NETWORKING 2008 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet: 7th International IFIP-TC6 Networking Conference Singapore, May

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 Alfred Kobsa University of California, Irvine, CA, 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 Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany

4982

Amitabha Das Hung Keng Pung Francis Bu Sung Lee Lawrence Wai Choong Wong (Eds.)

NETWORKING 2008 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet 7th International IFIP-TC6 Networking Conference Singapore, May 5-9, 2008 Proceedings

13

Volume Editors Amitabha Das Francis Bu Sung Lee Nanyang Technological University School of Computer Engineering Block N4, #2a-32, Nanyang Avenue, Singapore 639798, Singapore E-mail: {asadas, ebslee}@ntu.edu.sg Hung Keng Pung National University of Singapore School of Computing Department of Computer Science 3 Science Drive 2, Singapore 117543, Singapore E-mail: [email protected] Lawrence Wai Choong Wong National University of Singapore Department of Electrical and Computer Engineering 4 Engineering Drive 3, Singapore 117576, Singapore E-mail: [email protected]

Library of Congress Control Number: 2008925754 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 3-540-79548-0 Springer Berlin Heidelberg New York 978-3-540-79548-3 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 © IFIP International Federation for Information Processing 2008 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12264193 06/3180 543210

Preface

General Chairs’ Message Welcome to the proceedings of the 7th IFIP Networking Conference, which was held in Singapore during 5–9 May 2008. This was the first time that IFIP Networking Conference was held in Asia. An interesting program consisting of high-quality papers from researchers around the world was organized by the Program Chairs, Amitabha Das and Pung Hung Keng. There were a lot of opportunities for the participants to share their research and views. This was also a great opportunity for researchers and practitioners to network and we hope the friendship will continue beyond Singapore. The success of the conference is due to the hardwork of a lot of people. Our appreciation goes to the authors, who contributed to the conference through their presence and their high-quality research papers. Our sincere thanks to the Organizing Committee, who worked very hard handling the paper reviews, logistics, publication, financial matters, etc. to ensure that the conference ran smoothly. Special thanks to our committee members from overseas who helped us in publicizing the conference as well as providing valuable input and sharing their experiences with us. We would also like to thank the numerous paper reviewers for their effort and time. Finally, we thank the sponsors and the local institutions, Nanyang Technological University and National University of Singapore, for lending their support to the conference. May 2008

Lawrence W.C. Wong Francis Bu Sung Lee

Organization

Executive Committee General Co-chairs

Francis Bu Sung Lee (Nanyang Technological University, Singapore) Lawrence W.C. Wong (National University of Singapore, Singapore) Technical Program Co-chairs Amitabha Das (Nanyang Technological University, Singapore) Hung Keng Pung (National University of Singapore, Singapore) Technical Program Vice-chairs Sajal K. Das (University of Texas Arlington, USA) Archan Misra (IBM, USA) Edmundo Monteiro (University of Coimbra, Portugal) Tutorial Co-chairs Anwitaman Datta (Nanyang Technological University, Singapore) Sonja Buchegger (Deutsche, Telekom Lab) Workshop Co-chairs Teck Meng Lim (Nanyang Technological University, Singapore) Ilenia Tinnirello (Universit` a di Palermo, Italy) Publicity Co-chairs Clement Liang Tien Chia (Nanyang Technological University, Singapore) Andreas Kassler (Karlstad University, Sweden) Publication Chair Steven Kai Juan Wong (Nanyang Technological University, Singapore) Finance Chair Chai Kiat Yeo (Nanyang Technological University, Singapore) Local Arrangements Co-chairs Chuan Heng Foh (Nanyang Technological University, Singapore) Lek Heng Ngoh (Institute for Infocomm Research, Singapore)

VIII

Organization

Steering Committee Guy Leduc (IFIP TC6 Chair) Georg Carle (IFIP WG6.2 Chair) Ioannis Stavrakakis (IFIP WG6.3 Chair) Pedro Cuenca IFIP (WG6.8 Chair)

Technical Program Committee Abdelhamid Mellouk (University Paris XII, France) Afrand Agah (West Chester University of Pennsylvania, USA) Albert Levi (Sabanci University, Turkey) Alexandre Santos (University of Minho, Portugal) Alhussein Abouzeid (Rensselaer Polytechnic Institute, USA) Amit Mukhopadhyay (Bell Laboratories, Alcatel-Lucent, USA) Amitabha Das (Nanyang Technological University, Singapore) Ana Pont (Polytechnic University of Valencia, Spain) Andrea Bianco (Politecnico di Torino, Italy) Andreas Kassler (Karlstad University, Sweden) Antonio Capone (Politecnico di Milano, Italy) Archan Misra (IBM, USA) Arek Dadej (University of South Australia, Australia) As Madhukumar (Nanyang Technological University, Singapore) Athina Markopoulou (University of California, Irvine, USA) Azzedine Boukerche (University of Ottawa, Canada) Baochun Li (University of Toronto, Canada) Ben Liang (University of Toronto, Canada) Biswajit Nandy (Solana Networks, Canada) Boon Sain Yeo (SensiMesh, Singapore) Bu Sung Lee (Nanyang Technological University, Singapore) Burkhard Stiller (University of Z¨ urich and ETH Z¨ urich, Switzerland) Cedric Westphal (Nokia, USA) Chai Kiat Yeo (Nanyang Technological University, Singapore) Chang-Gun Lee (Seoul National University, Korea) Chih-Yung Chang (Tamkang University, Taiwan) Chris Blondia (University of Antwerp, Belgium) Christos Papadopoulos (Colorado State University, USA) Claudio Casetti (Politecnico di Torino, Italy) Dae Young Kim (Chungnam Nat’l University, Korea) Damla Turgut (University of Central Florida, USA) Daniel Zappala (Brigham Young University, USA) David Hutchison (Lancaster University, UK) Deyun Gao (Beijing Jiaotong University, P.R. China) Eckhart Koerner (University of Applied Sciences Mannheim, Germany) Edmundo Monteiro (University of Coimbra, Portugal) Eitan Altman (INRIA, France)

Organization

Eric Fleury (ENS Lyon/INRIA, France) Erwin Rathgeb (Universit¨ at Duisburg-Essen, Germany) Eusebi Calle (University of Girona, Spain) Evangelos Kranakis (Carleton University, Canada) Eylem Ekici (Ohio State University, USA) Falko Dressler (University of Erlangen, Germany) Fan Bai (General Motors, USA) Fang Zhu (Verizon, USA) Fengyuan Ren (Tsinghua University, P.R. China) Fernando Boavida (University of Coimbra, Portugal) Frank Huebner (AT&T Labs, USA) Georgios Kormentzas (University of the Aegean, Greece) Giacomo Morabito (University of Catania, Italy) Giovanni Pau (University of California Los Angeles, USA) Guenter Haring (Universit¨ at Wien, Austria) Guihai Chen (Nanjing University, P.R. China) Guoliang Xue (Arizona State University, USA) Guy Leduc (University of Liege, Belgium) Guy Pujolle (University of Paris 6, France) Hai Liu (University of Ottawa, Canada) Han-Chieh Chao (National Ilan University, Taiwan) Hao Zhu (Florida International University, USA) Harish Sethu (Drexel University, USA) Harry Perros (North Carolina State University, USA) Harry Skianis (National Centre for Scientific Research ‘Demokritos’, Greece) Hiroshi Esaki (The University of Tokyo, Japan) Hung-Keng Pung (University of Singapore, Singapore) Hung-Yun Hsieh (National Taiwan University, Taiwan) Ilkka Norros (VTT Technical Research Centre of Finland, Finland) Ioanis Nikolaidis (University of Alberta, Canada) Isabelle Guerin-Lassous (Universit´e de Lyon - LIP, France) Jerome Galtier (France Telecom R&D, France) Jiannong Cao (Hong Kong Polytechnic University, Hong Kong) Joe Finney (Lancaster University, UK) Jordi Domingo-Pascual (Technical University of Catalunya (UPC) Advanced Broadband Communications Center, Spain) Jorge Silva (University of Coimbra, Portugal) Jos´e Ruela (INESC Porto, Portugal) Josep Sole Pareta (UPC, Spain) Jun Guo (The University of New South Wales, Australia) Junehwa Song (KAIST, Korea) Jun-Hong Cui (University of Connecticut, USA) Kai-Juan Wong (Nanyang Technological University, Singapore) Karthikeyan Sundaresan (NEC LABS, USA) Ketan Mayer-Patel (University of North Carolina, USA)

IX

X

Organization

Kimon Kontovasilis (NCSR Demokritos, Greece) Konstantinos Psounis (University of Southern California, USA) Lars Wolf (Technische Universit¨ at Braunschweig, Germany) Laura Feeney (Swedish Institute of Computer Science, Sweden) Laura Galluccio (University of Catania, Italy) Laurent Mathy (Lancaster University, UK) Lawrence Wai-Choong Wong (National University of Singapore, Singapore) Lei Guo (Northeastern University China, P.R. China) Leszek Lilien (Western Michigan University, USA) Lichun Bao (University of California, Irvine, USA) Ling-Jyh Chen (Academia Sinica, Taiwan) Lorne Mason (McGill University, Canada) Luciano Bononi (University of Bologna, Italy) Luis Barbosa (Universidad de Castilla La Mancha, Spain) Manimaran Govindarasu (Iowa State University, USA) Manish Jain (Telchemy Inc., USA) Manuel Ricardo (INESC Porto, Portugal) Marco Gruteser (WINLAB / Rutgers University, USA) Marilia Curado (University of Coimbra, Portugal) Mario Freire (University of Beira Interior, Portugal) Mario Nunes (INESC, IST, Lisbon, Portugal) Martin Karsten (University of Waterloo, Canada) Matteo Cesana (Politecnico di Milano, Italy) Maurice Gagnaire (ENST, France) Mehmet Vuran (University of Nebraska-Lincoln, USA) Michael Menth (University of Wuerzburg, Germany) Michel Diaz (LAAS, France) Mike Myung-Ok Lee (Dongshin University, Korea) Mukund Seshadri (Sprint Applied Research, USA) Otto Spaniol (Aachen University of Technology, Germany) Ozgur Akan (Middle East Technical University, Turkey) Paolo Santi (CNR, Italy) Pascal Lorenz (University of Haute Alsace, France) Paulo Carvalho (University of Minho, Portugal) Paulo Mendes (INESC Porto, Portugal) Paulo pinto (Universidade Nova de Lisboa, Portugal) Paulo Simoes (University of Coimbra, Portugal) Peter Reichl (Telecommunications Research Center Vienna (ftw.), Austria) Philippe Owezarski (LAAS, France) Piet Van Mieghem (Delft University of Technology, The Netherlands) Premkumar (Nanyang Technological University, Singapore) Prosper Chemouil (France Telecom R&D, France) Raheem Beyah (Georgia State University, USA) Ramon Puigjaner (UIB, Spain) Raquel Hill(Indiana University, USA)

Organization

Ratan Ghosh (Indian Institute of Technology, Kanpur, India) Reza Rejaie (University of Oregon, USA) Rui Aguiar(Universidade de Aveiro, Portugal) Rui Rocha (Instituto de Telecomunicac˜ oes, Portugal) Sajal K. Das (University of Texas Arlington, USA) Sambit Sahu (IBM Research, USA) Sebastia Galmes (Universitat de les Illes Balears, Spain) Shantidev Mohanty (Intel Corporation, USA) Shigeki Goto (Waseda University, Japan) Shrikant Naidu (Motorola India Research Lab, India) Sirisha Medidi (Washington State University, USA) Srihari Nelakuditi (University of South Carolina, USA) Stephan Eidenbenz (Los Alamos National Laboratory, USA) Sunghyun Choi (Seoul National University, Korea) Taieb Znati (University of Pittsburgh, USA) Tamer ElBatt (Lockheed Martin Advanced Technology Center, USA) Teresa Vazao (Instituto Superior T´ecnico, Portugal) Thomas Kunz (Carleton University, Canada) Thomas Plagemann (University of Oslo, Norway) Torsten Braun (University of Bern, Switzerland) Tricha Anjali (Illinois Institute of Technology, USA) Tuna Tugcu (Bogazici University, Turkey) Turgay Korkmaz (University of Texas at San Antonio, USA) Vana Kalogeraki (University of California, Riverside, USA) Vassilis Tsaoussidis (Demokritos University, Greece) Vera Goebel (University of Oslo, Norway) Wei Wu (Research In Motion, USA) Wendong Xiao (Institute for Infocomm Research, Singapore) Xavier Masip-Bruin (Technical University of Catalonia (UPC), Spain) Xiaoyan Hong (University of Alabama, USA) Yevgeni Koucheryavy (Tampere University of Technology, Finland) Yingfei Dong (University of Hawaii, USA) Yong Cui (Tsinghua University, China, P.R. China) Yongbing Zhang (University of Tsukuba, Japan) Yuh-Shyan Chen (National Taipei University, Taiwan)

Referees Abdalkarim Awad Ageliki Tsioliaridou Ahren Studer Albert Cabellos Alexander Klein Alexandre Fonte Ali Tosun

Alicia Trivi˜ no Anahit Martirosyan Andr´e Rodrigues Andreas Maeder Anwitaman Datta Aravind Venkataraman Bart Braem

Bartomeu Serra Bruno Dias Carla-Fabiana Chiasserini Carlos Juiz Carlos Pinho Catalina Llad´ o

