0.829 2.460 468.79 493.70 511.94 2.010 0.899 2.185 ∞ 1.342 ∞ 636.74 585.92 585.40 2.010 2.010 2.186 ∞ ∞ ∞ 636.74 636.74 585.40

(b) CWmin = 32, m = 5, N = 10 λ (pkts/s) Load 102 2·102 3·102 4·102

0.06 0.33 1.34 1.79

E[Dacc ] (ms) E[Dtotal ] (ms) Throughput (kbps) Analysis Simulation Analysis Simulation Analysis Simulation Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 0.678 0.527 0.576 0.706 0.543 0.604 127.59 127.75 127.88 1.382 0.728 1.674 1.705 0.801 3.163 246.48 252.94 255.80 4.119 2.305 4.479 ∞ 5.443 ∞ 310.78 320.59 285.74 4.119 4.119 4.479 ∞ ∞ ∞ 310.78 310.78 285.79

We further proceed to modify the minimum contention window CWmin , which is the most influential protocol parameter. The maximum backoff stage has a much smaller influence, especially in non-saturation. We take numerical results for both models in a wide range of CWmin values. In Fig. 3 we plot simulated and best approximative analytical values for the mean channel access delay, as a more sensitive metric to changes of CWmin , compared to the mean total delay or throughput. Filled points are used to discern cases for which Model 2 was more accurate; as shown in the figure, this occurrs for a range of low loads (note that for fixed other parameters the load increases or decreases with CWmin proportionally to E[Dacc ]). Inaccuracies are more pronounced in both models as CWmin increases, but except for exorbitantly large CWmin values, the system’s behavior is accurately followed. Qualitative aspects of behavior are well captured by both models. We saw in Table 2 that both models demonstrate the occurrence of instability. Instability may also occur for very large values of CWmin , because of the increased waiting time prior to an attempt. This is confirmed by the numerical and simulation results in Fig. 3, for λ = 3·102 and CWmin equal to 512 and 1024 (the corresponding values coincide with the ones of the saturation case). The most important qualitative aspect concerns the decrease of the optimal CWmin value as the arrival rate decreases. The optimal CWmin equals 32 in the saturation case, for N = 5 stations, while for λ = 3 · 102 packets/sec it is equal to 8 and for λ = 102 it falls down to 4, as shown in Fig. 3.

448

I. Koukoutsidis and V.A. Siris 7 Analysis, Saturation Simulation, Saturation Best approximation, λ=3⋅102 Simulation, λ=3⋅102 2 Best approximation, λ=102 Simulation, λ=10

6

E[Dacc] (ms)

5 4 3 2 1 0 4

8

16

32

64 CWmin

128

256

512

1024

Fig. 3. Mean channel access delay for different CWmin values, and load conditions (N =5 stations, m = 5). Filled points refer to cases for which Model 2 was more accurate.

5

Concluding Remarks

We have examined modeling approximations for the 802.11 protocol in nonsaturation conditions, with respect to the accuracy of performance evaluation measures. A regenerative ON/OFF structure was used to calculate more accurately the actual throughput experienced by a station. This can be used as a simple extension to the load model, or as a stand-alone model in low load conditions. These modelings can also be extended to the case of multiple service classes suggested in 802.11e, based on modifications of the saturation analysis shown e.g. in [3, 7].

References 1. Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Select. Areas Commun. 18(3) (March 2000) 535–547 2. Kumar, A., Altman, E., Miorandi, D., Goyal, M.: New insights from a fixed-point analysis of single cell IEEE 802.11 WLANs. In: Proc. IEEE Infocom 2005, Miami, FL, USA (March 2005) 1550–1561 3. Ramaiyan, V., Kumar, A., Altman, E.: Fixed point analysis of single cell IEEE 802.11e WLANs: Uniqueness, multistability, and throughput differentiation. In: Proc. ACM Sigmetrics 2005, Banff, Canada (June 2005) 109–120 4. Clifford, P., Duffy, K., Foy, J., Leith, D., Malone, D.: Modeling 802.11e for data traffic parameter design. In: Proc. IEEE WiOpt 2006, Boston, MA, USA (April 2006) 28–37 5. Engelstad, P., Østerbø, O.: Non-saturation and saturation analysis of IEEE 802.11e EDCA with starvation prediction. In: Proc. ACM/IEEE MSWiM 2005, Montreal, Quebec, Canada (October 2005) 224–233 6. Ergen, M., Varaiya, P.: Throughput analysis and admission control for IEEE 802.11a. Mobile Networks and Applications 10(5) (2005) 705–716

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7. Hegde, N., Prouti`ere, A., Roberts, J.: Evaluating the voice capacity of 802.11 WLAN under distributed control. In: Proc. IEEE LANMAN 2005, Chania, Greece (September 2005) 8. IEEE Computer Society: IEEE Std 802.11. (1999) 9. IEEE Computer Society: IEEE Std 802.11a-1999 (R2003). (2003) 10. Wolff, R.: Stochastic Modeling and the Theory of Queues. Prentice-Hall, Englewood Cliffs, NJ (1989)

Appendix: ON/OFF Arrivals Let XON , XOFF denote the durations of the ON and OFF periods respectively. We will calculate the mean number of frames sent successively in an ON period, mON , and the mean number of attempt opportunities in an OFF period, mOFF , only relying on expectations of XON , XOFF . If M packets are sent in an ON period, the total time to send these is XON = M acc j=1 Dj . Employing Wald’s theorem, we have mON =

E[XON ] . E[Dacc ]

(15)

Now suppose L generic slots occur in an OFF period, the total duration of  (N −1) which is TOFF = L . Then TOFF = XOFF +RL , where RL is the residual j=1 Sj time of the last generic slot which begun before the end of the OFF period. If we assume that the end of the OFF period occurs randomly within the last generic (N −1) 2 ) ] slot, we approximately have E[RL ] = E[(S (see e.g., [10]). 2E[S (N −1) ] Then from TOFF = XOFF + RL , taking expectations in both sides, dividing by E[S (N −1) ], and employing Wald’s theorem, we have mOFF =

E[(S (N −1) )2 ] E[XOFF ] + . E[S (N −1) ] 2(E[S (N −1) ])2

(16)

It is noted that in the case where XOFF is considerably larger than the time of a generic slot the residual term can be removed without significant error. Following the notations in Sect. 3.2, the attempt probability for a station writes 1 mON · (1−c) E[R] p= (17) = ¯ + mOFF . E[X] mON · E[X] As always, the collision probability is c = 1 − (1 − p)(N −1) , so that a system of equations is constructed to solve for p, c.

Performance and Equilibrium Analysis of Heterogeneous IEEE 802.11 Based WLANs Hao Zhu Telecommunication and Information Technology Institute Florida International University 10555 W. Flagler Street, Miami, FL 33174 [email protected]

Abstract. This paper presents a general model to study the medium access control (MAC) layer performance and equilibrium of WLANs consisting of nodes with different MAC parameters (e.g., backoff window size). Our model can be used in general 802.11-based WLANs since it captures the important factors such as non-uniform backoff behavior, channel errors and unsaturated traffic. We first formalize the performance of 802.11 based MAC protocols with simultaneous fixed point equations. We then derive the average per-flow service time of each flow, and apply it to calculate throughput. More importantly, based on our model, we use interval analysis to formally study the existence and uniqueness of the network’s equilibrium, and find the sufficient condition for the uniqueness of equilibrium. We validate our model through simulations and the simulation results show that the model is quite accurate.

1

Introduction

With the low cost and high data rates, IEEE 802.11 based wireless local area networks (WLANs) have been widely deployed in residences, hotels, hospitals and other public areas as a communication infrastructure for high speed wireless Internet access. The core technology of 802.11 WLANs follows the medium access control (MAC) and physical layer (PHY) specifications [1] finalized by IEEE 802.11 working groups. In these specifications, the building block of medium access control mechanism is called distributed coordination function (DCF). DCF is a random access scheme and relies on the carrier sense multiple access with collision avoidance (CSMA/CA). Binary exponential backoff mechanism is used for data retransmission upon a collision or transmission failure. IEEE 802.11 working group has proposed a family of 802.11 MAC protocols such as 802.11 b/g/e [1,2] to improve the system performance in different aspects (e.g. channel capacity, service differentiation). Therefore, it is expected that future WLANs may need to accommodate nodes running heterogeneous 802.11 based MAC protocols. In order to have deep insights into the performance of heterogeneous 802.11 based WLANs, it is critical to develop a general model for 802.11 based WLANs considering the coexistence of heterogeneous MAC parameters since I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 450–461, 2007. c IFIP International Federation for Information Processing 2007 

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they play important roles in determining the bandwidth share of each node and the bandwidth utilization of the network. There have been a number of studies focusing on the performance model of DCF. Bianchi [3] modeled the binary exponential backoff under saturated traffic conditions as a two-dimensional discrete Markov Chain model. The similar Markovian technique has been subsequently adopted by many other works [4,5] in modeling WLANs under unsaturated traffic. Carvalho and Garcia-LunaAceves [6] modeled the packet service time in single-hop WLANs given that the state probabilities of a node’s backoff operation are known. With the results from [3], the first two moments of the service time were studied. Kumar et al. [7] proposed a simpler model by viewing the backoff procedure as a renewal process. Ramaiyan et al. [8] refined such approach to study the stability of the network in 802.11e WLANs [2] under saturated traffic conditions and give sufficient conditions for the existence and uniqueness of equilibrium. Kim and Hou [9] modeled the service time in large-scale single-hop WLANs for fast simulations. Medepalli and Tobagi [10] applied the average cycle time analysis to model the average service time in wireless ad hoc networks. Their model captures the impacts of unsaturated traffic and channel errors among contending nodes. However, the above-mentioned works are not sufficient to model the performance of heterogeneous 802.11 based WLANs under both saturated and unsaturated traffic. In this paper, we present a general model of heterogeneous 802.11 based WLANs. The model is fairly simple and quite accurate. Our work differs from previous works in at least one of the following three aspects (especially the third aspect): first, our model captures the performance of general 802.11 based WLANs since we take into account of channel errors and realistic network operating scenarios (e.g., unsaturated traffic). Second, our model can be used to study the performance of heterogeneous 802.11 WLANs in which each node may have different system parameters (e.g. back-off window size). Third, we theoretically study the sufficient condition for the uniqueness of equilibrium in a general heterogeneous 802.11 WLANs. Our sufficient condition is a superset of that in [8]. We first model the performance of the MAC protocol using the fixed point analysis [7] using renewal process theory [11]. The time average performance of the network can be obtained by solving simultaneous non-linear equations. Then, we apply interval analysis [12] to study the sufficient condition for the uniqueness of root, which indicates the network will be in a unique equilibrium. In the previous study [7] for homogeneous 802.11 WLANs, it has been shown that the network has a unique equilibrium if the mean backoff window sizes at different backoff stages are a non-decreasing sequence. Through our analysis, we prove that this sufficient condition still holds for a general heterogeneous 802.11 WLAN. We verify our model through simulations and the results show that our model is able to accurately capture the average behavior of the network. The rest of the paper is organized as follows. Section 2 states the system model. Section 3 describes the details of the proposed analytical model. We evaluate our model in Section 4. Section 5 concludes this paper.

452

2

H. Zhu

System Model

We study a single-cell WLAN with fixed number n of contending nodes that access the same wireless channel following 802.11 DCF [1]. For simplicity, these nodes are within the transmission range of each other, and there is not hidden terminal problem. We consider general traffic situation for each node. In particular, node i can send a packet to any other nodes in the network, and its aggregate traffic rate is λi (in packet per slot). In order to capture the impact of unsaturated traffic sources, we assume that each node has the probability that its queue is non-empty, denoted by ρi . Suppose the average service time for a packet once it becomes the head of the queue is E(Si ) (in slots), according to the Little’s Theorem [11], we have ρi = λi E(Si )

(1)

Queueing theory [11] has shown that the average packet delay of node i exponentially increases with ρi . According to delay requirements, we assume node i operates with a targeted ρi by controlling λi based on Eq.(1). The control is deterministic if E(Si ) can be uniquely determined by (ρ1 , ..., ρn ). We will prove that this assumption actually holds. According to the traffic and channel condition statistics, we assume the mean bit error rate of node i is beri and data packets sent by node i have a mean length of DAT Ai . We assume the RTS/CTS handshaking mechanism is always switched on. As shown in [3,9], there is not significant differences in modeling these two mechanisms. We also consider heterogeneous MAC parameters of each node. Even though the results in [8] can be used to model the impact of arbitrary inter frame space (AIFS) [2], it has been indicated in [8] that differentiating AIFS or the exponential component p would starve low priority traffic as the load of system increases. On the contrary, using initial back-off window can produce a bounded throughput ratio between different classes of traffic. Thus, we simply focus on the case that the nodes may have heterogeneous backoff parameters. Specifically, backoff parameters (for node i) are: – Ki : At the (Ki +1)th attempt either the impending packet of node i succeeds or is discarded – bi,k : The mean backoff (in slots) at the kth attempt for an impending packet of node i, where 0 ≤ k ≤ Ki . Other parameters not mentioned are the same for all nodes. Following the design principle of standard IEEE 802.11 DCF [1], we assume that bi,k , k ≥ 0 is a nondecreasing sequence.

3

The Proposed Model

In this section, we first introduce the proposed model and then study the network’s equilibrium. To make the presentation clear, we omit the inter frame spaces since their are much shorter than the transmission time of control and data packets.

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3.1

453

Modeling the Attempt Rate

Suppose node i sends a data packet DAT Ai with beri , the probability of transmission failures, denoted by pe,i , can be calculated as: pe,i = 1 − (1 − peri (RT S))(1 − peri (CT S))(1 − peri (DAT Ai ))(1 − peri (ACK))(2)

where the packet error probability peri (p) is defined by: peri (p) = 1 − (1 − beri )p.length

(3)

When a transmission failure happens at node i, the amount of bandwidth waste, denoted by αi can be calculated as: αi = tx(RT S) + tx(CT S) + (1 − ci )(tx(DAT Ai) + tx(ACK))

(4)

where tx(p) is the total transmission time of packet p and ci =

(peri (RT S) + peri (CT S)) pe,i

(5)

Renewal Cycle

0

Bi

1

Bi

3

2

Bi

0

Bi

Bi

Success

Collisions Transmission Error

Fig. 1. The aggregate attempt process of node i

Whenever the channel is occupied due to either data transmissions or collisions, each node freezes its backoff timer until the channel is available. Therefore, similar to [7], we can remove all busy periods of the channel and study the attempt rate of each node according to the aggregate attempt process in the idle periods of the channel. As shown in Figure 1, the aggregate attempt process of a node can be seen as a renewal process in which the reward is the number attempts in each renewal cycle. Since the each node enters the next backoff stage when it has a collision or transmission failure, suppose the conditional collision probability seen by node i when it attempts to access channel is pc,i , the conditional probability that an attempt of the node fails, denoted by γi follows: γi = (1 − pe,i )pc,i + pe,i

(6)

With the decoupling assumption introduced by Bianchi [3] and the renewal reward theorem, the attempt rate of node i, denoted by βi , is given by: Ki

j=0

βi = Ki

γij

j j=0 bi,k γi

where bi,k is the mean k th backoff period of node i.

(7)

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3.2

H. Zhu

Modeling the Service Time

Before studying the existence and uniqueness of equilibrium, we first model the service time of node i, denoted by E(Si ). Equation (7) only describes the saturation attempt probability. Given the non-empty queue probability of node i, the actual attempt probability of node i is equal to ρi βi . Thus the conditional collision probability of node i can be calculated by: n 

pc,i = 1 −

(1 − ρj βj )

(8)

j=1,j=i

idle

1111transmission 0000 0000 1111 collision

1111111 0000000 0000000 1111111 0000000 1111111

11111 00000 00000 11111 00000 11111 a cycle

a cycle

Time

a cycle

Fig. 2. The Illustration of Channel Activities

Since, as shown in Figure 2, the aggregate channel activity can be viewed as a renewal process, each of which contains an idle period followed by a collision or a transmission. For node i, its average service time E(Si ) is the average delay for a packet from the time it reaches the head of the queue at node i to the time the packet is transmitted or dropped. For the aggregate channel activity, the mean renewal time is the mean channel idle time plus the mean busy time for a transmission or a collision. We then study E(Si ) conditioned on node i having a packet in its queue. In this situation, the probability that a slot is idle , denoted by Pidle , is Pidle,i = (1 − βi )

n 

(1 − ρj βj )

(9)

j=1,j=i

Thus, the aggregate attempt rate is geometrically distributed with parameter 1 − Pidle,i , thus the mean channel idle time seen by node i follows (Note that we use a slot as time unit): E(tidle,i ) =

1 1 − Pidle,i

(10)

When the idle period ends, an attempt happens and the channel becomes busy due to a transmission or a collision. The probability of node k performing an attempt seen by node i follows: Pa (k, i) =

⎧ ⎨ ⎩

1−(1−βi ) 1−(1−βi )

 nβi

j=1,j=i

(1−ρj βj )

j=1,j=i

(1−ρj βj )

 ρnk βk

,k=i , else

(11)

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With Eq. (8), given the conditional collision probability pc,k , the probability that node k’s attempt is collision free seen by node i, denoted by Ptr (k, i), is equal to:  Ptr (k, i) =

Pa (i, i)(1 − pc,i ), k=i 1−βi , else Pa (k, i)(1 − pc,k ) 1−ρ β i i

(12)

When the channel is busy, the collision probability of the system seen by node i, denoted by Pcoll (i), is equal to: Pcoll (i) = 1 −

n

Ptr (k, i)

(13)

k=1

For the attempt process of node i, node i’s attempts can be separated by renewal cycles. Each cycle consists an idle period followed by a busy period. The mean channel busy time seen by node i before it obtains its transmission chance follows: E(tbusy,i ) =

n

Ptr (k, i)δk + Pcoll (i)Tc

(14)

k=1

where δk = (1 − pe,k )tx(DAT Ak ) + pe,k αk

(15)

αk is defined by Eq. (4), and Tc is the mean time the channel is sensed busy during a collision, and is equal to tx(RT S) + EIF S. The mean number of renewal cycles between node i’s consecutive attempts follows geometric distribution with parameter Pa (i, i). Thus, the mean time period E(tidle,i )+E(tbusy,i ) between node i’s consecutive attempts is equal to . ConsiderPa (i,i) ing collisions and channel errors, the mean number of attempts node i needed for a successful transmission follows geometric distribution with parameter 1 − γi . Since each packet at node i will be dropped after Ki + 1 transmissions, the adK +1  Ki 1−γi i justed mean number of attempts for a packet follows: k=0 (1−γi )γik = 1−γ . i 1−γ

Ki +1

i With Lhopital’s rule for 00 , limγi →1 1−γ i vice time of node i can be obtained by:

E(Si ) =

= Ki + 1. Therefore, the mean ser-

1 − γiKi +1 E(tidle,i ) + E(tbusy,i ) ) ( 1 − γi Pa (i, i)

(16)

We can use the service time to derive throughput. With queueing theory [13], the throughput of node i, denoted by Ti , follows: Ti =

ρi DAT Ai (1 − pd,i ) E(Si )δ

(17)

Where δ is the length of a time slot. pd,i reflects the impact of packet dropping, and follows: pd,i = 1 −

Ki

(1 − γi )γik = γiKi +1

k=0

(18)

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3.3

The Existence of Equilibrium n Let Γ (pc,i ) = 1− j=1,j=i (1−ρj Gj (pc,j )), for 1 ≤ i ≤ n, where Gi (pc,i ) = βi . We can write these n equations compactly in the following form of the simultaneous fixed point equations. (pc,1 , pc,2 , ..., pc,n ) = (Γ (pc,1 ), Γ (pc,2 ), ..., Γ (pc,n ))

(19)

Definition 1. Given ρ = (ρ1 , ..., ρi ), the network is in equilibrium if the conditional collision probability vector pc = (pc,1 , ..., pc,n ) satisfies the simultaneous equations (19). Because Eqs. (19) are non-linear equations, the network will be in multiple equilibria if the equations have multiple roots. In contrast, the network stays in a steady-state equilibrium if the equations have a unique root. The steady-state equilibrium can be directly used to describe the network’s time average performance1 , which is of fundamental interest. Since ρ and pc are continuous real vectors in [0, 1]n , Eqs.(19) are continuous function mapping from [0, 1]n to [0, 1]n . Hence by Brouwer’s fixed point theorem there exists fixed points in [0, 1]n for Eqs. (19). Many existing algorithms can be used to calculate the roots of Eq. 19. For example, Quasi-Newton algorithms (e.g. the Broyden algorithm [14]) can be used to solve the equations efficiently. During our study, the equations can be solved quite fast and the root can be found with less than 7 iterations. 3.4

Uniqueness of Equilibrium

The network has a unique equilibrium if and only if Eqs.(19) has a unique root. At the first glance, the uniqueness of solution may be affected by system parameters, BERs and the traffic arrival rate λs. Through detailed analysis, we can show that the uniqueness of equilibrium is only related to the settings of backoff parameters (i.e. bi,k ). In the following sub-sections, we give the details of analysis. Interval Extensions of Rational Functions. We start our analysis by introducing basics of interval analysis. A real interval A is the bounded, closed set of the real numbers defined by A = [a, a] = {x ∈ R|a ≤ x ≤ a}

(20)

where a, a ∈ R and a ≤ a. When a = a, A is called a singleton. An interval vector V in I(Rn ) has n components, each of which is an interval, Vi ∈ I(R), i = 1, ..., n. For A, B ∈ I(R), the operators +, −, ∗, / are defined as follows: A + B = [a + b, a + b] A − B = [a − b, a + b] A ∗ B = [min(ab, ab, ab, ab), max(ab, ab, ab, ab)] A/B = A ∗ [1/b, 1/b] 1

The time average is the long term time average, and does not necessarily prevent the short-term unfairness problem found in [8].

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For an interval matrix A with interval coefficients [aij , aij ], its norm follows: ||A|| = maxi

n

max(|aij |, |aij |)

j=1

As state in [15] that, given a rational function f (x), x ∈ Rn , its interval extension F (X), X ⊆ I(Rn ) can be obtained by simply replacing the real operations by interval operations and the variables by intervals. One simple example is, f (x) = x(1 − x); F (X) = X ∗ (1 − X) Testing the Uniqueness of Solution. We apply the interval fixed point theorem to get the sufficient condition for the unique solution of Eqs.(19). In particular, given a initial x0 ∈ X, where X is an interval vector. The Krawczyk operator [16] is defined as: K(X, x0 ) = x0 − Y f (x0 ) + (I − Y J(X))(X − x0 ))

(21)

where Y is an arbitrary nonsingular matrix and J(X) is the interval extension of the Jacobian of F (x), which is denoted by J(x). Specifically, Jij (X) is obtained by applying interval extension to Jij (x). Krawczyk [16] has shown that if X has a solution to F (x) = 0, where F (.) is a function, then so does K(X, x0 ). According to the Moore’s theorem [15], the sufficient condition for the uniqueness of solution for F (x) = 0 is as follows: ||I − Y J(X)|| < 1

(22)

where I is the identity matrix with the same dimension of Y J(X). The norm of interval matrix A with interval coefficients Aij = [aij , aij ] is defined in Eqs. (21). The reason behind is that, when Eq.(22) holds, the Krawczyk operator performs contractive mapping in X and guarantees a unique root. In order to guarantee the uniqueness of solution, we need to find a nonsingular matrix Y which satisfies condition (22). Supposed we have a nonsingular matrix Y  , we relax condition (22) to reduce the computational complexity. Specifically, ||I − Y  J(X)|| = ||Y  (Y −1 − J(X))||

Since all matrix norms satisfy the submultiplicative property ||AB|| ≤ ||A||||B||

Letting Y = Y

−1

, we have ||Y −1 (Y − J(X))|| ≤ ||Y −1 ||||Y − J(X)||

Since ||I|| = 1, to satisfy ||Y −1 ||||Y − J(X)|| < 1, we obtain ||Y −1 ||||Y − J(X)|| < ||Y −1 ||||Y || ⇒ ||Y − J(X)|| < ||Y ||

(23)

Therefore, we need to study if we can find a nonsingular matrix Y to satisfy Ineq. (23).

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The Jacobian J(x) is calculated by:

Jij (x) = ∂Γ (xi ) ∂xj

−1,

∂Γ (xi ) , ∂xj

i=j else

(24)

is calculated as follows: n  ∂Γ (xi ) = (1 − ρk Gk (xk ))ρj Gi (xj ) ∂xj k=1,k=i,j

(25)

Since Jij (x) is a rational function, we can easily apply interval extension to Jij (x) over the interval [0, 1]. We have G([0, 1]) = [G(1), G(0)] n  (1 − ρk G([0, 1]) = ⇒ k=1,k=i,j

[

n 

(1 − ρk G(0)),

k=1,k=i,j

n 

(1 − ρk G(1))]

(26)

k=1,k=i,j

Since Gi (.) resides in [0, 1] and is a monotonically decreasing function [7] provided that bi,k is a non-decreasing sequence, Gi (.) < 0. Thus, we have Gi ([0, 1])

 (1 − pe,i )ρi n k=1,k=i,j (1 − ρk Gi (1)) = ∗ Ki ( k=0 bi,k pke,i )2

Ki Ki Ki Ki (Ki + 1) kbi,k pk−1 pke,i − bi,k , 0] [ e,i 2 k=1 k=0 k=0

(27)

Therefore, J([0, 1]) follows:  Jij ([0, 1]) =

[−1, −1], i = j [aij , aij ], else

(28)

where [aij , aij ] = Gi ([0, 1]) ∗ [

n 

k=1,k=i,j

(1 − ρk G(0)),

n 

(1 − ρk G(1))]

(29)

k=1,k=i,j

Theorem 1. For each node i, if bi,k is non-decreasing, then the network has a unique equilibrium and the corresponding λ is also unique. Proof. The existence of equilibrium has been proven in Section 3.3. Because all parameters are finite and G(.) is a non-increasing function, [aij , aij ], 1 ≤ i, j ≤ n are compact and aij < aij = 0. Given J([0, 1]), we can always construct a nonsingular matrix Y following the algorithm in Table 1.

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Table 1. The algorithm for constructing the matrix Y  k=argmax1≤i≤n n j=1 |aij |; /*Note that ai < 0*/ Arbitrarily select a real positive number τ , and matrix Y is defined by: ⎧ i=j ⎨ −1, Yij = akj − τ, i = k ∧ i = j ⎩ 0, else

(30)

Applying the algorithm in Table 1, it is obvious that Y exists since [aij , aij ], 1 ≤ i, j ≤ n are compact. In addition, Y is nonsingular since det(Y ) = (−1)n . Applying Y to the left-hand of Ineq (23), we have: ||Y − J([0, 1])|| = (n − 1)

n

|akj − τ |

j=1

= ||Y || − 1 < ||Y ||

Therefore, Ineq. (23) is satisfied and, given ρ, Eqs. (19) has a unique root pc . As a result, the network equilibrium is unique. In addition, according to the analysis in Section 3.2, the service time of node i, E(Si ), is uniquely decided by ρ and pc . Recalling Eq.(1), the mapping from ρ to λ is one-to-one, which guarantees the flow control vector λ is also unique.

4

Model Validations

We validate our model via comparing analytical results to simulation results. The simulations are performed using ns-2 [17]. Without loss of generalization, packet size is assumed to be 1000 bytes and the transmission rate of each node is assumed to be 2 Mbps with a certain BER. Due to the limitation of space, we study a homogeneous network where each node follows the standard 802.11b

Our Model Simulation

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Fig. 3. The average throughput

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specifications. In order to study both saturated and unsaturated traffic scenarios, ρi is defined by min(0.05 ∗ n, 1.0), where n is the number of nodes. We evaluate the average per-flow throughput in two scenarios, which are errorfree and error-prone respectively. When channel is error-prone, the BER of each flow is assumed to be equal to 10−5 . We evaluate the average throughput as the function of the number of nodes. The results are shown in Figure 3 (a) and (b). As shown in the Figure, our model is able to accurately capture of average throughput in the system. Note that when the number of nodes is greater or equal to 20, every flow becomes saturated.

5

Conclusion

In this paper, we present a simple and accurate analysis model for the MAC performance of heterogeneous IEEE 802.11 based WLANs. We take into account of many practical factors (such as channel errors and unsaturated traffic) which may have significant impact on the network performance. With fixed point analysis, we first model the performance of the network with a simultaneous non-linear equations. Then, the service time of each flow is modeled and is further used to obtain the average throughput. We then theoretically examine the equilibrium of a general heterogeneous WLAN by studying the property of the corresponding simultaneous non-linear equations. We find the sufficient condition for the uniqueness of the equilibrium. Our model is validated through simulations and the results prove that our model is quite accurate to model the time average performance of the network. As future work, we will extend our model for 802.11-based multi-hop wireless ad hoc networks and study the existence and uniqueness of equilibrium as well.

References 1. IEEE: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Spec. IEEE 802.11 standard (1999) 2. 802.11 Work Group: Draft Supplement to Standard for Telecommunications and Information Exchange between Systems-LAN/MAN Specific Requirements- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Medium Access Control (MAC) Enhancements for Quality of Serice (QoS). IEEE 802.11e Draft 3.1 (2002) 3. Bianchi, G.: Performance analysis of the ieee 802.11 distributed coordination function. IEEE Journal on selected areas in communication 18(3) (2000) 535–547 4. Erge, M., Varaiya, P.: Throughput analysis and admission control for ieee 802.11a. Mobile Networks and Applications 10(5) (2005) 705–716 5. Garetto1, M., Chiasserini, C.F.: Performance analysis of 802.11 wlans under sporadic traffic. In: 4th International IFIP-TC6 Networking Conference (NETWORKING 2005). (2005) 6. Carvalho, M., Garcia-Luna-Aceves, J.: Delay analysis of ieee 802.11 in single-hop networks. In: IEEE ICNP’03. (2003) 7. Kumar, A., Altman, E., Miorandi, D., Goyal, M.: New insights from a fixed point analysis of single cell ieee 802.11 wlans. In: IEEE INFOCOM’05. (2005)

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8. Ramaiyan, V., Kumar, A., Altman, E.: Fixed pointed analysis of single cell ieee 802.11e wlans: Uniqueness, multistability and throughput differentiation. In: ACM SIGMETRICS’05. (2005) 9. Kim, H., Hou, J.C.: A fast simulation framework for ieee 802.11-operated wireless lans. In: ACM SIGMETRICS’04. (2004) 10. Medepalli, K., Tobagi, F.A.: Towards performance modeling of ieee 802.11 based wireless networks: A unified framework and its applications. In: IEEE INFOCOM’06. (2006) 11. Ross, S.M.: Introduction to Probability Models (7th Ed.). Academic Press (2000) 12. Mermaier, A.: Interval Methods for Systems of Equations. Cambridge University Press (1990) 13. Gross, D., Harris, C.M.: Fundamentals of Queueing Theory (Wiley Series in Probability and Statistics). Wiley-Interscience, 3rd edition (1998) 14. Broyden, C.G.: A class of methods for solving nonlinear simultaneous equations. Methematics of Computation 19 (1965) 577–593 15. Moore, R.E.: Interval Analysis. Prentice-Hall (1966) 16. Krawczyk, R.: Newton-algorithmen zur bestimmung von nullstellen mit fehlerschranken. Computing (by Springer Wien) 4(3) (1969) 187 – 201 17. VINT Group: Network simulatior version 2 (ns-2). http://www.isi.edu/nsnam/ns/ (1999)

Exploring a New Approach to Collision Avoidance in Wireless Ad Hoc Networks Jun Peng1 and Liang Cheng2 1

Department of Electrical Engineering, University of Texas - Pan American, Edinburg, TX 78541 USA [email protected] 2 Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015 USA [email protected]

Abstract. 1 We propose in this paper a new approach of bit-free control frames to improving the performance of the IEEE 802.11 DCF. Basically, a bit-free control frame does not contain any meaningful bits; instead, its length (i.e., airtime) is encoded with the control information. This new approach has two advantages over the traditional control frames. First, the airtime of a bit-free frame is easy to detect and robust against channel effects. Second, bit-free control frames can be very short because no headers or preambles are needed for them. Our investigation demonstrates that the new approach improves the performance of the IEEE 802.11 DCF significantly (network throughput gains from fifteen to more than one hundred percent).