XI

XII

Organization

Cheng Peng Fu Chi Zhang Christian Jacquenet Christina Stoica Christoph Sommer Christos Samaras Chuan Heng Foh Csaba Kiraly Dahai Xu Damien Magoni Daniel Schlosser Denis Collange Dennis Pfisterer Di Wang Dimitris Vassis Dirk Staehle Emmanouil Kafetzakis Erwin Van de Velde Eun Jung Kim Eva Mar´ın-Tordera Evsen Yanmaz Fotis Lazarakis Fr´ed´eric Nivor Gareth Tyson George Gardikis George Mastorakis Germ´an Santos-Boada Giulio Galante Guanhua Yan Guo Song Hans-Martin Zimmermann Helen Karatza Herve Debar Himali Saxena Horacio Oliveira Hristo Djidjev Huai-Jen Liu Ilenia Tinnirello Ioannis Lambadaris Ioannis Psaras Ishyan Hwang Ivan Gojmerac Jan de Meer Jean-Laurent Costeux

Jeff Joslin Jen-Chun Chang Jeroen Van Velthoven Jianfeng Guan Jikai Li Jing Ai John Felix Charles Joseph Joke Lambert Jonas Karlsson Jorg Liebeherr Jose Barcelo Junghee Han Kaouthar Sethom Kau-Lin Chiu Kin-Hon Ho Kostas Karasavvas Kuei-Ping Shih Lanier Watkins Lefteris Mamatas Lei Wang Mang Li Marc Brogle Marcel Castro Marcelo Yannuzzi Marcin Matuszewski Marco Liebsch Marco Mellia Marco Valero Marco Fiore Mario Serafim Nunes Markus Anwander Markus Waelchli Markus Wulff Martijn Kuipers Martina Umlauft Matti Siekkinen Maurizio Munafo’ Maxwell Young Mehmet Isik Miao Pan Michael Duelli Michela Meo Mickael Meulle Mikhail Smirnov

Miroslaw Klinkowski Nabil Seddigh Nawaporn Wisitpongphan Nicola Altan Nikos Sagias Niu Yanchao Olivier Dugeon Ovidiu Valentin Drugan Ozgur Ercetin Ozgur Gurbuz Pallab Datta Paolo Giaccone Patrick Brown Patrick Lauer Paul Kuehn Paul Smith Paul J. Kuehn Paulo Montezuma Pedro Sousa Pedro Miguel Fonseca Marques Ferreira Peng Chor Leong Pep-Lluis Ferrer Per Hurtig Peter Dely Pierre Borgnat Pierre Levis Pieter Audenaert Qi Zhang Qiang Shen Rana Rahim-Amoud Rastin Pries Razvan Pitic Reed Newman Rehan Akbani Rene Serral-Gracia Renato Lo Cigno Ren-Hung Hwang Robert Henjes Rongsheng Huang Ruediger Martin Rute Sofia Salman Baset Sanghwan Lee

Organization

Satyajayant Misra Senhua Huang Shenghui Liao Sidi-Mohammed Senouci Silvano Mignanti Silvia Farraposo Simon Oechsner Solange Lima Sophia Kaplantzis Stelios Thomopoulos Steve Uhlig Steven Simpson Stylianos Dimitriou Suk-Bok Lee

Sunggeun Jin Tebbani Badis Teck Meng Lim Theodoros Amanatidis Thierry Rakotoarivelo Thomas Zinner Tieyan Li Timothy Talty Tiziano Inzerilli Tobias Limmer Tsung-Hung Lin Utku Acer Venkatesh Ramaswamy Wai-Leong Yeow

XIII

Wantanee Viriyasitavat Wim Torfs Xiaotao Wu Xiaoyan Yin Xin Fei Yanchao Zhang Yohan Chae Yu Gu Yu Zhang Yue-Shan Chang Yunhuai Liu Zhenghao Zhang Zhenzhen Ye Zoran Despotovic

Table of Contents

Ad-Hoc and Sensor Networks ASN: Design and Optimization XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics for Ad Hoc Networking . . . . . . . . . . . . . . . . . . . . . . . . . Herv´e A¨ıache, Vania Conan, Laure Lebrun, J´er´emie Leguay, St´ephane Rousseau, and Damien Thoumin Cross-Layer Optimized Congestion, Contention and Power Control in Wireless Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eren G¨ urses

1

14

ASN: MAC Protocol Early Overhearing Avoidance in Wireless Sensor Networks . . . . . . . . . . . . Siquan Hu and Mehul Motani

26

Preamble MAC Protocols with Non-persistent Receivers in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdelmalik Bachir, Martin Heusse, and Andrzej Duda

36

ASN: Overlay Networking An Overlay Maintenance Protocol for Overlay Routing on Top of Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sandrine Calomme and Guy Leduc An Overlay Architecture for Vehicular Networks . . . . . . . . . . . . . . . . . . . . . Luigi Liquori, Diego Borsetti, Claudio Casetti, and Carla-Fabiana Chiasserini

48

60

ASN: Routing DBR: Depth-Based Routing for Underwater Sensor Networks . . . . . . . . . . Hai Yan, Zhijie Jerry Shi, and Jun-Hong Cui

72

Channel Allocation for Multiple Channels Multiple Interfaces Communication in Wireless Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . Trung-Tuan Luong, Bu-Sung Lee, and C.K. Yeo

87

XVI

Table of Contents

Resilience to Dropping Nodes in Mobile Ad Hoc Networks with Link-State Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ignacy Gawedzki and Khaldoun Al Agha 

99

ASN: Miscellaneous 3-D Localization Schemes of RFID Tags with Static and Mobile Readers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mathieu Bouet and Guy Pujolle

112

Performance Evaluation and Enhancement of Surface Coverage Relay Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antoine Gallais and Jean Carle

124

On Extending Coverage of UMTS Networks Using an Ad-Hoc Network with Weighted Fair Queueing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. El-Azouzi, R. El-Khoury, A. Kobbane, and E. Sabir

135

Next Generation Internet NGI: Authentication A Performance Analysis of Authentication Using Covert Timing Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reed Newman and Raheem Beyah

149

PassPattern System (PPS): A Pattern-Based User Authentication Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Rakesh Kumar and S.V. Raghavan

162

A Secure and Efficient Three-Pass Authenticated Key Agreement Protocol Based on Elliptic Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meng-Hui Lim, Chee-Min Yeoh, Sanggon Lee, Hyotaek Lim, and Hoonjae Lee

170

NGI: Modeling and Performance Evaluation On the Robustness of Complex Networks by Using the Algebraic Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Jamakovic and P. Van Mieghem

183

On the Social Cost of Distributed Selfish Content Replication . . . . . . . . . . Gerasimos G. Pollatos, Orestis A. Telelis, and Vassilis Zissimopoulos

195

On Performance Evaluation of Handling Streaming Traffic in IP Networks Using TFRC Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kacper Kurowski and Halina Tarasiuk

207

Table of Contents

XVII

NGI: Multicast Efficient Heuristic for Minimum Cost Trees Construction in Multi-Groups Multicast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Keen-Mun Yong, Gee-Swee Poo, and Tee-Hiang Cheng

215

Delay Bounds and Scalability for Overlay Multicast . . . . . . . . . . . . . . . . . . Gy¨ orgy D´ an and Vikt´ oria Fodor

227

A Distributed Algorithm for Overlay Backbone Multicast Routing in Content Delivery Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Guo and Sanjay Jha

240

NGI: Network Measurement and Testbed Network Performance Assessment Using Adaptive Traffic Sampling . . . . . Ren´e Serral-Graci` a, Albert Cabellos-Aparicio, and Jordi Domingo-Pascual

252

On the Applicability of Knowledge Based NAT-Traversal for Home Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andreas M¨ uller, Andreas Klenk, and Georg Carle

264

IP Performance Management Infrastructure for ISP . . . . . . . . . . . . . . . . . . Atsuo Tachibana, Yuichiro Hei, Tomohiko Ogishi, and Shigehiro Ano

276

Can Critical Real-Time Services of Public Infrastructures Run over Ethernet and MPLS Networks? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaime Lloret, Francisco Javier Sanchez, Hugo Coll, and Fernando Boronat

289

Shim6: Reference Implementation and Optimization . . . . . . . . . . . . . . . . . . Jun Bi, Ping Hu, and Lizhong Xie

302

A Measurement Study of Bandwidth Estimation in IEEE 802.11g Wireless LANs Using the DCF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael Bredel and Markus Fidler

314

NGI: Optical Networks Hierarchical Logical Topology in WDM Ring Networks with Limited ADMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tomoya Kitani, Nobuo Funabiki, Hirozumi Yamaguchi, and Teruo Higashino A Novel Class-Based Protection Algorithm Providing Fast Service Recovery in IP/WDM Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wojciech Molisz and Jacek Rak

326

338

XVIII

Table of Contents

End-to-End Proportional Loss Differentiation in OBS Networks . . . . . . . . Miguel A. Gonz´ alez-Ortega, Jos´e C. L´ opez-Ardao, Pablo Argibay-Losada, Andr´es Su´ arez-Gonz´ alez, C´ andido L´ opez-Garc´ıa, Manuel Fern´ andez-Veiga, and Ra´ ul F. Rodr´ıguez-Rubio

346

NGI: Peer-to-Peer and Overlay Networking The Effect of Peer Selection with Hopcount or Delay Constraint on Peer-to-Peer Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Tang, H. Wang, and P. Van Mieghem T2MC: A Peer-to-Peer Mismatch Reduction Technique by Traceroute and 2-Means Classification Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangyu Shi, Youshui Long, Jian Chen, Hao Gong, and Hongli Zhang Towards an ISP-Compliant, Peer-Friendly Design for Peer-to-Peer Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haiyong Xie, Yang Richard Yang, and Avi Silberschatz Cache Placement Optimization in Hierarchical Networks: Analysis and Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenzhong Li, Edward Chan, Yilin Wang, Daoxu Chen, and Sanglu Lu Towards a Two-Tier Internet Coordinate System to Mitigate the Impact of Triangle Inequality Violations . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Ali Kaafar, Bamba Gueye, Francois Cantin, Guy Leduc, and Laurent Mathy

358

366

375

385

397

NGI: Peer-to-Peer Services Efficient Multi-source Data Dissemination in Peer-to-Peer Networks . . . . Zhenyu Li, Zengyang Zhu, Gaogang Xie, and Zhongcheng Li

409

Modeling Priority-Based Incentive Policies for Peer-Assisted Content Delivery Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Niklas Carlsson and Derek L. Eager

421

AQCS: Adaptive Queue-Based Chunk Scheduling for P2P Live Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Guo, Chao Liang, and Yong Liu

433

E2E Blocking Probability of IPTV and P2PTV . . . . . . . . . . . . . . . . . . . . . . Yue Lu, Fernando Kuipers, Milena Janic, and Piet Van Mieghem

445

Table of Contents

Cooperative Replication in Content Networks with Nodes under Churn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eva Jaho, Ioannis Koukoutsidis, Ioannis Stavrakakis, and Ina Jaho

XIX

457

NGI: QoS Congestion Avoiding Mechanism Based on Inter-domain Hierarchy . . . . . Marc-Antoine Weisser, Joanna Tomasik, and Dominique Barth

470

A Novel Bandwidth Broker Architecture Based on Topology Aggregation in Delay|Bandwidth Sensitive Networks . . . . . . . . . . . . . . . . . . Walid Htira, Olivier Dugeon, and Michel Diaz

482

Client-Side Adaptive Search Optimisation for Online Game Server Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grenville Armitage

494

A Model for Endpoint Admission Control Based on Packet Loss . . . . . . . Ignacio M´ as and Gunnar Karlsson

506

NGI: Routing Convergence of Intra-domain Routing with Centralized Control . . . . . . . . Jing Fu, Peter Sj¨ odin, and Gunnar Karlsson

518

Improving the Interaction between Overlay Routing and Traffic Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gene Moo Lee and Taehwan Choi

530

Designing Optimal iBGP Route-Reflection Topologies . . . . . . . . . . . . . . . . Marc-Olivier Buob, Steve Uhlig, and Mickael Meulle

542

A Study of Path Protection in Self-Healing Routing . . . . . . . . . . . . . . . . . . Qi Li, Mingwei Xu, Lingtao Pan, and Yong Cui

554

The CPBT: A Method for Searching the Prefixes Using Coded Prefixes in B-Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Behdadfar and Hossein Saidi

562

On the Effectiveness of Proactive Path-Diversity Based Routing for Robustness to Path Failures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chansook Lim, Stephan Bohacek, Jo˜ ao P. Hespanha, and Katia Obraczka

574

NGI: Security On Robustness and Countermeasures of Reliable Server Pooling Systems Against Denial of Service Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . Thomas Dreibholz, Erwin P. Rathgeb, and Xing Zhou

586

XX

Table of Contents

DDoS Mitigation in Non-cooperative Environments . . . . . . . . . . . . . . . . . . Guanhua Yan and Stephan Eidenbenz Generalized Self-healing Key Distribution Using Vector Space Access Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ratna Dutta, Sourav Mukhopadhyay, Amitabha Das, and Sabu Emmanuel A Model for Covert Botnet Communication in a Private Subnet . . . . . . . Brandon Shirley and Chad D. Mano

599

612

624

NGI: Traffic Engineering Traffic Engineering and Routing in IP Networks with Centralized Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Fu, Peter Sj¨ odin, and Gunnar Karlsson

633

Distributed PLR-Based Backup Path Computation in MPLS Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohand Yazid Saidi, Bernard Cousin, and Jean-Louis Le Roux

642

Adaptive Multi-topology IGP Based Traffic Engineering with Near-Optimal Network Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ning Wang, Kin-Hon Ho, and George Pavlou

654

NGI: Transport Protocols Modeling TCP in Small-Buffer Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mark Shifrin and Isaac Keslassy Active Window Management: Performance Assessment through an Extensive Comparison with XCP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Barbera, M. Gerla, A. Lombardo, C. Panarello, M. Sanadidi, and G. Schembra

667

679

AIRA: Additive Increase Rate Accelerator . . . . . . . . . . . . . . . . . . . . . . . . . . Ioannis Psaras and Vassilis Tsaoussidis

691

Performance Evaluation of Quick-Start TCP with a Linux Kernel Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael Scharf and Haiko Strotbek

703

Lightweight Fairness Solutions for XCP and TCP Cohabitation . . . . . . . . Dino M. L´ opez Pacheco, Laurent Lef`evre, and Congduc Pham

715

Table of Contents

Concurrent Multipath Transfer Using SCTP Multihoming: Introducing the Potentially-Failed Destination State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preethi Natarajan, Nasif Ekiz, Paul D. Amer, Janardhan R. Iyengar, and Randall Stewart

XXI

727

NGI: Miscellaneous Energy-Efficient Mobile Middleware for SIP on Ubiquitous Multimedia Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Felipe Garcia-Sanchez, Antonio-Javier Garcia-Sanchez, and Joan Garc´ıa-Haro

735

A Secure Mechanism for Address Block Allocation and Distribution . . . . Damien Leroy and Olivier Bonaventure

748

Asset Localization in Data Centers Using WUSB Radios . . . . . . . . . . . . . . N. Udar, K. Kant, and R. Viswanathan

756

Improving the Performance of DCH Timing Adjustment in 3G Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaspar Pedre˜ no, Juan J. Alcaraz, Fernando Cerd´ an, and Joan Garc´ıa-Haro

768

Wireless Networks WiNet: MAC Performance Link Adaptation Algorithm for the IEEE 802.11n MIMO System . . . . . . Weihua Helen Xi, Alistair Munro, and Michael Barton

780

Thorough Analysis of 802.11e Star Topology Scenarios in the Presence of Hidden Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katarzyna Kosek, Marek Natkaniec, and Luca Vollero

792

A Mobility-Adaptive TDMA MAC for Real-Time Data in Wireless Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Johannes Lessmann and Dirk Held

804

High Performance Distributed Coordination Function for Wireless LANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haithem Al-Mefleh and J. Morris Chang

812

Turning Hidden Nodes into Helper Nodes in IEEE 802.11 Wireless LAN Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haithem Al-Mefleh and J. Morris Chang

824

Performance Evaluation of 802.11 Broadcasts for a Single Cell Network with Unsaturated Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashwin Rao, Arzad Kherani, and Anirban Mahanti

836

XXII

Table of Contents

WiNet: Mesh Networks Optimal Placement of Mesh Points in Wireless Mesh Networks . . . . . . . . Suk Yu Hui, Kai Hau Yeung, and Kin Yeung Wong A Distributed Channel Access Scheduling Scheme with Clean-Air Spatial Reuse for Wireless Mesh Networks . . . . . . . . . . . . . . . . . . . . . . . . . . Yuan-Chieh Lin, Shun-Wen Hsiao, Li-Ping Tung, Yeali S. Sun, and Meng Chang Chen A Unified Service Discovery Architecture for Wireless Mesh Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin Krebs, Karl-Heinz Krempels, and Markus Kucay

848

856

865

WiNet: Mixed Networks Using Predictive Triggers to Improve Handover Performance in Mixed Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaiyu Liu, Christian Maciocco, and Vijay Kesavan

877

Medium Access Cooperations for Improving VoIP Capacity over Hybrid 802.16/802.11 Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deyun Gao, Jianfei Cai, and Chuan Heng Foh

889

WiNet: Miscellaneous ERA: Effective Rate Adaptation for WLANs . . . . . . . . . . . . . . . . . . . . . . . . Saˆ ad Biaz and Shaoen Wu

901

On the Probability of Finding Non-interfering Paths in Wireless Multihop Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Waharte and R. Boutaba

914

Multi-commodity Flow Problem for Multi-hop Wireless Networks with Realistic Smart Antenna Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Osama Bazan and Muhammad Jaseemuddin

922

Evolutionary Power Control Games in Wireless Networks . . . . . . . . . . . . . Eitan Altman, Rachid El-Azouzi, Yezekael Hayel, and Hamidou Tembine

930

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

943

XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics for Ad Hoc Networking∗ Hervé Aïache, Vania Conan, Laure Lebrun, Jérémie Leguay, Stéphane Rousseau, and Damien Thoumin Thales Communications 160, boulevard de Valmy, BP82 92704 Colombes Cedex, France {firstname.name}@fr.thalesgroup.com

Abstract. In the highly dynamic and unpredictable environment of MANETs, cross-layer design is receiving growing interest but lacks experimental validation tools. This paper presents XIAN (Cross-layer Interface for wireless Ad hoc Networks), a generic framework for experimenting cross-layer designs in Linux testbeds with 802.11 wireless cards using the MadWifi driver. XIAN can be used as a service by other layers or system components to access MAC/PHY configuration and performance information. It provides experimenters with an open framework to create automatically complex metrics from both local and neighbour node measurements. The defined and implemented software architecture introduces the XIAN Nano-protocol and its automated management. We exemplify their benefits through the implementation of the well-known ETX (Expected Transmission count) metric and we provide results from real experimentations.

1 Introduction In Mobile Ad hoc Networks (MANETs [1]), nodes can be mobile and share one or more wireless channels without centralized control. In such dynamic and unpredictable distributed environments, traditional networking principles, such as the layer isolation of the OSI (Open Systems Interconnection) model, are challenged. Recent research studies have explored more flexible approaches to networking, called CrossLayer approaches, that revisit the classical IP stack design. The central idea of cross layering consists in allowing a more flexible exchange of status or control information between the different components of the communication system. With a better knowledge of available resources from different layers of the ad hoc network stack, the system is expected to be more reactive to the wireless environment and responsive to quality requirements of applicative-oriented elements. When compared to the usual OSI reference model, existing cross-layer solutions span a wide spectrum of options: some advocate global exchange of information between components (e.g. Conti et al. [2]), others prefer to limit them to adjacent layers (e.g. Kawadia et al. [3]), depending on how they impact or differ from this reference ∗

This work has been partially supported by the European IST FP6 CHORIST project and the French RNRT Airnet project.

A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 1–13, 2008. © IFIP International Federation for Information Processing 2008

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model. In any case, cross-layering calls for software architectures and implementations that support a more flexible sharing of information and status reports between the processes and functional modules of the communication system. Experimenting with cross layer design for MANETs remains difficult, most ad hoc testbeds making use of 802.11 cards which lack appropriate API support. This paper builds upon our earlier work [4] to define XIAN (Cross-layer Interface for wireless Ad hoc Networks) which consists in a framework that facilitates crosslayer integrations and experimentations by easing the access to information from MAC/PHY layers. The ultimate goal of XIAN is to encourage and facilitate crosslayer studies and experimentations over MANET testbeds. It has been implemented and is available in open source [7] for Linux over the MadWifi 802.11 driver [5]. This paper presents a major extension to the earlier basic framework by supporting the implementation of complex cross-layer metrics that can combine both local information and measurements obtained from the node’s neighbours by the XIAN Nanoprotocol. We exemplify its use with the implementation of the ETX (Expected Transmission Count) metric proposed by De Couto et al. [6] and we provide experimental demonstration of potential benefits. The remainder of the paper is structured as follows. Section 2 presents the XIAN approach and software architecture. Section 3 describes the XIAN programming interfaces and examples of accessible metrics. Section 4 and Section 5 present the use and experimental benefits of the proposed XIAN extension for the implementation of ETX. Section 6 concludes the paper, discussing directions for future work.

2 XIAN: Cross-Layer Interface for Wireless Ad hoc Networks This section introduces the overall XIAN framework. We first present the Linux based software architecture. Then we show how to integrate new complex metrics to extend and customize the framework. Finally we describe the XIAN Nano-protocol, integrated at the MAC layer, that automatically handles metric exchanges between neighbour nodes. 2.1 XIAN Basic Components for Cross-Layer Exchanges MAC/PHY state information, such as number of transmission retries or number of transmitted frames with CTS enabled, is available at driver level and a large part of this information may be of interest to higher layers. To support flexible cross-layer access to this information XIAN implements a software architecture composed of three main components: •

The Kernel Space XIAN Interface (KSI) is dedicated to kernel space components (e.g. TCP or UDP implementations) and implemented as a Linux kernel module. It interacts directly with the MadWifi driver to retrieve its internal states or statistics or with the XIAN components involved in the definition of complex metrics (see section 2.2).

XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics

• •

3

The User Space XIAN Interface (USI) mimics the KSI but at user space level. This API is implemented as an ordinary C library in order to facilitate its integration with user space programs (e.g. routing daemons or applications). The XIAN Information Transport Module (ITM) allows to pass information and statistics from the kernel space to the user space, by connecting the two previous XIAN APIs. This module is implemented in this version of XIAN as a special character device.

In addition, a complementary component, called the XIAN User Space Extended Interface (or USEI), provides simple means for experimenters to perform additional processing of raw measurements, such as averaging, metric combination or notification of significant changes. Figure 1 illustrates how the components interact and how internal driver/MAC states or metrics are provided to other Linux system components. From the developer’s point of view, the two XIAN APIs (i.e. KSI and USI) are identical. The information exchanged through the ITM and accessible via the USI and the KSI are of two kinds: (1) basic metrics extracted from specific structures/states

Fig. 1. XIAN Framework software architecture

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maintained by the MadWifi driver, and (2) new metrics integrated to the framework thanks to the software components described in the following section. 2.2 Extension to Integrate and Manage Complex Cross-Layer Metrics XIAN offers mechanisms implementing bidirectional exchanges of metrics among neighbouring nodes (such as those classically needed by [6, 9, 10, 11]). This makes possible the creation and the integration within the XIAN framework of metrics that require information from neighbouring nodes (e.g. the number of packets that have been received by a neighbour). Implementing a new metric involves the definition of the metric calculation formula, i.e., it defines how to combine readings from several individual PHY/MAC layer metric values to obtain a new compound one. Obtaining the individual values involves configuring the XIAN framework, by setting which metrics to read from the node’s neighbours and at which frequency. The implementation of this major extension to XIAN is available in the new release on the official project web site [7]. As shown Figure 1, four software components, all implemented as Linux kernel modules, support the integration of such complex metrics within the XIAN framework: •

•

•

•

The XIAN Metric Manager (XMM), in charge of the registration and unregistration of the new cross-layer metrics instantiated as XIAN Cross-layer Metric modules (XCMs – An XCM mainly implements the metric calculation formula and configures the protocol exchanges involved in the calculation of the metric in terms of type of information and frequency – see section 3.2). The XIAN Nano-Protocol handler (XNP), which handles the XIAN Nanoprotocol messages containing the values of the metrics exchanged between neighbouring nodes. The XNP is implemented between the MadWifi driver and the Linux kernel implementation of the IP layer. Its role is to extract the XIAN Nano-protocol messages from in-coming 802.11 frames and to create and send out-going XIAN Nano-protocol messages to the desired neighbours. The XIAN Metrics Repository (XMR), which is responsible of recording the calculated values of the new cross-layer metrics introduced within the XIAN framework. Once recorded by the XMR, the metric values become accessible (via the KSI or the USI – see section 1.1) to other operating system components, along with all MadWifi driver information reported by default by XIAN. The XIAN Neighbouring Manager (XNM), which detects neighbour nodes and triggers updates necessary to automate XIAN Nano-protocol message exchanges.

To enable the exchanges of metrics between neighbouring nodes, we propose a simple MAC layer-oriented protocol, called the XIAN Nano-protocol. It is described in the following section. 2.3 The XIAN Nano-Protocol The XIAN Nano-protocol is responsible of exchanging the XIAN Nano-protocol messages. Figure 2 gives an example of such a message. It contains a XIAN Nanoprotocol Metrics Reports made up of (1) a header, which mainly indicates information

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5

about the report object itself and (2) a payload, which can embed several XIAN Nanoprotocol Metric Objects that provides metric values and associated meta information. Note that XIAN Nano-protocol messages are encapsulated into unicast or broadcast 802.11 data frames. The XIAN Nano-protocol Metrics Report is composed of the following fields: • • • •

Version indicates the used version of the XIAN Nano-Protocol. Sequence is the identifier of the XIAN Nano-protocol Metrics Report. Length indicates the number of XIAN Nano-protocol Metric Objects contained in the XIAN Nano-protocol Metrics Reports. Payload contains a set of XIAN Nano-protocol Metric Objects.

Fig. 2. Metrics Report format of the XIAN Nano-protocol

A XIAN Nano-protocol Metric Object is composed of the following fields: • • • • •

Type indicates the identifier associated to a metric. Id identifies a reference to the associated XCM (see section 3.2). Encoding_type indicates how the metric value is encoded (e.g. integer or float). Mac_addr indicates the MAC address to which the metric value belongs to. Value contains the effective numerical value of the metric.

Moreover, in order to optimize the number of messages sent to a given neighbour node, note that the XIAN Nano-Protocol handler (XNP) is able to aggregate several XIAN Cross-Layer Metrics Object of different types in a single XIAN Metrics Report.