1

Introduction

The hidden terminal phenomenon in wireless packet networks is interesting but problematic. Basically, even if two nodes in a wireless network cannot sense each other, they may still cause collisions at the receiver of each other [1]. If the hidden terminal problem is not well addressed, a wireless network may have a significantly degraded performance in every aspect, since frequent packet collisions consume all types of network resources such as energy, bandwidth, and computing power but generate no useful output. There are basically two existing approaches to the hidden terminal problem. One is the use of an out-of-band control channel for signaling a busy data channel when a packet is in the air [2,4,3]. This approach is effective in dealing with hidden terminals but requires an additional control channel. The more popular approach to the hidden terminal problem is the use of in-band control frames for reserving the medium before a packet is transmitted [5,7,6]. The popular IEEE 802.11 standard [9] uses this approach in its DCF. 1

The research was partly supported by the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA), and Lehigh University.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 462–474, 2007. c IFIP International Federation for Information Processing 2007 

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Basically, before an IEEE 802.11 node in the DCF mode transmits a packet to another node, it first sends out a Request to Send (RTS) frame after proper backoffs and deferrals. After receiving the RTS frame, the intended receiver responds with a Clear to Send (CTS) frame, which includes a Duration field informing its neighbors to back off during the specified period. In an ideal case, the hidden terminals of the initiating sender will successfully receive the CTS frame and thus not initiate new transmissions when the packet is being transmitted. However, control frames have limited effectiveness in dealing with hidden terminals because they may not be able to reach all the intended receivers due to signal attenuation, fading, or interference [8]. In addition, control frames have considerably long airtimes because they are recommended to be transmitted at the basic link rate in both narrow-band and broadband IEEE 802.11 systems for link rate compatibility among nodes. In addition, they also usually carry long physical layer preambles and headers. Therefore, in-band control frames still introduce significant network overhead, even though they do not require an out-of-band control channel. This paper explores a new approach of bit-free control frames to addressing the disadvantages of the traditional control frames. Basically, with the new approach, control information is carried by the airtimes instead of the bits of control frames. The airtime of a frame is easy to detect and robust against interference and channel effects. In addition, a bit-free control frame carries no meaningful bits so that no preamble or headers are needed for it (in-band bursts of variable lengths were used for priority scheduling in [10]). To investigate the potentials of the new approach, we have modified the IEEE 802.11 DCF by replacing the traditional control frames with bit-free control frames and have done extensive simulations with the modified protocol. Our investigation has shown that the modified protocol improves the average throughput of a wireless network by from fifteen percent to more than one hundred percent. The rest of the paper is organized as follows. Section 2 presents our modifications to the IEEE 802.11 DCF. We show in Section 3 the comprehensive simulation results comparing the modified protocol to the original one. Finally, we give our conclusions in Section 4.

2 2.1

Applying the New Approach Basics

The challenge in applying the new approach to the IEEE 802.11 DCF is the limited capability of the bit-free control frames in carrying control information. Particularly, only the airtime of a control frame can carry control information. To address this issue, we use two basic strategies. One is that the bit-free control frames only carry the indispensable information for medium access control, while the other is to use frame pairs for backoff duration control. For sending bit-free control frames, we assume that the IEEE 802.11 hardware has some modification so that it can be commanded to transmit the carrier for a specified amount of time. We also assume that the airtime of a control

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frame can be recorded with a degree of accuracy depending on the hardware, bandwidth, and channel conditions. One protocol parameter, the minimum guard gap between the lengths of two control frames, may be adjusted based on the recording accuracy. In fact, with its carrier sense capability, the existing IEEE 802.11 hardware may record the airtime of an incoming frame. In addition, a bit-free control frame can not be mistaken as a bit-based frame, since a bit-free frame does not include a physical layer preamble and thus the synchronization on the frame can not be done. A bit-based frame, however, may be mistaken as a bit-free frame if the synchronization on the frame fails. This kind of interference is usually filtered out due to the typically long airtime of a bit-based frame and the short airtime of a bit-free control frame. 2.2

Bit-Free Control Frames

The frame type needs to be specified for each frame so that the receiver knows how to interpret the bits in the bit-based frame case or the frame airtime in the bit-free frame case. Bit-free frames carry no meaningful bits so that the frame type information can only be delivered by their airtimes. Particularly, if the airtime of a bit-free frame falls into a specified range or ranges, then the frame belongs to the type of frame denoted by the range or ranges. Besides the frame type information, the other indispensable information in an RTS frame is the address of the receiver. The length of a bit-free RTS frame needs to fall into the designated range or ranges. We therefore may not be able to encode the address information of each single receiver into the airtime of a bit-free RTS frame. To address this problem, we apply a “Mod-n” calculation on each receiver address before it is encoded. Basically, we first divide the address by n and then encode the remainder into the frame airtime. Particularly, If r = M od(RA, n), then FL = RT S(r) where RA is the receiver address, n is an integer, r is the remainder, FL is the airtime of the bit-free RTS frame to send, and RT S(r) is an r-indexed element in the set of RTS lengths in microseconds. The Duration field in a bit-based RTS frame is also important because it specifies the period during which a receiver of the frame should back off. A bitfree RTS frame does not have the capacity for the duration information. Instead, a receiver of a bit-free RTS frame starts to back off upon receiving the frame and ends the backoff only after the medium has been sensed idle for a specified amount of time (more details later). In our proposed design with bit-free frames, all CTS frames have the same fixed length that distinguishes them from other bit-free frames. In addition, we use control frame pairs to communicate the backoff duration information of a traditional CTS frame, which will be introduced later. Similarly, all bit-free ACK frames in our design have the same fixed length that distinguishes them from other types of bit-free frames (the address issue of these frames is discussed in Section 2.5).

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In addition to the RTS, CTS, and ACK bit-free frames, we add another type of bit-free control frame named CTS-Fail frame in our design. A CTS-Fail frame has a fixed length and is sent by a CTS frame sender in two cases to notify other nodes to end their backoff. The first case is that a CTS frame sender does not receive any packet after SIFS (Short Interframe Space) plus propagation delays after sending the CTS frame. The second case is that a CTS frame sender receives a packet after sending the CTS frame but finds that either the packet is not intended for it or the packet has errors. In this case, the CTS-Fail frame is sent only after the packet is fully received. 2.3

Frames Working Together

To explain how the four types of bit-free control frames work together in the modified IEEE 802.11 DCF, we describe how a node contends for the medium when it has a packet to transmit. The IEEE 802.11 DCF is basically a CSMA/CA protocol, and our modifications to the protocol are only on the CA part. When a node has a packet to transmit, it starts to listen to the channel. If the channel has been found idle for a period of time longer than the DCF Interframe Space (DIFS), the node starts a random backoff timer whose value is uniformly drawn from the node’s contention window (CW). If the node detects no carrier before its backoff timer expires, it proceeds to transmit an RTS frame upon the expiration of its backoff timer. Otherwise, the node backs off. As soon as the backoff timer of the node expires, the node starts to transmit a bit-free RTS frame. As explained earlier, the airtime of the bit-free frame is determined by the address of the intended receiver. After finishing the transmission, the node waits for a CTS frame, whose airtime is fixed and known. After a neighbor of the initiating sender receives the bit-free RTS frame, it does the “Mod-n” calculation on its own address and compares the remainder to the length of the received frame in microseconds. If the remainder matches the length, the neighbor sends out a bit-free CTS frame and then waits for a packet. If the CTS frame sender does not receive any packet after a period of SIFS plus propagation delays, it sends out a CTS-Fail frame. On the other hand, if the remainder does not match the length of the received RTS frame, the neighbor will enter backoff and remain in the backoff until the medium has been sensed idle for a period of time that is SIFS plus either the CTS frame length or the ACK frame length, whichever is longer. After the initiating sender obtains the bit-free CTS frame, it waits for SIFS and then starts to transmit the packet. If for any reason the RTS frame sender fails to obtain the expected CTS frame, the sender starts over to contend for the medium. In such a case, the sender doubles its CW. On the other hand, if a node receives an unexpected bit-free CTS frame (i.e., the node is not an RTS frame sender), the node increases its CTS frame counter N umcts by one, starts a backoff monitor timer, and then enters backoff. Such a node exits the backoff in two cases. One is that its CTS frame counter N umcts reaches zero when the node decrements the counter by one after receiving an ACK or CTS-Fail frame, while the other is that its backoff monitor timer expires (more details later).

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After the initiating sender succeeds in contending for the medium, receives the expected CTS frame, and fully transmits the packet, it expects a bit-free ACK frame from the receiver. If the sender does not obtain the expected acknowledgment, it doubles its CW and starts to monitor the channel again for a retransmission. On the other hand, after a node receives the data packet, it checks if the packet is intended for it and free of error. If so, the node sends back a bit-free ACK frame. If the packet is not intended for it or the packet has errors, the node checks whether it has sent a CTS frame for the packet. If so, the node sends out a CTS-Fail frame to notify its neighbors to exit backoff. The whole process repeats until the initiating sender obtains an acknowledgment for the packet or the retry limit is reached. The node discards the packet in the latter case and resets its CW to the minimum size in both cases. 2.4

Some Design Considerations

The first design consideration on the modified MAC protocol is the choices of receive power thresholds for its bit-free control frames. Unlike bit-based frames, bit-free control frames can be correctly received as long as they can be sensed. The receive power threshold for a bit-free control frame may thus be adjusted for controlling the transmission range of the frame. As introduced earlier, a bitbased CTS frame may not successfully reach all the hidden terminals of the initiating sender [8]. A node with the modified protocol, therefore, needs a lower receive power threshold for bit-free control frames. The lowest power threshold that a node may use for receiving a bit-free control frame is the carrier sense power threshold. In such a case, a node decodes a bitfree frame if the frame can be sensed. The implementation in our simulations uses this conservative choice to ensure the coverage of bit-free control frames. However, there is an exception. When a node receives a bit-free RTS frame matching its address, the node responds with a CTS frame only if the received power of the RTS frame is above the receive power threshold for data frames, since the node should not respond if it can not correctly receive a packet from the other node. Another design consideration on bit-free control frames is the set of lengths in terms of airtimes that the frames should use. The basic rule is that control frames should be easy to detect and distinguish from one another. The shortest control frame in our simulations is 20-μs long and the minimum guard gap between two lengths in the set is 5μs, which corresponds to 5-bit airtime at the transmission rate of 1Mb/s (even in broadband systems such as 802.11g, the control frames are recommended to be transmitted at a basic link rate). In reality, the minimum guard gap should be set based on the length detection accuracy of bit-free frames, which may be affected by the hardware, bandwidth, and channel conditions. When choosing the length for a specific control frame that has a fixed length, we need to consider another factor. In particular, when multiple bit-free frames arrive at the same node in the same time segment, they may form a “merged” bit-free frame that has a length denoting another defined bit-free control frame.

Exploring a New Approach to Collision Avoidance

Case 1

1111111111111111 0000000000000000 0000000000000000 1111111111111111 000000000000000000 111111111111111111 000000000000000000 111111111111111111

0000000000000000 A1111111111111111 0000000000000000 1111111111111111

C

000000000000000000 111111111111111111 B 111111111111111111 000000000000000000

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1111111111111111 000000000000000000 111111111111111111 0000000000000000 000000000000000000 111111111111111111 0000000000000000 1111111111111111 000000000000000000 111111111111111111 0000000000000000 1111111111111111 000000000000000000 111111111111111111 0000000000000000 1111111111111111

Corrupted Frames

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Fig. 1. Merging of Control Frames

This kind of false control frame may appear when the merged frame has a longer airtime than any individual merging frame, as demonstrated by Case 3 in Fig. 1. The possible adverse effects of the merged frame phenomenon are alleviated by the discrete lengths of the defined control frames and the strict timelines for receiving CTS and ACK frames. Particularly, only when a merged frame matches a defined bit-free control frame, would it possibly cause some harm. Moreover, for control frames such as CTS and ACK, a false frame may be harmful only if it emerges in the right timeline and at the right node (if a false ACK appears accidentally at a sender, the lost packet will be recovered by upper layers). However, we may still further address the merged frame phenomenon by carefully choosing the lengths for the fixed-length control frames. We have three types of fixed-length control frames, which are CTS, ACK, and CTS-Fail. Among them, a false CTS frame would arguably generate the worst scenario, in which the nodes receiving the false frame enter backoff and wait for a non-existing ACK or CTS-Fail frame for exiting the backoff. Therefore, to avoid false CTS frames generated by merging frames, we need to assign a CTS frame the shortest length in the chosen length set for control frames. What happens if a false CTS frame emerges anyway due to a reason such as environmental noise? A backoff monitor timer is used to address this problem. When a node receives a CTS frame, it starts a backoff monitor timer before it enters backoff. The backoff monitor timer is set to a value Tm that is the transmission time of the largest allowable frame in the network. The node exits the backoff anyway when its backoff monitor timer expires. Additionally, a backoff monitor timer also solves the problem of lost ACK or CTS-Fail frames due to interference or failed nodes. In addition, it needs some extra caution to receive a CTS frame. A RTS frame may be interpreted by two or more nodes as being intended for them due to the “Mod-n” calculation design and thus two or more bit-free CTS frames may be generated for a single RTS frame. The consequence in such a case is that the received CTS frame may be slightly longer than usual because of the various propagation delays between the RTS frame sender and its receivers (besides, the medium may be reserved in a larger space than necessary in such a case). A degree of tolerance on length variation is therefore needed for decoding a CTS frame. Particularly, if we denote the transmission distance of a node by dtx and

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the signal propagation speed by c, then the decoding tolerance δ on the length of a CTS frame should be the maximum possible difference of round trip times for the receivers, i.e., dtx . (1) δ =2× c Finally, a bit-free ACK frame needs to have a longer length than a bit-free CTS-Fail frame. A data frame may have more than one active receivers because the length of an RTS frame is determined by a modulus calculation. In such a case, the false active receivers will respond with CTS-Fail frames after finishing receiving the data frame. To enable the sender to still recognize the ACK frame, the ACK frame must be longer than the CTS-Fail frames. 2.5

More Design Issues

One disadvantage of bit-free control frames is that they carry no specific addresses so that they may be interpreted by any receiver as legitimate. One basic observation, however, is that when an initiating sender is expecting a CTS or ACK frame, it has already notified its neighbors except the intended receiver to back off. Therefore, an initiating sender may only receive a CTS or ACK frame from the intended receiver in a general case. Moreover, an initiating sender sets a strict timeline for receiving a CTS or ACK frame. Therefore, an initiating sender can hardly receive a false and harmful CTS or ACK frame, which makes the lack of address information in the CTS and ACK frames almost harmless. There is a special case to consider, which is that two senders may start to transmit their RTS frames almost at the same time. If the two nodes can hear each other, there is usually no harm, since in such a case the sender with a shorter RTS frame will usually detect the other sender after it finishes its RTS frame transmission. If the two senders cannot hear each other, there may exist a harmful situation in which one sender overhears the CTS frame intended for the other and mistakenly starts to transmit its packet. This kind of harmful situation occurs, however, with low probabilities because two senders with different RTS frames have different timelines for receiving their CTS frames. If the two RTS frames have the same length, a collision may occur and the following random backoffs of nodes will resolve the issue.

3

Scheme Evaluations

We have done extensive simulations with ns-2 [11] to investigate the performance of the modified IEEE 802.11 DCF and compare it to the original protocol. As mentioned earlier, we only modified the collision avoidance (CA) part of the original protocol, while other parts of the original protocol were kept unchanged. For easy reference, we named the modified MAC protocol as CSMA/FP, which denotes Carrier Sense Multiple Access with Frame Pulses.

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Fig. 4. Average Medium Access Delay vs. Number of Nodes in the Network

Configuration Details

We first evaluated CSMA/FP in a wireless LAN with saturation traffic and compared it to the original protocol. We then used a more general scenario of a multihop ad hoc network to investigate its performance. Particularly, we evaluated the protocols from the perspective of an individual user in the ad hoc network. From an individual user’s perspective, the network is better if the user can have statistically higher flow throughput. Although a contention-based MAC protocol may not be always fair to contending nodes in terms of one-hop throughput, the statistical rate of a random flow in the network truthfully reflects the throughput of the network, especially when the transport layer does not apply rate control over the flows in the network, as configured in our simulations. The ad hoc network has 100 nodes in an area of 1000 by 1000 square meters. Each node uses a transmission power of 0.2 watt, which means a carrier sense range of about 500 meters with the default power threshold settings of ns-2. The link rate of each node is 1Mb/s (a higher rate means that more bits will be transmitted in the time saved by CSMA/FP for using more effective and efficient control frames). In addition, there are a maximum of 25 Constant Bit Rate (CBR) background flows. The routing protocol used in the simulations is the Dynamic Source Routing protocol (DSR) [12]. In modifying the IEEE 802.11 DCF with the bit-free control frame approach, we used an n of 20 in the “Mod-n” calculation over the receiver’s address for obtaining the length of an RTS frame. Twenty is the average number of nodes that fall into the transmission range of a node in the ad hoc network (however, we also investigated the impact of a halved n). The elements in the length set designated for RTS frames fall into two ranges for balancing the average length of an RTS frame with the average length of other control frames. One of the ranges is from 40 to 90μs, while the other is from 120 to 170 μs (with a guard gap of 5μs). In addition, a CTS frame, a CTS-Fail frame, and an ACK frame have fixed lengths of 20, 100, and 110μs, respectively. For other parameters, the modified protocol shares the default ns-2 configurations with the original protocol. For example, the minimum and maximum sizes of the CW of a node are 32 and 1024 timeslots, respectively, while a timeslot is 20 μs.

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Wireless LANs

Fig. 2 shows the wireless LAN throughput versus the number of nodes in the LAN. In the simulations, every node always has packets to send (i.e., saturation traffic) and the destination of each packet is randomly selected (all nodes are in the transmission range of each other). In addition, each packet is 512-byte long. As shown in Fig. 2, the modified protocol has a relative throughput gain of about 15% (an absolute gain of about 100kb/s) when there are 5 nodes in the network. As the number of nodes in the network increases, the throughput gain of the modified protocol increases too. When the number of nodes in the network reaches 25, the relative gain increases to 25% (an absolute gain of 150kb/s). The average maximum CW size and the average medium access delay for a packet in the network are shown in Fig. 3 and Fig. 4, respectively. As shown in the two figures, a packet experiences less delay when the modified MAC protocol replaces the original one in the network. These results conform to the throughput results shown earlier. For conciseness, we only show the throughput results for ad hoc networks in the following sections. 3.3

Ad Hoc Networks

We first tested the two protocols in the ad hoc network with stationary nodes. In particular, we run a series of simulations in which the rate of the background flows varied from 0.5*512 bytes/second (B/s) to 8*512 B/s with an increase factor of 100%. A test flow, meanwhile, kept its rate constant at 4*512 B/s to monitor the actual throughput that it could obtain in various cases of network load. Fig. 5 shows the percentage of the packets in the test flow that are successfully received by the flow receiver as the network load varies. As shown in Fig. 5, when the network load increases to some degree, more packets of the test flow are delivered by the network if the modified MAC protocol replaces the original IEEE 802.11 DCF. For example, when the rate of the background flows is 4*512 B/s, the throughput of the test flow increases from about 44% to 61% as the modified MAC protocol replaces the original one, which indicates a relative performance gain of about 39%. A similar relative

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performance gain is observed for the modified protocol when the rate of the background flows is 8*512 B/s. Fig. 6 shows the throughput of the test flow when the nodes in the network have random waypoint movement and have a minimum and maximum speed of 1.0 and 5.0m/s, respectively (the average pause time is 0.5 second). Fig. 7 shows the throughput of the test flow in a similar case but the maximum node speed increases to 10.0m/s. As shown in these two figures, the modified protocol, on average, has a relative performance gain of more than 50% in both cases of node mobility. Note that the network is more dynamic when the maximum node speed increases and a more dynamic network is more challenging for medium access control. 3.4

More Hidden Terminals

Using the case of a maximum node speed of 5.0m/s, the rest of the section further investigates the performance of the modified protocol. This section shows how the modified protocol performs when there is a higher probability of hidden terminals for a transmitter in the network. To increase the probability of hidden terminals, we increased the carrier sense (CS) power threshold of a node from less than one twentieth to half of its packet receive power threshold. The increase of the CS power threshold shrinks the carrier sense range of a node in the network. Fig. 8 shows the throughput of the test flow when the CS power threshold has been increased in the network. By comparing this figure to Fig. 6, we find that the modified protocol has even higher performance gains as the probability of hidden terminals is increased in the network. This is expected because the modified protocol is better in handling hidden terminals than the original protocol. 3.5

Link Losses

We also investigated the impact of link frame losses on the performance of the modified protocol. The bit-free control frames of the modified protocol are robust against channel effects2 , but a data frame may still experience link losses. We have considered both independent and burst link frame losses. Fig. 9 shows the results for the case of an independent link loss rate of 5%. As shown by Fig. 9 and Fig. 6, independent losses may actually increase the relative performance gains of the modified protocol over the original protocol. Similar results have been observed for other cases of link frame losses. 3.6

Environmental Noise

We also investigated the impact of environmental noise on the modified protocol. To test the impact of environmental noise, we placed a noise source at the center of the network and let it generate random-length noise signals at an average rate of 100 signals per second. Moreover, we restricted the noise signal lengths to the range from 1μs to 200μs, which were the range designated for the bit-free control 2

Note that a receiver is in its sender’s packet transmission range while bit-free frames can be received at the CS range of the sender.

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frames. The simulation results for this scenario are shown in Fig. 10. As shown by the comparison of Fig. 10 to Fig. 6, the modified protocol is not more sensitive to noise than the original one. In fact, after introducing the noise source in the network, the modified protocol shows higher relative performance gains over the original one. Note that a noise signal may not be able to change the length of a bit-free control frame even if when it damages a bit-based frame. Meanwhile, as explained in Section 2, a noise signal must have the right length, arrive at the right node, and possibly arrive at the right time for being harmful. 3.7

Protocol Resilience

The above subsections are about how external factors may impact the performance of the modified protocol. This subsection shows how the protocol’s own parameters affect its performance. We have investigated the three most important parameters of the protocol, which are the receive power thresholds for control frames, the length set for control frames, and the base n of the Mod-n calculations for obtaining RTS frame lengths. Fig. 11 shows how the modified protocol performs when all its control frames use the same receive power threshold as data frames, which deprives the modified protocol of its advantage of better hidden terminal handling. As shown in the figure, the performance of the protocol does degrade but still maintains significant gains over the original protocol. Fig. 12 shows the performance of the modified protocol as the average length of its control frames becomes similar to the average length of the bit-based control frames of the original protocol. As shown in this figure, the performance of the modified protocol degrades gracefully in this case. Fig. 13 shows how the modified protocol performs as the base n of the Mod-n calculation is halved. Halving the n is similar to doubling the node density of the network in terms of investigating how the redundant CTS frames for an RTS frame may affect the protocol performance. As shown in Fig. 13, the performance of the modified protocol has a graceful degradation when the n is halved.

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Conclusion

We have proposed in this paper a new bit-free control frame method for collision avoidance in wireless packet networks. Bit-free control frames do not need headers or preambles, so they can be short. In addition, bit-free control frames are robust against channel effects due to their simplicity. We have investigated the new approach by applying it to the IEEE 802.11 DCF and conducting extensive simulations. We have tested the new approach in both wireless LANs and ad hoc networks. We have also investigated how hidden terminals, link losses, and environmental noise may impact the new approach. Additionally, we have examined how protocol parameters such as the average length, the receive power thresholds, and the size of the length set of control frames may impact the performance of the new approach. Our conclusion is that the new bit-free control frame method is able to significantly improve the performance of the IEEE 802.11 DCF.

References 1. Tobagi, F. A., Kleinrock, L.: Packet Switching in Radio Channels: Part II - The Hidden Terminal Problem in Carrier Sense Multiple Access and the Busy Tone Solution. IEEE Transactions on Communications 23 (1975) 1417–1433 2. Kleinrock, L., Tobagi, F. A.: Packet switching in radio channels: Part I - carrier sense multiple-access modes and their throughput- delay characteristics. IEEE Transactions on Communications 23 (1975) 1400-1416 3. Peng, J., Cheng, L., Sikdar, B.: A new MAC protocol for wireless packet networks. IEEE GLOBECOM, San Francisco, CA (2006) 4. Haas, Z. J., Deng, J.: Dual Busy Tone Multiple Access (DBTMA) - A Multiple Access Control Scheme for Ad Hoc Networks. IEEE Transactions on Communications 50 (2002) 975-985 5. Karn, P.: MACA - A New Channel Access Method for Packet Radio. Proc. of the 9th ARRL Computer Networking Conference, Ontario, Canada (1990) 6. Bharghavan, V., Demers, A., Shenker, S., Zhang, L.: MACAW: a medium access protocol for wireless LANs. ACM SIGCOMM, London, United Kingdom (1994)

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7. Fullmer, C. L., Garcia-Luna-Aceves, J. J.: Floor acquisition multiple access (FAMA) for packet-radio networks. ACM SIGCOMM, Cambridge, Massachusetts (1995) 8. Xu, K., Gerla, M., Bae, S.: How Effective is the IEEE 802.11 RTS/CTS Handshake in Ad Hoc Networks? IEEE GLOBECOM, Taipei, Taiwan (2002) 9. IEEE 802.11 Wireless Local Area Networks http://grouper.ieee.org/groups/802/11/ 10. Sobrinho, J. L., Krishnakumar, A. S.: Real-time traffic over the IEEE 802.11 medium access control layer. Bell Labs Technical Journal 172-187 (1996) 11. The Network Simulator - ns-2 http://www.isi.edu/nsnam/ns/ 12. Johnson, D. B., and Maltz, D. A., Hu, Y. C.: The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR). IETF Interet draft,draft-ietf-manet-dsr-10.txt (2004)

Video Rate Adaptation and Scheduling in Multi-rate Wireless Networks Sourav Pal, Sumantra R. Kundu, Amin R. Mazloom, and Sajal K. Das Dept. of Computer Science and Eng. University of Texas at Arlington Arlington, TX 76019 – USA {kundu,spal,mazloom,das}@cse.uta.edu Abstract. Current scheduling techniques used for cellular networks do not suffice for the emerging multi-rate systems like cdma2000 and High Data Rate (HDR). Real-time applications like video streaming must comprehend the channel conditions and consequently the data rates that are currently being supported; accordingly the content and the amount of data to be transmitted needs to be adapted to the available bandwidth. In this paper, we have considered multimedia (MPEG-4) streaming as the application over HDR and propose a content aware scheduling scheme (CAS) that takes into consideration the different priorities of the MPEG-4 stream content. The proposed transmission scheme considers both the channel conditions as perceived by the user as well as the priority of the streams. In addition, CAS verifies the playout timestamp and discards stale packets ensuring higher throughput in the process. We capture the lag of the proposed adaptation scheme using the KullbackLeibler distance and show that the rate adaption scheme has a reasonably small lag. Simulation results demonstrate that the proposed scheme results in higher overall peak signal to noise ratio (PSNR) values of the entire movie, lesser number of dropped frames, and a better throughput utilization over existing schemes.

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The advent of multi-rate systems like cdma2000 [1], HDR [2], and EDGE [3] provide higher bandwidth but pose challenges in the channel rate estimation and scheduling. Although advanced video standards like MPEG-4 and H.264 can take advantage of the increased transmission bandwidth and offer powerful error resilient mechanisms, they are unable to handle the complexities like attenuation due to multi-path fading, shadowing, transmission errors, bandwidth fluctuations, spectrum scarcity associated with the wireless channel. In this paper, we focus on the scheduling aspect for such systems. In addition to round robin (RR) and first come first serve (FCFS) scheduling policies, video scheduling over HDR [6] have used Modified-Largest-WeightedDelay-First (M-LWDF) and Exponential (EXP) scheduling algorithm and variations of the two to reduce the percentage of video frames that do not meet 

This work is supported by NSF ITR grant IIS-0326505.

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the playback deadlines. However, even such sophisticated radio resource management schemes are unaware of the content, priority and timing requirements demanded by the video streams. In this paper, we provide rate adaptation techniques at the base station that adapts the transmission of the packets to the available bandwidth. It also ensures that packet buffer overflow/underflow does not take place at the client end. We also propose a scheduling algorithm specifically for streaming multimedia (MPEG-4) that not only adapts to the available bandwidth but also schedules the packet with respect to their priority for ensuring smooth video playout at the client end. On favorable channel conditions, the scheme transmits more packets than necessary for current viewing but honors the buffer overflow condition determined by the rate adaptation algorithm. The success of the proposed scheme is due to the priority structure of the MPEG-4 stream in which I frames have higher priority than B and P frames, since without the I frames video playout is not possible. Based on the channel conditions, the scheme not only adjusts the data but also drops the lower priority frames (in case of video), if necessary. We employ the Kullback Leibler [4] distance as a metric to measure the adaptation rate. In summary, the main contributions of this paper are as follows: – A rate adaptation scheme which takes into consideration the buffer overflow and underflow problems at the client end. – A content aware scheduling technique which ensures that higher priority MPEG-4 frames are transmitted prior to the lower priority ones and takes into consideration the playout timing requirements. – Experimental evaluation of the scheme using a framework consisting of Darwin Streaming Server (DSS), click modular router, and Mplayer as the client end media player reveals that the proposed technique achieves lower frame drop and higher PSNR values compared to the case with no content aware scheduling mechanism. The rest of the paper is organized as follows: We discuss the necessary background on HDR systems and MPEG-4 in Section 2. A rate adaptation scheme based on the estimated channel throughput is proposed in Section 3. Section 4 presents a case study of transmission of MPEG-4 over HDR using the proposed techniques. In Section 5, simulation results involving rate adaptation, scheduling algorithms, and performance of MPEG-4 transmission over HDR system are presented. Conclusions are drawn in the last section.