3 XIAN Programming Interfaces The previous section described the main building blocks of the XIAN architecture. This section details the programming interfaces offered by XIAN to implement view to access, refine, design and integrate MAC-oriented cross-layer metrics. 3.1 Accessing MAC-Oriented Basic Metrics XIAN eases the access to a large set of basic metrics offered by the MadWifi driver. The most important ones can be divided into two groups: •

Global metrics, similar to counters, this kind of metrics provides global statuses on the use of the 802.11 network interface. The reported information can be:

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•

the number of received frames dropped or with wrong BSSID, the number of transmitted frames with CTS or with RTS enabled, the relative signal strength indicator (RSSI) of the last ACK received, the number of failed receptions (due to queue overrun, bad CRC, PHY errors or decryption problems). Per link metrics, which stores per-neighbour information. For instance, this kind of metric reports the number of received/transmitted data frames or bytes, the relative signal strength indicator (RSSI) or the number of transmission retries.

Depending on the type of reported metric (aggregated or per-neighbour/link), the prototype of the function (for a given metric named metric_name) is defined as follows: •

For a global metric: u_int32_t /* returned metric */ get_metric_name(char * dev_name, /* Interface name */ unsigned int * code_err); /* Error code */

•

For a per-neighbour/link metric: u_int32_t get_node_metric_name( u_int8_t * macadd, char * dev_name, unsigned int * code_err);

/* returned metric value */ /* Neighbour node’s MAC address */ /* Interface name */ /* Error code */

About 180 such basic metrics are supported in XIAN, obtained from converting the corresponding readings of the local MadWifi driver. 40 of these measurements are given on a per-neighbour basis, the remaining ones being global values for the node. 3.2 Design and Integration of New Complex Metrics New cross-layer metrics are implemented in XIAN as XIAN Cross-layer Metric modules (XCMs). XCMs are usual Linux kernel modules which use specific APIs mainly defined by the XIAN Metric Manager (XMM). To define both the metric calculation formula and the protocol configuration, the XMM proposes the following interface to register a new XCM within the framework: void register_id (unsigned int id, unsigned int metric, char dev[IFNAMESIZ+1], char mac[IEEE80211_MAC_ADDR_LEN+1], unsigned int freq, void * pf)

/* /* /* /* /* /*

Conf. identifier */ New metric id. */ Net. interface */ Report dest. MAC */ Reports frequency */ Processing func. */

Once called, the XMM introduces the new metric identified within XIAN under the reference metric. The XMM will automatically send a report about this cross-layer metric (through XIAN Nano-protocol Metrics Reports) on the network interface identified as dev by the Linux kernel each freq milliseconds. Moreover, when a XIAN Nano-protocol Metric Object of type metric is received, the XMM calls the associated processing function pf to compute the new value of the metric (typically implementing the metric calculation formula). Note that the field id allows to uniquely identify an XCM to allow reuse of the same cross-layer metric for different processing or configurations.

XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics

7

Note that with such a design, experimenters do not have to take care about the XIAN Nano-protocol exchanges, but just have to configure its behaviour, meaning: the frequency of the metrics reports (i.e. the parameter called freq), what are the nodes involved in the calculation of the metric (i.e. the parameter mac) and the formula to calculate the metric (i.e. the processing function pf). In addition to the function register_id(), the XMM proposes another complementary interface which allows to un-register an XCM: void unregister_id(unsigned int id, /* Conf. Identifier */ unsigned int metric) /* Metric Id. */

This function un-register the metric identified within XIAN by its reference metric and its associated XCM (or configuration) identifier id. Therefore, as mentioned, a typical XCM implementation looks like to a classical Linux kernel module with specific calls to the functions register_id()and unregister_id(). In this way, a typical XCM would implement the following functions: •

•

•

An initialisation function, as for Linux kernel modules implementation, which is necessary to load the XCM into the Linux kernel and to register the new metric (or several) within the XIAN framework thanks to the XMM’s function called register_id(). A processing function, which is specific to XIAN and enables the reception of XIAN Nano-protocol Metric Objects in view to extract the metrics values and to apply the associated formula before updating the metric values through the XMR, the metrics repository of the XIAN framework. A cleanup function, as for Linux kernel modules implementation, which is important to unload properly the XCM from the Linux kernel and to unregister the given cross-layer metric (or several) from XIAN thanks to the XMM’s function called unregister_id().

Since the processing function performs operations on the metrics exchanged through the XIAN Nano-protocol messages, it follows a specific prototype: void processing_function(unsigned char *saddr, /* Source MAC */ struct metric_msg *metric) /* Metric object */

Thanks to this function, once the XCM is inserted inside the Linux kernel and registered within the XIAN framework, the XMM is able to pass the received XIAN Cross-Layer Metric Object in metric to the right XCM. Moreover, note that the XMM provides an additional information to the given XCM: the MAC address of the source node (indicated by the variable saddr), which sent the received XIAN CrossLayer Metric Object (i.e. contained in metric). 3.3 XIAN User Space Extended Interface Several metrics may have to be further combined or refined in order to be meaningful or at least more useful for specific system components. For example, the number of transmitted MAC frames (in bytes) would not be an interesting metric if no timecorrelation is introduced to reflect how this metric evolves during the life-time of the wireless communication system. In other cases, the average value of a given metric is

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more meaningful than an instantaneous measurement. Therefore, XIAN complements the interfaces introduced in Section 3.1 and 3.2 with another API, available in userspace, called the User Space XIAN Extended Interface (USEI), which provides: • • •

Measurement functions, which compute metrics resulting from the combination of several elementary metrics taken directly via the USI or from the refinement of an elementary metric (e.g. average values). Operation functions, which implement the corresponding mathematical operator required by the new defined calculated metrics (e.g. min or max functions). Relevance functions, which implement the corresponding comparator indicating if a significant difference occurs between two calculated values (typically between the previous and the new ones).

The USEI has been implemented as a usual C library to facilitate its use and integration with user space processes. Based on the description of the XIAN architecture and programming interfaces, the following section exemplifies their use with the implementation of the ETX (Expected Transmission Count [6]) metric as a XIAN Crosslayer Metric module (XCM) available as an example of complex metric in the new release of XIAN.

4 Implementation of a Complex Metric within XIAN Several cross-layer metrics have been proposed in the literature. De Couto et al. [6,8] have proposed the Expected Transmission count (ETX) which measures the bidirectional packet loss ratio of link. Awerbuch et al. [9] have introduced the Medium Time Metric (MTM) that selects high throughput paths. Déziel et al. [10] have defined the available bandwidth. Iannone et al. [11] have combined the packet success rate, the interference level, and the physical bit rate. In this section, we specifically illustrate how the new set of XIAN functionalities presented in this paper enable experimenters to easily implement the ETX metric. First, we recall the definition of this metric. Then, we show how to implement this metric within the XIAN framework. 4.1 The Expected Transmission Count (ETX) The Expected Transmission count (ETX) calculates the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination. ETX predicts the number of transmissions required using per-link measurements of packet loss ratios in both directions of wireless links. The ETX value for a route is the sum of the link metrics. ETX is a combination of two measurements: (1) the forward delivery ratio, called Df, which is the measured probability that data packets successfully arrive at the recipient, and (2) the reverse delivery ratio, Dr, which is the probability that ACKs packets are successfully received. The probability that a transmission is successfully received and acknowledged is Df x Dr. Then, ETX is expressed as follows:

XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics

9

ETX(link) = 1/(Df x Dr). The main issue when it comes to ETX implementation is that Df and Dr are not directly available locally. The only solution is to exchange delivery ratios among neighbours. These delivery ratio can be determined in various manners: (1) by looking at statistics reported in a per-neighbour fashion by XIAN (e.g., number of data transmission trials, number successful data transmissions), (2) by comparing the number of broadcast packets that have been received from neighbours to those that should have been received, assuming that nodes send broadcast messages every T milliseconds. In this work, to demonstrate how ETX can be implemented as a XIAN Cross-layer Metric modules, nodes exchange their delivery ratios for all their out-going links using XIAN Nano-protocol messages. They also take advantage of these message exchanges to calculate these delivery ratios using the solution (2) explained in the previous paragraph. 4.2 ETX Implementation in XIAN ETX has been implemented within the XIAN framework as an XCM, called ETX_XCM. At the load of the ETX_XCM, we configure the XNM to send every T milliseconds the metric Dr in a broadcast message. Thus we create a new configuration identifier and a new metric code for it. The parameter pf given to the function register_id() is a function called at each reception of the metric ETX, which has the following prototype: processing_ETX(unsigned char*saddr, struct metric msg *metric).

The broadcast message contains the values of Dr for the list of MAC addresses provided by the function insert_mac_to_broadcast() which is used for each received broadcast message when the MAC address is not present. At the reception of those messages, the ETX_XCM stores the Dr metric’s value thanks to the XMR by using its interface: update_xian_stat(). Then, ETX_XCM increments a counter and records it in the XMR. Thanks to Dr and Df, we can calculate ETX. The calculated ETX values are stored within the XMR in order to provide the metric and the possibility to estimate the quality of a link. In parallel, a thread is wake up every W milliseconds to recover the counter and to calculate the Df, thanks to the counter and the expected number of received messages during W milliseconds. Df is then recorded within the XMR and the counter reset to zero. At last, ETX is recalculated with the new value of Df and updated within the XMR. Note that, since the Linux kernel does not manage float values, all the metrics within the kernel space are under the format struct xian_float and accessible by the function struct xian_float get_xian_stat(), added to the KSI. In the user space, the results are available under the format float with the similar function float get_xian_stat(), offered by the USI. Moreover, to perform operations on struct xian_float, the addition, the multiplication and the division are available as functions provided by XIAN.

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Based on this implementation of ETX, the following section explains how the ETX_XCM has been used to perform measurements over a real 802.11 ad hoc testbed.

5 Experimental Testbed and Results To illustrate the benefit of XIAN to facilitate cross-layering experiments, we present experimental measurements oriented on QoS routing and performed with the ETX_XCM, the implementation of ETX with the XIAN APIs. The testbed we setup was composed of 3 machines equipped with Cisco Aironet Wi-Fi cards equipped by the Atheros chipset and configured to use the 802.11b standard at the bit rate of 11 Mbits/s in ad hoc mode (without RTS/CTS). On each machine, we used iperf [12] to generate TCP and UDP traffic and the STAF/STAX [13] framework to automate the experiment runs. We also use a simple program that logs, for each link, the metrics accessed through XIAN. We logged each of the following metrics every δ seconds: • • •

The RSSI (Relative Signal Strength Indicator): the wide-band received power within the used channel; The throughput: the sum of total data bytes received and sent at the MAC layer within the last δ seconds; The ETX metric measured over 10 beacons (XIAN Nano-protocol messages) sent every 200 milliseconds.

Fig. 3. Experimental setup and traffic generation

XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics

11

Figure 3 presents the triangle network topology that we considered and the different flows that we have generated. The link between the machine N2 and N5 is significantly longer than the other ones. The traffic generation sequence used (depicted in Figure 3(b)) is composed of the following phases each lasting 24 seconds: • • • • •

Phase (0): no traffic. Phase (1): UDP traffic between N3 and N5 via N2 at 4 Mbits/s. Phase (2): TCP traffic between N3 and N5 via N2. Phase (3): UDP traffic between N3 and N5 at 4 Mbits/s. Phase (4): TCP traffic between N3 and N5.

Figure 4 presents the measurements that we have performed. More specifically, Figure 4(b) and Figure 4(e) present the throughput respectively for links N3→N2 and N3→N5. We can see that when UDP and TCP flows issued from N3 are passing via N2 to reach N5, high throughput are achieved with average values equal respectively to 3.4 Mbits/s (saturation is achieved) and 2.4 Mbits/s. Whereas when UDP and TCP flows issued from N3 are passing directly over the link N3→N5, the average values are equal respectively to 0.1 Mbits/s and 0.1 Mbits/s. These results show that in our case, routing decisions based on hop-count can fail. Cross-layer metrics are then required to achieve acceptable performances. Figure 4(a) and Figure 4(d) look at RSSI values respectively for links N3→N2 and N3→N5. We can see that due to the higher length of the link N3→N5, RSSI values are lower on this link than those or the link N3→N2 with average values equal respectively to 12.5 Db and 36.1 Db. An average RSSI of 33.4 Db have been observed for the link N2→N5. In our case, we could have used this metric to make efficient routing

Fig. 4. Metric values measured with XIAN

12

H. Aïache et al.

decisions. However, the RSSI does not capture information regarding the congestion and contention levels at MAC layer and thus may not work in larger and more realistic conditions. Figure 4(c) and Figure 4(f) present the ETX values respectively for links N3→N2 and N3→N5. We can see that the link N3→N5 plots ETX values continuously higher than the one of the link N3→N2 (see especially values for the different phases (0) ) which denotes the fact that ETX captures the poor quality of links. In phases (3), as the UDP traffic is totally not able to be transferred over the link, node N3 runs out of buffer memory. This results in a large amount of drops for the beacons used in ETX computation and thus to high values of the metric (this impact can also be seen for the ETX value of link N3→N2). Some of the loses of beacons might have also caused by collisions over the wireless medium. We have seen here that ETX captures two crucial factors: (1) the weakness of links due to low RSSI values and (2) the local and neighbouring traffic load. This experiment illustrates how the ETX metric, implemented within the XIAN Framework through automated XIAN Nano-protocol exchanges, introduced in this paper, allows to make efficient QoS routing decisions. Note that the purpose of this work was to show how to implement the original ETX specifications in XIAN. With XIAN it is moreover possible to explore variants of this protocol, for example by monitoring more exactly the real traffic to estimate transmission count in place of the dedicated ETX probes, thus taking into account the packet size distribution.