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A brief description of the HDR system model and the functioning of MPEG-4 is presented in this section. 2.1

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We consider a single cell of a multi-rate wireless system with the base station serving N mobile terminals. We assume that the system employs data rate

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control mechanism on the forward link that adapts to the changing channel conditions by employing adaptive modulation and coding techniques, hybrid Auto Repeat-reQuest (ARQ), and best serving sector selection. The mobile terminals perform measurement of the current channel conditions (i.e., EIob the received energy per bit to interference) and predicts the achievable rate. Every mobile terminal updates the base station of the predicted rate via the pilot signal on the reverse link data rate control (DRC) channel. At any time slot t, the data rate that can be supported by the ith mobile terminal is Ri (t), 1 ≤ i ≤ N , where Ri (t) is one of the many rates supported by the system. For example, HDR supports 11 data rates [2]. Ri (t) is the mean rate actually provided to user i measured over a sliding window of length tc and is given by Ri (t + 1) = (1 −

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We initially present the adaptation algorithm and thereafter present the analytical modeling of the adaptation scheme and propose adaptation metrics using the Kullback Leibler distance. The process of adaptation of the application bit stream in response to the available transmission rate gives rise to the following three scenarios. We denote ri (t), Xb (t), and Xe (t) as the available transmission rate, the required base layer bit rate, and the enhanced layer bit rate (applicable only for FGS MPEG-4) respectively. – Case I: ri (t) < Xb (t): The adaptation scheme selectively drops base layer frames based on the proposed prioritization scheme. – Case II: Xb (t) ≤ ri (t) ≤ Xb (t)+Xe (t): The highest priority layer is encoded. For the enhanced layer, the adaptation scheme encapsulates whatever portion of the EL that can be packed into the remaining available bandwidth. – Case III: ri (t) > Xb (t) + Xe (t): This is the most favorable case where the encoding rate is higher than the bit stream rate and hence the entire data can be transmitted. Through the proposed adaptation scheme, the lowest possible granularity is achieved. However, a finite lag remains between the channel state and actual transmission of the data which in our case is the best achievable, i.e., lag by a slot. Note that during packetization the granularity is measured in terms of the decision period. Depending on the current buffer status at both the transmitter and user and the current channel state, the rate control system needs to determine the encoding rate. The currently encoded packets would only be served after the existing MAC PDU’s are served. Thus, the rate control system needs to determine the number of decision periods required to serve the existing MAC PDUs. Hence, prediction of the rates by transition probability computation is important which we discuss next.

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Transition Probability Calculation

We assume that the data rates are related to the distance of the user from the base station [13]. Though this assumption holds true under ideal conditions (i.e., no interference, no topological effects), we still use it for mathematical simplicity. If we assume that the system supports m rates, then a cell can be divided into m concentric rings; the innermost ring gets the maximum data rate and outermost ring gets the minimum. In one decision period, a user can go one ring above or below the current ring because of the limitation in speed. Let Puv denote the probability of a user switching from a data rate u to data rate v. Note, v can only be u − 1, u, or u + 1. Let the steady state probability of an user being in the uth data rate be denoted by πu . We define πu as the ratio of the ring area r 2 −r 2

by the cell area, and is given by πu = ur2 u−1 where ru is the radius of the uth m−1 m−1 ring. Clearly, u=0 πu = 1. The transitional probabilities Puv are computed geometrically and can be given as follows  2 s(s − r1 )(s − r2 )(s − d) − (θ1 r12 − θ2 r22 ) πr22 Ai = πr22 Abge = πr22

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Analytical Modeling of Rate and Adaptation

The encoding rate ri (t) for user i depends on the channel state, buffer status, and bandwidth allocated. This necessitates the computation of the number of decision periods needed to serve the existing MAC PDUs. The number of decision periods is basically the lag by which the encoder follows the channel state. If ξ be the number of decision periods needed to serve the MAC buffer Bi , then ξ 

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Conversely, if the mobile terminal undergoes favorable channel condition i.e., would support a higher data rate than the rate controller would be tempted to transmit more data provided the receiver buffer does not overflow. Transmitting more data than required would compensate if channel conditions deteriorate. The available buffer space (υi (t)) during that decision period is υi (t) = (1 − frb ) ×RBi −   ξ l Sil × Puv × Ril (t) + ρi × (τ ξ) l=0

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where 0 < frb < 1 is the receiver buffer fullness, RBi is the receiver buffer size, ρi is the rate of the playout curve rate, and τ is the time of each slot (τ = 1.67ms for HDR). The adaptation layer encoding rate, considering the buffer constraints at both the transmitter and receiver sides is ri (t) = min (χi (t), υi (t))

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Note that ri (t) is a random variable which gives the value of rate adaptation. Let p(·) denote the pdf for the random variable ri (t). Let fg (·) denote the pdf of the Group of Picture (GoP) size distribution of the gth video. We use the Kullback Leibler (KL) distance to characterize the performance of our adaptation scheme. Definition: Kullback Leibler distance which determines the relative entropy between two distributions is given as D(pq) =



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The granularity of the rate of adaptation is bounded by the decision period as given in Equations (3), (4), (5) and (6). Hence we analyze the adaptation with respect to the decision period. The time granularity of the decision period is denoted by s.

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The success of video streaming depends on devising mechanism to adapt to the changing channel conditions. The popular techniques for TCP rate adaptation [7]

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did not consider the fluctuation in channel conditions. Numerous link layer schemes [8,9] exist for enhancing TCP over wireless networks. But link layer aware techniques [10,11] use statistical measure of packet loss for adaptation. These techniques fail to capture the content level information which needs to be exploited for video applications. It is known that better rate control algorithms are obtained if stochastic channel behavior through a priori models are considered [12]. We incorporate the channel model as well as the video information in our rate adaptation algorithm at the MAC layer. The scheduler transmits more data than necessary when the conditions are favorable, whilst avoiding buffer overflow. Whereas it drops frames without compromising on the integrity of the video when the conditions are degraded. For example, successful transmission of I-frames is more crucial than others. This knowledge of frame priority at the MAC layer helps the rate adaptation. This is because the delay in retransmission of I-frames from the application layer is higher than retransmitting from the MAC layer. In addition, the MAC layer can drop less important frames based on the priority and closely follow the channel conditions by minimizing the Kullback-Leibler distance. Resync marker

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In general applications the working of these layers are mostly static in nature and do not consider the underlying network protocols or the possible variation in the channel conditions. We propose a hierarchical fragmentation, packetization and prioritization layer (FPPL), specifically designed for multi-rate wireless systems, that uses the packet resynchronization and exploits the underlying MAC and the channel conditions for packetization/fragmentation.This hierarchy is shown in Figure 1.

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Since FPPL is MAC-aware, the FPPL packet size is matched to the MAC protocol data unit (PDU) to prevent further fragmentation at the MAC layer. 4.2

Radio Resource Scheduling

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Depending on the application the FPPL generates the appropriate number of priority levels and performs fragmentation and packetization accordingly. We assume that k can be any number of priority levels depending on the application. The CAS models the MAC buffer into k number of queues one for each priority level. For example in case of the FGS MPEG-4 video data there would be 4 queues one each for the I, B, P-frames and the last one for the enhancement layer with the highest priority being given to the I-frames and the least to the enhancement layer. The generic MAC-S does not differentiate the priorities and simply serves the content in a first come first serve (FCFS) queuing discipline. However, the proposed CAS being content aware enhances the system goodput. In many cases for real time streaming applications, transmission of the higher priority content is essential for the success of the application. In addition, the proposed CAS is aware that the packets need to be transmitted before a certain deadline at the receiver side. The CAS is smart enough to discard packets if stale.

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For ith packet, we define a boolean variable SP (i) which determines whether the packet is stale or not. A packet i is defined stale if SP (i) = 1 and the condition is tcurr > T Si +tprop else SP (i) = 0 meaning the packet is not stale; where tcurr , T Si and tprop are the current system time, the time stamp of packet i prior to which it needs to be transmitted and the propagation time, respectively. This simple yet content aware scheduling mechanism prevents error propagation and increases the goodput. The MAC-S is unaware of the prioritization scheme and is unable to schedule efficiently. However, the CAS utilizes the priority information and also employs a Selective Adaptive Retransmit Control (SARC) mechanism for the highest priority packets. The SARC mechanism can be utilized till any level of priority i.e. for each buffer but for the sake of simplicity we restrict ourselves to the highest priority buffer. Additionally, the CAS employs Fast Transmit Scheme (FTS) to take advantage of favorable channel condition but does take into account of the upper limit Thus, in summary, the rationale for employing SARC are the following: 1. Provide unequal error protection (UEP) to high priority data and thereby increase the transmission quality and prevent error propagation. 2. Mobile terminal’s buffer fullness (frb ) is transmitted back to the base station through the SARC mechanism. frb is essential for boundary condition determination. The SARC mechanism of CAS keeps retransmitting the highest priority till the mobile terminal acknowledges the successful arrival of the frame. The number of retransmissions is bounded by the timing requirements, i.e., till the packet becomes stale. Continuing with the FGS MPEG-4 example the I-frame being the most important video data frame, multiple retransmissions might be required for it. Missing or corrupted I-frame results in wastage of the corresponding B, P and EL frames even if they arrive correctly. The FTS scheme enables the CAS to transmit more data than the playout curve rate of the mobile terminal when the channel conditions are favorable provided CAS has assigned sufficient slots by the MAC-S. Since the receiver buffer of the mobile terminal is finite, the CAS should restrain from transmitting data that would overflow the mobile terminal buffer. Similar to the ARQ mechanism in TCP, the mobile terminal transmits the available buffer space (β) in each acknowledgment packet. The CAS makes the rate control system module aware of β. Thereafter, RCS module takes into account of the available rate, buffer space at both the MAC and mobile terminal, and computes the upper bound for data transmission. It also determines whether the fast transmission scheme outlined earlier is achievable or not. The working principle of CAS is explained below. Without loss of generality let us explain the CAS scheduling of an application having k priority levels whose queues denoted by iHP where i {1, 2, .., k}. The highest priority level enabled with SARC in the K th decision cycle of the MAC-S scheduler.

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Scheduling Algorithm CAS() CAS ← control state if (Overflow Constraint not violated) then if 1HP not empty then if T S1HP −packet < T S2nd,...,kth−packet && !SP(1HP) then CAS schedules 1HP-packets CAS employs SARC //wait for ζ slots for ACK 7: Return 8: end if 9: end if 10: if CAS ← SARC feedback then 11: Retrieve retransmit packet numbers 12: Compute Overflow Constraint 13: if !SP(1HP-packet) then 14: CAS retransmits 1HP-packets 15: CAS employs SARC 16: if slot available then 17: pack 2nd HP, . . .,kth HP-packets respectively //wait for ζ slots for ACK 18: end if 19: Return 20: end if 21: end if 22: i=2 23: if ith -Buffer not empty then 24: CAS transmits ith Buffer packets. 25: Increment i 26: end if 27: end if

1: 2: 3: 4: 5: 6:

CAS initiates transmission with the highest priority packets employing Selective Adaptive Retransmit Control and following up with the rest of the frames without employing any ARQ on the rest of the packets. For CAS, goodput would be more effective measure than throughput. We define goodput for real time data as the number of packets transmitted per decision cycle by the CAS scheduler that the mobile terminal successfully utilizes. The calculation of CAS goodput is done in section 4.3. 4.3

CAS Goodput

In this section, we perform the analytical modeling of the Content Aware Smart Scheduler and also derive the goodput of the system using CAS. First, we model the SARC mechanism. Let Pb (m) and Pe (m) denote the probability of the bit error and packet error respectively for sending m packets simultaneously. If Ps (m) denotes the probability of successful packet transmission then Ps (m) = 1 − Pe (m) = [1 − Pb (m)]

L

(9)

where L denotes the length of the MAC protocol data unit (PDU). To provide more protection to the highest priority packets, error correction coding (ECC) is employed. The modified probability for packet transmission with ECC at any point of time is Ps (m, y) =

y    L L−o Pb (m)i [1 − Pb (m)] o i=0

(10)

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where o is the number of errors corrected and y is the total number of correctable bit errors. Thus Ps (m, y) denotes the probability for successful packet transmission for highest priority packets. However, the number of retransmissions for these packets is limited by , the maximum number of retransmits for the ith 1HP packets. We compute as =

T S1HP −packet if !SP(1HP-packet) tprop + tarq

(11)

where tprop , tarq and T S denote the propagation, the time after which the ACK transmitted by the mobile terminal is received, and the timestamp of the corresponding packet before being marked stale, respectively. The mean number of retransmissions for 1HP-packets is given by δ1HP =

 

(1 − Ps )(i−1) Ps × i

i=1

= Ps ×

1 − Ps (1 − Ps )2

(12)

Let nk be the total number of MAC-PDUs generated by FPPL for the k th data segment and let nk1HP and nkR be the number of 1HP-frame packets and 2, . . . , k- frame packets respectively such that nk = nk1HP +nkR . The timestamp of the frames is dependent on the data size distribution. We model δRj as the parameter which determines whether 2, . . . , k- frame are stale. Consider that δRj = SP(j), thus the effective number of packets transmitted per decision period by CAS is given by Δi = nkI × δI + nkR × δRj

(13)

Therefore, the average goodput (ρgp ) of CAS is given by K ρgp =

i=1

K

Δi

(14)

where K is the total number of decision periods for which video is transmitted. Note that throughput of such systems would fail to capture the actual system performance since it would not consider adaptive selective retransmission and selective frame dropping based on packet staleness.

5

Simulation Results

We have conducted simulation experiments where a single multi-rate cell with multiple users was simulated to illustrate the performance of the proposed adaptation technique with respect to the existing scheme for HDR. The video test sequences chosen comprises of both the simple profile (SP) and advanced simple profile (ASP). In order to ensure the performance of the proposed schemes, we use representative test sequences of foreman, paris and football which have

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Table 1. Specification of the files used in our simulation Resolution Clip name fps QCIF

paris

QCIF

bitrate

15fps 64kbps

foreman 25fps 64 kbps

CIF

football

15fps 1Mbps

26 CIF,15fps, 1Mbps QCIF,25fps 64kbps QCIF, 15fps ,64kbps

24

PSNR in db

22

20

18

16

14

12

0

200

400 600 Number of Errors in the BitStream

800

1000

Fig. 3. Observe the smooth variation of PSNR with BER using our CAS algorithm 0.12

Slow Fading Very Slow Fading

Percentage lag in Adaptation

0.11

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Fig. 4. The variation of CAS adaptation due to mobility

varying resolution, frame rate and bit rate. The specifications of the streams are listed in Table 1. Figure 3 highlights the graceful degradation of PSNR values using the CAS scheduler. As for the lag in adaptation, we analyzed equation 8 numerically. Figure 4 shows how the system is able to learn and adapt if a sufficiently long window of observation is allowed. As we do not deal with fast fading channels, we compared ‘slow’ and ‘very slow’ fading channels. Obviously, the adaptation is

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Percentage of Dropped I Frames

70 Non− CAS 60

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50

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0 4.5

5

5.5

6

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7

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Fig. 5. Improved throughput of I-frames due to CAS. The data transmission quality by CAS is also impressive and lesser bits are flipped due to adaptability nature of the CAS.

Percenatge of Frames Dropped

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40

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0 4.5

5

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6

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7

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Percentage of BER (10−6)

Fig. 6. Error resiliency of CAS and throughput of I-frames. As the channel condtion degrades the CAS shows significantly better adaptability to erroneous medium and results fewer Ipacket drops.

better for slower fading channels. In Figures 5 and 6, we show how the throughput of I-frames in multimedia streaming could be improved in the presence of CAS.

6

Conclusion

This paper deals with rate adaptation at the MAC layer which is necessary for streaming multimedia over multi-rate wireless systems. We propose a rate adaptation technique where the application layer encoding rate is dynamically adjusted to the varying channel conditions perceived by the user. We have also enhanced the fragmentation, packetization and prioritization scheme for FGS MPEG-4 and tailored it for the multirate wireless systems. We have shown how content-aware

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scheduling at the MAC layer can enhance the performance for video data transmission by selectively deciding on the nature and importance of the content.

References 1. 3GPP2 C.S0024 Ver3.0, “cdma2000 High Rate Packet Data Air Interface Specification”, Dec. 5, 2001. 2. P. Bender, P. Black, M. Grob, R. Padovani, N. Sindhushayana, and A. Viterbi, “CDMA/HDR: A Bandwidth-Efficient High-Speed Wireless Data Service for Nomadic Users”, IEEE Comm. Magazine, July 2000. 3. A. Furuskar, S. Mazur, F. Muller and H. Olofsson, “EDGE: Enhanced Data Rates for GSM and TDMA/136 Evolution,” IEEE Personal Communication Magazine, pp 56-66, June 1999. 4. S. Kullback and R. A. Leibler, “On information and sufficiency”, Ann. Math. Stat., 22:7986, 1951. 5. R. Koenen, “Overview of the MPEG-4 Standard”, ISO/IEC JTCI.SC29/WG11 M4030, 2001. 6. K. Gribanova and R. Jntti, “On Scheduling Video Streaming Data in the HDR System”, Vehicular Technology Conference, 2004. 7. M. Chen and A. Zakhor, “Rate Control for Streaming Video over Wireless”, Infocom, 2004. 8. H. Balakrishnan and R. Katz, “Explicit Loss Notification and Wireless Web Performance”, IEEE Globecom Internet Mini-Conference, 1998. 9. S. Biaz, N. H. Vaidya, “Distinguishing congestion losses from wireless transmission losses: a negative result”, Computer Communications and Networks, 1998. 10. H. Balakrishnan, V. N. Padmanabhan, S. Seshan and R. H. Katz, “A Comparison of Mechanisms for Improving TCP Performance over Wireless Links”, Proc. ACM Sigcomm 1996, Stanford, CA, August 1996. 11. S. Cen, P. C. Cosman, and G.M. Voelker, “End-to-end differentiation of congestion and wireless losses”, Proc. Multimedia Computing and Networking (MMCN) conf. 2002, pp. 1-15, San Jose, CA, Jan 23-25, 2002. 12. S. Aramvith, I. Pao and M. Sun, “A Rate-Control Scheme for Video Transport over Wireless Channels”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, no. 5, May 2001. 13. T. Bonald and Alexandre Proutiere, “Wireless downlink data channels: user performance and cell dimensioning”, Proceedings of the 9th annual international conference on Mobile computing and networking, San Diego, CA, USA. 14. A. Jalali, R. Padovani, and R. Pankaj, “Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system,” in IEEE Proc. of Vehicular Technology Conf. 2000-Spring. 2000, vol. 3. 15. L. D. Soares and F. Pereira, “MPEG-4: a flexible coding standard for the emerging mobile multimedia applications”, IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications, Volume: 3, pp. 1335-1339, 1998. 16. W. Chung, H. W. Lee, and J. Moon, “Downlink Capacity of CDMA/HDR”’,in the Proc. IEEE 2001 Vehicular Technology Conference (VTC2001-Fall), Atlantic City, NJ, USA, October 2001. 17. C. Bormann, L. Cline, G. Deisher, T. Gardos, C. Maciocco, D. Newell, J. Ott, G. Sullivan, S. Wenger, and C. Zhu, “RTP Payload Format for the 1998 Version of ITU-T Rec. H.263 Video (H.263+),” Internet Engineering Task Force, RFC 2429, Oct. 1998. 18. T. V. Laksman, A. Ortega and A. R. Reibman, “VBR video: Trade-offs and potentials,” Proc. IEEE, vol 86. pp. 952-973, May 1998.

On Scheduling and Interference Coordination Policies for Multicell OFDMA Networks G´abor Fodor Ericsson Research, Kista, Sweden [email protected]

Abstract. In orthogonal frequency division multiple access systems there is an intimate relationship between the packet scheduler and the inter-cell interference coordination (ICIC) functionalities: they determine the set of frequency channels (sub-carriers) that are used to carry the packets of in-progress sessions. In this paper we build on previous work - in which we compared the so called random and coordinated ICIC policies - and analyze three packet scheduling methods. The performance measures of interest are the session blocking probabilities and the overall throughput. We find that the performance of the so-called Fifty-Fifty and What-It-Wants scheduling policies is improved by coordinated sub-carrier allocation, especially in poor signal-to-noise-and-interference situations. The performance of the All-Or-Nothing scheduler is practically insensitive to the choice of the sub-carrier allocation policy. Keywords: Orthogonal Frequency Division Multiple Access, Radio Resource Management, Interference Coordination, Scheduling.

1 Introduction The 3rd Generation Partnership Project (3GPP) has selected orthogonal frequency division multiple access (OFDMA) as the radio access scheme for the evolving universal terrestrial radio access (E-UTRA). Packet scheduling (PSC) and inter-cell interference coordination (ICIC) are important radio resource management (RRM) techniques that together determine the set of OFDMA resource blocks (essentially the sub-carriers) that are taken into use when a packet is scheduled for transmission over the radio interface [2], [3]. In broad terms, PSC is responsible for determining the session(s) that can send a packet during a scheduling interval and the number of sub-carriers that the session may use. The number of the assigned sub-carriers has a direct impact on the instantaneous bit-rate and thereby can be seen as part of the rate control mechanism. The ICIC function, in turn, is concerned with allocating the particular sub-carriers to the session taking into account the instantaneous channel conditions and the ICIC policy. Such ICIC policy may coordinate which sub-carriers should be taken into use by the schedulers in neighbor cells. The impact of these two RRM functions on the session-wise and overall throughput has been for long recognized by the standardization and research communities. Sections 11.2.4 and 11.2.5 of [2] and Chapter 6.12 of [3] describe the roles of the PSC and ICIC functions and discuss their relation. From a performance analysis perspective, I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 488–499, 2007. c IFIP International Federation for Information Processing 2007 

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Letaief et al. developed a model that jointly optimizes the bit and power allocation in OFDMA schedulers [5]. ICIC has been the topic of research for long (for a classical overview paper, see [4]). The paper by Liu and Li proposed a so called ”Radio Network Controller algorithm” that determines the set of allowed resources in each base station under its control, while the ”Base Station algorithm” schedules packets for transmission [9] (see also Chapter 8 of [10]). These works demonstrate that already with a single dominant interfering neighbor cell, the total throughput increases when an appropriate ICIC policy is employed by the packet scheduler. The contribution of the current paper is that we (1) explicitly take into account that traffic is elastic and (2) propose a flexible model to capture the behavior of a wide range of schedulers under two different ICIC policies. With regards to (1) we allow the bitrates of the sessions to fluctuate between the associated minimum and maximum rates. This model allows the maximum rate to be large so that the behavior of TCP-like greedy sources can be captured. Regarding (2), we introduce the notion of the scheduler policy vector that specifies the probability that a session is granted a certain amount of subcarriers when there are competing sessions in the system. We add this rather general scheduler model to the interference coordination model described in [8] and analyze the model in a sequence of steps (Steps 1-6) detailed in the paper. The performance analysis gives insight into the potential gains that inter-cell interference coordination can give when employing different packet scheduling policies. The paper is organized as follows. In the next section, we describe the scheduling and ICIC policies that we study and introduce the policy vector as a convenient tool to characterize these policies. Next, in Section 3 we state the performance analysis objective in terms of the input parameters and the performance measures of interest. The solution is summarized in a sequence of steps (as described above). Section 4 discusses numerical results. We highlight our findings in Section 5. We note that the proofs of the lemmas as well as further details, numerical results and the conclusions are available in the longer version of the paper [1].

2 Scheduling and Inter-cell Interference Coordination Policies We consider an OFDMA cell that comprises S orthogonal frequency channels (subcarriers). The number of in-progress sessions is denoted by i and represents the state of the system. When the system is in state i, the scheduler determines the number of sub-carriers that are assigned to each session. For a particular session under study, this implies that the session is assigned s number of sub-carriers with probability P (s); S s=0 P (s) = 1. We refer to the mechanism that (in each system state) establishes P (s) as the scheduling policy. The scheduling policy vector is a vector of dimension (S + 1) whose sth element specifies the probability that the session under study (and thereby any session) is allocated s channels, s = 0 . . . S. (We note that the indexing of the (S + 1) elements of the policy vector runs from 0 to S.) In the following subsections we describe three such scheduling polices. Throughout we assume that the sessions belong to the same service class that is ˆ and a maximum slowdown factor a characterized by a peak rate requirement R ˆ ≥ 1. ˆ a. Also, when The minimum accepted (guaranteed) bit rate for a session is Rmin = R/ˆ

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a session is granted s number of frequency channels, its ideal bit-rate (assuming a given and fixed modulation and coding scheme, MCS) and assuming zero packet error/loss ˆ is set to RS (that is the peak bit-rate rate (P ER = 0) is denoted by Rs . When R requirement is the bit-rate that is provided when all resources are assigned to a single session), we say that the session is greedy. We will also use the operator S(R) that returns the number of required channels in order for the session to experience R bitrate (again assuming P ER = 0). That is, when a session is admitted into the system, ˆ the number of allocated channels s (in the long term) must fulfil: Rmin ≤ Rs ≤ R. This implies that we assume that an admission control procedure operates in the system such that the maximum number of simultaneously admitted sessions remain under Iˆ  S  S(R/ˆ ˆ a) . We say that state i is an under-loaded, critically loaded or overloaded state if ˆ is less than, equal to or greater than S respectively. S(i · R) 2.1 The What-It-Wants Scheduling Policy ˆ channels to the sessions The What-It-Wants scheduling policy attempts to grant S(R) ˆ as long as i · S(R) ≤ S; i > 0. Otherwise, in overloaded states, it grants either  Si  or  Si  channels. Specifically, the What-It-Wants scheduling policy is defined by the ˆ ≤ S: following Policy Vector. If i · S(R) ⎧ ˆ 1 if s = S(R) ⎪ ⎪ ⎨ → − (granting peak rate with prob. 1), P W IW (s) = (1) ⎪ ⎪ ⎩ 0 otherwise. For overloaded states, we need to distinguish between two cases. If Si is an integer number, then: ⎧ S ⎪ ⎪ 1 if s = i ⎨ → − (granting an equal share with prob. 1), P W IW (s) = (2) ⎪ ⎪ ⎩ 0 otherwise. When Si is not an integer number, the following relations must hold. The scheduler grants  Si  channels with probability P1 and  Si  number of channels with probability 1 − P1 . Clearly:  



S S S S S P1 · + (1 − P1 ) · = ; P1 = − . i i i i i (3) Thus, the policy vector in this case takes the form: ⎧  P1 if s = Si ⎪ ⎪ ⎪ ⎪ ⎨

 → − P W IW (s) = 1 − P1 if s = Si ⎪ ⎪ ⎪ ⎪ ⎩ 0 otherwise.

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2.2 The All-Or-Nothing Scheduling Policy In the All-Or-Nothing scheduling policy all resources are assigned to the scheduled session. This type of scheduling is employed in High Speed Downlink Packet Access (HSDPA) systems when code multiplexing is not used. Thus, a session with peak rate ˆ would need to be scheduled with probability S(R)/S ˆ requirement R in order for it to receive its peak rate. However, when there are i ≥ 1 on-going sessions, any given session cannot get scheduled with higher probability than 1/i. That is, in the All-OrNothing scheduling policy, in system state i, a session gets scheduled with probability ˆ 1/i]. The scheduling policy takes the following form: P2 = Min[S(R)/S, ⎧ P2 if s = S ⎪ ⎪ ⎪ ⎪ ⎨ → − P AoN (s) = 1 − P2 if s = 0 (5) ⎪ ⎪ ⎪ ⎪ ⎩ 0 otherwise. 2.3 The Fifty-Fifty Scheduling Policy The Fifty-Fifty scheduling policy can be seen as a policy in between the What-It-Wants and All-Or-Nothing policies. When there are i sessions in the system, the scheduler divides the resources (almost) equally between the competing sessions (similarly to WhatIt-Wants). However, similarly to the All-Or-Nothing policy, in under-loaded states this ˆ Thus, would mean that the sessions receive more resources in the long term than S(R). S S in this policy, in underloaded state i, if i is not integer, a session receives  i  channels with probability P31 ,  Si  number of channels with probability P32 and no channels ˆ < S and S is not with probability 1 − P31 − P32 . Clearly, in states for which i · S(R) i an integer number:   

   

S S S S S S ˆ : − P31 · + P32 · = S(R), and P31 : P32 = − . (6) i i i i i i If Si is integer, the session is assigned Si number of channels with probability P33 and zero channels with probability 1 − P33 : P33 ·

S ˆ = S(R); i

and:

P0 = 1 − P33 .

ˆ ≥ S) the channels are fully utilized For critically and overloaded states (i · S(R) (P34 + P35 = 1):  

S S P34 · i · + P35 · i · = S. i i In the critically loaded and overloaded states, if Si is integer, the number of allocated sessions for each session is Si with probability 1. Based on these observations, the scheduling policy vector for the Fifty-Fifty policy is straightforward to determine (although a bit tedious to formally specify it, see [1].

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Cell-0 Cell-1 Cell Under Study Dominant Interfering Cell

K0

Collision

K1

Cell-1

Cell-0 Random Allocation of frequency channels

Cell-0

Cell-1

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S Collision

No Collisions

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Fig. 1. Random and Coordinated ICIC policies. Coordinated ICIC can be realized by assigning a cell specific ordered list of the frequency channels to each cell such that the “collisions” of frequency channels are avoided as long as there are non-colliding pairs. Assuming a single (dominant) interfering cell (as in [9] and [10]), devising such ordered lists is straightforward. For many cells, coordinated ICIC implies careful frequency planning, as described in for example [4].

2.4 A Numerical Example Consider an OFDMA cell that supports S = 64 sub-carriers (channels). Sessions have ˆ = 4 channels. When there are 6 ina peak rate requirement that corresponds to S(R) progress sessions, the system is under-loaded (6 · 4 < 64), the three scheduling policy vectors are as follows: → − P W IW = [0, 0, 0, 0, 1, 0 . . . , 0] ;   → − 60 4 , 0, . . . , 0, P AoN = ; 64 64   40 8 16 , 0, . . . , 0, , , 0, . . . , 0 , (7) PF F = 64 64 64 where the PF F vector has non-zero elements at positions 0, 10 and 11 (corresponding to 1, 11 and 12 scheduled channels). Since the system is underloaded, the What-It-Wants policy grants the peak rate with probability 1 (4 channels), the All-Or-Nothing policy allocates all the 64 channels with probability 4/64. The Fifty-Fifty policy (A = 0.0625, B = 0.03125 so P31 = 0.125 and P32 = 0.25) either allocates 10 or 11 channels to any given session (with probabilities 8/64 and 16/64 respectively) or it does not schedule the session (zero channels with probability 40/64). (All three policies allocate 4 channels in the long term average in this system state.) 2.5 ICIC Policies: Random and Coordinated Sub-Carrier (Channel) Allocation Basically, there are two approaches as to how the sub-carriers out of the available ones are selected when a session requires a certain number of sub-carriers (see Figure 1). The

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simplest way is to pick sub-carriers out of the ones that are available (i.e. scheduled) randomly such that any available sub-carrier has the same probability to get allocated to an arriving session. Random allocation of sub-carriers is attractive, because it does not require any coordination between cells, but it may cause collisions even when there are free sub-carriers. In contrast, a low complexity coordination can avoid collisions as long as there are non-colliding sub-carrier pairs in the two-cell case and non-colliding tuples in the multiple-cell case. We refer to this method as coordinated sub-carrier allocation. (Further details about these ICIC policies in general can be found in [8].)

3 Performance Measures of Interest and Solution Approach 3.1 Input Parameters and Performance Measures We consider a single OFDMA cell with S channels at which sessions belonging to the same (elastic) service class arrive according to a Poisson process of intensity λ. Each session brings with itself a file whose size is an exponentially distributed random variable with parameter μ. The session requests a radio bearer that is characterized by its ˆ (for which: S(R) ˆ ≤ S) and minimum rate R/ˆ ˆ a, where a peak rate R ˆ ≥ 1 is the maximum slowdown factor associated with the session. If, at the time instant of the arrival of the new session, the admission of the new session brought the system into a state in which the minimum rate (governed by the particular scheduling policy) cannot be granted, the session is blocked and leaves the system. The single cell is disturbed (interfered) by a single dominant interferer cell, such as in [9]. In this paper we characterize the load in this dominant interfering cell by the number of used sub-carriers K1 ≤ S. When an allocated sub-carrier in the cell under study and one of the K1 disturbing channels use the same sub-carrier frequency, we say that the two sub-carriers collide and suffer from co-channel interference [4]. The performance measures of interest are the session-wise blocking probability and the mean file transfer time. These two quantities represent a trade-off since more admitted sessions imply lower per-session throughput and thereby longer file transfer times. This trade-off in a WCDMA environment has been investigated by Altman in [6] and subsequently by Fodor et al. in [7]. 3.2 Step 1: Determining the Distribution of the Allocated Sub-Carriers Recall that in each system state the scheduling policy vector determines the probability that a given session is allocated s channels. When a session is given s channels (which → − happens with probability P (s)), we need to calculate the conditional distribution of the number of the totally allocated number of channels (that is to all sessions) in the cell (denoted by K0 ), given that the session under study is given s channels. This is because K0 and the number of disturbing channels K1 determine the distribution of the colliding and collision-free channels in the cell, which in turn determine the performance measures of interest. We cannot give a closed form formula for the (conditional) distribution of K0 . However, in [1] we provide the pseudo code description of the algorithm that calculates it.

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3.3 Step 2: Determining the Distribution of the Colliding Sub-Carriers Under the Random and Coordinated Sub-Carrier Allocation Policies Lemma 1. Let S denote the total number of available sub-carriers in each cell and let K0 ≤ S and K1 ≤ S denote the number of allocated channels in Cell-0 and Cell-1 respectively. Let N1 (c) denote the number of possible channel allocations in Cell-0 and Cell-1 such that the number of collisions is c. Then, the distribution and the mean of the number of collisions under the random allocation policy (γ1 ) are as follows: cMIN = M ax[0, K0 + K1 − S],

cMAX = M in[K0 , K1 ],

      S S−c S − K0 N1 (c) = · · ; c K0 − c K1 − c

E[γ1 |K0 , K1 ] =

cM AX  c=cM IN

c · N1 (c) , T OT 1

P r{γ1 = c|K0 , K1 } = 

where T OT 1 =

S K0

N1 (c) , T OT 1

   S · . K1

Lemma 2. Using similar notation as in Lemma 1, the distribution and the mean number of collisions under the coordinated allocation policy (γ2 ) is given by:  1 if c = c0 N2 (c) = 0 otherwise,  c0 =

0 K0 + K1 − S

P r{γ2 = c} = N2 (c),

if K0 + K1 < S, otherwise.

E[γ2 ] =

cM AX 

c · N2 (c).

c=cM IN

3.4 Step 3: Determining the Packet-Wise Effective SINR The scheduling policy vector specifies the probability that s channels are used in Cell-0, whereas Lemmas 1-2 determine the probability that the number of colliding channels is c. We will use the following lemma to determine the probability that the number of colliding channels in a packet of size L is γ when the number of scheduled channels (for the session under study) is s and the total number of colliding channels is c ≤ s. Lemma 3    c   s−c  L γ L−γ P r {γ ≤ c channels out of L are colliding} = ·  s  ·  s−γ  γ γ L−γ

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3.5 Step 4: Calculating the SINR Level in Case of Collisions for the Downlink Lemmas 1-3 determine the probability that the number of colliding channels is γ and the number of non-colliding channels is L − γ in a packet of a session under study. We now need to determine the impact of the collision on a channel’s signal-to-noise-andinterference (SINR) ratio. For this, we use the path loss model recommended by the 3GPP (described in [15]) and a result from [8]. Let θ be a predefined threshold and let X  rr01 be a random variable representing the distance ratio between the mobile station distances from its serving and disturbing base station respectively. Also, let Q0 and Q1 denote the power that the serving and the neighbor base station uses on the colliding channels respectively. Furthermore, let G0 and G1 denote the path gains from the serving base station (that is in Cell-0) and the dominant neighbor base station (that is in Cell-1) respectively to the mobile station under study. Then, the probability that the SINR remains under this threshold can be approximated as follows [8]:  G0 · Q0 <θ ≈ Pr G1 · Q1 + N0 



Max[X]

  fX (x)g(x) dx;

0

  xμ θ 5 ln Q0 /Q1 1 · . g(x)  erfc − 2 bς ln 10

(9)

where fX (x) is the probability density function of X; b, ς and μ are the parameters of the 3GPP path loss model as described in [15]. 3.6 Step 5: Calculating the Effective SINR and the Packet Loss Probability We are now in the position that the packet loss probability in each system state can be determined. When one or more of the channels that are used to carry a packet are hit by collisions, an efficient way to characterize the overall SINR quality of the packet is to use the notion of the effective SINR. This concept has been proposed in [12] and used in for instance [13], in which a method to calculate the packet error probability for a given value of the effective SINR was also proposed. A specific method to calculate the effective SINR (based on the SINR of the composing channels) that is applicable in cellular OFDM systems is also recommended by the 3GPP [11]. In this paper we employ the 3GPP method that can be summarized as follows. Suppose that there are L sub-carriers that carry a data packet and each has a SINR value of SIN Ri . Then, the effective SINR that is assigned to the packet is given by:   L  SIN R   i −1 1 SIN Reff = α1 · I I , (10) L i=1 α2 where I(·) is a model specific function and I −1 (·) is its inverse. The parameters α1 and α2 allow to adapt the model to characteristics of the considered modulation and coding scheme. The exponential effective SINR metric proposed in [11] corresponds

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to I(x) = exp(−x). In [13] it is shown that for QPSK and 16-QAM modulation, the parameters α1 and α2 can be chosen as follows: α1 = 1 and α2 = 1. In [13] a method to determine the packet error rate (σ) as a function of the effective SINR is presented. Essentially, this method maps (in a 1-1 fashion) the effective SINR onto a (modulation and coding scheme dependent) packet error rate. 3.7 Step 6: Determining the Performance Measures of Interest We now make use of the assumption that the session arrivals form a Poisson process and that the session size is exponentially distributed. We choose the number of admitted sessions as the state variable and thus the number of states in the system is Iˆ + 1. The transitions between states are due to an arrival or a departure of a session. The arrival rates are given by the intensity of the Poisson arrival processes. Due to the memoryless property of the exponential distribution, the departure rate from each state depend on the nominal holding time of the in-progress sessions, and also on the slow down factor and the packet error rate in that state. Specifically, when the slow down factor is ai (n), and the packet error rate is σ(n) its departure rate is (1 − σi (n))μi /ai (n). The Markovian property for such systems was observed and formally proven by Altman et al. in [14]. Thus, the system under these assumptions is a continuous time Markov chain whose state is uniquely characterized by the state variable n.

4 Numerical Results In accordance with the 3GPP recommendation, we here (in a somewhat simplified fashion) assume that a downlink resource block (sometimes referred to as a chunk) occupies 300 kHz and 0.5 ms in the frequency and time domains respectively. A chunk carries 7 OFDM symbols on each sub-carrier; therefore the downlink symbol rate is Rsymbol =140 symbols/chunk/0.5ms. Assuming a 10 MHz spectrum band, and considering some overhead due to measurement reference symbols and other reasons, this corresponds to 30 chunks in the frequency domain (S = 30), that is 8400 ksymbol/s. The actual bit-rate depends on the applied modulation and coding scheme, in this paper we do not model adaptive modulation and coding (AMC), we simply assume a fixed binary phased shift keying (BPSK) so that each symbol carries nMCS = 2 bits. Sessions arrive according to a Poisson process of intensity λ = 1/8 [1/s]. A session is characterized by the amount of bits that it transmits during its residency time in the system (we may think of this quantity as the size of the file that is to be downloaded). We assume that this file size is an exponentially distributed random variable with mean value ν = 4 ∗ S ∗ Rsymbol ∗ nMCS . The blocking probability and file download meantime results are shown in Figures 2-3. On the x axis we let the number of disturbing channels (i.e. the occupied channels in the neighbor cell) increase (K1/5 = 1 . . . 6), while the y axis shows the blocking probabilities and the mean session residency times. The upper graphs in each figure correspond to the case when there is no channel allocation coordination between the cells, while the lower graphs assume coordination (channel segregation). This system is highly loaded so that when a ˆ is set to 1, the blocking probabilities increase from 5.5% up to 7%, while the file download time increases from 30.5 s to 33 s (not shown here). The figures correspond to the case when a ˆ = 2. We observe

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that when the sessions tolerate some slowdown, the blocking probability dramatically decreases (down to 0.06% !) without much increasing the download time (from around 33s to around 34s). Secondly, we note that coordinated allocation is beneficial when the What-It-Wants or the Fifty-Fifty scheduling method is employed, and has no effect when the All-Or-Nothing scheduling is used. The curve denoted “ideal” corresponds to the case when the packet error rate σ is zero in all system states. We refer to [1] for further results and analysis.

5 Conclusion Inter-cell interference coordination is an important radio resource management function for (O)FDMA based cellular systems in general [4] and for the evolving Universal Terrestrial Radio Access Network (E-UTRA) in particular [2], [3]. We proposed the notion of the (scheduling) policy vector to model the behavior of the packet scheduler. Using the policy vector, we were able to derive the conditional distribution of the number of colliding and collision free channels in the cell under study for all three cases. This in turn allowed us to determine the distribution of the number of colliding and collision free (i.e. co-channel interference free) channels in each scheduled packet. We used this knowledge to calculate the effective SINR and from it the packet error rate and thereby the useful packet throughput of the system. This useful throughput determines the session wise blocking probabilities and the time it takes for elastic sessions to complete a file transfer. Our major finding is that the performance of the ICIC function (its impact on the system throughput) depends on the employed scheduler. Specifically, for peak rate limited ˆ < S), when frequency domain scheduling (“narrow band”) traffic (that is when S(R) is used in combination with time domain scheduling, it is useful to employ coordinated channel allocation in neighbor cells. Coordinated ICIC has little impact when the scheduler is pure time domain based. We also note that our further numerical results indicate that ICIC is only necessary for cell edge users, whose SINR is negatively impacted by frequency domain collisions [1].

References 1. G. Fodor and M. Telek, “Modeling and Performance Analysis of Scheduling Poicies for OFDMA Based Evolved UTRA”, Technical Report, 2006. http://webspn. hit.bme.hu/∼telek/techrep/multicell.pdf. 2. 3GPP Technical Report TR 25.912, Feasibility Study for Evolved Universal Terrestrial Radio Access (E-UTRA), Release 7, 2006. 3. 3GPP Technical Report TR R3.018, Evolved UTRA and UTRAN Radio Access Architecture and Interfaces, Release 7, 2006. 4. I. Katzela and M. Naghshineh, “Channel Assignment Schemes for Cellular Mobile Telecommunication Systems: A Comprehensive Survey”, IEEE Personal Communications, pp. 1031, June 1996. 5. Ying Jun Zhang and Khalid Ben Letaief, “Multiuser Adaptive Subcarrier-and-Bit Allocation With Adaptive Cell Selection for OFDM Systems”, IEEE Transactions on Wireless Communications, Vol. 3, No. 5, pp. 1566-1575, September 2004.

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6. E. Altman, “Capacity of Multi-service Cellular Networks with Transmission-Rate Control: A Queueing Analysis”, ACM Mobicom ’02, Atlanta, GA, September 23-28, 2002. 7. G. Fodor and M. Telek, “Performance Anlysis of the Uplink of a CDMA Cell Supporting Elastic Services”, in the Proc. of IFIP Networking 2005, Waterloo, Canada, Springer LNCS 3462, pp. 205-216, 2005. 8. G. Fodor, “Performance Analysis of a Reuse Partitioning Technique for OFDM Based Evolved UTRA”, 14th IEEE International Workshop on QoS, New Haven, CT, USA, pp. 112-120, June 2006. 9. G. Li and H. Liu, “Downlink Dynamic Resource Allocation for Multi-cell OFDMA System”, 58th IEEE Vehicular Technology Conference, VTC 2003-Fall, Vol. 3, pp. 1698-1702, 6-9 October 2003. 10. H. Li and G. Liu, “OFDM-Based Broadband Wireless Networks: Design and Optimization”, WILEY, 2005, ISBN: 0471723460. 11. 3GPP Technical Report TR 25.892, Feasibility Study for OFDM for UTRAN Enhancements (Release 6), V6.0.0, 2004-06. 12. S. Nanda and K. M. Rege, “Frame Error Rates for Convolutional Codes on Fading Channels and the Concept of Effective Eb /N0 ”, IEEE Transactions on Vehicular Technologies, Vol. 47, No. 4, pp. 1245-1250, November 1998. 13. K. Brueninghaus, D. Ast´ely, T. S¨alzer, S. Visuri, A. Alexiou, S. Karger, G-A Seraji, “Link Performance Models for System Level Simulations of Broadband Radio Access Systems”, 16th IEEE International Symposium on Personal, Indoor and Mobile Communications, PIMRC, 11-14 Sepetmeber 2005. 14. E. Altman, D. Artiges and K. Traore, “On the Integration of Best-Effort and Guaranteed Performance Services”, INRIA Research Report No. 3222, July, 1997. 15. 3GPP TR 25.942, Radio Frequency System Scenarios, 2005.

Distributed Uplink Scheduling in CDMA Networks Ashwin Sridharan1 , Ramesh Subbaraman2 , and Roch Gu´erin2, Sprint Advanced Technology Labs [email protected] 2 University of Pennsylvania {rameshrs,guerin}@seas.upenn.edu 1

Abstract. Ever more powerful mobile devices are handling a broader range of applications, so that giving them greater control in scheduling transmissions as a function of application needs is becoming increasingly desirable. Several standards have, therefore, proposed mechanisms aimed at giving devices more autonomy in making transmission decisions on the wireless uplink. This paper explores the impact this can have on total throughput in CDMA systems, where this control has traditionally been centralized. The investigation relies on a simple distributed policy that helps provide insight into the impact of distributed decisions on overall system efficiency, and identify guidelines on how to best mitigate it.

1

Introduction

With the power and versatility of mobiles1 rivaling that of stationary platforms, the diversity and communication requirements of applications they run have also been expanding. This has resulted in a push to give mobiles more autonomy in making transmission decisions. This, however, often conflicts with the centralized operation of current wireless systems, e.g., the control exercized by base stations or the use of 802.11 RTS/CTS handshakes between devices and access points. In this paper, we explore the tension this creates in the uplink of CDMA systems. Traditional CDMA base stations tightly control transmission schedules and power to maintain acceptable signal to interference levels. Several standards for modern 3G/4G cellular networks, e.g., 1xEV-DO Rev. A [1], HSUPA [2], have, however, introduced mechanisms that give devices significant autonomy in deciding when to transmit and at what rate. As stated in [3], a major driver was to define a “wide-area-mobile wireless Ethernet,” where devices had greater independence in making transmission decisions best matched to their applications. The price for this flexibility is potentially higher interferences, and a corresponding degradation in performance. Investigating this issue is what motivated this paper. 

1

The work of these authors was supported in part by a Gift from the Sprint Corporation and through NSF Grant CNS-0627004. We use mobiles, devices and users interchangeably.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 500–510, 2007. c IFIP International Federation for Information Processing 2007 

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One proposed mechanism for allowing distributed transmission decisions while maintaining some control on resource sharing, is a token bucket [1] similar to that used in wired networks [4]. Each token grants access to a certain amount of “resources,” with token generation rate and token bucket depth imposing limits on resource consumption. Mobile devices decide how to spend their tokens to achieve transmission rates (and latencies) best suited to their applications. Unlike wired networks where the token “currency” is in bytes, tokens are now in units of transmission power, the primary resource in a CDMA system. This leads to resource sharing models fundamentally different from the “queueing systems” that capture buffer and bandwidth consumption in wired networks. Instead, as discussed in Section 4, the sharing of resources among users is measured through the resulting signal to interference and noise ratio (SINR). We develop models that reflect this sharing, with users making independent but constrained transmission decisions, where constraints arise from token bucket mechanisms. We first investigate a simple distributed policy, with users randomly and independently alternating between idle and active periods. Token bucket constraints are introduced next that limit the frequency of active periods. We derive expressions in both settings for the achieved user rates as functions of the frequency of active periods and rate selection. This enables us to explore the impact of distributed transmission decisions on performance and assess the efficacy and effect of token buckets. Our evaluation is carried out in the context of uplink transmission in a single cell. Extensions to the multi-cell scenario will be considered in future work. The rest of the paper is structured as follows. Section 2 provides basic background on CDMA systems and reviews related work. Section 3 is a short tutorial on the operation of 3G/4G cellular uplinks. Our resource sharing model is covered in Section 4, while Section 5 describes our evaluation framework. Our analysis is covered in Section 6, with Section 7 comparing its results to simulations. Section 8 summarizes our findings and points to future work.

2

CDMA Reverse Link: Related Work

In CDMA systems [1,2] the reverse link or uplink, i.e., from mobiles to the access network, is well-known to be interference limited. Because users share the same spectrum and their signals are not perfectly orthogonal, the throughput they see is a function of both their own transmission power and that of other users whose transmissions are perceived as interference. This introduces a tradeoff as increasing either ones own signal strength or transmission frequency also increases interferences to others. A user must therefore first decide what transmission power to utilize and second, when and how often to transmit. There is a large literature addressing optimum transmission power selection. See, for example [5,6,7,8,9,10] that address the problem of joint allocation of transmission power and associated QoS functions (e.g., rate). These studies however do not address the second issue of uplink scheduling. Off late, this topic has received some attention, notably [11,12,13] where authors proposed

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joint scheduling and power allocation algorithms that take multi-user interferences into account. These works, however, assume a centralized control of users transmissions, which places a heavy burden on the system in terms of signaling overhead and scalability.

3

The 3G/4G Cellular CDMA Reverse Link

We now describe two key features of the operation of 3G/4G CDMA uplinks, which play important roles in enabling and affecting distributed scheduling policies. The first, pilot assisted transmission, governs the transmission power level of devices. Each device transmits a pilot signal on the uplink whose strength is set by the access network through a fast closed control loop to ensure that all pilot signals are received with equal power. When selecting an uplink transmission data rate, the device then sets its transmission power relative to the strength of the pilot signal. Specifically, if at time slot t the pilot strength of device i is i (R, t): PSi (t), transmission at a data rate R requires a transmission power PD i (R, t) = T xT 2P [R] · PSi (t) PD

(1)

where T xT 2P [R] is an a priori specified proportionality factor function of the target rate R. This mechanism does not yield optimal transmission power selections, but ensures a level playing field to all devices by equalizing the strength of all pilot signals at the receiver. The fast power control loop allows the pilot signal to track variations of the wireless channel, and as a result the transmission power for the data is de-coupled from the problem of coping with fading and attenuation on the wireless channel. As we shall see later, this is a critical aspect of the system. The second and more recent feature of CDMA systems is the use of a tokenbucket2 to control how devices access the uplink. Each MAC (layer 2) flow is assigned a token bucket which can hold σ “power-tokens” and is filled at a rate of ρ. To transmit at a data rate of R, a device must have T xT 2P [R] tokens that are then subtracted from its bucket3 . The higher R, the higher T xT 2P [R], and hence the faster the token bucket drains. Through the token bucket parameters (σi , ρi ), the network limits the maximum transmission power and frequency of devices, and therefore controls both the total uplink power and its allocation across devices. On the other hand, it relinquishes scheduling decisions to devices by letting them control the use of their power tokens.

4

System Model

The system consists of a single cell with n + 1 homogeneous and continuously backlogged users sharing a time-slotted uplink. Denote the data transmission 2 3

Also known as Grants in HSUPA [2]. In practice there are only a finite set of rates a device can transmit at(e.g., six in [1]).

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i power of user i in slot t when transmitting at rate Ri as PD (Ri , t). Under the CDMA sharing model, the SINR of user i in slot t is then given by [14]:

Si (Ri , t) =

i G(Ri ) · Giloss (t) · PD (Ri , t) ,  j j 2 σ +θ Gloss (t) · PD (Rj , t)

(2)

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where θ ∈ [0, 1] quantifies the orthogonality of the codes, σ 2 is the thermal noise, Giloss (t) the path loss4 of user i in time slot t, and G(Ri ) = W/Ri its processing gain, where W is the spread-spectrum bandwidth. Recall from Section 3 that the pilot signal of each device is controlled by a fast control loop so that received pilot strengths are all identical at the base station. This can be modeled as each pilot signal seeking a common target SINR 1/φ. Let PSi (t) be the pilot strength of user i in slot t. Assuming perfect power control and unconstrained transmission power, it is then easy to see that the pilot power control loop requires each device to set PSi (t) such that, Giloss (t) · PSi (t) = Δ =

σ2 ,. φ − θp n

(3)

where θp is the orthogonality factor for the pilot signal. Since, the data transmisi (R, t) is relative to the pilot strength, using Eqn. (1) and Eqn. (3), sion power PD Eqn. (2) can be written as Si (Ri , t) =

G(Ri ) · T xT 2P [Ri ] · Δ  . σ2 + θ T xT 2P [Rj ] · Δ

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Eqn. (4) states that with perfect power control and no power constraint, the SINR of a user is influenced only by other users rate choices and not the channel.

5

Evaluation Framework

Given our assumption of continuously backlogged and homogeneous users, a metric of interest is long-term throughput. For simplicity, we first approximate the effective rate achieved by a user in a time slot as linearly proportional to its SINR. Specifically, the effective rate achieved by a user i in time slot t, given that it transmitted at rate Ri , is given by Ci (t) =

Si (Ri , t) Ri So

(5)

where Si (Ri , t) is given by Eqn. (4) and So is the target data SINR. The linearity assumption is typically valid at small SINR values [14] as long as the modulation scheme remains the same. A limitation of Eqn. (5) is, however, that whenever Si (Ri , t) is greater than So , it yields an effective rate greater than the 4

Function of distance to base station and fast fading.

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transmission rate Ri . This is clearly not possible, and hence, the above relation is modified to Ci (t) = min(Ri ,

Si (Ri , t) Ri ) . So

(6)

We refer to Eqn. (5) as the Linear Model and Eqn. (6) as the Bounded Model.  = E[Ci (t)]. In both cases, the metric of interest is average user achieved rate, C  Apart from C, for token bucket constrained systems, the token efficiency, i.e., the achieved effective rate per token expended, is another metric of interest. If T is the expected number of tokens expended per time slot, the token-bucket  . efficiency is defined as η = C/T 5.1

The Scheduling Policy

We consider a transmission scheme where in each slot when a user has enough tokens it either transmits at rate R with probability p, or doesn’t transmit at all with probability 1 − p. All users are assumed independent and identical in their transmission behaviour, i.e., p and R are the same for all users. Although simple, this policy is interesting for several reasons. First, it is inherently distributed, which when combined with its simplicity makes it eminently practical. Indeed, it has direct equivalents in wireline networks, e.g., Aloha, CSMA etc. Second, by virtue of easily controllable parameters, it lets us explore and understand key system properties, e.g., impact of cell load, transmission rate, etc., which we show can strongly influence performance. In addition, it captures a hybrid sharing model between pure CDMA (all users transmitting) and a slotted-system (one user transmitting at a time) that has the potential to enable distributed control while improving performance.

6

Analysis of Scheduling Behaviour

In this section, we analyze the performance of the on-off scheduler, and identify how the value of p that maximizes throughput depends on both the number of users in the cell (n + 1) and their selected transmission rate R. The analysis is based on Eqn. (4) and assumes perfect power control. We also assume that So in Eqn. (5) is large enough that the rate obeys the Linear Model. The impact of these assumptions is explored in Section 7. Due to space limitations, we refer the reader to the technical report [15] for all proofs. 6.1

Scheduler Behaviour: No Token Bucket

i (R, t) = From Section 3, the signal power to transmit at data rate R is PD i T xT 2P [R] · PS (t). For ease of exposition, assume that R is fixed and let T xT 2P [R] = K. Let Ki be the random variable denoting the transmission power used in a slot by user i. Under the on-off scheduler with no token constraints, Ki is a

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Bernoulli random variable that takes values K and 0 with probability p and 1 − p, respectively. After minor algebraic manipulations of Eqn. (4) and using  Eqn. (5) and G(R) = W/R, the expected achieved data rate C(p) is found to be   n  W 1 n j  C(p) = ·p· (7) p (1 − p)n−j , θ · So j + δ j j=0 φ − θp n γ = . We now state two propositions that capture and θK θK  elicit the impact of δ on the scheduling parameter p and the achieved rate C(p). where δ =

 Proposition 1. If δ ≥ 1, then the expected achieved rate C(p) attains its max∗ imum value at p = 1.  Proposition 2. If δ < 1, then the expected achieved rate C(p) has a unique ∗ maximum at p = p < 1. In either case, p∗ satisfies the following equation: n    n j 1 1 = . (8) p (1 − p)n−j j j + δ (n + 1)p −1+δ j=0 φ − θp n Since δ = , assuming φ is fixed5 , Propositions 1 and 2 reflect the θK impact of the number of users and the selected rate on the optimal p. Specifically, with few users or low enough transmission rates so that δ ≥ 1, the optimal policy yields a pure CDMA system (p∗ = 1) where everybody transmits, exploiting the orthogonality of the CDMA codes. As the load increases (n or K ) so that δ ≤ 1, the increased interference triggers a transition to a hybrid slot-division/CDMA allocation with only some users active in any given slot. This reflects the trade-off between reducing interference (p ) and increasing transmission opportunities (p ). Propositions 1 and 2 characterize the transition point precisely through δ. Similar results were obtained in [12], albeit in a centralized setting. Next, we study the impact of selecting different transmission rates in the on state on the ∗ . optimal achieved rate C ∗ be the optimal achieved rates when the trans∗ and C Proposition 3. Let C 1 2 mission rates in the on state are R1 and R2 , respectively. If R1 > R2 , then ∗ > C ∗ . C 1 2 The proposition states that under an on-off scheduler, increasing the transmission rate R always improves throughput. Hence, one should select the (R, p∗ ) combination with the highest R. However, this is true only for an unconstrained system, and need not hold when token constraints are present. In such a setting token efficiency matters, and lower rates may fare better than higher ones that consume more tokens. 5

A typical target Pilot SINR 1/φ is between −26 dB and −17 dB and the same for homogeneous users.

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Incorporation of a Token Bucket

The previous section established that controlling transmission frequency and rate matters when devices make independent decisions. This can be realized by a token bucket with parameters (ρ, σ). The token rate ρ bounds transmission frequency and rate, while the bucket depth σ affords flexibility in scheduling decisions. Next, we use the results of Section 6.1 to explore how to spend tokens (transmissions at rate R cost K tokens) to maximize throughput. Consider the on-off scheduler, but now operating under token bucket constraints. Specifically, if p is the conditional transmission probability given enough tokens (≥ K), the token bucket evolution can be modeled as a Markov chain to obtain the stationary distribution πl of having l tokens in the bucket [15]. The unconditional transmission probability ptok is then given by Eqn. (10) that  so that the optimum pair together with Eqn. (7) can be used to approximate C, ∗ ∗ (K , p ) is the solution of the non-linear program N1 :  K) N1 : max C(p,

(9)

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  n  1 n j  K) = W · ptok · C(p, ptok (1 − ptok )n−j , θSo j + δ j j=0 ptok = p · (1 −

K−1 

πk ),

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0 ≤ ptok ≤ 1 . An algorithm that solves program N1 is described in [15], and evaluated in Section 7.2.

7

Simulation Results

We explore the validity of the analysis of Section 6 and the roles of rate selection and transmission probability in both unconstrained (Section 7.1) and token bucket constrained systems (Section 7.2). Results are obtained using a detailed simulator of the uplink that incorporates key characteristics of the channel model and transmission system. The target pilot strength is set to −17dB, which allows up to 50 active users to share the uplink. Simulations for both perfect and imperfect power control yielded very similar results. Hence, only results for the former are presented. Results for the latter can be found in [15] together with additional details on the simulator itself. All results have a 90% confidence interval. 7.1

Unconstrained System Evaluation

Our first goal is the validation of δ = 1 as a transition point for the optimal policy, i.e., from p∗ = 1 to p∗ < 1. We focus on the linear model on which the

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analysis of Section 6.1 is based, and plot in Fig. 1 the achieved throughput for two configurations: A lightly loaded system, R = 76.8 kbps and 24 users, for a load ≤ 50% and δ > 1; and a highly loaded system, R = 76.8 kbps and 45 users for a load ≈ 90% and δ = 0.23 < 1. The figure highlights the different optimal policy of each configuration (p∗ = 1 for the former and p∗ < 1 for the latter) confirming predictions made in Propositions 1 and 2. Similar results were also obtained for the bounded rate model. Next we explore the differences that exist between the linear and bounded rate models. We use a scenario with δ < 1, and plot in Fig. 2 the achieved throughput as a function of p for the two rate models. For the linear model, the optimal p∗ ≈ 0.03 agrees with the solution of Eqn. (8). For the bounded rate model, the figure, however, highlights the impact of limiting the rate even as the SINR keeps increasing. The rate capping translates into a higher optimal p∗ ≈ 0.2, or in other words in allowing more simultaneously active users. The figure also illustrates for both rate models the benefits of the hybrid allocation of the on-off scheduler (p < 1) over both a pure CDMA system (p = 1) and a pure slot-based scheme (plotted on the right y-axis), where the latter was realized (for the bounded rate model) through a round robin scheduler that allowed only one user to be active in any time slot. 7.2

Token Bucket Constrained System Evaluation

Fig. 2 (R = 153.6 kbps, 45 users) and Fig. 1 (R = 76.8 kbps, 45 users) also validate Proposition 3, as they show that for the linear rate model, an (R, p∗ ) combination with a higher R is indeed better. As discussed earlier, this however ignores token efficiency. Indeed, Fig. 3 shows that R = 76.8 kbps has higher token efficiency for a 24 user system. We explore next how this affects throughput under token bucket constraints.