6 Conclusions and Future Work This paper presented XIAN a cross-layer interface implementation specialized in the building of experimental setups for validating a large variety of use cases of IEEE 802.11 cross-layering and its extensions to support complex user-defined metrics. The extension proposed in this paper turns XIAN into an extensible framework dedicated to the creation of complex cross-layer metrics. Moreover, the decomposition of crosslayer metric definitions in metric calculation formula and protocol exchanges configuration, translated into software components, facilitates their design and their integration within the XIAN framework. It is thus possible to define and to implement cross-layer metrics as new components in XIAN that can use both local and neighbouring measurements transparently exchanged with the XIAN Nano-protocol. We exemplified its use with the implementation of the ETX (Expected Transmission Count) metric and we provided experimental demonstration of its potential benefits. Finally, we released our code that can be downloaded from [7]. Future work along these lines would include the development of interfaces working in a publish/subscribe manner. This kind of interface may improve further the integration of the MAC and routing layers as it would allow, for instance, reporting of link up and link down events and help the system react more quickly to topology changes. Finally, one could wish to extend the generic APIs to support other chipsets than Atheros in the spirit of the Wireless Tools.

XIAN Automated Management and Nano-Protocol to Design Cross-Layer Metrics

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References 1. Corson, S.: RFC 2501, Mobile ad hoc networking (MANET): Routing protocol performance issues and evaluation considerations. In: IETF (January 1999) 2. Conti, M., Maselli, G., Turi, G., Giordano, S.: Cross-layering in mobile ad hoc network design. IEEE Computer, 48–51 (February 2004) 3. Kawadia, V., Kumar, P.R.: A cautionary perspective on cross layer design. IEEE Wireless Communication Magazine (July 2003) 4. Aïache, H., Conan, V., Leguay, J., Levy, M.: XIAN: Cross-layer interface for wireless ad hoc networks. In: Proc. Med-Hoc-Net (2006) 5. MadWifi, http://www.madwifi.org 6. De Couto, D.S.J., Aguayo, D., Bicket, J., Morris, R.: A high-throughput path metric for multi-hop wireless routing. In: Proc. MobiCom. (2003) 7. XIAN., http://sourceforge.net/projects/xian/ 8. De Couto, D.S.J., Aguayo, D., Chambers, B.A., Morris, R.: Performance of multi-hop wireless networks: Shortest path is not enough. In: Proc. HotNets, ACM SIGCOMM (2002) 9. Awerbuch, B., Holmer, D., Rubens, H.: High throughput route selection in multi-rate ad hoc wireless networks. In: Proc. WONS. (2004) 10. Déziel, M., Lamont, L.: Implementation of an IEEE 802.11 link available bandwidth algorithm to allow cross-layering. In: Proc. WiMob. (2005) 11. Iannone, L., Khalili, R., Salamatian, K., Fdida, S.: Cross-layer routing in wireless mesh networks. In: Proc. ISWCS. (2004) 12. Iperf, http://dast.nlanr.net/Projects/Iperf/ 13. STAF: Software Testing Automation Framework, http://staf.sourceforge.net

Cross-Layer Optimized Congestion, Contention and Power Control in Wireless Ad Hoc Networks Eren G¨ urses Centre for Quantifiable QoS in Communication Systems Norwegian University of Science and Technology, O.S. Bragstads Plass, 2E, N-7491 Trondheim, Norway [email protected]

Abstract. Despite some scepticism, cross-layer optimization has largely been accepted as an indispensable part of protocol design for wireless networks. In this respect, generalized network utility maximization (GNUM) has become a widely employed systematic approach for understanding the interconnections between layers and designing efficient cross-layer protocols. In this paper we adopt the GNUM approach and propose a cross-layer optimized congestion, contention and power control algorithm for transport, MAC (medium access control) and physical layers respectively. First we develop an abstract MAC layer model to capture the effects of multiple access in a wireless medium, then express the effective link capacities by relating physical layer capacities to the MAC layer model through average interference. Secondly we construct the GNUM based congestion, contention and power control problem, and devise a primal-based distributed algorithm to solve it. Results show that distributed algorithm obtains very similar results with the centralized one.

1

Introduction

Following the seminal paper of Kelly et.al. [1], network utility maximization (NUM) has been a versatile tool for understanding, reverse engineering and designing Internet congestion control protocols. Lately starting with [7], a generalized form of NUM has been adapted to wireless networks and widely used in order to expose the interconnections between layers and design alternative layering (or cross-layering) schemes in general [2]. The generalized network utility maximization (GNUM) puts the sum of source utilities and other application imposed needs to the objective function to maximize while relating the other protocol layer parameters through constraints. Source utilities are functions of source rates and different forms of them represent different measures of fairness. In many works following [7], different cross-layer designs based on the GNUM have been proposed. Here, we only discuss the relevant works from this literature which has a special focus on transport, MAC (medium access control) and 



Author has been affiliated with David R. Cheriton School of Computer Science, University of Waterloo, ON, Canada since Jan 2008. E-mail: [email protected] Centre for Quantifiable QoS in Comm. Systems, CoE, is appointed by The Research Council of Norway, and funded by the Research Council, NTNU and Uninett.

A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 14–25, 2008. c IFIP International Federation for Information Processing 2008 

Cross-Layer Optimized Congestion, Contention and Power Control

15

physical layers. The joint congestion-contention problem is addressed in [3], [4], [5] and [6]. In [3] a link scheduling algorithm based on deterministic approximations is proposed. However this algorithm entails a continuous messaging overhead to conduct link scheduling and can not be a replacement of standard simple distributed coordination function (DCF) of 802.11 [9]. Furthermore, in [6] it has been shown that deterministic approximations can not accurately model random access-based MAC. In [4], [5] very similar joint congestion-contention control problems are addressed. However, MAC models used in these works assume saturation conditions for the operation which relies on the existence of at least one packet in MAC layer transmit buffer. This assumption makes the MAC layer model identical to slotted Aloha [10]. In contrast to this, we allow less restrictive non-saturation conditions in the MAC layer operation which is practically the case if transport layer is adapting its rate in coordination with the MAC layer. Actually in our work, link access probabilities regulates the flow to the MAC buffer which prevent saturation mode operation. In [8] a joint power scheduling (power adaptation and link scheduling) and congestion control problem is addressed. They develop a probabilistic model and propose a joint power scheduling and congestion control algorithm using stochastic subgradient method. However the resulting algorithm has the similar disadvantages of [3]. On the other hand [7] addressed the problem of joint congestion control and power adaptation. Although the work claims the use of CDMA based MAC layer, the convex optimization process is completely relying on exclusive scheduling of closeby nodes in order to achieve high SIR (signal-to-interference-ratio). However neither an exclusive scheduling algorithm nor its impact on effective link capacities is discussed. In this paper we extend the congestion control and power adaptation framework in [7] to include the effect of MAC layer. However, integrating the effect of MAC layer to the framework in [7] leads to a set of difficulties like non-convexity and non-separability and necessitates a completely different approach to overcome them. The contributions of this paper start at this point and are given as follows. First we develop an abstract MAC layer model to represent the nonsaturation mode of operation through successful channel access probabilities, al . Second we describe physical layer link capacities c(P, a), based on average interference model in order to consider the effect of MAC layer and then construct the resulting convex GNUM problem. Finally we propose a cross-layer optimized distributed congestion, contention and power control algorithm using primal-based techniques. The rest of the paper is organized as follows. In Section 2, we first present the system model including the contributions on MAC and physical layer and then construct the GNUM problem for optimal congestion, contention and power control. Next after in Section 3, we first convert the given NUM problem to a convex one and then introduce the distributed primal-based subgradient projection algorithm to solve this convex but non-separable problem. Finally, the simulation results are presented in Section 4 and followed Section 5 where we conclude the paper and give an outlook of possible future work.

16

E. G¨ urses

Flows: A: 1 → 4 → 6 → 7

D

B: 4 → 6 C: 3 → 2

xD

D: 5 → 6

2

E: 8

5 xB

xA

A

7

B 1

4

6

E

8 xE

3

d xC

: Sources

C

: Relaying nodes

Fig. 1. A sample wireless ad hoc network topology

2

System Model

We consider a wireless ad hoc network composed of N nodes and L links where each link is unidirectional and only a number of S ≤ N nodes are acting as traffic sources. The set of all links and all sources are denoted as L and S respectively. Packets from sources are routed to their destinations according to the established routes which are assumed to be fixed by the network layer at the time of optimization. In our network model the routing matrix R of size L × S corresponds to these established routes where entries rls of this matrix take the value of 1 if source s is using link l, and 0 otherwise. In the following subsections, we first introduce the parameters and necessary notations on transport, MAC and physical layers, then present the resulting GNUM formulation.

Max. clique 1: 1, 2, 3, 4 Max. clique 2: 1, 3, 4, 6 Max. clique 3: 2, 4, 5 Max. clique 4: 4, 5, 6, 7 Max. clique 5: 6, 7, 8

2

1

5

7

4

3

6

8

Fig. 2. Conflict graph corresponding to the topology

2.1

Transport, MAC and Physical Layer Parameters

The main functionality of the transport layer is to control the end-to-end congestion in a network while maximizing the individual source rates xs of nodes s ∈ S according to a given fairness criteria. Congestion control is simply done by controlling xs in order to keep the aggregate flows on links l ∈ L less than

Cross-Layer Optimized Congestion, Contention and Power Control

17

or equal to the effective link capacities observed above MAC layer, while at the same time maximizing the rate of each individual source, generally through a utility function Us (xs ) where different forms of it correspond to different fairness policies. This constrained optimization problem is modeled as a NUM problem and it was recently shown that variants of TCP, together with AQM (Active Queue Management) techniques, are designed to approximately solve it for different forms of utility functions Us (xs ). In classical Internet congestion control problem, NUM formulation is limited to the case of fixed link capacities. However in wireless networks, effective link capacities are functions of MAC and physical layer parameters, and these parameters should be optimized to get a higher network performance. In the rest of this section, we introduce the MAC and physical layer parameters first and then introduce the new generalized network utility maximization (GNUM) problem. In wireless networks, wireless links implement MAC protocols to have multiple access to the shared medium. In order to capture this behaviour, first we construct an abstract MAC layer model by using conflict graphs. However constructing the conflict graph of a given topology requires the MAC protocol to be specified. Therefore, in the rest of the paper we assume contention based CSMA/CA (carrier sense multiple access with collision avoidance) protocol is employed at the MAC layer. This protocol solves the hidden and exposed terminal problems via a reservation scheme (RTS/CTS) and as a result links within a 2-hop neighborhood contend to capture the medium and if successful, exclusively use it during their transmission. This MAC layer operation enables the links to operate in high SINR region since high interference caused by the simultaneous use of nearby links (i.e. within the 2-hop neighborhood) is eliminated. Later it is shown that this feature is crucial in terms of the convexity of the resulting optimization problem. In the given topology Fig. 1, we assume a separation of d between all neighboring nodes. Assuming that the conflict region of a transmitter is limited to d and the hidden/exposed terminal problems are eliminated by the MAC layer, we can draw the conflict graph in Fig. 2 which helps to visualize the contending links in the network. Note that this graph is not required by the algorithm proposed in Section 3. Only local contention information (within 2-hops distance) is required. In the conflict graph, we label each wireless link of a given topology (Fig.1) with a unique number and represent it as a link node. Each line connecting the two link nodes in the conflict graph means these two links are not allowed to be active at the same time by the CSMA/CA (due to the high mutual interference). All link nodes that are connected to each other form a subgraph that is referred to as a clique. A maximal clique is a complete subgraph that is not contained in any other complete subgraph. Hence the list  of maximal cliques for the topology is given as {1, 2, 3, 4}, {1, 3, 4, 6}, {2, 4, 5}, {4, 5, 6, 7}, {6, 7, 8} . We define al as the MAC layer parameter which determines the successful medium access probability on link l after taking the effect of collisions into account. This parameter can be interpreted as the probability (or percentage of time) that link l captures the medium for transmission among the other contending links within a maximal clique m. We define a conflict matrix

18

E. G¨ urses

F of dimension M × L which describes how the different wireless links are contending for the channel where M and L denote the total number of maximal cliques and links in the network respectively. The entries fml of conflict matrix F take the value of 1 for all maximal cliques that the link l is involved and 0 otherwise. For each maximal clique m, the sum  of medium access probabilities of links that conflict should satisfy the inequality l fml al ≤ m , where m ∈ [0, 1] denotes the usable portion of a channel after excluding the effect of collisions and interframe spacing (DIFS). The value of m is depending on the number of contending links in the max. clique m and can be set adaptively using the results in [10]. m = 1 corresponds to the case of perfect scheduling where there is no collision. After running the distributed 2-hop maximum clique identification algorithm, each link l knows the set of links that can not be active at the same time with itself and denote it as I  (l). Then, the links excluded by I  (l) can interfere with l and is given by I(l) = L\I  (l). For example if we consider l = 1, by using the list of maximal cliques in Fig. 2, every link excluded by I  (1) = {1, 2, 3, 4, 6} can interfere with l = 1 (i.e. I(1) = {5, 7, 8}). In interference-limited wireless networks, we can write the physical layer link capacities as cl (P ) = W log(1 + KSINRl (P )) for a large family of modulations, where P is the transmit power, W is the bandwidth, SINRl (P ) = Gll Pl /( k =l Gkl Pk + nl ) is the signal-to-interference-plus-noise-ratio and K = (−φ1 )/(log(φ2 BER)) is a constant defined as functions of required bit error rate BER and other constants φ1 , φ2 depending on the modulation. However the expression cl (P ) for link l does not consider the effect of MAC layer duty cycle of the interfering links k  = l. Considering the effect of the MAC layer, we say only links k ∈ I(l) can be active together with l and interfere. Since an interfering link k can be active only with probability ak , then we conclude that l observes an average interference power level of Qk = ak Pk . Hence we can modify the standard SINR in order to obtain the new signal-to-average interference-plus-noise-ratio SINRl (P, a) in (1) which takes the effect of MAC layer into account. Gll Pl k∈I(l) Gkl ak Pk + nl

SINRl (P, a) = 

(1)

Then by taking the effect of MAC layer into account, physical layer link capacities can be similarly written as cl (P, a) = W log(1 + KSINRl (P, a)). However using this expression, we can safely say that high SINRl can always be maintained since CSMA/CA based MAC layer prevents the nearby (i.e. 2-hop) nodes to operate at the same time. High SINRl allows us to approximate cl (P, a) as below, where this function is later shown to be concave in (14) after proper change of variables. cl (P, a) ≈ W log(KSINRl (P, a)) 2.2

(2)

Generalized Network Utility Maximization Formulation

Given the protocol layer parameters, GNUM (Generalized Network Utility Maximization) framework is a systematic way of modeling the operation of network

Cross-Layer Optimized Congestion, Contention and Power Control

19

as a whole through these parameters. In its general form, GNUM appears as a non-linear constrained optimization problem. Similar to NUM, it maximizes the sum of source utilities at the objective function while relating the all protocol layer parameters through the constraints. The GNUM formulation given in (3)-(7) ties the transport, MAC and physical layer parameters together. The solution gives the optimal transport layer source rates xs , MAC layer channel access probabilities al and physical layer power levels Pl that maximize the total network utility. The network utility given as the objective function of (3) corresponds to the sum of individual utilities Us (xs ) of each source s which simply describes the flow fairness criteria in the network. In the rest of the paper we will use proportional fairness by setting the utility function to Us (xs ) = log xs . The values that xs , al and Pl can take are defined by the constraints in (6)-(7). The first constraint in (4) concerns all three layers to be optimized but essentially states that traffic traversing wireless links is bounded by the amount of bandwidth each link actually can maintain to the layers above MAC. The second constraint in (5) describes the link access probability constraint imposed by the MAC layer as given in detail in Section 2.1.  max Us (xs ) (3) x,a,P

s. t.

s



rls xs ≤ al c¯l (P, a)

, ∀l∈L

(4)

fm,l al ≤ m

, ∀m∈F

(5)

, ∀l∈L , ∀s∈S

(6) (7)

s

 l

0 ≤ al ≤ 1 ; Pmin ≤ Pl ≤ Pmax xmin ≤ xs ≤ xmax

3

Optimal Congestion, Medium Access and Transmit Power Control

Layering as Optimization Decomposition (LOD) [2] is a popular approach for systematically and distributively solving the cross-layer optimization problems in the form of GNUM. It simply decomposes the optimization problem into subproblems using dual decomposition and assign them to different layers. Each subproblem is solved separately by the assigned layer where the subproblems are coordinated among the layers through Lagrange dual variables. Two necessary conditions for applying LOD are convexity and separability. Separability enables the feasibility of dual-based solutions, whereas the convexity implies the identity of this dual solution with the primal one. However the given GNUM formulation in (3)-(7) is neither convex and nor separable in its dual. Due to its non-separable nature over a and P , we focus on primal-based algorithms. Nevertheless, convexity is required by primal-based algorithms to guarantee the global maximum since any local optimum is necessarily the global one in a convex optimization problem. Converting the original optimization problem into a convex one is achieved by change of variables

20

E. G¨ urses ˆ

ˆ

xs = exˆs , al = eaˆl , Pl = ePl . Defining Ql = eQl and using the expression for ˆl = a ˆ l − Pˆl we average power Ql = al Pl we obtain Q ˆl + Pˆl . Replacing a ˆl = Q ˆ ˆ conclude that the following optimization problem over xˆ, Q, P is convex, if both ˆ Pˆ ) is concave [11]. Us (ˆ xs ) and c¯l (Q,  max Us (ˆ xs ) (8) ˆ Pˆ x ˆ,Q,

s. t.

s



ˆ ˆ ˆ Pˆ ) rls exˆs −Ql +Pl ≤ c¯l (Q,

, ∀l∈L

(9)

, ∀m∈F

(10)

, ∀l∈L

(11)

, ∀s∈S

(12)

s



ˆ

ˆ

fm,l eQl −Pl ≤ m

l

ˆ min ≤ Q ˆl ≤ Q ˆ max ; Q

Pˆmin ≤ Pˆl ≤ Pˆmax

xˆmin ≤ x ˆs ≤ xˆmax Us (ˆ xs )

The new utility function obtained as =x ˆs is concave. On the other hand, ˆ ˆ ˆ we change the variables al = eQl −Pl and Pl = ePl in (1) and (2). Then the new  ˆ Pˆ ˆ = SINRl (ePˆ , eQ− link SINR is obtained as (13) after setting SINRl (Pˆ , Q) ). ˆ

Gll ePl



ˆ =  SINRl (Pˆ , Q)

k∈I(l)

Gkl eQˆ k + nl

(13)

Similarly, new physical layer link capacities are given by (14) after setting ˆ Pˆ ˆ Pˆ ) = c¯l (ePˆ , eQ− c¯l (Q, ) and using (13). Note that this is a concave function ˆ since log of exponentials is convex[11]. Hence the problem in (8)of Pˆ and Q, (12) is convex.    ˆ ˆ Pˆ ) = 1 c¯l (Q, log (KGll ) + Pˆl − log ( Gkl eQk + nl ) (14) Kmax k∈I(l)

3.1

Primal-Based Algorithm

We propose a primal-based algorithm for the distributed solution of the nonseparable convex optimization problem in (8)-(12). For this purpose, we write ˆ Pˆ ) ≤ rate and contention constraints in (9) and (10) in the standard form hl (ˆ x, Q, ˆ ˆ ˆ ˆ ˆ ˆ 0 and wm (Q, P ) ≤ 0 respectively where hl (ˆ x, Q, P ) and wm (Q, P ) are given as:   ˆ ˆ ˆ Pˆ ) = ˆ Pˆ ) hl (ˆ x, Q, rls exˆs −Ql +Pl − c¯l (Q, (15) s



ˆ Pˆ ) = wm (Q,



ˆ

ˆ

fml eQl −Pl − m

(16)

l

ˆ Pˆ ) Then, by using penalty function method the new objective function V (ˆ x, Q, is obtained as follows where κ is a positive constant [11].    ˆ Pˆ ) = ˆ Pˆ )} − κ ˆ Pˆ )} V (ˆ x, Q, Us (ˆ xs ) − κ max{0, hl (ˆ x, Q, max{0, wm (Q, s

l

m

(17)

Cross-Layer Optimized Congestion, Contention and Power Control

21

Hence, instead of the original one, the following optimization problem can now alternatively be solved over the primal variables. ˆ Pˆ ) max V (ˆ x, Q,

(18)

ˆ Pˆ x ˆ,Q,

ˆ min ≤ Q ˆl ≤ Q ˆ max s. t. Q xˆmin ≤ x ˆs ≤ xˆmax Pˆmin ≤ Pˆl ≤ Pˆmax

, ∀l∈L , ∀s∈S , ∀l∈L

ˆ Pˆ ) is not differentiable, the primal-based solution for the probSince V (ˆ x, Q, lem in (18) can be distributively obtained by using subgradient projection algorithm given by (19)-(21). Subgradients are later described in (24)-(26). Each source s and link l respectively use the equations (19) and (20)-(21) to update their primal parameters at each iteration. And since the problem is convex, any local optimum obtained by this algorithm is guaranteed to be the global one. 

ˆ l , Pˆl ) xˆ =ˆx (t), x ˆs (t + 1)= xˆs (t)+β(t)gxˆs (ˆ xs , Q s s

xˆmax (19)

ˆ l =Q ˆ l (t), Q x ˆmin Pˆl =Pˆl (t)



Qˆ max ˆ ˆ ˆ ˆ Ql (t + 1)= Ql (t)+β(t)gQˆ l (ˆ xs , Ql , Pl ) xˆs =ˆxs (t),

(20)



Pˆmax ˆ ˆ ˆ ˆ Pl (t + 1)= Pl (t)+β(t)gPˆl (ˆ xs , Ql , Pl ) xˆs =ˆxs (t),

(21)

ˆ l =Q ˆ l (t), ˆ Q Qmin Pˆl =Pˆl (t)

ˆ l =Q ˆ l (t), ˆ Q Pmin Pˆl =Pˆl (t)

First we define the functions γm and δl in (22)-(23) which can be respectively interpreted as the indication of high contention in max. clique m and congestion on link l.   ˆ ˆ 0 , for l fml eQs −Pl ≤ m γm = (22) 1 , otherwise 

0 δl = 1

 ˆ ˆ ˆ Pˆ ) , for s rls exˆs −Ql +Pl ≤ c¯l (Q, , otherwise

(23)

After defining γm and δl it is easy to calculate subgradients. However for the  Q ˆ ˆ  ˆ l , Pˆl ) = sake of brevity, we denote the common terms as D1 (Q e l −Pl m fml γm  ˆ ˆ  ˆ l , Pˆl ) = and D2 (ˆ xs , Q δl e−Ql +Pl s rls exˆs where finally the subgradients are given by:

22

E. G¨ urses

ˆ l ,Pˆl ) =1 − κexˆs gxˆs(ˆ xs ,Q



ˆ

ˆ

δl rls e−Ql +Pl

(24)

l



ˆ l ,Pˆl ) =κ D1 (Q ˆ l , Pˆl )−D2 (ˆ ˆ l , Pˆl )+ δl gPˆl(ˆ xs ,Q xs , Q (25) Kmax ˆl

Q  δj Glj  ˆ l ,Pˆl ) =κ D2 (ˆ ˆ l ,Pˆl )−D1 (Q ˆ l ,Pˆl )− e ˆj , Q) ˆ gQˆ l(ˆ xs ,Q xs ,Q SINR ( P j Kmax ePˆj Gjj j∈I(l) (26) In the given subgradient projection algorithm, positive subgradients correspond to an increase in the related primal variables. D1 is a contention related term. If there is high contention in any of the max. cliques that link l belongs to, ˆ l as respectively then D1 leads to an increase in Pˆl and decrease in a ˆl through Q given by subgradients in (25) and (26). On the other hand a non zero D2 indicates congestion on link l. Then D2 leads to a decrease in Pˆl (25) and increase ˆ l on link l according to (25) and (26) respectively. Besides D1 in a ˆl through Q and D2 , a congested link increases its power Pˆl due to the term δl /Kmax in (25). Similarly considering the last term of (26), in case of congestion on a mutually interfering link j ∈ I(l) with link l, a ˆl is decreased in order to reduce the interferˆ l . As a result, reducing interference on j ence on that remote link j by reducing Q necessarily increases SINRj and physical layer link capacity c¯j (P, a). The reader should notice that due to this last term, the update of (26) requires exchange  ˆ lj /ePˆj Gjj from remotely located, interfering and of the message SINRj (Pˆj , Q)G congested link j to l as given in (26). However as the algorithm converges the number of congested links in the network will reduce and the amount of message exchange will steadily decrease.

4

Simulation and Discussion

In our simulations, we use the simple ad hoc wireless multi-hop network topology in Figure. 1 to illustrate the optimal congestion, contention and power control algorithm. Although we consider a simple topology for the ease of showing the optimization variables, it should be noted that the proposed algorithm is fully distributed and yet scalable. The given topology is composed of N = 8 nodes, L = 8 links, S = 5 sources and M = 5 max. cliques where the separation between all neighboring nodes is 50m. According to the performance analysis of 802.11, we select m = 0.85 due to collisions and interframe spacing. We set BER = 10−3 , φ1 = φ2 = 1, W = 5MHz and Glk = d−4 lk where dlk is the distance between transmitter of link l and receiver of link k. Then we determine the noise level nl on each link by assuming an 8dB SNR level when transmitter power is set to 2.5mW. We normalize link capacities in (2) with W Kmax where Kmax = 4. We set (xmin , xmax ) = (0, 1) after the normalization and (Pmin , Pmax ) = (0.8, 28) mW. Finally we set the parameters related to algorithm as κ = 4 and β(t) = 0.01. Results of the proposed optimal congestion, contention and power control algorithm is given in Fig. 3. It is first observable that flows requiring less resources

Cross-Layer Optimized Congestion, Contention and Power Control 10

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(c) Power adaptation Fig. 3. Primal variables that describe (a) transport layer rates xs , (b) MAC layer link access probabilities al , (c) physical layer transmit power levels Pl

from the network (for. ex. number of hops) are getting higher bandwidth. Furthermore we can also observe that source rates xs are converging very fast where the other primal variables need more iterations to converge. However besides these observations, it is hard to understand how al and Pl are optimal (i.e. maximizing network utility) since the problem is not separable over them. Therefore rather than examining al and Pl separately, we investigate the effective capacity observed above MAC layer al c¯l (P, a) which is a function of both al and Pl . These effective capacities al c¯l (P, a) are plotted in Fig. 4(b) and can be calculated by using al in Fig. 3(b) and c¯l (P, a) in Fig. 4(a). In the following discussion we denote any link k ∈ {1, ..., 8} as Lk in short. From the topology given in Fig. 1 we can see that L6 (short notation for link 6) is used by 3 flows, which implies increasing the effective capacity on L6 may lead to a rate increase in 3 flows xA , xB and xD . Although L4 carries 2 flows (second most used link), these flows xA and xB use the link L6 as well, hence capacity of L4 is bounded by the effective capacity of L6. On the other hand L8 is used by a single flow xE , and this flow is not using any other link. Then any increase in effective capacity of L8 directly results in an increase in the utility of xE . Hence effective capacities on L6 and L8 are the most important ones for maximizing the network utility. Therefore