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Fig. 3. Token Efficiency (24 users)

Fig. 4 plots the achieved bounded rate throughput as a function of the conditional transmission probability p, i.e., probability of transmission given enough tokens in the bucket, for a 24 user system and a token bucket with ρ = 7 dB and σ = 21.5 dB. Based on the recommendations in [1], transmissions at R = 153.6 kbps require 18.5 dB worth of tokens and 13.5 dB at R = 76.8 kbps6 . When p is low, R = 153.6 kbps yields better throughput than 76.8 kbps because the bucket is rarely exhausted and hence token efficiency is not critical. However, this is no longer true at higher values of p where the better token efficiency of 76.8 kbps yields a higher throughput. Overall, 76.8 kbps yields the highest achieved throughput because it provides a better compromise than R = 153.6 kbps between token efficiency and realized rate. 6

See [15] for a full list of rate to token mappings.

Distributed Uplink Scheduling in CDMA Networks

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Last, we discuss the solution of program N1 of Section 6.2 that relies on the linear rate model and explore its differences with the bounded rate model (Fig. 4). Table 1 presents the optimal transmission probabilities p∗A and achieved ∗ obtained by solving N1 for both R = 76.8 kbps and R = 153.6 kbps rates CA in a 24 user system. It also gives the optimal values obtained by simulation for the bounded rate model. As expected, because the bounded rate model caps rates, its achieved rates are significantly lower. When it comes to optimal transmission probabilities however, the analytical results are in good agreement with simulations for R = 153.6 kbps. For R = 76.8 kbps the analytical p∗ = 1.0 is higher than that predicted by simulations p∗sim = 0.35. However, comparing the last column Csim (p∗A ) in Table 1, which shows the throughput achieved in ∗ simulations using the analytically computed p∗A , with the optimal Csim , we see that they are very close for both R = 76.8, 153.6 kbps. This indicates that the p∗A obtained from solving N1 provide very reasonable estimates for setting the transmission probabilities in practice. Table 1. Token Bucket - Bounded Rate Model Rate Token Bucket (kbps) Analysis :N1 Simulation ∗ ∗ A sim p∗A p∗sim C Csim (p∗A ) C 76.8 1.0 26.4 0.35 17.84 16.56 153.6 0.21 42.9 0.25 10.63 10.59

8

Conclusions and Future Work

In this paper, we investigated the performance of a CDMA uplink, when transmissions decisions are distributed to mobiles. This was motivated by standard

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proposals [1,2] that introduced support for such distribution. The investigation relied on a simple on-off scheduler to explore the impact of distributed transmission decisions, and identified both analytically as well as via simulations, key factors that affect system performance and how to account for them in designing a scheduling policy. The paper also investigated the realization of such distributed scheduling decisions through a token bucket, and how the token bucket operation affected the scheduler. There are many possible extensions to this work, and we mention two we are currently exploring. The first is identification of the optimal capacity region for distributed decisions and how to achieve it. The second and possibly more important direction involves using the token bucket to provide differentiated services to users. Preliminary results can be found in [15].

References 1. QualComm: 1xEV: 1x EVolution, IS-856 TIA/EIA Standard (2004) 2. 3GPP TS 21.101 UTRAN-based 3GPP System, Rel. 6: High Speed Uplink Packet Access (2005) 3. Bhusha, N., Lott, C., R. Attar, P.B., Jou, Y.C., Fan, M., Ghosh, D., Au, J.: CDMA2000 1xEV-DO Revision A: A physical layer and MAC layer overview. IEEE Comm. Mag. 44(2) (2006) 4. Turner, J.: New directions in communications (or which way to the information age?). IEEE Comm. Mag. 24(10) (1986) 5. Foschini, G., Miljanic, Z.: A simple distributed autonomous power control algorithm and its convergence. IEEE Trans. Veh. Technol. 42(4) (1993) 6. Holliday, T., Bambos, N., Goldsmith, A.J., Glynn, P.: Distributed power control for time varying wireless networks: Optimality and convergence. In: Proc. 41st Allerton Conf. (2003) 7. Hande, P., Rangan, S., Chiang, M.: Distributed Uplink Power Control for Optimal SIR Assignment in Cellular Data Networks. In: Proc. INFOCOM. (2006) 8. Price, J., Javidi, T.: Decentralized and Fair Rate Control in a Multi-Sector CDMA System. In: Proc. WCNC. (2004) 9. Yim, R., Shin, O.S., Tarokh, V.: A CDMA Reverse Link Rate Control algorithm with Fairness Gaurantee. In: Proc. IWCT. (2005) 10. Tinnakornsrisuphap, P., Lott, C.: On the fairness and stability of reverse-link MAC Layer in cdma2000 1xEV-DO. In: Proc. ICC. (2004) 11. Elbatt, T., Ephremides, A.: Joint Scheduling and Power Control for Wireless Adhoc Networks. In: Proc. INFOCOM. (2002) 12. Kumaran, K., Qian, L.: Uplink Scheduling in CDMA Packet-Data Systems. In: Proc.INFOCOM. (2003) 13. Cruz, R.L., Santhanam, A.V.: Optimal Routing, Link Scheduling and Power Control in Multi-hop Wireless Networks. In: Proc. INFOCOM. (2003) 14. Viterbi, A.J.: Principles of Spread Spectrum Communication. Addision-Wesley (1995) 15. Sridharan, A., Subbaraman, R., Gu´erin, R.: Distributed Uplink Scheduling in CDMA Systems. Research Report RR06-ATL12070139, Sprint ATL (2006)

Resource Allocation in DVB-RCS Satellite Systems Andr´e-Luc Beylot1 , Riadh Dhaou1 , and C´edric Baudoin2 1

IRIT/ENSEEIHT, 2 rue C. Camichel, BP7122, F-31071 Toulouse Cedex 7, France [email protected], [email protected] 2 Alcatel Alenia Space, 26 avenue JF Champollion BP1187, F-31037 Toulouse Cedex 1, France [email protected]

Abstract. This paper compares several approaches for dynamic allocation in geo-stationary networks based on DVB-RCS system. Each Satellite Terminal (ST) regularly sends requests to the Network Control Center (NCC) which in turn allocates resource to the users. Unfortunately, this delayed request-assignment makes the dynamic bandwidth allocation very difficult. Simple mechanisms such as a fixed allocation or requests based on the current size of the terminals’ queue are compared to predictive methods based on control theory techniques which have been previously proposed. A lower bound is also derived by considering that the actual size of the buffer can be instantaneously known. It is shown that if the traffic is not really bursty, a fixed allocation which implies lighter signalling mechanisms leads to good results. In bursty traffic conditions, simple mechanisms for which the requests correspond to the actual size of the buffer may lead to the best performance results. Keywords: Resource allocation, Modeling and performance evaluation.

1

Introduction

DVB-RCS (Digital Video Broadcasting - Return Channel by Satellite) satcom systems provide a shared uplink between various users of terminals. Dynamic allocation mechanisms, taking into account the needs for each terminal, have been investigated to enable an optimal use of the expensive and limited bandwidth. The capacity requests are calculated according to the generated traffic in the terminal. Several modes of calculation are proposed which are based on the size of the buffer information and on the input and/or output rates with possible use of a calculation window taking into account the satellite delay. The allocation requests are then processed in the Network Control Center (NCC). This one carries out a control on the radio resource according to the requests and to their priority. In this paper, we define several methods of calculation of requests and allocations. The allocation loop is characterized by an important delay, a shared access to the resource, a granularity of allocation. The methods aim at decreasing the average latency of the traffic in the buffers while insuring fairness I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 511–521, 2007. c IFIP International Federation for Information Processing 2007 

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between satellite terminals. The suggested loops are modelled and evaluated to compare their performance, their complexity and their sensitivity to the traffic type.

2 2.1

Dynamic Resource Allocation The System

The DVB-RCS standard considers various classes of service on the MAC level and associated allocation methods. Packets have a fixed size. – CRA: Constant Rate Assignment. This class is not the subject to allocation requests, the capacity is allocated (in slot/frame) at the connection setup and remains constant. This operation is used for delay sensitive traffics (voice). – RBDC: Rate Based Dynamic Capacity. This category is used for the traffics with variable flow which can tolerate the response time of the MAC scheduler. The bandwidth is granted on request to run out the instantaneous traffic rate. – VBDC: Volume Based Dynamic Capacity. Used for the traffics without any time constraints, VBDC requests are expressed in number of slots and satisfied if there are remaining slots. The duration of a frame Tf is equal to 30ms. An allocation cycle will include propagation delay and various computing times (terminal and NCC) and is set to d = 23 frame durations. Thus, the request for frame n must be calculated and sent in frame n − 23. In such a context several allocation methods have been proposed. 2.2

Predictive Allocation Techniques

Several techniques of control were recently applied to communication networks. They lead to performance gains given adequate traffic conditions. The Smith’s Predictor (SP) [7] is a popular and very effective long dead-time compensator for stable processes. The main advantage of the SP method is that the time delay is effectively taken outside the control loop in the transfer function. Classical SPs are used to remove potentially destabilising delays from the feedback loop by employing “loop cancellation” technique. The SP has been successfully used in designing congestion control algorithms in TCP [5] and ATM [6] networks. Authors in [4] proposed a DAMA mechanism based on SP controller, making it possible to adapt the load on-demand for satellite networks. Results are limited to the study of simple parameters and to systems which can be described by first order’s models with a long delay (the multiple combinations of parameters and the study of systems with models of a higher nature are still open). The SP algorithm can be described as follows. Let yi (n) denote the allocation request

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emitted by terminal i during frame n and ui (n) its buffer size at the beginning of the same frame. (K is a gain factor 0 < K < 1, only integer values for the requests are considered): yi (n) = K(ui (n) +

n−1 

yi (j))

(1)

j=n−d

In the present paper, the allocation technique mainly concerns VBDC traffic. Consequently, if the total amount of the requests is lower than the actual number of slots C, all the requests are satisfied. Else, the allocation is proportional to the request.   yi (n − d) if k yk (n − d) ≤ C (2) ui (n) = yi (n−d)   yk (n−d)  + εi (n − d) otherwise k

As results may not be integer values, the remaining slots are randomly and uniformly distributed among active terminals whose request has not been fulfilled. εi (n − d) stands consequently for an additional allocated slot. A novel approach for dynamic bandwidth allocation (DBA) in satellite networks has been presented in [1,2,3]. Each ST uses a local adaptive predictor to forecast the future input traffic flow along with a local predictive (recedinghorizon) controller to generate a bandwidth request to the NCC. This approach gives good results when the traffic model is well known. The difficulties to adapt the predictor to actual traffic conditions and the complexity of the optimization of the controller have been evaluated [8] but will not be discussed in this paper. Moreover, several papers [13,14,15] shown that adaptive resource allocation policies may outperform the fixed policies. 2.3

Other Allocation Techniques

In the present paper, we aim at comparing simple algorithms to the SP method. The first one supposes that the controller knows instantaneously the buffer state of the terminals. Of course, this method cannot be implemented because the necessary delay to indicate packet arrivals is exactly the problem we face. Nevertheless, it leads to optimal performance results to which other techniques may be compared. This method is named “Big Brother” in the present paper. Many algorithms may be chosen to implement fairness principles between STs. When considering infinite buffers, all the work-conserving policies lead to the same value of the mean delay. We did not choose any of them. The second method which has been considered is a Fixed Allocation. Terminals receive a constant number of slots per frame. This allocation may be done during the set-up period of the terminal. In the present paper, uniform traffic conditions are considered. The number of allocated slots may vary according to the number of active sessions using a simple procedure and with a very light signalling mechanism. For the evaluation, the duration of the allocation period is supposed to be large enough to consider that a terminal is allocated a constant number of slots during a frame period. If it has fewer packets to send, the

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corresponding slots will be lost. The principle is that the round-trip delay is so long that it is not efficient to implement solutions at a packet level. The third method is exactly the opposite of the previous one: a signalling procedure is implemented. The terminals send requests reflecting their current buffer size. This mechanism is named “Candid Algorithm” because no intelligence is required to calculate the size of the requests. The controller collects all those requests, calculates the corresponding numbers of allocated slots and sends back these values.

3 3.1

Model and Analysis of the System Model of the System and Traffic Assumptions

In the present paper, we do not investigate the actual scheduling of the slots or the exact position of the allocated slots in the frame. Bulk departures are considered at the beginning of the frame during which packets are actually sent. Different traffic models will be considered. For simplicity, we will first assume that packets arrive to the queues according to independent Poisson processes. Interrupted Poisson Processes (IPP) will then be considered in order to show the influence of the burstiness of the input traffic on the performance criteria. 3.2

Models “Big Brother” and “Candid” - Poisson Arrivals

In the case of Poisson Arrivals, the analysis of the two methods can be done through a M/D(H) /1 queue with bulk departures where H is the maximal size of the burst. This size is constant and is equal to C in “Big Brother” case and to C N in “Candid method” (N is the number of terminals). The evolution of such a queue at departure epochs can be described as follows: Xn+1 = M ax {Xn − H, 0} + An

(3)

where Xn is the number of packets in the queue (or in the aggregate queue in the Big Brother case) just before departure and An the number of arrivals during frame n. This queue has been extensively studied. Using a z-transform approach, classical solutions consist on finding the roots of the denominator of: X(z) =

A(z)(S(z) − z H ) A(z) − z H

(4)

where X(z), A(z), S(z) are the pgf of the stationary queue length, the number of arrivals and the number of departures. The mean number of packets in the queue can be expressed as follows E(X) = ρ +

A (1) − S  (1) 2(H − ρ)

(5)

ρ is the mean number of arrivals during a frame ρ = A (1) = S  (1). We propose new bounds of such a term using the following considerations. The first lower

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bound consists on observing that the second moment of the number of departures is lower than the second moment of the number of arrivals. It is a lower bound because the number of departures is bounded by the maximal size of the bulk. This bound is reached if the number of departures is exactly equal to the number of arrivals. The second lower bound considers that either 0 or H packets are served. As S  (1) = ρ, we get S  (1) ≤ (H − 1)ρ. An upper bound of the mean queue size is derived by considering that the variance of the number of departures is positive thus, S  (1) ≥ ρ2 − ρ. Finally, M ax(ρ +

ρ2 − (H − 1)ρ ρ , ρ) ≤ E(X) ≤ ρ + 2(H − ρ) 2(H − ρ)

(6)

In the present paper, we also implemented the very smart solution proposed in [9]. It consists on inverting arrivals and services and considering that Eq. 3 corresponds to the evolution of the response time of a discrete D/G/1 queue with, in this case, a service time which is distributed according to a Poisson Process. The mean number of packets in the queue is thus equal to: +∞ +∞  1  E(X) = ρ + (i − kH)ci,k k k=1

(7)

i=kH

where ci,k is the probability that i packets arrives during k frames. The main interest of this method is that these expressions are root-free. To implement this method, we set a maximal value for the infinity. Classical bounds, such as Kingman’s formula [10] of G/G/1, can also be applied E(X) ≤ r + r2 (Ca2 + Cs2 )2(1 − r)λ

(8)

ρ , λ is the arrival rate of packets, Ca2 and Cs2 are respectively the squared r= H coefficient of variation of “arrivals” and “service times” of the D/G/1 queue. We ρ can easily find that Ca2 = 0 and that Cs2 = ρ1 . Consequently, E(X) ≤ ρ + 2(H−ρ) which is exactly the upper bound that has been derived in Eq. 6. In order to derive the mean delay, we can substract from all the previous results the mean number of departure ρ, then apply Little’s formula [11] and finally add the mean time between arrival time and the beginning of the following frame. As Poisson arrivals are considered, this parameter is equal to 12 frame duration (a more formal proof can be obtained by deriving the mean number of packets at arrival instant and applying then Little’s formula). Thus, the mean delay (expressed in number of frames) is equal to:

E(R) =

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E(X) 1 − ρ 2

(9)

Models “Big Brother” and “Candid” - Bursty Arrivals

Many models can be considered to take into account the burstiness of the traffic. In the present paper, we consider Interrupted Poisson Process in order to derive

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some numerical results. Our aim was mainly to compare the different methods rather than finding exact performance results. As it has been mentionned earlier, if the optimal allocation policy depends to much on the traffic characteristics, an erroneous model of traffic may lead to bad performance results. IPP processes were introduced in order to model ON/OFF traffic with “ON” and “OFF” period duration exponentially distributed (with respective parameter α, β) and Poisson arrivals during the “ON” period (rate γ). The superposition of such processes is not IPP [12]. The performance criteria will thus be derived through the analysis of a IP P/D(H) /1 queue for “Candid” method and of a n − IP P/D(H) /1 queue in “Big Brother” case. We will consider the embedded Markov Chain at departure epochs, by considering the number of packets in the queue and the number of active source(s). The number of arrivals during a frame period An depends on the number of active sources. For “Candid” mechanism, we thus analyzed the continuous process corresponding to the number of packets generated since the beginning of the period N (t) and the state of the source E(t). The transient analysis of this process have  been previously been performed [12] using a z-transform approach Φi (z, t) = k P r[E(t) = i, N (t) = k]z k . In the present paper, we prefer a Laplace-Transform approach. Let Pj (k, i, t) = P r[N (t) = k, E(t) = i|E(0) = j] and Pj∗ (i, k, s) denote its Laplace transform. We easily get the initial values:  ∗  ∗ P1 (0, 1, s) = α+s P0 (0, 1, s) = ατ τ (10) s+β+γ ∗ P0 (0, 0, s) = τ P1∗ (0, 0, s) = βτ where τ = s2 +(α+β+γ)s+αγ. The following terms can be recursively described γ(s+α) ∗ ∗ ∗ Pj (k − 1, 1, s) as follows: Pj∗ (k, 0, s) = βγ τ Pj (k − 1, 1, s) and Pj (k, 1, s) = τ Let s1 and s2 denote the poles of the denominator τ . After some calculations, those Laplace transforms can be inverted. They are of the following forms: Pj (k, i, t) =

k+1 

tj−1 (uk,i,m exp(+s1 t) + vk,i,m exp(+s2 t)) (j − 1)! m=1

(11)

Coefficients uk,i,m and vk,i,m are recursively computed [8]. A numerical method has been implemented to derive the values of those functions in t = Tf and we 1 only consider a finite number of terms. Let Rj,i,k = Pj (k, i, Tf ). The numerical solution leads to the steady state probabilities (number of packets, state of the and thus the mean number of packets just before departure is equal source) xk,j to E(X) = k k(xk,0 + xk,1 ). Using the same argument as in the Poisson arrival case, we can again apply Eq. 9. In Candid method, we have to study a n − IP P/D(H) /1 queue. We nearly applied the same method. The number of arrivals during a frame period duration given the number of active sources are recursively derived by using previous equations. m+1 = Rj,i,k

1  k  p=0 q=0

m 1 Rj,i,q R0,p,k−q (1 −

i i m 1 ) + Rj−1,i,q R1,p,k−q m m

(12)

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4 4.1

517

Results Analytical Results - Poisson Arrivals

In the present work we consider a high load ρ = 0.8 (for lighter traffic conditions, predictive methods are less useful). The frame duration Tf is equal to 30ms. The maximum aggregate bit rate is set to 2Mbps which nearly leads to a maximal number of slots per frame equal to C = 140. In figure 1, we represented the mean access delay as a function of the number of terminals N . The case N = 1 corresponds to “Big Brother” mechanism. We compared the different analytical methods (upper bound, lower bound, numerical solution) to simulation results. The numerical method leads to excellent results. They are nearly equal to those obtained using a discrete event simulation whose confidence intervals are extremely low. The upper bound is quite accurate. The load is high, it corresponds to traffic conditions for which Kingman’s formula is known to be accurate. The difference between this upper bound and simulation results is between 3% and 20%. The lower bounds are also accurate; the one considering that the variance of the number of departures is equal to the variance of the number of arrivals leads to good results when the number of terminals is low. In this case, the number of slots allocated to a terminal is high and consequently, most of the packets arrived during frame n will be emitted in frame n + 1. The other lower bound is exact when each terminals are allocated one slot per frame and leads to inconsistent results when the number of terminals decreases. 4.2

Comparison of the Allocation Methods, Poisson Arrivals

We will compare the fixed allocation method, the SP technique (for various values of K), and the lower bound under Poisson arrivals. The standardized load of the system is 0.8 and we vary the number of terminals. SP leads to worse results than a fixed allocation especially when the number of terminals is large. With regard to the SPs parameter setting, we note that larger values of K lead to better results. The value K = 1 was considered, even if it constitutes an extreme value (the series of requests diverge). We can note that for this value of K the difference with the fixed allocation method is lower. We are inclined to say that if the system is heavily loaded, all the resources will be very intensely used and that devoting them to a terminal or another does not change the global result. As Poisson arrivals are considered, the lower bound is nearly equal to 0.5 (a packet has to wait at least for the beginning of the following frame) and does not depend on the number of terminals (we did not plot a curve ; it corresponds to the fixed technique with N = 1 terminals). We can finally think that these good results are due to Poisson arrivals which are not sporadic enough compared to real traffic. Poisson sources result in a rather good regularity in arrivals flows, which could correspond to an unfavourable configuration for the SP. Under a sporadic traffic, in particular with larger durations of activity, SP may have time enough to stabilize itself and thus to outperform a fixed allocation.

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4.3

519

Influence of the Burstiness, IPP Arrivals

Let us now compare the results obtained with fixed allocation, SP, Candid and “Big Brother” methods under bursty traffic conditions. We consider again a normalized load of 0.8. IPP processes are characterized by three parameters: the mean duration of “OFF” periods α1 , the mean duration of ON periods β1 α and the arrival rate during “ON” period γ. Thus, ρ = N C γ α+β γ is to packets per frame during active periods. We took several values of the duration rate of active periods β and we deduced the duration rate of silence periods α as a function of the number of terminal N . Results are depicted in Fig. 3 and 4. In Fig. 3, the average duration of “ON” periods is short (1 frame). The difference between the allocation mechanisms and the lower limit remains reasonable. The fixed allocation gives better results than the SP; the performance is degraded when we increase the number of terminals. The “candid method” gives the worst results but these results are less sensitive to the number of terminals. In this configuration, the best results obtained with the SP correspond to the value K = 1.0 for the same reasons invoked when considering Poisson arrivals. We also note that the durations of the “ON” periods remain short compared to the supposed reactivity of the SP. Load=0.8, gamma=15, beta=1, static method vs smith predictor vs candid method Big Brother-Simulation Big Brother-Numerical Fix-Simulation Fix-Numerical Smith k=1 Smith k=0.8 Smith k=0.6 Candid

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Fig. 3. Mean Access Delay as a function of the number of terminals, IPP arrivals

In Fig. 4, the mean duration of the “ON” periods is set to 100 frames. The results derived from the different allocation methods are now very far away from the lower bound. The system becomes extremely weak; the periods of overload

520

A.-L. Beylot, R. Dhaou, and C. Baudoin Load=0.8, gamma=15, 1/beta=100, static method vs smith predictor vs candid method 400 Big Brother-Simulation Big Brother-Numerical Fix-Simulation Fix-Numerical Smith k=1 Smith k=0.8 Smith k=0.6 Candid

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Fig. 4. Mean Access Delay as a function of the number of terminals, IPP arrivals

are very long. Even in the “big brother” case, the performance deteriorates. The performance is clearly degraded when we increase the number of terminals with the fixed allocation whereas dynamic methods allow on a limited horizon a more efficient absorption of traffic bursts. SP and candid methods are less sensitive than the fixed allocation on the number of terminals. If we consider N = 10 terminals, all the methods nearly lead to the same results. Starting from N = 14, we observe for the first time the interest of the SP compared to the fixed allocation method that does not work properly. In this configuration, in the fixed allocation scheme, terminals are allocated 7 slots in each frame, which does not make it possible any more to absorb instantaneously peaks of traffic. The benefit of the absence of signalling is reduced. The optimal value of K is K = 0.6. The SP is better (except for K = 1.0) than the fixed allocation... but in these cases, “Candid” method leads to better performance results.

5

Conclusion

In GEO Satellite systems, dynamic resource allocation is a difficult problem because of the very important delay which separates requests from their response and of the strong potential variability of injected traffics. The Smith’s predictor has been extensively studied in communication networks. If traffic variations or delays are low, this method can be interesting. Nevertheless, fixed allocation or multi-step allocation appears more effective in the satellite context. Taking into account of a predictor of traffic is undoubtedly important. However, our

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experiment shows that if the traffic has not the same nature as the modelled traffic, results are questionable. In our experiments, it appears that for not very sporadic traffics, the fixed allocation is the most efficient (because it does not generate significant signalling’s overhead and delay) and that for much more sporadic traffics, a “naive” method aiming at emitting the current size of the buffer absorbs more efficiently traffic variations in particular over long durations. In conclusion, the methods based on the Smith’s predictor may possibly be better than simpler fixed allocation methods when the systems are heavily loaded or when the durations of the burst periods are large. However, if these periods are really long, some signalling could make it possible to switch from a step of fixed allocation to another one. It could be advisable to consider, in further analysis, real traffic traces and to focus also on throughput and fairness.

References 1. Chisci L, Fantacci R, Pecorella T: Multi-terminal dynamic bandwidth allocation in GE0 Satellite Networks. In: IEEE VTC04 Spring (2004) 2797-2801. 2. Chisci L, Fantacci R, Pecorella T: Predictive bandwidth control for GEO satellite networks. In: IEEE ICC 2004 (2004) 3958–3962. 3. Chisci L, Fantacci R, Francoli F, Pecorella T: Dynamic Bandwidth Allocation via Distributed Predictive Control in Satellite Networks. In:IEEE ISCCSP04 (2004) 373–376. 4. Delli Priscoli F, Pietrabissa A: Load-adaptive bandwidth-on-demand protocol for satellite networks. In:IEEE ICDC02, Las Vegas (2002) 4066-4071. 5. S. Mascolo S: Congestion control in high-speed communication networks using the Smith principle. Automatica, Vol. 35 (1999) 1921–1935. 6. Mascolo S, Di Sciascio E, Grieco A: End-to-End Congestion Control and Bandwidth Estimation in High Speed ATM Networks. In:International Conference ITI 2001, Pula, Croatia (2001) 57–62. 7. Smith O.J.M: Closer control of loops with dead time. Chemical Engineering Progress. Vol. 53 (1957) 217–219. 8. Beylot AL, Dhaou R: Optimisation de boucles dallocation. Alcatel Space Grant Final Report (2006). 9. Janssen A, van Leeuwarden J: Analytic computation schemes for the discrete-time bulk service queue. Queueing Systems. Vol. 50 Springer (2005) 141-163. 10. Kingman J: On the Algebra of Queues. Methuen, London (1966). 11. Little J: A proof for the queuing formula: L = λW , Op Research Vol. 9 (1961) 383-387 12. Fischer W, Meier-Hellstern K: The MMPP cookbook. Perf Eval. Vol. 18 (1992) 149-171 13. Alagoz F, Vogcic B-R, Walters D, Altrustamani A, Pickholtz R-L: Fixed versus adaptive admission control in direct broadcast Satellite networks with return channel. IEEE Jour. on Sel. Areas in Comm. Vol. 22 (2004) 238-249. 14. Baglietto M, Davoli F, Marchese M, Mongelli M: Neural approximation of openloop feedback rate control in satellite networksi. IEEETrans on Neural Net., Vol. 16 (2005) 1195-1211. 15. Celandroni N, Davoli F, Ferro E, Gotta A: Networking with multi-service GEO satellites: Cross-layer approaches for bandwidth allocation. Int. Jour. of Sat. Comm. and Net., Vol. 24 (2006) 387-403.

Enhanced Downlink Capacity in UMTS Supported by Direct Mobile-to-Mobile Data Transfer Larissa Popova, Thomas Herpel, and Wolfgang Koch University Erlangen-Nuremberg, Germany {popova,koch}@LNT.de [email protected]

Abstract. The goal of this work is first to analyze the feasibility of a peer-to-peer file sharing technique in mobile cellular environments, taking into account key characteristics and peculiarities of the UMTS Radio Access Network (UTRAN). The concept is referred here to as mobile-tomobile (m2m). Next, our research efforts explore the performance benefits of m2m file sharing applications in UMTS networks in terms of releasing overall downlink capacity, which can be used to provide better Quality of Service (QoS) for real-time services. To evaluate the performance of the proposed m2m concept we conducted extensive simulation studies with appropriately modified radio propagation models for low antenna heights for both, transmitter and receiver, as it is typical for m2m. Two alternative scenarios of serving user requests (m2m network mode and conventional UMTS mode) have been constructed and analyzed. The results indicate a dramatic increase in service probability and overall throughput gain of up to 85 % in a UMTS network, supported by the m2m data transmission mode. Furthermore, results show that by a well-designed m2m routing policy and proper utilization of currently not used uplink resources (due to asymmetric uplink/downlink traffic load) substantial reduction of the expected file download time can be achieved. Keywords: Direct mobile-to-mobile data transfer, WCDMA, released downlink capacity, asymmetric uplink/downlink traffic load, unified radio interface, data exchange policy.

1

Introduction

Rapid growth of new multimedia services such as real-time streaming, distributed video conferences, with their demand for high data rates, as well as downloading of popular movies or music files put a considerable load onto the valuable and limited resources of radio networks. It is well known that in UMTS networks the traffic load is asymmetrically distributed between uplink/downlink. Typically, the downlink is the potential bottleneck, while free resources may be available in the uplink. Thus, a proper management of the air interface resources is a challenging task in order to utilize the spectral resources more efficiently. I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 522–534, 2007. c IFIP International Federation for Information Processing 2007 

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Nowdays, mobile networks are widely used for so-called background services such as digital camera images, mp3 file downloads or new movie trailers. Although these services are interesting for many users, conventional solutions for capacity utilization such as multicast or broadcast are not applicable since users request service at different times. The above considerations stimulate research for employing new paradigms in the architecture of cellular communication systems, as well as in policies in handling of user service requests. In this work we propose an approach to overcome the capacity limitations of cellular networks and to prevent exhaustion of the downlink capacity by enabling direct data transmission among the background users within a radio network. Currently, there are only a few proposals for a mobile peer-to-peer file sharing architecture. In [7] the authors are motivated by the potential UMTS capacity improvement of embedding WLAN (Wireless Local Area Network) systems in UMTS. The effort of this research is focused on the basic applicability and enhanced routing of the information in the proposed hybrid network by using a series of short mobile-to-mobile hops between already connected mobiles to extend coverage into areas not covered by the conventional UMTS cell. The propagation model they used is the free-space model, which is not realistic for terrestrial scenarios. The feasibility of the eDonkey Internet protocol in a GPRS environment was investigated in [4,6] and extended to UMTS radio networks in [5]. The main focus lies on resource mediation and control by using different strategies for data caching in the wired part of the network. Thus, signalling and data traffic is displaced from the air interface to the core network. No direct data transmission among the users is intended. All the above mentioned studies are based on entirely different approaches and have other goals than the concept, proposed in this paper. In our concept the users that are interested in downloading a popular file form a mobile cooperative community (groups) and using the fact that traffic load of multimedia services is asymmetrically distributed between uplink and downlink, contribute their own currently not used uplink capacity for providing the packets of the content to other users in the group (in their coverage range) in multicast mode on the uplink carrier frequencies. Our concept is denoted as m2m (mobileto-mobile). Although we focus on the FDD (Frequency Division Duplex) mode of WCDMA (Wideband Code Division Multiple Access) the principle can be applied to other systems as well. Particularly, in hotspot environment users increasingly demand ubiquitous data availability. Thus, the main focus of our analysis lies in the optimization of data availability to users in hotspots (e.g. airports, railway stations) by using the m2m file sharing technique. We consider the background user population, dynamically joining and leaving the system. The users organize communities which are interested, for example, in the movie trailer or the latest computer games. Groups are dynamically reshaped, so that the members of each group represent a relatively loosely coupled formation. Instead of transmission of the complete data file by providing individual links from the Node B to each user, the original file, which is available somewhere in the network, is divided into

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m logical packets and distributed packet by packet to active background users in order to generate one complete copy of this file in every radio cell. A user which received a packet from Node B acts as a server for that particular packet. The mobile terminals (MTs) are assumed to be able to receive in both uplink and downlink and each new user brings further uplink resources into the system. By this cooperative data transfer among the users, the Node B does not need to spend a lot of its valuable resources to accommodate the population of the above mentioned background user-class, as a result a major part of the traffic is shifted away from the downlink, making the released downlink capacity available for other (e.g. real-time demanding) services. The main goal of this work is to show the feasibility of the proposed m2m technique for cellular radio networks like UMTS and to analyze advantage of using m2m application in some specific wireless scenarios, which, we believe, are of practical importance. The performance results were evaluated taking into account the specifics of UTRAN, e.g. wireless interference, realistic propagation model, etc.

2 2.1

M2M Model Characteristics and Assumptions M2M Concept

To enable the wireless peer-to-peer technique we revised the idea of a mesh cooperative architecture for the fixed-line Internet [2] and appropriately extended it for the UTRA-FDD (Frequency Division Duplex). In Figure 1 the main concept of the m2m technique is demonstrated. MTs, which are interested in downloading a popular content participate in the file sharing via direct m2m data transfer with the purpose to reconstruct this original popular content, which is distributed in the network. However, in contrast to Internet peer-to-peer applications in a cellular system the number of users who can cooperate with each other is limited by the transmit power of the MT and its coverage range, which will be typically less than a cell. Therefore, in case of wireless cooperative community formation, mobile communities are location and radio propagation dependent. Thus, to cooperate with each other, m2m users must be organized into groups with nearby located users. Grouping is not restricted to users of one cell. The decision to which group a user should be assigned is based strictly on current propagation conditions, described in Section III. Upon arrival each new user, which is interested in download of the popular content, establishes contact to the Node B/RNC (Radio Network Controller) in order to get an authorization to participate in m2m file transfer and to get information about nearby located m2m users. All authorized m2m users must allow using of their uplink capacity for providing the packets of the content, he has requested, to other m2m users, which are interested in it. In order to reduce the transmission of identical packets on the network links the m2m transfer is performed in multicast mode on the normally only partly used uplink carrier frequency, whereas receivers in the group switch to listen on the uplink.

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Fig. 1. M2M Concept

2.2

Radio Interface Restrictions

In the following some restrictions and potential solutions of implementing a peerto-peer technique in UMTS Radio Access Network (UTRAN) are listed. – One of the main restrictions of UTRAN is wireless interference. In order to avoid interference from an MT transmitting in m2m mode on other signals at the Node B receiver, the transmit power is set to the minimum, which is -44 dBm according to 3GPP specifications [1]. – Compared to fixed-line networks the air interface of wireless systems has a relatively limited transmission capacity. Furthermore, the effect of user blocking, as a consequence of congestion control, is expected to occur more often than in fixed-line environments in both directions. Thus, in order to increase the efficiency of uplink bandwidth usage it is necessary to maximally reduce the signalling information. This can be done by taking advantage of already existing infrastructure of mobile communication systems, e.g. the network providers know the online status and service agreement of the mobile users. – The limitation of battery capacity of the handsets results in a lower online time compared to the fixed-line desktop PCs. Thus, a well designed file sharing organization between mobiles is essential. – There is a need for appropriate propagation models for wireless peer-to-peer links. The problems and challenges such as a fair scheduling, battery life, billing, security and rights management could be mitigated by strategies such as 1) offering bonus system (e.g. upload credits) to network subscribers who choose to allow using their uplink capacity to provide the contents to other users, 2) by implementing restrictions on the amount of traffic that a particular terminal is permitted to distribute, to save the battery life. The contents liable to cost can be also distributed with m2m strategy, might be, however, protected, for example, via DRM (Digital Rights Management). However, all analysis in this work are mainly focused on the technological characteristics of mobile peer-to-peer applications and the proper consideration of above mentioned issues is not a topic of this paper.

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Traffic Model

We study the performance of the proposed m2m concept in a UMTS network with dynamic user arrival and departure pattern. User arrivals are modelled by a Poisson process and the MTs are randomly distributed over the cell area. All users are assumed to be pedestrians. We assume that there are mobile specific content types, like mp3, to be distributed with m2m strategy. The sizes of popular mp3 files used in our simulation have been taken from [4]. 2.4

M2M Propagation Model

Successful data transmission in wireless systems depends on radio propagation conditions. The characteristic of the m2m channels is significantly different from conventional Node B - MT links because of the low antenna heights of both receiver/transmitter and a relatively short propagation distance. Pathloss and Shadowing: In our work the shadowing process is characterized by a lognormal distribution with standard deviation σ=6 dB and the pathloss between two MTs is calculated using extended Okumura-Hata model [3]. Although, the application of this model is generally restricted to antenna heights of Node B starting from 30 m, the direct comparison of this model with a model from [8] tailored for low antenna heights at both sides shows a good suitability of the model of [3] for the present application. Figure 2 shows a scatter plot for the received power depending on transmitter/receiver separation (a minimum transmit power of -44 dBm was assumed). Fading: For the simulated traffic model, cf. Subsection C, flat small-scale fading (constant within one radio frame (0.01 sec)) is assumed for the m2m links. Received power samples for pairs of m2m−sender/receiver 0

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Algorithm

We simulate a population of users that are interested in downloading a popular file, which originally is only available in the core network. Since downlink capacity is limited, Node B does not serve all users simultaneously, and the popular file is divided into m logical packets, each with an individual ID, and distributed periodically (every 10 frames) packet by packet to active background users in order to generate one complete copy of this file in every radio cell. A user which received a packet from the Node B behaves as a server for that particular packet and uses its uplink resources for providing the packet. The users are organized into dynamically reshaped multiple groups to cooperate with each other with the purpose to reconstruct the original file. 3.1

Group Organization Policy

In the following general assumptions for the organization of dynamic groups in m2m file sharing are presented. – Each new user looks for a group to join when trying to receive popular content. In case that no appropriate group for a new m2m user is found he forms a group with stand-alone user only and waits until the next group update. – MTs form groups, which satisfy the following conditions: 10lg PT X −Lij − Λij ≥ −112dBm | PT X = PT X,min , ∀i, j ∈ group, where Lij is the pathloss between MTs i and j and Λij is the random variable describing the shadowing process. – The simplest way to inform a new MT about all other MTs, already requesting the same content in its coverage range is to transmit “Hello” packets by all MTs, periodically. But this procedure puts considerable load on signalling channels. Thus, it would be more efficient if upon arrival each new m2m user contacts a Node B that provides information about all other m2m users already in the system in the range of tens of meters, to determine the potential members of the group (triangulation or GPS). Only MT, assigned to a multiple member group sends a “Hello” packet to get an appropriate information about the pathloss to any other MTs from its group. In such a way we can reduce the signalling information between stand-alone MTs and prolong the limited battery life of MTs. – The size of the groups is restricted to a maximum number of members. – Users in the group dynamically join and leave the group at any time (battery life, handover). Each MT can be a member of only one group at the same time. Groups are periodically updated (every 100 radio frames) and reshaped in order to check positions of MTs and their radio propagation characteristics on the one hand, and to track and authorize new m2m users or handover-users in the group in case they fulfill the above mentioned “joingroup” criteria, on the other hand.

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– Information about the link quality can be obtained e.g. from “Hello” packets, periodically transmitted by MTs. – Grouping is not restricted to one cell. 3.2

Data Exchange Policy

Using the m2m service, the MT concurs in the employment of its own free uplink resources (service level agreement). We assume that each user knows about the packets it has downloaded and the packet IDs that are available at its neighbors. The data exchange algorithm finds an appropriate “sender” candidate, based on a local “most-utile-packet” scheme, in order to maximize the number of users for which the packet can be useful. This procedure is performed framewise. After the best candidate is found, the admission control procedure verifies whether the system has enough uplink capacity to accept the connection. If there is more than one sender candidate or if a sender candidate has more than one packet to send, the packet with the lowest ID will be distributed. In order to avoid packet collisions caused by wireless interference from other groups, to reduce identical packets on the network links and in turn to utilize uplink bandwidth more efficiently, our concept uses the so-called multicast mode among users within a group. Such a parallel packet downloading policy improves the performance of the system, in terms of number of simultaneously served m2m users. Users, which have not found any useful packets within a specified time interval, try to connect to the Node B for packet delivery.1 We now summarize the main characteristics/assumptions of our data exchange policy: – – – –

Initially, no packet is available among m2m users. The data exchange policy is based on a local “most-utile-packet” scheme. Packets can be distributed in an arbitrary order. The m2m data transfer is performed in multicast mode on the uplink carrier frequency; identification of the sender is done using a unique scrambling code. – The MTs must be able to receive in both uplink and downlink. – No physical data channel on Node B is needed to control the intra-group data transfer. – The Node B/RNC responsibilities are 1) to distribute at least one complete copy of the original file in every radio cell (time interval between Node B “packets upload sessions” is 10 radio frames), 2) to support the data exchange process in the m2m groups with signalling information such as “listen on the uplink frequency”, etc. 3) to serve timeout requests from MTs. On the one hand the last point of the download policy gives the MTs better chances of finding missing packets and finishing the download faster, on the other hand, however, it puts additional load on the downlink resources. To more or less overcome this problem and to enable a more efficient data exchange by minimizing the probability of serving m2m requests via Node B, it is necessary to estimate the optimal group size. 1

The MT can receive the single packets of a data file in a random order.

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Performance Evaluation

In this section we evaluate the performance of the proposed m2m concept through simulations. We first list the assumptions and parameter settings we employed in our simulations: – The MTs are distributed randomly over the cell area. The radius of the cell is 50 m (hotspot scenario). – The MTs generate a Poisson arrival process with rate λ of requests for some popular content. – The maximum group size varies between 3-10. – Users within the group dynamically join and leave the group at any time (handover, battery discharging). – The MTs depart from the system immediately after finishing their download. – The size of a logical packet is equal to one UMTS radio frame.2 – The quality of the wireless channel in each group remains constant within each radio frame, but can vary from frame to frame (block-fading channel). – If the packet is incorrect after detection, we declare a packet loss. – The simulation time is 400 - 1200 sec and we collect data framewise. 4.1

Comparison of M2M with Conventional File Sharing

First, we focus on the download data volume characteristics when only one popular file is dispersed with the m2m technique. We compare performance results with those for the conventional UMTS mode, where a continuous transmission of data is organized by individual links from Node B to each user. We consider three performance measures: – Overall downlink throughput gain: data volume reduction in the downlink. – Service probability gain: number of served users. – Download time gain: download time reduction. We define the download time as the time window, in which the user receives the complete file. The criterion for the download time gain is the 90% quantile of finished downloads in the system. The most important simulation parameters are summarized in Table 1. Figure 3 shows the system performance versus the offered traffic load. The upper graphs demonstrate the efficiency of the m2m file sharing mechanism for medium and heavy traffic load in terms of the download time reduction. The bottom graphs depict the m2m performance gain in terms of released downlink resources. As one would expect, the results show that the higher the traffic load, the more efficient is the performance of m2m file sharing. Obviously, with increased traffic load m2m users have better opportunities to find the content of interest, which 2

Depending on the coding scheme and spreading factor this leads to corresponding packet sizes.

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Traffic and environmental settings Traffic load (max. number of m2m users/cell) Maximum m2m group size Antenna type Cell radius Moving process for MT User profile Radio interface and algorithmic settings File size Required data rate Maximum user data rate with 1/2-rate coding Size of logical packets for m2m data Receiver sensitivity Transmission power in m2m mode Eb /N0 target Inner loop power control for m2m sender Simulation step Group update period

10 (low), 30 (medium), 50 (high) 3, 7, 10 omni-directional 50 m Gaussian Random Walk Pedestrian 500 KByte 60 kb/s 30 kb/s 225 bit (coded) -112 dBm -44 dBm 3 dB OFF 1 radio frame (0.01 sec) 100 radio frames (1 sec)

positively influences the file downloading time, on the one hand and increases downlink throughput capacity on the other hand, making the released Node B capacity available to other services. Table 2 shows some numerical values for the overall downlink throughput gain and relative download time reduction for complete file download in m2m network mode compared with conventional UMTS data transmission (+: m2m slower, -: m2m faster). The rows “Data volume in DL in conv. mode [MB/cell]” and “Data volume in DL in m2m [MB/cell]” show how many Mbyte of data had to be sent via the downlink channels in order to distribute the data file of 500 Kbyte size to the users within one cell. We examine now some parameters and their influence on the system performance. 4.2

Impact of Group Size

We consider again the same m2m scenario with maximum group size 3, 7, 10 and investigate the effect of the group size on the performance of the proposed Table 2. Overall downlink throughput gain Load low medium high Data volume in DL in conventional mode [MB/cell] 5.73 16.51 34.44 Data volume in DL in m2m mode [MB/cell] 1.33 2.93 4.67 Released DL capacity [%] 76.70 82.23 86.44 Time m2m [sec] +189.3 -20.1 -142.3

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technique for low, medium and high traffic load. In figure 4 the influence of the group size on the file download time for a high loaded UMTS system is illustrated. Intuitively, the larger the group size, the higher the multicast efficiency. The number of needed senders is lower, hence, for the same number of members in

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Table 3. Impact of uplink interference for different group sizes and traffic scenarios (erroneous data [%]) Different System Scenarios low load medium load high load

Erroneous data, % group size 3 group size 7 group size 10 3.71 1.86 2.07 13.70 5.56 6.25 18.96 8.88 8.99

the system large groups consume less bandwidth. Equivalently, with the same uplink resource consumption more members will get a service when the group size is bigger. With increased number of multiple coexisting groups (group size 3) in the network some performance degradation of the m2m technique was observed. This effect is influenced by admission control, wireless interference and user mobility. Obviously, with increasing the number of groups the number of sender candidates that can be admitted is bounded by the uplink capacity. It results in a rejection of some link admission requests of sender candidates. Besides, if the group size is too small and the mobility of users is low (as assumed in this work), the probability to find in each frame a missing packet is quite low; the number of packet requests from m2m users to Node B increases and puts additional load on the downlink resources. Another important performance criterion is the average rate of the successfully delivered packets. For systems with small group size we observed enormous m2m uplink interference, which leads to significant performance degradation and in turn to an increase of download time. Figures 5 and 6 demonstrate the impact of the uplink interference on m2m data transmission for group sizes 3 and 7 in a high loaded UMTS system (effective user data rate is 30 kb/s). Relative losses of link quality (number of corrupted data in %) for different group sizes and traffic scenarios are shown in Table 3. Impact of uplink interference on m2m data transmission, group size 7 (high load)

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Table 4. The average number of multicast receivers per group for different m2m traffic scenarios Load Users per one UL-channel Service probability gain, % m2m (low) 1.91 46.92 m2m (medium) 2.43 58.40 m2m (high) 3.11 67.30

4.3

Impact of Multicast Technique

As the required data rate for each m2m request is always the same it is correct to evaluate the effectiveness of the proposed concept with respect to the relative service probability gain by using our knowledge about the number of successfully delivered packets in each multicast group every frame. Figure 7 and Table 4 demonstrate the benefit of using multicast in terms of the relative gain in the number of MTs, that can be supported by m2m concept. Comparing our performance results with those of the conventional UMTS mode, one can observe a dramatic increase in service probability by using the proposed m2m technique.

5

Conclusions and Perspectives

In this paper, a concept based on direct m2m data exchange on uplink channels is presented and its performance for UMTS is analyzed. Our concept targets the distribution of large files in a dynamic wireless environment, where multiple user groups coexist in the UMTS network by taking wireless interference among the neighboring groups into account. The main focus of our analysis lies in the optimization of data availability to users in hotspots (e.g. airports, railway stations), since in hotspot environment users increasingly demand ubiquitous data availability. This technique does not require any centralized knowledge of the transfers in the rest of the network to perform the intra-group data transfer, but requires cooperative MTs. The flexible nature of the cooperative community formation makes the proposed algorithm robust to sudden departure of any member in the group. Furthermore, we demonstrated through extensive simulations the performance advantages of using m2m mode in UMTS network for distribution of popular non-real time contents compared to the conventional UMTS mode. The following conclusions can be drawn from the numerical results: – The higher the arrival intensity and mobility of m2m users, the more bandwidth becomes available. As a consequence, the more efficient is the m2m file sharing. – The performance benefits provided by the m2m technique in terms of released downlink capacity are up to 85% compared to the conventional UMTS mode. – Furthermore, we observe up to 21% of download time reduction for complete file download in a UMTS network, supported by m2m data transmission mode.

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– Besides, the number of MTs that can be supported by such a system is more than three times higher than in conventional UMTS. In this work only a few examples of numerical results are shown. In our studies we have conducted many more simulations, than can be presented here with different parameter settings to investigate the feasibility of our concept. Our purpose was to demonstrate in this paper the advantage of using m2m application in some specific wireless scenarios, which, we believe, are of practical importance. According to the achieved results, the proposed algorithm might be a promising alternative for distribution of popular content in cellular radio networks like UMTS and motivate further analysis of using m2m technique for a wide range of scenarios.

References 1. UE Radio Transmission and Reception. 3GPP TS 25.101. 2. B. Cohen. Incentives Build Robustness in BitTorrent. In Workshop on Economics of Peer-to-Peer Systems, Berkeley, CA, USA, May 2003. 3. E. Damosso. Digital Mobile Radio towards future generation systems, COST Report 231. In Proc. of IEEE PIMRC, Taipei, Taiwan, October 1996. 4. T. Hossfeld, K. Tutschku, and F. Andersen. Mapping of File-Sharing onto Mobile Environments: Feasibility and Performance of eDonkey with GPRS. In Proc. of IEEE WCNC, New Orleans, USA, March 2005. 5. T. Hossfeld, K. Tutschku, and F. Andersen. Mapping of Filesharing onto Mobile Environments: Enhancement by UMTS. In Proc. of IEEE Pervasive Computing and Communications (PerCom), Kauai Island, Hawaii, March 2005. 6. T. Hossfeld, K. Tutschku, F. Andersen, H. de Meer, and J. Oberender. Simulative Performance Evaluation of a Mobile Peer-to-Peer File-Sharing System. In Proc. of IEEE Next Generation Internet Networks (NGI), Rome, Italy, April 2005. 7. D. Tacconi, C. Saraydar, and S. Tekinay. Ad Hoc Enhanced Routing in UMTS for Increased Packet Delivery Rates. In Proc. of IEEE WCNC, New Orleans, USA, March 2005. 8. Z. Wang, E. Tameh, and A. Nix. Statistical Peer-to-Peer Channel Models for Outdoor Urban Environments at 2GHz and 5GHz. In Proc. of IEEE VTC Fall, Los Angeles, USA, September 2004.

Impact of Technology Overlap in Next-Generation Wireless Heterogeneous Systems Ahmed Zahran1 , Ben Liang1 , and Aladdin Saleh2 1

Department of Electrical and Computer Engineering, University of Toronto 2 Wireless Technology, Bell Canada

Abstract. The integration of different wireless access technologies is propelled by the need to support new services and better resource utilization in next-generation wireless networks. This integration complicates the system design due to the interaction of different factors including network-oriented, application-oriented, and user-oriented system parameters. In this work, we present an analytical framework to estimate different session-level performance metrics in two-tier systems, using the 3G-WLAN integrated network as an example. We investigate the impact of the amount of coverage overlap and the topology of the underlay technology on different session performance metrics as well as the total session cost. The obtained results show that clustering can significantly reduce the vertical-handoff signaling load and the forced termination probability of different applications in comparison with a random topology. Additionally, the proposed cost analysis provides design guidelines for developing economical WLAN management mechanisms to maintain reduced session cost with extended WLAN coverage.

1

Introduction

Next-generation wireless network will converge the service of different pervasive access technologies while providing the user with a large set of novel revenue generating multimedia-based applications. Hence, heterogeneity will naturally become the main system feature due to the huge diversity in the characteristics of access technologies and applications. The 3G-WLAN integrated system is an example of this wireless heterogeneous networking paradigm that has received great support from industrial and standardization bodies [1, 2, 3]. Currently, both technologies are among the most pervasive wireless access approaches that complement each other. On the one hand, 3G networks provide an expensive universal coverage; on the other hand, WLANs provide ample networking resources for the users at a cheaper cost wherever available. Hence, users will generally enjoy the best of each access technology, and service providers will enjoy better utilization of their resources. 

This research was made possible thanks to Bell Canada’s support through its Bell University Laboratories R&D program.

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The integrated system is very rich in its parameter set, including technologyoriented parameters, application-oriented parameters, and user-oriented parameters. All these parameters are interwoven together in an inter-technology roaming enabled environment creating a real challenge for system analysis and performance evaluation. For example, in the 3G-WLAN integrated model, the bandwidth provided to the mobile terminal (MT) in both networks may vary by one order of magnitude after any change of network attachment point. Combining this fact with the bandwidth greediness of some applications due to their buffering or pre-fetching capabilities, one can directly conclude that inter-technology roaming, commonly known as vertical handoff (VHO) [4], will significantly influence next-generation session dynamics. This influence affects different system design aspects such as resource utilization, signaling, and quality-of-service (QoS). Consequently, investigating the impact of VHO on system performance under different scenarios is crucial for better design and performance evaluation of next-generation systems. The performance evaluation of heterogeneous wireless network is a challenging task due to the system complexity and the large set of parameters that should be considered. To the best of our knowledge, very few papers [5, 6] deal with mathematical modeling and performance analysis of heterogeneous wireless networks. In [5], the authors study the admission region of voice and data traffic within the integrated system. They derive expressions for the dropping and blocking probabilities of data and voice calls in 3G cell and WLAN. Their main result is that voice traffic in the double coverage area should be restricted from occupying all the WLAN bandwidth. In [6], we propose a novel mobility model for two-tier integrated system using Coxian phase-type structures to represent the cell residence time. Additionally, we develop a generic framework based on matrix-geometric theory and Markov reward models to estimate different celllevel metrics such as network utilization times and VHO rate. In this paper, we present an analytical framework to estimate different sessionlevel performance metrics. The obtained results are used to study the impact of WLAN coverage and topology variation on the session performance and total session cost. The analytical framework is validated through simulation. Additionally, the cost analysis shows that WLAN clustering in extended WLAN deployment can greatly reduce the session cost provided that the increased network management cost of extended WLAN deployment is eliminated. In Section 2, we present our 3G-WLAN integrated network model. Section 3 presents an analytical framework for estimating different session parameters and the session cost. We present the numerical results in Section 4 and conclude in Section 5.

2

Network Model

In this work, we assume that a cellular network provides universal coverage with WLANs overlapping at a portion Pow of the total area. Consequently, the percentage of the unique cellular coverage area is Poc = 1 − Pow . Without loss of generality, we assume that active MTs prefer WLAN due to its larger bandwidth

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and lower cost. Hence, any active MT will always handoff to WLAN when it is encountered. Additionally, we adopt the extended-Coxian model [6] for user mobility in this integrated network. This model is based on phase-type (PH) distributions [7]. Generally, a PH random variable is defined as the absorption time of an evanescent finite-state Markov process to a single absorbent state. Additionally, a PH random variable is conventionally denoted as P H(α, T), where α and T represent the initial transient state distribution and the transition rates among transient states, respectively. The proposed model employs the Coxian structure to represent cell residence time as a probabilistic sum of WLAN and cellular technology residence times (TRT), defined as the duration spent by the MT in a specific technology.

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Figure 1 shows the extended-Coxian mobility model where each stage is labeled with a letter corresponding to the visited technology and a number representing the stage sequence within the intended cell. The model consists of consecutive stages such that upon exiting any stage i, the MT may proceed to another technology within the same cell with probability ai or move to a neighbor cellular cell (i.e. get absorbed) with probability bi . The TRT of stage i is PH distributed and denoted as P H(αi , Ti ). Hence, the cellular residence time can be expressed as P H(αm , Tm ) ⎞ ⎛ T1 a1 t1 α2 0... ..0 ⎜ 0 T2 a2 t2 α2 ..0 ⎟ ⎟ (1) Tm = ⎜ ⎝ . . . ⎠ 0.. 0 Tk αm = [α1 0]

(2)

where ti = −Ti e [7]. Note that, in a two tier system, there are two mobility sub-models; each sub-model has a different initial phase technology. Hence, we use C-type and W-type cell to differentiate models with initial cellular and initial WLAN phases respectively. Furthermore, we assume that the MT application session has exponentially distributed holding times denoted as tch and twh with parameters λch and λwh for cellular network and WLAN respectively. The exponential session

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holding time assumption is common in wireless networks due to their time-based pricing strategies and the limited power resources of mobile devices.

3

Cost Analysis

In this section, we estimate a total session cost as a function of session resource utilization, session QoS, and network administrative cost. First we present our session model in Section 3.1. Then, we present a generic framework to estimate the total session network utilization and total session VHO load in Section 3.2. Then we present our proposed total session cost function in Section 3.3. 3.1

Session Modeling

In our session model, we combine the mobility model and application characteristics to represent both application activity and user mobility. Consequently, the main conceptual difference between the session and mobility models is the interpretation of absorption. On contrary to the mobility model in which absorption only occurs due to cell exit, the session may get absorbed for different reasons including normal session termination, denoted as the TERM state, successful handoff to a neighbor cell, denoted as the SHH state, session dropping during horizontal handoff, denoted as the HHFT state, or session dropping during vertical handoff, denoted as the VHFT state. Hence, the generator matrix of the Markovian session process will have the following structure:  QT T QT erm QSHH QHHF T QV HF T , (3) QS = 0 0 0 0 0 where QT T , QT erm , QSHH , QHHF T , QV HF T can be expressed as shown in [6]. Note that different cases are distinguished in the analysis according to different combinations of mobility sub-models and session types. We use A to denote mobility sub-model that is determined by the initial technology of the visited cell, where A ∈ {c, w} for C-type and W-type cells respectively. Additionally, we use B to denote the session type, where B ∈ {n, h} for new and handoff sessions respectively. As a notational remark, we use superscripts to define the sessiontype and mobility sub-model combination. Following this notation, we denote AB AB AB PTAB ERM , PSHH , PV HF T , and PHHF T as the absorption probabilities to Term, SHH, VHFT, and HHFT states respectively for a B-type session starting in an A-type cell. These absorption probabilities can be estimated from the embedded Markov chain of the session model as shown in [8]. 3.2

Session Performance Analysis

In this subsection, we present a generic analytical framework to estimate different session-level performance metrics from their corresponding cell-level metrics of different session types and mobility sub-models. The derivations of the cell-level performance metrics are omitted here for brevity. However, interested readers

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are referred to [6] for the details. The following analytical framework is used to estimate session cellular network utilization, session WLAN utilization and session VHO rate, denoted as Lsc , Lsw and μsv respectively. Due to page limitation, in the following derivation, only μsv is shown for illustration purpose. For exh ample, Lsc can be estimated by replacing μAB cv and μcv with their corresponding AB h metrics Lcc and Lcc respectively. The session VHO rate is defined as the expected total number of VHOs performed by the MT during an entire session. Given that the session starts in the W-type cell, a conditional session VHO rate, μw sv , can be expressed as wn μw sv = μcv +

∞

P (n = k|W )kμhcv ,

(4)

k=1

where μwn cv is the VHO rate per cell of a new session starting in a W-type cell, P (n = k|W ) is the probability that a session starting in a WLAN will exactly visit k handoff cells before termination, and μhcv is the VHO rate per cell of a ch wh handoff session and is expressed as μhcv = Pwh μwh cv + Pch μcv , in which μcv and ch μcv represent the VHO rate per cell of a handoff session starting in a W-type and C-type cells respectively, and Pwh and Pch represent the probability that the session will handoff to a W-type and C-type cells respectively. Note that Pwh and Pch are topology dependent and equal the percentage of the cell edge with dual and unique coverage respectively. Clearly, μw sv can be expressed as wn w h μw sv = μcv + μhs μcv ,

(5)

where μw hs is the expected number of visited handoff cells per session given that the session starts in a W-type cell. In order to estimate μw hs , we define the following probabilities – Phh : the probability that a handoff session will successfully perform a horiwh ch zontal handoff to a neighbor cell. Hence Phh = Pwh PSHH + Pch PSHH . – P hh : the probability that a handoff session will be terminated in the same cell either due to normal termination or due to forced termination during VHO. – Ps : the probability that a handoff session will visit exactly one more cell. wh ch Hence, Ps = Pwh PSHH P hh + Pch PSHH P hh . Consequently, the marginal distribution function of the number of successful w , can be expressed as HHO given that the session starts in a W-type cell, NHH ⎧ wn wn ⎨ PT erm + PVwn HF T + PHHF T , k = 0 wn w PSHH P hh , k = 1 P (Nhs = k) = . (6) ⎩ k−2 wn Phh Ps , ∀k ≥ 2 PSHH Hence, μw hs is calculated as μw hs =

∞

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w kP (Nhs = k|W ) .