24

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8 Link 1 Link 2 Link 3 Link 4 Link 5 Link 6 Link 7 Link 8

al.cl (Effective link capacity) (Mbps)

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Fig. 4. (a) Physical layer link capacities c¯l (P, a), (b) effective link capacities al .¯ cl (P, a)

these two most important links L6 and L8 get the highest effective link capacity in Fig. 4(b). On the other hand effective capacities of L1 and L7 are the least important ones in terms of their contribution to the network utility, since the only flow xA using them also uses L4 and L6. Hence the effective capacities of L1 and L7 are bounded by L4 and L6. Therefore in Fig. 4(b) they acquire the lowest values for effective link capacities. Finally, L2 and L3 are used only by the single flow xC which couples the effective capacities of these links. Since any increase on these link capacities excluding the other, do not contribute to the utility of xC . This coupled behaviour is also clearly observable in Fig. 4(b) where the values of effective link capacities follow each other. Finally we compare the results of the distributed algorithm with the results of MATLAB’s fmincon(.) which solves (8)-(12) in a centralized way. However fmincon returns the maximum possible values for primal variables eventhough they do not increase the objective function. Therefore the optimal power levels Pl∗ for L1 and L7 are significantly different from results of the distributed algorithm. However the remaining results are conforming with the results of the distributed algorithm (with some error). Table 1. Results of the centralized solution obtained by fmincon(.) Sources A B C D E x∗s 1.2375 1.6570 3.4787 2.0328 5.0031 (Mbps) Links 1 2 3 4 5 6 7 8 a∗l 0.0534 0.3911 0.2388 0.1666 0.2364 0.3911 0.0559 0.4030 Pl∗ 14.9776 0.9992 3.3494 4.8014 1.8542 2.0454 14.3182 1.9851 (mW) a∗l .c∗l 1.0683 3.4787 3.4787 2.8945 2.0328 4.9273 1.1175 5.0031 (Mbps)

5

Conclusions and Future Work

In this paper we formulate the optimal joint congestion, contention and power control problem in wireless ad hoc networks through GNUM. Considering the

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25

effect of MAC layer on physical layer link capacities, the original GNUM problem become non-convex and non-separable which can not be directly solved by LOD. After converting the problem to a convex one, we devise a primal-based distributed algorithm to solve the given cross-layer optimization problem. Simulation results show that the results of the centralized optimal solution obtained by MATLAB can be reached without any global coordination. In this work we limit our GNUM model to the basic modes of operation in PHY, MAC and network layers. An immediate step in the future is to evaluate the performance of the proposed algorithm in an actual network setting. Another possible future direction is to extend these basic modes of operation to more complicated ones like PHY layer cooperative diversity, MAC layer multi-channel assignement and network layer optimal routing which could lead to a dramatic increase in the network performance.

References 1. Kelly, F.P., Maulloo, A., Tan, D.: Rate control for communication networks: Shadow prices, proportional fairness and stability. Journal of Operations Research Society 49(3), 237–252 (1998) 2. Chiang, M., Low, S.H., Calderbank, A.R., Doyle, J.C.: Layering as optimization decomposition: A mathematical theory of network architectures. Proceedings of the IEEE 85(1), 255–312 (2007) 3. Chen, L., et al.: Joint congestion control and media access control design for ad hoc wireless networks. In: Proc. of IEEE INFOCOM, vol. 3, pp. 2212–2222 (2005) 4. Wang, X., et al.: Cross-layer rate control for end-to-end proportional fairness in wireless networks with random access. In: Proc. ACM Mobihoc, pp. 157–168 (2005) 5. Lee, J.-W., Chiang, M., Calderbank, R.A.: Jointly optimal congestion and contention control based on network utility maximization. IEEE Communication Letters 10(3), 216–218 (2006) 6. Lee, J.-W., Chiang, M., Calderbank, R.A.: Utility-optimal medium access control: Reverse and forward engineering. In: Proc. of IEEE INFOCOM, pp. 1–13 (2006) 7. Chiang, M.: Balancing transport and physical layers in wireless multihop networks: Jointly optimal congestion control and power control. IEEE Journal on Selected Areas in Comm. 23(1), 104–116 (2005) 8. Lee, J.-W., Mazumdar, R., Shroff, N.B.: Joint opportunistic power scheduling and end-to-end rate control for wireless ad hoc networks. IEEE Trans. on Vehicular Technology 56(2), 801–808 (2007) 9. IEEE 802.11 WG: International standard for information technology local and metropolitan area networks-specific requirements Part 11: Wireless LAN MAC and PHY specifications (1999) 10. Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Comm. 18(3), 535–547 (2000) 11. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

Early Overhearing Avoidance in Wireless Sensor Networks Siquan Hu and Mehul Motani Department of Electrical and Computer Engineering, National University of Singapore, 117576 Singapore {husq,motani}@nus.edu.sg

Abstract. To reduce the overhearing cost of long preambles in low power listening (LPL), we propose an early overhearing avoidance technique, in which the destination address is embedded into the continuous preamble. The overhearing can be stopped after receiving a short preamble block, which is much earlier than after the whole data packet is received. Based on this idea, a novel low power media access control (MAC) protocol called asynchronous pseudo preamble listening (APPL) is designed to improve the power efficiency of LPL. Both analysis and experiments show that APPL saves more power than LPL, especially on nodes with more neighbors and fewer children. Keywords: Overhearing avoidance, Wireless sensor networks, Medium access control.

1

Introduction

Increasing the network lifetime is a key challenge in low power sensor networks. In this scenario, the MAC protocols used must be energy efficient by reducing potential energy wastage. In resource-constrained wireless sensor networks, one of the primary mechanisms to achieve low power operation is duty cycling, in which each sensor node periodically cycles between awake and sleep states. Most low power MAC protocols proposed for wireless sensor networks can be roughly categorized into scheduling-based and sampling-based approaches, along with hybrid combinations. Scheduling-based protocols include TDMA, FDMA, CDMA and variations such as S-MAC [1], T-MAC [2] and TRAMA [3]. Sampling-based MAC protocols include Aloha with preamble sampling [4], BMAC [5], WiseMAC [6], etc. Hybrid protocols also exist that combine a synchronized protocol with asynchronous sampling [7]. In scheduling-based protocols, a specific schedule of media access is used in time, frequency, or code domains. For instance, in TDMA family of MAC protocols for wireless sensor networks, nodes schedule their radio states in discrete time intervals (slots) and synchronize slots with neighbors allows nodes to only power the radio on when needed, significantly reducing idle listening. Generally, scheduling-based protocols require some kind of synchronization and lack scalability. Scheduling reduces energy cost by ensuring that listeners and transmitters A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 26–35, 2008. c IFIP International Federation for Information Processing 2008 

Early Overhearing Avoidance in Wireless Sensor Networks

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have a short period to rendezvous and can sleep at other times. Schedule maintenance and idle listening in contention windows cause extra overhead. The sleep interval can be extended to save more power, but this can potentially reduce throughput. In sampling-based MAC methods, the sensor nodes do not coordinate their wakeup/sleep schedules by explicit synchronization. Rather, nodes periodically wake up and only start receiving data if they detect channel activity by sampling based either on channel energy or successful symbol decoding. Periodic radio sampling allows a node to conserve energy by keeping its radio off most of the time. Although different kinds of schedule synchronization may be introduced into sampling protocols, as a pure asynchronous sampling-based MAC, low power listening (LPL) is still the most attractive protocol in wireless sensor networks. The duty cycles of the sender and receiver can be completely decoupled, which provides great flexibility and scalability. The simplicity of this asynchronous design removes the need for, and the overhead introduced by, synchronized wakeup/sleep schedules. The code size is also reduced which is valuable to the light-weight protocol design requirement for most WSN application, especially body sensor networks. Unlike scheduling based protocols, which send regular data packets, LPL must send long, expensive messages to ensure that the receiver can receive the payload in the message. In every transmission, the sender sends a preamble before the payload. The preamble length must be at least as long as the receiver’s sampling interval. When sharing a common wireless medium, the data transmitted by one node can reach all the other nodes within its transmission range. The receiver will wake up periodically, detect the preamble, and stay awake to receive the data. If the receiver is not the targeted node, the receiving wastes energy, which we refer to as overhearing. Idle listening is a dominant factor of power inefficiency in many sensor network applications. LPL reduces idle listening successfully by shifting the burden of synchronization to the sender and lowering the radio duty cycle by turning the radio off much of the time. The other source of energy waste is overhearing, which occurs when a node receives packets that are not destined to itself. LPL doesn’t address this problem, which can become severe in dense deployments. Reducing the cost of overhearing could improve the power efficiency of LPLbased protocols. In this paper, we propose an early overhearing avoidance technique to reduce the overhearing cost in LPL. The core idea is that the destination address is embedded into the preamble so that the untargeted nodes can go to sleep quickly after parsing the destination address. Furthermore, we propose an asynchronous pseudo preamble listening (APPL) MAC for wireless sensor networks, which preserves the advantage of asynchronous LPL and reduces the overhead of overhearing based on the pseudo preamble structure. APPL has higher power efficiency than LPL without decreasing the throughput and latency performance. The remainder of this paper is organized as follows. Section 2 describes related work. The APPL MAC protocol is presented in Section 3 and its performance is

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analytically evaluated in Section 4. Implementation of APPL protocol on Mica2 and the experimental results are described in Section 5. Section 6 concludes this paper.

2

Related Work

LPL is an approach where the channel is sampled very briefly for the presence of activity rather than for specific data. The idea of sending a wakeup signal by simply adding preambles in front of each transmitted packet was initially introduced into wireless sensor networks by Hill [8] and El-Hoiydi [4] independently. Hill proposed to periodically sample the energy of analog baseband of the radio and use a low power radio frequency wakeup signal to reduce the duty cycle of the radio. El-Hoiydi’s Aloha with preamble sampling protocol proposed that packets be sent with long preambles to match the channel check period. B-MAC [5] implemented LPL in TinyOS with a well-defined interface for applications to perform fine-grained control over the MAC. Several groups have improved specific aspects of LPL. WiseMAC [6] is an iteration on Aloha with preamble sampling and it improves previous LPL protocols by reducing the preamble length after an initial unicast packet with a long preamble. The receiver piggybacks its next sampling time in the ACK packet, allowing the sender to send the next packet with a short preamble. SP [9] similarly proposes a scheme for a node to piggyback its channel sampling schedule when it sends a data packet with the long LPL preamble. Both WiseMAC and SP have limitations caused by clock drift. UBMAC [10] further reduces the long preambles in B-MAC by precisely estimating the clock drift between packet transmissions. It requires fine-grain time synchronization which requires high-resolution timers and has very high CPU cost. Halkes et al. [7] study the benefit of directly utilizing LPL in the listening intervals of S-MAC and T-MAC. SCP-MAC [11] trades off the penalty of long LPL preambles with the cost of synchronizing schedules based on worst-case clock drift. It also adjusts the sampling interval based on traffic. Rather than introducing schedule either by fine or coarse schedule, our work differs from each of the above schemes to improve LPL’s power efficiency by overhearing avoidance without losing the flexibility of long preambles. SCP-MAC does include a kind of overhearing avoidance for LPL which is based on MAC headers. To do this, a receiver examines the destination address of a packet immediately after receiving its MAC header, before completely receiving the packet. If it is a unicast packet destined to another node, it immediately stops the reception and places the radio to sleep. Since the decision can only be made after the header is received and the LPL preamble is much longer than the real packet, the power saved is very limited. Our work is different from X-MAC [12], although both of them embed the destination address into preamble. X-MAC use a series of strobe preambles which are separated by slots trying to receive an ACK from target nodes. At the sender side, the cost is increased by frequent radio switching between transmitting and

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receiving states. It also has difficulty in supporting broadcast or multicast because of lack of coordination among the sender and multiple receivers. Our APPL approach uses the continuous preamble of LPL and restructures it into continuous sequence of Pseudo Preambles, which preserve the asynchronous advantage of LPL as much as possible while reducing the cost of overhearing. Miller and Vaidya [13] also propose to include the destination address in the wakeup packet through the primary channel of a two-radio architecture. However, our work doesn’t require such special hardware and APPL can work on current practical WSN platforms, most of which are using a single radio chip.