(7)

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 c i w Using the mathematical identity ∞ i=0 ic = (1−c)2 , |c| < 1, μhs can be expressed as   2 − Phh w wn μhs = PSHH P hh + Ps . (8) (1 − Phh )2 Similarly, the expected number of successful HHO for a session starting in the cellular network, μchs , can be expressed as   2 − Phh c cn μhs = PSHH P hh + Ps . (9) (1 − Phh )2 Similar to (5), the expected total session VHO rate assuming the session starts in a C-type cell, μcsv , can be expressed as c h μcsv = μcn cv + μhs μcv .

(10)

Hence, using the total probability theorem, the total session VHO rate, μsv , is c μsv = Pwh μw sv + Pch μsv .

3.3

(11)

Total Session Cost

The total session cost, χ, is calculated as a function of session performance metrics and system administrative cost. This cost function is expressed as χ = Cc Lsc + Cw Lsw + Cvh μvh + Chh μhh + Cq Pf t + Cm ∗ η Pwo ,

(12)

where μhh represents the session horizontal handoff rate, Pf t represents the session forced termination probability, Cc and Cw represent cellular network and WLAN utilization cost coefficients respectively, Cvh and Chh represent session vertical and horizontal handoff signaling cost coefficients respectively, Cq represents the session forced termination probability cost coefficient, Cm represents the management cost, and η is a WLAN area cost base. The derivation of μhh and Pf t is presented in [6]. Clearly, this cost function considers different aspects including resource utilization cost, signaling load cost, QoS factor, and administrative cost. The latter cost is considered due to its substantial increase in extended WLAN deployment [9]. The main source of the increase in management cost under extended WLAN deployment is administrative tasks such as RF monitoring and configuration, load balancing support, security issues, and station mobility support.

4

Numerical Results

In addition to the analysis, we have simulated an integrated heterogeneous system with square cells for simplicity of illustration. Each cell is sub-divided into N subdivisions, where WLANs are located. We consider two different WLAN topologies: random and clustered. In the random topology, WLANs are randomly

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distributed in the cell and the cell encounter a new random topology as it moves to each new cell. In the clustered topology, all WLANs are aggregated in one hotspot that is randomly located within the cell. In order to emulate practical MT operation, a handoff area [10] of dH seconds is assumed between overlay 3G cells. This delay corresponds to the hysteresis introduced in handoff algorithms to decrease the ping-ponging impact during horizontal handoff. Additionally, the MT 3G-WLAN handoff is delayed with ds seconds as a typical delay required for WLAN discovery and handoff signaling. In the simulation, we adopt a twodimensional Gauss-Markov movement model from [11] for MT mobility. In its discrete version, at time n, the MT velocity in each dimension, vn , is given by  vn = αv vn−1 + (1 − αv )μv + 1 − α2v xn−1 , (13) where αv represents a past velocity memory factor such that 0 ≤ αv ≤ 1, μv is the asymptotic mean of vn , and xn is an independent and stationary Gaussian process with zero mean and standard deviation σv , where σv is the asymptotic standard deviation of vn . The MT mobility parameters for the shown results are α = 0.9, μv = 0, and σv = 2.5. Table 1 lists the default values of analysis and simulation parameters. Table 1. Analysis and simulation parameters Parameter Value Parameter Value Parameter Value dH (sec) 5 ds (sec) 3 N 100 Pvb 0.01 Phb 0.01 Cq 10000 Cc 20 Cw 2 η 10 Cvh 75 Chh 75

In this study, we categorize the applications as symmetric and asymmetric. The former preserves the same level of resource utilization independent of the available network resources, while the latter has a greedy nature and can consume as much bandwidth as the network can provide. Conversational applications, such as voice over IP and video conferencing, are examples of the former, while streaming applications with buffering capabilities, such as video on demand and radio on demand, are examples of the latter. Generally, asymmetric applications benefit from resource rich networks such as WLANs by increasing the download rate for smoother playback. In the simulation and analysis, we consider different combinations of session duration (long and short) and application type (symmetric and asymmetric) whose parameters are shown in Table 2. The following figures plot the analysis and simulation results (including 95% confidence intervals) for different session metrics. The obtained results show a good match between the analysis and simulation results. This match demonstrates the accuracy of the derived analytical framework in estimating different metrics.

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4.1

Session Performance

Figure 2(a) plots the session VHO rate versus WLAN coverage for different applications in random and clustered topologies. Clearly, the figure shows that WLAN aggregation significantly reduces VHO signaling load. This reduction reaches more than 70% at 50% coverage for all the simulated applications. Generally, the VHO rate is mainly driven by the circumference of WLAN hotspots. Hence, the VHO rate of symmetric applications always increases as WLAN coverage increases in the clustered topology, but it starts dropping as WLAN coverage increases beyond 50% in the random topology. Additionally, the VHO rate of asymmetric applications decreases as WLAN coverage increases due to the beneficial higher bandwidth of WLAN, which decreases the session duration of such applications. Figure 2(b) shows the session forced termination probability of different applications versus WLAN coverage in random and clustered topologies. Similar to VHO, clustering WLANs significantly reduces the forced termination probability of different applications compared to the random topology. For example, a 45% reduction is achieved at 35% coverage overlap for different applications. Figure 2(c) shows HHO rate variations for different applications versus WLAN coverage for both random and clustered topologies. Generally, the HHO rate of symmetric applications is independent of WLAN coverage, while the HHO rates of asymmetric applications significantly decrease as WLAN coverage increases. Additionally, this figure shows that the applications perform more HHOs in the clustered topology compared to the random one. However, this slight increase is a logical consequence for extended application lifetime due to the reduced session forced termination probability in the former topology. Figures 2(d) and 2(e) plot the total-session cellular network and WLAN utilization, respectively, versus WLAN coverage for different applications in random and clustered topologies. Generally, a specific network utilization for a symmetric application is linearly proportional to its corresponding coverage independent of the WLAN topology. On contrary, as WLAN coverage is extended, the cellular utilization of asymmetric applications exponentially decreases with an insignificant increase in WLAN utilization. Additionally, the figure shows that the clustered cellular network and WLAN utilization are slightly larger when compared to the random topology. However, this is also due to the longer session lifetime in the clustered topology.

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Figures 3(a) and 3(b) plot the total session cost versus the percentage of WLANcellular technology overlap for Cm = 0 and Cm = 5000 respectively. Figure 3(a)

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shows that increasing WLAN coverage reduces the total session cost in this scenario. This reduction may reach 50% and 22.5% for asymmetric and symmetric applications respectively as the WLAN coverage increases from 10% to 35%. However, the administrative costs of extended coverage approximately eliminates the integration cost benefits for the considered application set, except for asymmetric applications with large session duration, as shown in Figure 3(b). Even for this application, there exists an optimal WLAN coverage, e.g. approximately 50% overlap for the asymmetric application under consideration, beyond which the overall session cost is dominated by its administrative component. Hence, maintaining the cost reduction in an extended WLAN scenario mandates developing economical WLAN management schemes, such as remote management. Figure 3(c) plots the total session cost versus the ratio between forced termination and cellular utilization costs for different applications at 36% coverage

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overlap. The figure suggests that WLAN clustering greatly reduces the total session cost as the application sensitivity to forced termination increases. Hence, clustering WLANs is highly recommended for systems dominated with this type of applications.

5

Conclusion

The inherent heterogeneity of next generation systems complicates system analysis and performance evaluation especially with the unavoidable interaction of different system parameters. A generic framework is presented to estimate different session-level performance metrics from their corresponding cell-level metrics. These metrics are used to study the impact of the amount of coverage overlap and the topology of the underlay technology on the total session cost. The obtained results show that WLAN clustering can reduce the VHO signaling load and the forced termination probability of different applications by up to 70% and 45%, respectively. Additionally, the cost analysis shows that increased administrative cost due to extended WLAN coverage can significantly reduce the integrated system cost benefits. Hence, developing economical WLAN management mechanisms is crucial to achieving real cost benefits from extended WLAN coverage.

References 1. Buddhikot, M.M., Chandranmenon, G., Han, S., Lee, Y.W., Salgarelli, S.M.L. (2003): Integration of 802.11 and third generation wireless data networks. In Proc. of IEEE INFOCOM, San Francisco, US, 503-512. 2. 3GPP2 (2003): Feasibility study on 3GPP systems to. wireless local area network WLAN interworking. Technical report, 3GPP TR 22.934. 3. ETSI (2001): Requirements and architectures for interworking between HIPERLAN/3 and 3rd generation cellular systems. Technical report, ETSI TR 101 957. 4. Stemm, M., Katz, R.H. (1998): Vertical handoffs in wireless overlay networks. ACM Mobile Networks and Applications 3(4) 335-350 5. Song, W., Jiang, H., Zhuang, W. (to appear): Performance analysis of WLAN-first scheme in Cellular/WLAN interworking. IEEE Trans. on Wireless Commun. 6. Zahran, A.H., Liang, B., Saleh, A. (2006): Modeling and performance analysis for beyond 3G integrated wireless networks. In Proc. of IEEE International Conference on Communications (ICC). 7. Latouche, G., Ramaswami, V. (1999): Intorduction to Matrix analytic Methods in Stochastic Modeling. ASA-SIAM series on Statistics and Applied Probability. 8. Papoulis, A., Pillai, S. (2002): Probability, Random Variables and Stochastic Processes. 4th edn. McGraw-Hill. 9. Hara, B.O., Calhoun, P., Kempf, J. (2005): Configuration and provisioning for wireless access points (CAPWAP) problem statement. RFC 3990. 10. Wang, J., Zeng, Q.A., Agrawal, D.P. (2003): Performance analysis of a preemptive and priority reservation handoff schemefor integrated service-based wireless mobile networks. IEEE Trans. Mob. Comput. 2(1) 65-75. 11. Liang, B., Haas, Z.J. (2003): Predictive distance-based mobility management for multi-dimensional pcsnetworks. IEEE/ACM Trans. on Net. 11(5) 718-732.

An On-Line Measurement-Based Admission Control for VBR Video Traffic in Wireless Multimedia Home Networks Yi-Hsien Tseng1 , Eric Hsiao-Kuang Wu2 , and Gen-Huey Chen1, 1

2

Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan [email protected] Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan

Abstract. Variable bit rate (VBR) video traffic with high data rate is expected to be the largest proportion of traffic carried by future wireless home networks. In order to guarantee quality-of-service (QoS) requirements of such type of VBR video traffic and improve channel utilization, this paper proposes an on-line measurement-based admission control scheme. The proposed scheme is designed based on the property of the aggregate VBR video traffic is lognormal distributed and consists of two main components which are measurement process and admission decision respectively. The measurement process applies a linear Kalman filter to estimate statistical parameters of aggregate VBR video traffic. Then, the estimated statistical parameters are used to calculate the effective bandwidth for admission decision. The proposed scheme is computational efficiently and accurate without much prior information of traffic. Simulation results confirm its accuracy and also show that the proposed scheme performs well for small number of connections as well as large number of connections.

1

Introduction

Because of ease of installation or relocation, wireless home networks are particularly attractive to ensure required “wire-like” performance for many indoor multimedia applications. Consequently, Variable bit rate (VBR) video traffic is expected to be the largest proportion of traffic carried by future wireless home networks. In order to guarantee quality-of-service (QoS) requirements of VBR video traffic, an admission control scheme is needed by the network to decide whether a new connection is accepted or rejected. VBR encoding can offer higher picture quality and greater opportunity for statistical multiplexing gains [1] (with the same average bandwidth) than constant bit rate (CBR) encoding. However, an admission control scheme may make decision under uncertain conditions because 

Corresponding author.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 546–557, 2007. c IFIP International Federation for Information Processing 2007 

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the required bandwidth of VBR video traffic varies with time. A good admission control scheme for VBR video traffic should improve channel utilization through statistical multiplexing and guarantee QoS at the same time. Many admission control schemes have been proposed to date (e.g, [2]-[7]). In [8], the overview and the three-way taxonomy were provided. They commented that the admission control schemes proposed by [2] and [3] are simple and can achieve high bandwidth efficiency. These two schemes were both based on effective bandwidth and using measurement. In this paper, we propose an on-line Measurement-based admission control (MBAC) scheme for VBR video traffic. The type of VBR video which we deal with is the full-motion video generated by a Motion Picture Expert Group (MPEG) encoder [9]. Several probability distribution functions, such as Gamma, Weibull, lognormal etc., can be used to fit the distribution of MPEG-coded video trace (the sequence of bits per frame in the compressed video traffic). The proposed scheme consists of two main components which are measurement process and admission decision respectively. A linear Kalman filter [10] is applying in the measurement process to estimate statistical parameters of aggregate VBR video trace. The estimated statistical parameters are used to calculate the effective bandwidth for admission decision. The effective bandwidth is devised from the assumption that the aggregate VBR video trace is lognormal distributed. The proposed scheme can achieve good performances which include channel utilization and QoS guarantee for VBR video traffic. Meanwhile, it is computation effectively and can be adopted to be an on-line MBAC scheme for real-time VBR video traffic. Moreover, the proposed scheme needs not to know much statistical characteristic (only peak rate) of individual VBR video traffic in advance. The rest of this paper is organized as follows. In Section 2, an on-line MBAC scheme for VBR video traffic is proposed. Simulation and comparison results are shown in Section 3. Finally, this paper concludes with some remarks in Section 4.

2

A MBAC with Aggregate Effective Bandwidth Estimation

In this section, we propose a MBAC scheme with aggregate effective bandwidth estimation (MBAC-AEBE) for VBR videos. The MBAC-AEBE is based on the assumption that VBR video trace is lognormal distributed and consists of two main components which are measurement process and admission decision respectively. 2.1

Network Model and Aggregate VBR Video Traffic Model

The network model that we consider is the IEEE 802.15.3 [11] wireless home network. The IEEE 802.15.3 MAC protocol uses time division multiple access (TDMA) to allocate channel time among devices, in order to prevent conflicts, and it allocates new channel time for a connection only when enough bandwidth is available.

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The elementary topology unit for the IEEE 802.15.3 MAC layer is a piconet. A piconet contains a number of independent data devices (DEVs) that are allowed to exchange frames directly with each other. The master/slave relationship was adopted for these DEVs; a particular DEV, named piconet coordinator (PNC), acts as the master and the others are slaves. Timing for a piconet is realized by superframes and is shown in Fig. 1.

Fig. 1. Timing for a piconet in IEEE 802.15.3 wireless home networks

The MBAC-AEBE is performed in the PNC to determine if a new VBR video traffic is accepted or not. The VBR video traffic that we consider is the fullmotion video traffic generated by a MPEG encoder. MPEG-coded video traces used by this paper can be found in [12]. The MPEG introduced three frame types: intra-frame (I), inter-frame (P) and bidirectional-frame (B). These frame types are organized into so called groups of pictures (GoPs). The size of each GoP is measured by the MBAC-AEBE and is used for admission control. It was shown in [13] that the distribution of VBR video trace can be fitted well by a lognormal distribution. A random variable X with a lognormal distribution has PDF: 2 2 1 √ e−(lnx−μ) /2σ , (1) f (x; μ, σ) = xσ 2π where μ and σ 2 are the mean and variance of the random variable Y = ln(X) which is Gaussian (Normal) distributed. If aggregate GoP sizes are lognormal distributed, they are easy to be transformed into be Gaussian distributed. Then the mean μ and variance σ 2 of the Gaussian distribution can be estimated by their good unbiased estimators X and S 2 as follows respectively.

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X1 + ... + Xn . (2) n n (Xi − X) 2 S = i=1 . (3) n−1 However, it may not be easy to find good unbiased estimators for other distributions. X=

2.2

Measurement Process Using Kalman Filter

From above discussion, we denote a aggregate GoP trace as a random process x(n) with lognormal distribution. Then, the y(n) = ln(x(n)) is a random process with Gaussian distribution. The measurement process of the MBAC-AEBE adaptively estimates the mean and variance of y(n) in each GoP period. This is done by an optimization scheme based on a linear Kalman filter. The Kalman filter can provide an optimal least-square estimate of the system state for a linear system. The Kalman filter is a set of mathematical equations that implement a predictor-corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance. The Kalman filter addresses the problem of estimating the state x ∈ Rn of a discrete-time controlled process that is governed by the process equation xk = Axk−1 + wk , with a measurement z ∈ R

m

(4)

which measurement equation is zk = Bxk + vk ,

(5)

where the n × n matrix A relates the state at the previous time step k − 1 to the state at the current step k. The m × n matrix B relates the state xk to the measurement zk . The random variables wk and vk are the process and measurement noise respectively and are assumed to be independent, white, and with normal probability distributions p(w) ∼ N (0, Q),

(6)

p(v) ∼ N (0, R),

(7)

where Q is the process noise covariance and R is the measurement noise covariance. The kalman filter estimates the process state at some time and then obtains corrections from measurements. The Kalman filter consists of five equations to solve above problem. The equations for the Kalman filter are categorized to two groups which are time update equations and measurement update equations respectively. The time update equations can also be thought of as predictor equations and are listed as follows.

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xˆ− xk−1 , k = Aˆ

(8)

Pk− = APk−1 AT + Q,

(9)

x ˆ− k

where is a priori state estimate at step k given knowledge of the process prior to step k. x ˆk−1 is a posteriori estimate which was updated in the measurement update equations. Pk− is a priori estimate error covariance. Pk−1 is a posteriori estimate error covariance which was updated in the measurement update equations. The measurement update equations can be thought of as corrector equations and are listed as follows. Kk = Pk− B T (BPk− B T + R)−1 ,

(10)

x ˆk = x ˆ− ˆ− k + Kk (zk − B x k ),

(11)

Pk = (I − Kk B)Pk− ,

(12)

where Kk is the Kalman gain. Moreover, zk is known from observing a system. A, B, Q and R are also known parameters while designing a system. The initial values of x ˆk−1 and Pk−1 are must determined before running Kalman filter. About the guideline for determining the initial values can be found in [14]. Since the measurement process of the MBAC-AEBE attempts to estimate the mean and variance of y(n), the state for our system is defined as xk = [Mk , Vk ], where Mk and Vk are the mean and variance of y(n) at the k-th step. If there is no any traffic entering or leaving the system, the state does not change from step to step so the A in the process equation (4) is set to identity matrix (I). The process equation of our system is: xk = xk−1 + wk−1 .

(13)

And our noisy measurement is of the state directly so the B in the measurement equation (5) is also set to I. So the measurement equation of our system is: (14) zk = xk + vk , ¯ k , S 2 ]. X ¯ k is the sample mean of y(n) at the k-th where zk is defined as zk = [X k step and can be obtained from calculating (2). Sk2 is the sample variance of y(n) at the k-th step. One way to obtain is to calculate (3). Since Mk will adaptively approximate to the mean of the system, we find that the better way to calculate Sk2 is: n (yi − Mk−1 ) . (15) Sk2 = i=1 n−1

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Then, the state xk of our system can be estimated by calculating the equations from (8) to (12) at each step. The process noise covariance Q is set to zero-matrix because we fully “trust” that the state does not change from step to step. The measurement noise covariance R is usually measured prior to operation of the filter. From our experiences, we set R = [0.1 0.1]T ·[0.1 0.1]. However, the value of R is not critical while Q is set to zero-matrix. The initial value of x ˆk−1 called xˆ0 is set to [0 0]T because we observe nothing from y(n) before starting. Furthermore, we choose the initial value of Pk−1 called P0 to be [1 1]T · [1 1]. In fact, we can choose almost P0 = O and the filter will eventually converge. The values of Mk and Vk are used to calculate aggregate effective bandwidth when a new traffic requests a connection. The admission decision based on the aggregate effective bandwidth will be discussed in next section. The measurement process continues to run without any change if the new request is rejected. On ¯ k and S 2 should be reset when a new request the contrary, the values of Pk−1 , X k is accepted or an existing traffic is finished. Since the mean and variance of y(n) will change after a new request is accepted or an existing traffic is finished. We must make xk to approximate the new state (i.e. new mean and new variance). Thus, Pk−1 is reset as the value of P0 . This makes the filter does not believe xk is true enough and make xk to approximate ¯ k and S 2 should be calculated without consider the new state. The values of X k historical aggregate GoP sizes before the k-th step. 2.3

Admission Decision Using Aggregate Effective Bandwidth

For the admission decision of the MBAC-AEBE, we consider a model that a PNC has accepted some connections and the capacity is C. We assume that the current aggregate bandwidth is E. The aggregate GoPs x(n) demands a loss probability no larger than  which is as follows. ¯ p > E} < , P r{X

(16)

¯ p = 1/p[x(1) + ... + x(p)]. The MBAC-AEBE tries to find the value of where X E while given the value of  and adopts E for admission decision when there is a new request with peak rate R. If E + R < C, the connection is accepted; otherwise it is rejected. ¯ p is also a Gaussian random variable. FurIf x(n) is a Gaussian process, X ¯ p approaches to be a Gaussian thermore, from the Central Limit Theorem, X random variable for p is large enough (p ≥ 30) even that x(n) is not a Gaussian process. However, it is difficult to find E accurately by solving (16) if x(n) is non-Gaussian especially for p is small. ¯ p ) is exactly a Since we have known that x(n) is lognormal distributed, ln(X Gaussian random variable. The assumption of a Gaussian distribution allows us to adopt standard approximations to estimate tail probability of the distribution. Then, we can estimate E by solving ¯ p ) > ln(E)} < . P r{ln(X

(17)

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2 ¯ For solving (17), the mean μ ˆlnX¯ and variance σ ˆln ¯ of ln(Xp ) must be known. X From the measurement process of the MBAC-AEBE, the mean Mk and variance Vk of y(n) = ln(x(n)) have been known. Then, the mean μ ˆ x and variance σ ˆx2 of x(n) can be calculated by Fenton’s approach [15] as follows:

μ ˆx = exp(Mk +

Vk ), 2

σ ˆx2 = exp(2Mk + Vk ){exp(Vk ) − 1}.

(18) (19)

2 ¯ p is μ ¯ Moreover, it is known that the mean μ ˆX¯ of X ˆ x and the variance σ ˆX ¯ p of Xp 2 2 is σ ˆx /p. The μ ˆlnX¯ and σ ˆlnX¯ can be obtained by calculating as follows [16]: 2 σ ˆln ¯ = ln( X

2 σ ˆX ¯ + 1), 2 μ ˆX¯

2 σ ˆln ¯ X . 2 Finally, the approximation[17] of E is given by:

μX¯ ) − μ ˆ2lnX¯ = ln(ˆ





E  exp(ˆ μlnX¯ + α σlnX¯ ), with α =

 −2ln() − ln(2π).

(20)

(21)

(22)

In brief, it needs to calculate (18)-(22) for the admission decision. Moreover, the value of p in (16) can be non-integer and can be controlled to handle different level of burstness of aggregate traffic. The value of p can be decreased for handling higher level of burstness. The admission decision is involved only when a DEV requests a new connection. The computational complexity of the admission decision is not high. Recalling that the measurement process of the MBAC-AEBE based on the linear Kalman filter is also computational efficiently. Therefore, the MBAC-AEBE is suitable for on-line usage.

3

Simulation Results

The scenario of our simulation is described as follows. Different numbers of DEVs are required to transmit VBR video traffic to other DEVs within the same piconet. We accept connections of these different numbers of traffic to evaluate the performance of the measurement process of the MBAC-AEBE and the accuracy of the effective bandwidth for the admission decision of the MBAC-AEBE. We assume that the channel is ideal, in order to focus our attention on the performance of the MBAC-AEBE. The performance of the measurement process of the MBAC-AEBE is evaluated by checking if the estimated Mk and Vk can approach to the mean μ and variance σ 2 of logarithmic aggregate GoP sizes. The logarithmic aggregate GoP sizes with length 3333 GoPs are fitted by the Gaussian distribution and the values of its μ and σ 2 are found as 13.395 and 0.074 respectively. Furthermore, the sample mean and sample variance are calculated by considering previous 30

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aggregate GoP sizes at each step (GoP index). And the sample mean and the sample variance are fed into the measurement equation (14) of our measurement process. It is shown in Fig. 2 that the value of the Mk well approaches to the value of μ, whereas the sample mean varies largely over time. Therefore, using the estimated Mk is much better and more stable than using the sample mean for admission decision.

Fig. 2. Performance of the measurement process for estimating the mean (μ)

Since the Mk is the good estimation of μ, the MBAC-AEBE adopts (15) to calculate the sample variance and feeds it into the measurement equation. Then, as shown in Fig. 3, the value of the Vk also well approaches to the value of σ 2 . However, the sample variance calculated by (15) still varies largely over time. Therefore, using the estimated Vk is much better and more stable than using the sample variance for admission decision. Moreover, we evaluate the performance of the admission decision of the MBAC-AEBE. The performance of the admission decision is checked by evaluating the accuracy of the aggregate bandwidth E calculated by (22). The value of loss ratio is more close to the desired QoS (i.e. desired loss ratio) if the value of E is more accurate. The overestimation of the aggregate effective bandwidth may decay channel utilization, whereas the underestimation of the aggregate effective bandwidth may not guarantee the desired QoS. The desired loss ratio is set to 1e-4 in our simulations. The accuracy of E is evaluated for different numbers of connections which are only 1 connection (Fig. 4), aggregating 12 connections (Fig. 5) and aggregating 18 connections (Fig. 6). It is also compared with the method propose in [17] which assumed that the aggregate traffic is Gaussian distributed. This method is labelled as JSAC91 in our simulations.

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Fig. 3. Performance of the measurement process for estimating the variance (σ 2 )

Fig. 4. Accuracy comparison of estimated effective bandwidth for only one connection

Observing the results shown from Fig. 4 to Fig. 6, the accuracy of the JSAC91 is increasing when the number of connections is increasing. This conforms to the Central Limit Theorem because the JSAC91 assumed Gaussian. On the contrary, the MBAC-AEBE is much accurate no matter how many connections are. The resulting loss ratios for the MBAC-AEBE approach to the desired QoS very well such as the MBAC-AEBE with p=2.58 in Fig. 4, the MBAC-AEBE with p=1.71 in Fig. 5 and the MBAC-AEBE with p=1.44 in Fig. 6. In practice, the best value of p can be found by using a steepest-descent algorithm [14].

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Fig. 5. Accuracy comparison of estimated aggregate effective bandwidth for 12 connections

Fig. 6. Accuracy comparison of estimated aggregate effective bandwidth for 18 connections

4

Conclusion

This paper proposed an on-line measurement-based admission control scheme called MBAC-AEBE in order to guarantee quality-of-service (QoS) requirements of real-time VBR video traffic. The MBAC-AEBE consisted of two main components which were measurement process and admission decision respectively.

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For on-line usage, the measurement process of the MBAC-AEBE adaptively estimated the mean and variance of logarithmic aggregate GoP sizes in each GoP period by an optimization scheme based on a linear Kalman filter. The Kalman filter could provide an optimal least-square estimate of the system state for a linear system. Then, the estimated mean and variance by measurement process were used to calculate the effective bandwidth for admission decision. Simulation results showed that the measurement process of the MBAC-AEBE could provide accurate estimations of the mean and the variance of logarithmic aggregate GoP sizes. We also showed that the aggregate effective bandwidth estimated by the MBAC-AEBE could provide loss ratio much approached to the desired QoS. The performance of the admission decision depended on the accuracy of the estimated aggregate effective bandwidth. Moreover, the performance of the admission decision was evaluated for different numbers of connections. It showed that the MBAC-AEBE was much accurate no matter how many connections are.

References 1. I. Dalqic and F. A. Tobaqi, “Performance evaluation of ATM networks carrying constant and variable bit-rate video traffic,” IEEE Journal of Selected Areas in Communications, vol. 15, pp. 1115-1131, 1997. 2. Z. Dziong, M. Juda, and L. G. Mason, “A framework for bandwidth management in ATM networks - aggregate equivalent bandwidth estimation approach,” IEEE/ACM Transactions on Networking, vol. 5, no. 1, pp. 134-147, Feb. 1997. 3. K. Shiomoto, S. Chaki, and N. Yamanaka, “A simple bandwidth management strategy based on measurements of instantaneous virtual path utilization in ATM networks,” IEEE/ACM Transactions on Networking, vol. 6, no. 5, Oct. 1998. 4. H. Saito and K. Shiomoto, “Dynamic call admission control in ATM networks,” Journal of Selected Areas in Communications, vol. 9, no. 7, pp. 982-989, Sept. 1991. 5. T. E. Tedijanto and L. Gun, “Effectiveness of dynamic bandwidth management mechanisms in ATM networks,” Proceedings of IEEE INFOCOM ’93, pp. 358-367, 1993. 6. K. Shiomoto and S. Chaki, “Adaptive connection admission control using real-time traffic measurements in ATM networks,” IEICE Transactions on Communications, vol. E78-B, no. 4, pp. 458-464, Apr. 1995. 7. R. J. Gibbens, F. P. Kelly, and P. B. Key, “A decision-theoretic approach to call admission control in ATM networks,” IEEE Journal of Selected Areas in Communications, vol. 13, no. 6, pp. 1101-1114, Aug. 1995. 8. K. Shiomoto, N. Yamanaka and T. Takahashi, “Overview of measurement-based connection admission control methods in ATM networks,” IEEE Communications Surveys & Tutorials, vol. 2, no. 1, pp. 2-13, First Quarter 1999. 9. D. Le Gall, “MPEG: A video compression standard for multimedia applications,” Communications of the ACM, vol. 34, pp. 47-58, Apr. 1991. 10. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transaction of the ASME-Journal of Basic Engineering, pp. 35-45, Mar. 1960. 11. IEEE standard 802.15.3: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for High Rate Wireless Personal Area Networks (WPANs), Inst. Elec. Electron. Eng., New York, USA, 2003.

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12. M. Reisslein et al., “Traffic and Quality Characterization of Scalable Encoded Video: A Large-Scale Trace-Based Study,” Arizona State University, Dept. of Elect. Eng., Tech. Rep., Dec. 2003. Video traces available from http://trace.eas.asu.edu. 13. M. Krunz, R. Sass and H. Hughes, “Statistical characteristics and multiplexing of MPEG streams,” Proceedings of IEEE INFOCOM ’95, vol. 2, pp. 455-462, 1995. 14. S. Haykin, Adaptive Filter Theory. Englewood Cliffs, NJ: Prentice-Hall, 1991. 15. L. F. Fenton, “The sum of lognormal probability distributions in scatter transmission systems,” IRE Transactions on Communications Systems, pp 57-67, 1960. 16. K. Nagarajan and G. T. Zhou, “A new resource allocation scheme for VBR video traffic source,” Proceedings of 34th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 1245-1249, 2000. 17. R. Guerin, H. Ahmadi, and M. Naghshineh, “Equivalent capacity and its application to bandwidth allocation in high-speed networks,” IEEE Journal of Selected Areas in Communications, Vol. 9, pp 968-981, 1991.

On Event Signal Reconstruction in Wireless Sensor Networks ¨ ur B. Akan Barı¸s Atakan and Ozg¨ Next Generation Wireless Communications Laboratory Department of Electrical and Electronics Engineering Middle East Technical University, 06531, Ankara, Turkey {atakan,akan}@eee.metu.edu.tr http://www.eee.metu.edu.tr/∼ nwc/ Abstract. In Wireless Sensor Networks (WSN), the effective detection and reconstruction of the event signal is mainly based on the regulation of sampling and communication parameters used by the sensor nodes. The aim of this paper is to understand the effect of these parameters on the reconstruction performance of event signal in WSN. Theoretical analysis and results show that with proper selection of sampling and communication parameters, the event signal can be satisfactorily reconstructed at the sink. Furthermore, this study also reveals that the non-uniform and irregular sampling of the event signal outperform the uniform sampling in terms of the reconstruction performance while providing significant energy conservation. Moreover, it is also shown that node density is closely related with the quality. Keywords: Wireless sensor networks, event signal reconstruction, non-uniform sampling, irregular sampling.

1

Introduction

In Wireless Sensor Networks (WSN), energy-efficient and reliable communication is mainly based on the regulation of sampling and communication parameters such as reporting frequency1 (or sampling frequency), number of source nodes, size of the source node selection area around the event. Therefore, to effectively reconstruct the observed physical phenomenon at the sink, it is imperative to understand the effect of these parameters over the reconstruction performance. There has been some research efforts about the reconstruction of the observed phenomenon in WSN. In [2], the effect of the compression over the reconstruction of event signal is investigated. The upper bound for the compression rate which can achieve the given distortion bound is given. In [3], to effectively reconstruct 

1

This work was supported by the Turkish Scientific and Technical Research Council (TUBITAK) Career Award under grant KARIYER-104E043. The sampling frequency of a sensor node is the number of samples taken from the sensed event signal. However, since in WSNs each data packet includes a number of samples, reporting frequency which is defined as the number of transmitted data packet per unit time is directly proportional with sampling frequency. Therefore, throughout this paper, we mainly use the sampling frequency.

I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 558–569, 2007. c IFIP International Federation for Information Processing 2007 

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the correlated observations of sensor nodes, both asymmetric and symmetric encoder settings are proposed. In [4], a source-channel matching approach is proposed to estimate the observed field, which can provide the analysis for the effect of the parameters such as number of nodes, power, field complexity over the estimation. In [5], to efficiently reconstruct the observed field, the tradeoff between coding rate and reliability characterized by the loss probability is investigated. In [6], the relation between the system throughput and reconstruction distortion is investigated for the case in which the reconstruction is performed by a large scale sensor network with mobile agents. However, none of these studies consider actual signal reconstruction. They use either estimated or compressed version of sensor samples to reach to an estimation of the event signal instead of an actual event signal reconstruction. Furthermore, they do not incorporate the sampling and communication parameters to investigate their effects on the actual reconstruction of the event signal. In this paper, a theoretical analysis for the reconstruction of event signal at the sink is performed. The aim of this theoretical analysis is to understand the effect of the sampling and communication parameters over the performance of event signal reconstruction in WSN. The main contributions and results of this work can be outlined as follows: 1. Exact event signal reconstruction is performed using the sensor readings instead of an estimation of the event features. It is shown that with proper selection of sampling and communication parameters, an event signal can be satisfactorily reconstructed at the sink. 2. For a given application-specific reconstruction error constraint, the uniform sampling scheme, in which source nodes use the same sampling frequency, is inefficient for the reconstruction of the event signal with high frequency components. However, the non-uniform and irregular sampling schemes, in which source nodes use heterogeneous sampling frequencies, enable sensor network to effectively reconstruct the event signal with high frequency components as well as providing significant energy conservation. 3. The reconstruction performance can also be affected by the size of the source node selection area around the event. When this area is decreased, the lower reconstruction error can be obtained as well as significant energy conservation can be achieved with smaller number of sources. This highlights the node density problem coupled with the reconstruction process. That is, to achieve a certain level of reconstruction error, certain node density is imperative. The remainder of this paper is organized as follows. In Section 2, we introduce the theoretical model for event signal reconstruction. In Section 3, by using the theoretical model given in Section 2, we first give the results on the performance of event signal reconstruction with uniform, non-uniform and irregular sampling schemes, respectively. Then, we present the comparative results of these three sampling schemes. The concluding remarks are given in Section 4.

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Reconstruction of Observed Event Signal

In WSN applications, the physical phenomena which are collectively observed by the sensor nodes can be modeled by a single point source or a field source. Here, we adopt the point source model [7] for event signal, and then we introduce the model for the event signal reconstruction. Note that, event signal reconstruction analysis introduced in this paper can be directly extended to the field source case. 2.1

Modeling of Observed Event Signal

In WSN applications, the event signal modeled by a point source is assumed to generate a continuous random process fS (t) having a variance σS2 and a mean μS . Assuming that the point source is at the center of the coordinate axis [7], the sensor node situated at location (x, y) receives the signal given by  √ x2 + y 2 − xθ2 +y2 s )e (1) f (x, y, t) = fS (t − v where v denotes the diffusion velocity of fS (t) and θs denotes the attenuation constant of event signal fS (t). Since f (x, y, t) is the delayed and attenuated version of fS (t), the variance and mean of f (x, y, t) can be expressed by μE (x, y) = μs e

−

2 (x, y) = (σs e− σE

√

x2 +y2 θs

(2)

√

x2 +y2 θs

)2

(3)

Using the observations which are attenuated and delayed version of the event signal, each sensor node generates its data packets. The k th data packet generated by sampling the received signal f (xi , yi , t) by sensor node ni at location (xi , yi ), i.e., Si,k , is defined as   (4) Si,k = f (xi , yi , tkp ) f (xi , yi , tkp+1 ) ... f (xi , yi , t(k+1)p ) where Si,k is p × 1 vector, p and k denote the packet length and packet number, respectively, f (xi , yi , tkp ) is a sample of event signal f (xi , yi , t), taken at time tkp . Next, we introduce the model for the reconstruction of event signal at the sink. 2.2

Event Signal Reconstruction

In WSNs, after the detection of an event, sensor nodes sample the event signal and send the generated data packets (Si,k ) to the sink. During this process, sensor circuitries add noise to these packets (Si,k ) as  = Si,k + Ni,k Si,k

(5)

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 where Si,k is the noisy version of the sensor packet Si,k and Ni,k is the observation 2 ). In addition to the additive noise, due to the noise, where Ni,k ∼ N (0, σN constraint in cost, power, and communication, in WSNs, packet losses often arise during the communication between the sensor nodes and the sink. In this paper, to model the packet losses, we assume an overall packet loss probability λ which includes loss due to channel errors, collision, and congestion. Hence, we define the lossy and noisy version of the sensor packet (Xi,k ) as  0 with probability λ Xi,k = Si,k + Ni,k with probability (1 − λ)

In WSNs, the packet losses heavily depend on the increasing contention in the forward path which is mainly based on the excessive communication load which results in collision, and congestion in the forward path. Therefore, the overall packet loss probability in a sensor network can be modeled by the sampling and communication parameters affecting the communication load over sensor network. Using ns-2 network simulator [11], we conduct comprehensive set of simulation experiments to model the overall packet loss probability (λ) based on sampling and communication parameters used by sensor nodes. The simulation setting used in these experiments is given in Table 1. The experiment results given in Fig. 1 show that the overall packet loss probability (λ) is mainly based on the number of source nodes (M ) and their sampling frequency (f ). Using OriginLab [10] data fitting toolbox, we analytically express the overall packet loss probability λ as a function of number of source nodes and their sampling frequency as follows −M 15

λ = 1 − e−0.01f /(150e

+5)

(6)

where M is the number of source nodes, f is the sampling frequency of source nodes (samples/sec.). Since uncoded transmission considerably outperforms any approach based on the separation paradigm when the measured physical phenomenon is reconstructed within some prescribed distortion level [8], we assume that the sensors perform uncoded transmission. When the sensor nodes transmit the uncoded observations, minimum mean square error (MMSE) estimation is the optimal decoding technique. Therefore, the decoded version of the sensor packet Si,k , i.e., Zi,k , can be expressed as follows   2 σE (xi , yi ) + μ2E (xi , yi ) (Xi,k )   (7) Zi,k = 2 (x , y ) + μ2 (x , y ) + σ 2 (1 − λ) σE i i E i i N By using decoded sensor packets (Zi,k ∀i, k), to generate the samples used for reconstruction of the event signal fS (t), sink first averages the decoded sensor packets and then, arranges the successive averaged packets as follows As =

M M M

1  Zi,1 Zi,2 . . . Zi,( τ f ) p M i=1 i=1 i=1

(8)

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Fig. 1. The overall packet loss probability (λ) for the varying M and f

where As (1 × τ f vector) consists of the samples used for reconstruction of the event signal fS (t), τ is a reconstruction interval (second). In order to obtain a ˆ reconstruction of the event signal fS (t), i.e., S(t), As is provided as the input to an ideal reconstruction (low-pass) filter [9] with frequency response Hr (jΩ) and impulse response hr (t). Then, the output of the filter is expressed as ˆ = S(t)

τf

As [n]hr (t −

n=1

n ) f

(9)

Here, assuming that the event signal fS (t) is a band-limited with the highest frequency component of ΩF , a cutoff frequency Ωc of the filter must be ΩF < Ωc < πf − ΩF as long as πf > 2ΩF is satisfied.2 Hence, for cutoff frequency πf , the corresponding impulse response hr (t) can be given by hr (t) =

sinπf t πf t

(10)

ˆ as follows and by substituting hr (t) into (9), we obtain S(t) ˆ = S(t)

τf n=1

As [n]

sin[π(f t − n)] π(f t − n)

(11)

Using (7), (8), (9), (10), (11), the overall reconstruction operation can be formulated as in (12). 2

This is known as Nyquist Sampling Theorem and ΩF and 2ΩF are called as the Nyquist Frequency and Nyquist Rate, respectively.

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  2 2 σ (x , y ) + μ (x , y ) Xi,k [j] i i i i E E ˆ τ, f, M, p) = 1   S(t, × M − M e−0.01f /(150e 15 +5) k=1 j=1 i=1  

 sin π f t − ((k − 1)p + j)    ×   2 (x , y ) + μ2 (x , y ) + σ 2 π f t − (k − 1)p + j σE i i E i i N τ f /p p M 

(12) ˆ τ, f, M, p) is a continuous time signal Since the reconstructed event signal S(t, as the event signal fS (t), to measure the reconstruction efficiency, we resample ˆ τ, f, M, p) and fS (t) with a sampling frequency which is higher than Nyquist S(t, ˆ τ, f, M, p) and fS (t), we define the rate. Based on the resampled version of S(t, deterministic reconstruction error Er used for measuring the efficiency of the event signal reconstruction as Er =

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Reconstruction Performance Analysis and Results

Here, we consider the scenario in which 300 sensor nodes are randomly deployed in a 100 × 100m field and a point source which characterizes the observed event signal is located at the center. We first perform the event signal reconstruction with three different sampling schemes: – Uniform Sampling: In this scheme, source nodes sample the event signal with the same sampling frequency (f ). – Non-uniform Sampling: In this scheme, source nodes select their sampling frequency according to their distance to the event location such that source nodes closer to event location use higher sampling frequency. – Irregular Sampling: In this scheme, source nodes take samples from the event signal with a probability while sampling the event signal with a sampling frequency. Then, we present the comparative results on the reconstruction performance of these sampling schemes. To make results more reliable, we take 100 Monte Carlo runs for every result in each scenario. For the event signal reconstruction process, we use fS (t) = sin(2π50t)+sin(2π120t) as an event signal which consists of 50Hz and 120Hz sinusoidal components as an event signal. According to the Nyquist Sampling Theory, since fS (t) includes 50Hz and 120Hz sinusoidal components, fS (t) can be reconstructed with the sampling frequency larger than the Nyquist rate 240 Samples/sec. We set the simulation parameters in Table 1.

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3.1

Event Signal Reconstruction with Uniform Sampling

In this section, we perform the reconstruction of event signal with the uniform sampling scheme. In Fig. 2, Er is given with varying f for different values of M . As observed, for f = 50 and f = 100, Er takes its maximum value because the sampling frequencies 50 and 100 are less than the Nyquist rate (240 Samples/sec). However, although the sampling frequency 200 is less than the Nyquist rate, it can reduce Er . This is mainly because that the sampling frequency 200 can be sufficient for the reconstruction of the 50Hz sinusoidal component in fS (t), which decreases the overall reconstruction error. In Fig. 3 (a), the event signal fS (t) is shown. In Fig. 3 (b) the reconstructed event signal is shown by using the sampling frequency 500. Since the sampling frequency 500 is larger than the Nyquist rate, the event signal fS (t) can be satisfactorily reconstructed at the sink. The amplitude difference between fS (t) and the reconstructed signal results from the attenuation in the event signal from source nodes to sink. The common technique for improving the reconstruction accuracy is to utilize oversampling. However, as observed in Fig. 2, it cannot be always possible to decrease Er by increasing f . While f increases, as observed in Fig. 1, λ increases. Therefore, at the higher f , the sufficient number of samples required for the reconstruction cannot be successfully delivered to sink and Er increases. This highlights the reconstruction problem for the event signal with high frequency components, which necessitates high sampling frequency according to the Nyquist Sampling Theory. Therefore, the uniform sampling does not stand as an effective sampling scheme to reconstruct the event signal with higher frequency components. Reconstruction accuracy is also influenced by M such that while M increases, due to the increasing contention, λ increases as shown in Fig. 1. Therefore, as M increases, it is getting more difficult to deliver the sufficient number of samples to sink for the event signal reconstruction. Due to this result, as observed in Fig. 2, the smaller Er cannot be obtained at the higher f by using higher M . However, as M decreases, the smaller Er cannot be obtained at the lower f .

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Therefore, M must be selected according to the event signal requirements such that if fS (t) has larger bandwidth, M must be selected as smaller value and if fS (t) has smaller bandwidth, M must be selected as higher value to obtain smaller Er .

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Event Signal Reconstruction with Non-uniform Sampling

Here, to realize non-uniform sampling, we divide 100 × 100m environment to 5 levels called L1 , L2 , L3 , L4 and L5 such that from L1 to L5 , the size of the levels are 20 × 20m, 40 × 40m, 60 × 60m, 80 × 80m and 100 × 100m, respectively. While the source nodes in L1 use the sampling frequency f , the source nodes between L1 and L2 use f /2 and the source nodes between L2 and L3 use f /3 and so on. Consequently, there exist 5 different levels of sampling frequency from f to f /5. In Fig. 4, Er is shown with varying f for different M . For the illustration purposes, in f axis of Fig. 4, we show only the largest sampling frequency in L1 . For example, f = 1000 means that the source nodes in L1 use f = 1000 and the source nodes between L1 and L2 use f = 500 and so on. Moreover, to compute λ, we use the average sampling  represents all  frequency, fa , which f f f f sampling frequencies in 5 levels, i.e., fa = f + 2 + 3 + 4 + 5 /5. As observed in Fig. 4, similar to uniform sampling, in non-uniform sampling, Er is not be reduced at the higher f . However, since fa is less than f used in uniform sampling, as observed in Fig. 1, smaller λ can be obtained at the higher sampling frequencies with respect to uniform sampling. This is mainly because decrease in f results in less contention at the forward path. Therefore, at the higher f , non-uniform sampling enables WSN to deliver more information to sink and to obtain smaller Er . This allows WSN to reconstruct the event signal with higher frequency components which necessitates the higher sampling frequency. 3.3

Event Signal Reconstruction with Irregular Sampling

In irregular sampling, source nodes take the samples from the event signal with the probability α while they sample the event signal with the sample frequency f .

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Therefore, in irregular sampling, to compute λ we use αf as a normalized sampling frequency fn . To observe the effect of α on the event signal reconstruction, in Fig. 5, Er is shown with varying f for different α. It can be possible to choose α according to the reconstruction error requirements and the event signal bandwidth such that as α decreases, since fn decreases, less contention and overall packet losses in the forward path can be obtained. Therefore, it can be possible to deliver more information to sink at the higher f and this enables the reconstruction of event signal with higher frequency components. However, for the reconstruction of event signal with low frequency components, as α decreases, since the number of delivered samples decreases, this results in increase in Er . Therefore, to obtain lower Er at lower f , α must be increased. 3.4

Comparative Results on Three Sampling Schemes

To compare the uniform, non-uniform and irregular sampling schemes, in Fig. 6, Er is shown with varying f for these three sampling schemes. In terms of Er , the non-uniform sampling scheme outperforms the uniform and irregular sampling

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scheme. For an application-specific Er constraint (Er = 0.85) shown in Fig. 6, non-uniform sampling scheme enables the sensor network to reconstruct the event signal having higher frequency components as well as providing smaller Er with respect to non-uniform and irregular sampling. For smaller f , the uniform and irregular sampling have almost the same Er performance.

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However, for higher f , irregular sampling provides smaller Er performance with respect to uniform sampling scheme. Therefore, with a given applicationspecific Er constraint, it can be possible to reconstruct the event signal having higher frequency components using irregular sampling scheme. To show the energy efficiency of three sampling schemes, we evaluate the average energy consumption for the three reconstruction schemes. We assume that each source node consumes one unit of average energy denoted by Eav to transmit a sample of the event signal to sink. Using the same simulation parameters (M = 48, α = 0.2, τ = 10s) in Fig. 6, we can compute the average energy consumption as τ f M Eav . For all sampling schemes, τ , M and Eav are

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the same. However, f changes according to the sampling schemes such that while in the uniform sampling each source node transmits f samples per second, in irregular sampling, each source node transmit fn = αf samples per second and in the non-uniform sampling, on average, fa samples per second. According to this energy model, in Fig. 7 (a), for three sampling schemes, the average energy consumption is shown with varying sampling frequency. The irregular and nonuniform sampling significantly outperforms the uniform sampling in terms of energy consumption, because the irregular sampling and the non-uniform sampling transmit the less number of samples per unit time (fn and fa , respectively) to reconstruct the event signal with respect to the uniform sampling. The reconstruction error can also be affected by the attenuation in the received event signal. The attenuation in the received signal increases with the distance between the point source and the source nodes. In Fig. 7 (b), for the different size of source node selection area (A) around the event and for M = 48, the reconstruction error is shown with varying sampling frequency by using the uniform sampling scheme. The reconstruction error (Er ) decreases while A decreases because the attenuation in the received signal decreases while A decreases. Furthermore, it is seen in Fig. 7 (b) that the minimum Er is achieved for A = 20x20m and M = 48. Therefore, to achieve this minimum reconstruction error, in each 10x10m area, there should exist at least 12 sensor nodes. This highlights the node density problem in the reconstruction process such that for a certain level of reconstruction error, a certain node density is imperative.

4

Conclusion

In this paper, theoretical analysis and results show that with proper selection of sampling and communication parameters, the observed physical phenomenon can be satisfactorily reconstructed at the sink node. It is also shown that the uniform sampling is inefficient for the reconstruction of the event signal with high frequency components and non-uniform sampling and irregular sampling significantly outperform the uniform sampling for the reconstruction of event sig-

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nal with high frequency components and energy conservation. Therefore, unlike the existing congestion control mechanisms, non-uniform and irregular sampling based rate control stands as a promising approach. Furthermore, as a result of the attenuation in the sensed event signal, it is also shown that to achieve a certain level of reconstruction error, certain node density is imperative.

References 1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci: Wireless Sensor Networks: A Survey. Computer Networks Journal (Elsevier) vol. 38, Issue 4, (2002) 393–422 2. A. Kashyap, L. Alfonso, L. Monta, C. Xia, Z. Lui: Distributed Source Coding in Dense Sensor Networks In Proc. IEEE DCC (2005) 3. S. S. Pradhan, K. Ramchandran: Distributed Source Coding: Symmetric Rates and Applications to Sensor Networks. In Proc. IEEE DCC (2000) 363–372 4. W. Bajwa, A. Sayeed, R. Nowak: Matched Source-Channel Communication for Field Estimation in Wireless Sensor Networks. In Proc. IPSN’05 (2005) 332–339 5. D. Marco, D. L. Neuhoff: Reliability vs. Efficiency in Distributed Source Coding, In Proc. IPSN’04, (2004) 6. M. Dong, L. Tong, B. M. Sadler: Source reconstruction via mobile agents in sensor networks: throughput-distortion characteristics In Proc. IEEE MILCOM 2003, Boston, USA, (2003) 7. M. C. Vuran, O. B. Akan: Spatio-temporal Characteristics of Point and Field Sources in Wireless Sensor Networks In Proc. IEEE ICC 2006, stanbul, June (2006) 8. M. Gastpar, M. Vetterli, Source-Channel Communication in Sensor Networks: In Proc. IPSN’03, Palo Alto, USA, (2003) 9. A. V. Oppenheim, R. W. Schafer, J. R. Buck: Discrete-Time Signal Processing Prentice Hall, 1999. 10. http://www.originlab.com/ 11. The Network Simulator, ns-2, http://www.isi.edu/nsnam/ns/

Peer-Assisted On-Demand Streaming of Stored Media Using BitTorrent-Like Protocols Niklas Carlsson and Derek L. Eager Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada {carlsson,eager}@cs.usask.ca

Abstract. With BitTorrent-like protocols a client may download a file from a large and changing set of peers, using connections of heterogeneous and timevarying bandwidths. This flexibility is achieved by breaking the file into many small pieces, each of which may be downloaded from different peers. This paper considers an approach to peer-assisted on-demand delivery of stored media that is based on the relatively simple and flexible BitTorrent-like approach, but which is able to achieve a form of “streaming” delivery, in the sense that playback can begin well before the entire media file is received. Achieving this goal requires: (1) a piece selection strategy that effectively mediates the conflict between the goals of high piece diversity, and the in-order requirements of media file playback, and (2) an on-line rule for deciding when playback can safely commence. We present and evaluate using simulation candidate protocols including both of these components. Keywords: BitTorrent-like systems, peer-assisted streaming, probabilistic piece selection.

1 Introduction Scalable on-demand streaming of stored media can be achieved using scalable server protocols such as patching [1] and Hierarchical Stream Merging [2], server replication as with CDNs, and/or peer-to-peer techniques. This paper concerns peer-to-peer approaches. A number of prior P2P protocols for scalable on-demand streaming have used a cache-and-relay approach [3-6]. With these techniques, each peer receives content from one or more parents and stores it in a local cache, from which it can later be forwarded to clients that are at an earlier play point of the file. Some work of this type concerns the problem of determining the set of servers (or peers) that should serve each peer, and at what rate each server should operate [7, 8]. Related ideas, based on application-level multicast architectures, have been used in protocols for live streaming [9, 10]. The above approaches work best when peer connections are relatively stable. Motivated by highly dynamic environments where peer connections are heterogeneous with highly time-varying bandwidths and peers may join and/or leave the system frequently, recent work by Annapureddy et al. [11] has considered the use of I.F. Akyildiz et al. (Eds.): NETWORKING 2007, LNCS 4479, pp. 570–581, 2007. © IFIP International Federation for Information Processing 2007

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BitTorrent-like protocols [12] for scalable on-demand streaming. (Other recent work has considered use of such protocols for live streaming [13-15].) In BitTorrent-like protocols, a file is split into smaller pieces which can be downloaded (in parallel) from any other peer that has at least one piece that the peer does not have itself. In the approach proposed by Annapureddy et al. for on-demand streaming of stored media files, each file is split into sub-files, each encoded using distributed network coding [16]. Each sub-file is downloaded using a BitTorrent-like approach. By downloading sub-files sequentially, playback can begin after the first sub-file(s) have been retrieved, thus allowing a form of “streaming” delivery. Note that use of large sub-files results in large startup delays, while using very small sub-files results in close to sequential piece retrieval, which can lead to poor performance as will be shown in Section 3.3. The best choice of sub-file sizes would be workload (and possibly also client) dependent, although the method requires these sizes to be statically determined. The authors do not elaborate on how the sizes can be chosen, or how startup delays can be dynamically determined. Rather than statically splitting each file into sequentially retrieved sub-files or using a small window of pieces that may be exchanged (as in BitTorrent-like protocols that have been proposed for live streaming [13]), in this paper we propose an approach in which any of the pieces needed by a peer may be retrieved any time they are available. As in BitTorrent, selection of which piece to retrieve when a choice must be made is controlled by a piece selection policy. For the purpose of ensuring high piece diversity, which is an important objective in download systems (where the file is not considered usable until fully downloaded) [17], BitTorrent uses a rarest-first policy, giving strict preference to pieces that are the rarest among the set of pieces owned by all the peers from which it is downloading. On the other hand, in the context of streaming it is most natural to download pieces in-order. The piece selection policy proposed in this paper attempts to achieve a good compromise between the goals of high piece diversity, and in-order retrieval of pieces. We also address the problem of devising a simple on-line policy for deciding when playback can safely commence. The remainder of the paper is organized as follows. Section 2 provides a brief overview of BitTorrent. Section 3 defines and evaluates candidate piece selection policies. Section 4 addresses the problem of dynamically determining the startup delay. Finally, conclusions are presented in Section 5.

2 Overview of BitTorrent With BitTorrent files are split into pieces, which themselves are split into smaller subpieces. Multiple sub-pieces, potentially of the same piece, can be downloaded in parallel from different peers. A peer is said to have a piece whenever the entire piece is downloaded. A peer is considered interested in all peers that have at least one piece that it currently does not have itself. BitTorrent distinguishes between peers that have the entire file (called seeds), and peers currently downloading the file (called leechers).

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In addition to the rarest-first piece selection policy, BitTorrent uses a number of additional policies that determine which peers to upload to. While each peer establishes persistent connections with a large set of peers (e.g., 80 [17]), at each time instance, each peer only uploads to a limited number of peers. Only peers that are unchoked may be sent data. Generally, clients re-evaluate the set of unchoked peers relatively frequently (e.g., every 10 seconds, each time a peer becomes interest/uninterested, and/or each time a new connection is established/broken). To discourage free-riding, BitTorrent uses a tit-for-tat policy in which leechers give upload preference to the leechers that provide the highest download rates to them. Without any measure of the upload rates from other peers, it has been found beneficial if seeds give preference to recently unchoked peers [17]. Periodically (typically every third time the set of unchoked peer is re-evaluated), each client uses an optimistic unchoke policy to probe for better pairings (or in the case of a seed, allow a new peer to download pieces).

3 Piece Selection Section 3.1 defines candidate policies, Section 3.2 describes our simulation model, and Section 3.3 evaluates the performance of the piece selection policies defined in Section 3.1. 3.1 Candidate Policies To allow playback to begin well before the entire media file is retrieved, pieces must be selected in a way that effectively mediates the conflict between the goals of high piece diversity and the in-order requirements of media file playback. Assuming that peer j is about to request a piece from peer i, we define two baseline policies: • Rarest: Among the set of pieces that peer i has and j does not have, peer j requests the rarest piece among the set of all pieces held by peers that j is connected to. Ties are broken randomly. • In-order: Among the set of pieces that peer i has, peer j requests the first piece that it does not have itself. We propose using simple probabilistic policies. Perhaps the simplest such technique is to request an in-order piece with some probability and the rarest piece otherwise. Other techniques may use some probability distribution to bias towards earlier pieces. We have found that the Zipf distribution works well for this purpose. The specific probabilistic policies considered here are as follows: • Portion (p): For each new piece request, client j uses the in-order policy with a probability p and the rarest policy with a probability (1–p). • Zipf (θ): For each new piece request, client j probabilistically selects a piece from the set of pieces that i has, but that j does not have. The probability of selecting each of these pieces is chosen to be proportional to 1/(k+1–k0)θ, where k is the index of the piece, and k0 the index of its first missing piece.

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Note that parameters p and θ can be tuned so that the policies are more or less aggressive with respect to their preference for earlier pieces. For the results presented here the parameters are fixed at the following values: p =50%, p = 90%, and θ = 1.25. 3.2 Simulation Model A similar approach is used as in prior simulation studies of BitTorrent-like protocols [18, 16]; however, rather than restricting peers to a small number of connections, it is assumed that peers are connected to all other peers in the system. It is further assumed that pieces are split into sufficiently many sub-pieces that use of parallel download is always possible when multiple peers have a desired piece. It is assumed that a peer i can at most have ni concurrent upload connections and that no connections are choked in the middle of an upload. The set of peers that a peer i is uploading to may change when (i) it completes the upload of a piece, or (ii) some other peer becomes interested and peer i is not utilizing all its upload connections. The new set of upload connections consists of (i) any peer currently in the middle of an upload, and (ii) additional peers up to the maximum limit ni. Additional peers are selected from the set of interested peers. To simulate optimistic unchoking, with a probability 1/ni a random peer is selected, and with a probability of (ni–1)/ni the peer which is uploading to peer i at the highest rate is selected. Random selection is used to break ties. This ensures that seeds only use random peer selection. For simulating the rate at which pieces are exchanged, it is assumed that connection bottlenecks are located at the end points (i.e., either by the upload bandwidth U at the sender or by the download rate D at the receiver) and the network operates using max-min fair bandwidth sharing (using TCP, for example). Under these assumptions each flow operates at the highest possible rate that ensures that (i) no bottleneck operates above its capacity, and (ii) the rate of no flow can be increased without decreasing the rate of some other flow operating at the same or lower rate. 3.3 Performance Comparisons Throughout this paper it is conservatively assumed that there is a single persistent seed and that all other peers leave the system as soon as they have received the entire file (i.e., act only as leechers). In a real system peers are likely to continue serving other peers as long as they are still playing out the media file, and some peers may choose to serve as seeds beyond that time. With a larger aggregate available download bandwidth and higher availability of pieces the benefits of more aggressive piece selection techniques are likely to be even greater than presented here. Without loss of generality, downloading of a single file is considered, with size and play rate both equal to one using normalized units. With these normalized units, the volume of data transferred is measured in units of the file size and time is measured in units of the time it takes to play the file data. Hence, all rates are expressed relative to the play rate, and all startup delays are expressed relative to the playback time. The file is split into 512 pieces, and unless stated otherwise, peers are assumed to have three times higher download capacity than upload capacity. Each peer is assumed to upload to at most four peers simultaneously. Throughout this section, policies are evaluated with regard to the lowest possible startup delay.

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This section initially considers a simple scenario in which peers do not leave the system until having fully received the file, requesting peers arrive according to a Poisson process, and peers are homogenous (i.e., have the same upload and download bandwidth). Alternative scenarios and workload assumptions are subsequently considered. To capture the steady state behavior of the system, the system is simulated for at least 4000 requests. Further, measurements are only done for requests which do n