3

Design of Asynchronous Pseudo Preamble Listening

The design goal of APPL is to preserve the advantage of asynchronous duty cycling techniques and to improve LPL’s power efficiency by early overhearing avoidance. Low-power listening is an approach to reduce idle listening, by introducing a duty-cycle, which operates at the physical layer. The basic idea of low-power listening is to shift the cost from the receiver to the transmitter by increasing the length of the preamble. This allows the receiver to periodically turn on the radio to sample for incoming data, and detect if a preamble is present or not. If it detects a preamble, it will continue listen until the start symbol arrives and the message can be properly received. If no preamble is detected the radio is turned off again until the next sample. LPL’s inherent asynchronous nature makes it preferable to synchronized protocols in terms of energy efficiency, latency, and throughput. In part, this is because they do not incur overhead due to synchronization. In addition, asynchronous techniques do not have to share schedule information and only stay awake long enough to sample the radio when they are not receiving or transmitting data. Hence, the awake period can be significantly shorter than that of synchronized methods. With a shorter awake period, asynchronous protocols can wake up more often while still maintaining a low duty cycle. Consequently, they experience reduced latency and higher throughput. In the networking stack, power efficiency is not only involved at any single layer. To achieve the primary goal of energy saving, cross-layer design is becoming more and more important. Most previous works make use of parameters propagated from upper layers to adapt the medium access protocol. Our work applies cross-layer design in a different way. APPL MAC exploits the physical layer’s potential of power saving. In APPL, to save the cost of overhearing for untargeted nodes, we replace the long preamble of LPL with a serial of short pseudo preamble blocks followed by one short true preamble. The last true preamble is followed by the start symbol and MAC header. All pseudo preamble blocks, true preamble, start symbol are a continuous bit stream. Each pseudo preamble block is composed of three parts. The first part is the ordinary preamble bit pattern; the length of the preamble bit pattern should be the minimum required length of preamble in 100% duty cycle LPL, i.e. no

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Fig. 1. Structure of the pseudo preamble block for Mica2. (PSS is the pseudo start symbol, Dest is the destination address of current packet.)

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Fig. 2. Comparison of the timelines of LPL and APPL. (SS is the start symbol, PP is the pseudo preamble block, P is the short standard preamble, MacH is the MAC header.)

power management. The second part is a pseudo start symbol which is different from the regular start symbol. The pseudo start symbol is used to determine the bit shift of followed address information. The third part is the destination address information. Fig. 1 is an example of pseudo preamble block which using ’010101...’ bit pattern such as CC1000 radio chip. Similar to LPL, each node in APPL periodically wakeups and samples the preamble. If the radio strength is strong enough, the radio transfer to RX state and receives the preamble. From the short pseudo preamble block or the true preamble followed by the MAC header, the destination address of the packet can be identified. If the local node doesn’t match the destination address, it aborts the reception in progress and goes to sleep quickly. If local node is the target node, then it will continue to detect the start symbol to receive the real data. Similar to the standard start symbol, the pseudo start symbol can be used to determine the bit shift of the bytes in the preamble block, so the cost of bit synchronization doesn’t increase in APPL. Fig. 2 illustrates the timelines of LPL and APPL.

4

Performance Analysis

In this section we analyze the energy performance of LPL and APPL. Both LPL and APPL are asynchronous, giving them flexibility and simplicity without synchronization overhead. We will only analyze their energy efficiency. Our analysis

Early Overhearing Avoidance in Wireless Sensor Networks

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Table 1. Parameters of Mica2 platform Symbol

Value

Description

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60mW

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Prx

45mW

Psampling 15.5mW

Power in receiving Power in radio sampling

Psleep

0.09mW

Power in sleeping

TB

0.000416s

Time to TX/RX a byte

Tsampling 0.00255s Tch

Time to sample radio

0.125s

Radio Check Interval

Tgen

180s

Data generate Interval

Lpacket

36 bytes

Packet Length

Lpreamble 308 bytes

LPL preamble length

Lpp

8 bytes

pseudo preamble block length

Npp

38

number of pseudo preamble blocks

n

10

neighborhood density

is based on a multi-hop network model where each node periodically generates packets at a fixed interval. Each node in the network will sample the channel at a uniform rate and detect the preamble when each of its n neighbors transmits a packet. In the configuration using LPL, the node will receive the data regardless of the packet’s destination. Whereas in configuration using APPL protocol, the node will determine whether to receive the data or go to sleep based on the detected preamble. The model parameters are given in Table 1 and the typical values on Mica2 platform are also listed (the values are sourced from [5] and the voltage is assumed to be 3 Volts). Consider a node i in the network, the number of neighbors surrounding the node i is denoted by n, which is neighborhood density of node i. Since our interest is power efficiency at the MAC layer, we will ignore the details of the routing layer. Furthermore, without loss of generality, we use a function c(i) to represent the number of children of node i in the multihop network. To simplify the analysis, we will ignore the boundary condition in which one node among neighbors of node i will forward the message out of the neighborhood of i. Our analysis focuses on the energy consumption by the motes, including the radio and CPU; we do not model the sensors. There are four stable radio states: transmitting, receiving, sampling and sleeping. The power (energy per unit time) drawn in each state is denoted by Ptx , Prx , Psampling and Psleep , respectively. The energy consumption of the mote is determined by how much time it spends in transmitting, receiving, sampling and sleeping, denoted as ttx , trx , tsampling     and tsleep respectively for LPL, and denoted as ttx , trx , tsampling and tsleep respectively for APPL. In our analysis, all these time values are normalized to one second. They represent the fractions of time in one second the node is in the

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different states. For both LPL and APPL, the expected energy consumption per node, is the sum of the expected energy spent in each state: E = Etx + Erx + Esampling + Esleep . For LPL, Elpl = Ptx ttx + Prx trx + Psampling tsampling + Psleep tsleep .

(1)

For APPL, 







Eappl = Ptx ttx + Prx trx + Psampling tsampling + Psleep tsleep .

(2)

So we only need to know the time spent on each state to deduce the total energy. Actually the time spent on channel sampling is the same for LPL and APPL when using the same channel check interval. So 

tsampling = tsampling = Tsampling /Tch .

(3)

To compare the energy efficiency of LPL and APPL, we use the same length of preamble for both. So Lpreamble = (Npp + 1)Lpp − 4,

(4)

which assumes the length of pseudo start symbol and true start symbol is 2 bytes and the address is 2 bytes. So the time spent on transmitting in LPL and APPL equal to  ttx = ttx = (Lpreamble + 2 + Lpacket )TB /Tgen . (5) The energy spent on receiving is varying because start point of receiving is random; however, its expected value can be estimated. Let Lrx denotes the

Early Overhearing Avoidance in Wireless Sensor Networks

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expected value of received bytes, then Lrx = 0.5Lpreamble + 2 + Lpacket . For LPL, trx = nLrx TB /Tgen . (6) For APPL,



trx = (1.5(n − c(i))Lpp + c(i)Lrx )TB /Tgen .

(7)

And the remaining time in 1 second is the sleep time. So for LPL, the sleep time is tsleep = 1 − ttx − trx − tsampling . (8) And for APPL, the sleep time is 







tsleep = 1 − ttx − trx − tsampling .

(9)

Then the energy spent in 1 second for LPL can be computed from formula (1)(3)(4)(5)(6)(8), and from formula (2)(3)(4)(5)(7)(9) to get APPL’s total energy in 1 second. From (6) and (7), we can know if the children number of node i decreases, APPL’s time spent on receiving will become smaller so more energy is saved. However, LPL’s receiving time doesn’t change as the number of children changes. In Fig. 3, the energy spent on 1 second for different c(i) are shown as real lines. The dotted lines in Fig. 3 show the effect of neighborhood density; we can see that the higher density, more energy saved by APPL compared to LPL. To match the pulse check interval, APPL’s total preamble length including all pseudo preamble blocks are the same as LPL’s preamble provided that they have the same pulse check interval. Both of them have the same latency and throughput performance. At most one packet can flow through a node in a pulse check interval. This means APPL improves the energy performance of LPL MAC without decreasing the latency and throughput.

5

Experimental Evaluation

The main contribution of our work is to propose to embed the destination address into the preamble so that the overhearing can be stopped after the destination address is parsed, which is much earlier than after the whole data packet is received. We call this early overhearing avoidance, and have proposed a novel low power MAC, APPL, to improve the power efficiency of LPL. The analytical performance computations show that APPL saves more power than LPL, especially when neighborhood density is high, and the saving is more significant in those nodes which have fewer children. We have implemented both LPL and APPL protocol on the Mica2 platform on TinyOS to validate our analysis. The parameters are the same as those listed in Table 1 except for neighborhood size. We use the deployment topology shown in Fig. 4 to verify experimental results with the analytical results. The arrows in the topology map represent the routing from a child node to its parent node. Node 0 is the base station. Nodes 1 - 6 are deployed in the radio range of each

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0

2

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5

1

4

3

Fig. 4. The topology map of the test Table 2. The power consumption results Power Consumption (mW) Node 2 Node 1 Node3-6 LPL

0.54

0.48

0.46

APPL

0.48

0.35

0.28

other to test the effect of overhearing. Only node 1 and node 2 are in the radio range of base station. The topology used is static, which is sufficient for MAC layer performance evaluation. The base station is on full power state and doesn’t generate any packets. The neighborhood size of nodes 1-6 is 6. The number of children of each node is shown in the map. As noted before, c(i) is the number of children of node i, so c(1) = 1, c(2) = 3, for other node i(i = 3, 4, 5, 6), c(i) = 0, which means they are leaf nodes. We measure the energy consumption at each node by recording the time spent by the radio in the different states and normalize them to 1 second. The experiment results are averaged over a 1 hour running time and listed in Table 2. For leaf nodes 3-6, the energy efficiency has been increased by 39%; for node 1 who has 1 child, the increased efficiency is 27.1%, and node 2 with 3 children is 11%. The experiment matches well with our analysis in section IV that APPL outperforms LPL especially for nodes who have less children.

6

Conclusion

This paper has presented an early overhearing avoidance technique by embedding the destination address into the preamble. We also evaluated a novel MAC, APPL, which uses the early overhearing avoidance technique to improve the power efficiency of LPL. Both analytical computations and experimental evaluation show that APPL is more power efficient than LPL especially on nodes with fewer children and more neighbors. Though APPL builds upon LPL, we

Early Overhearing Avoidance in Wireless Sensor Networks

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believe that adding a small amount of information into the long sparse preamble and exploiting early overhearing avoidance will improve the performance of any underlying preamble sampling-based MAC protocol.

Acknowledgment This work was supported by the EHS-II project funded by A*STAR, Singapore.

References 1. Ye, W., Heidemann, J.S., Estrin, D.: An energy-efficient MAC protocol for wireless sensor networks. In: INFOCOM, pp. 1567–1576 (2002) 2. van Dam, T., Langendoen, K.: An adaptive energy-efficient MAC protocol for wireless sensor networks. In: ACM SenSys. pp. 171–180 (2003) 3. Rajendran, V., Obraczka, K., Garcia-Luna-Aceves, J.J.: Energy-efficient collisionfree medium access control for wireless sensor networks. In: ACM SenSys. pp. 181–192 (2003) 4. El-Hoiydi, A.: Aloha with preamble sampling for sporadic traffic in ad hoc wireless sensor networks. In: IEEE ICC., pp. 3418–3423 (2002) 5. Polastre, J., Hill, J., Culler, D.: Versatile low power media access for wireless sensor networks. In: ACM SenSys. pp. 95–107 (2004) 6. El-Hoiydi, A., Decotignie, J.D.: WiseMAC: An ultra low power MAC protocol for multi-hop wireless sensor networks. In: First Int. Workshop on Algorithmic Aspects of Wireless Sensor Networks, July 2004, pp. 18–31 (2004) 7. Halkes, G.P., van Dam, T., Langendoen, K.G.: Comparing energy-saving MAC protocols for wireless sensor networks. Mob. Netw. Appl. 10(5), 783–791 (2005) 8. Hill, J., Culler, D.: Mica: A wireless platform for deeply embedded networks. IEEE Micro. 22(6), 12–24 (2002) 9. Polastre, J., Hui, J., Levis, P., Zhao, J., Culler, D., Shenker, S., Stoica, I.: A unifying link abstraction for wireless sensor networks. In: ACM SenSys. pp. 76–89 (2005) 10. Ganeriwal, S., Ganesan, D., Shim, H., Tsiatsis, V., Srivastava, M.B.: Estimating clock uncertainty for efficient duty-cycling in sensor networks. In: ACM SenSys. pp. 130–141 (2005) 11. Ye, W., Silva, F., Heidemann, J.: Ultra-low duty cycle MAC with scheduled channel polling. In: ACM SenSys. pp. 321–334 (2006) 12. Buettner, M., Yee, G.V., Anderson, E., Han, R.: X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks. In: ACM SenSys. pp. 307–320 (2006) 13. Miller, M.J., Vaidya, N.H.: A MAC protocol to reduce sensor network energy consumption using a wakeup radio. IEEE Trans. Mobile Comput. 4(3), 228–242 (2005)

Preamble MAC Protocols with Non-persistent Receivers in Wireless Sensor Networks Abdelmalik Bachir, Martin Heusse, and Andrzej Duda Grenoble Informatics Laboratory, Grenoble, France

Abstract. In preamble sampling MAC protocols, nodes keep their radios off most of the time to reduce idle listening and periodically wake up for a short time to check whether there is an ongoing transmission on the channel. Such access methods result in substantial energy savings in low traffic conditions. In this paper, we compare several representative preamble sampling MAC protocols in which receivers are non-persistent. Our analysis takes into account bit error rate and traffic load to compute energy consumption and link reliability. Our results show that two access methods obtain the longest normalized lifetime for a wide range of bit error rates: MFP (Micro Frame Preamble) and DFP (Data Frame Preamble).

1

Introduction

We consider energy consumption and link reliability in Wireless Sensor Networks. Previous studies on evaluating various medium access methods in such networks have neglected the effect of transmission errors that have significant impact on reliability and energy consumption due to frame retransmissions. In this paper, we analyze a class of access methods for sensor networks: preamble sampling schemes with non-persistent receivers (a companion paper considers the schemes with persistent receivers [2]). Preamble sampling, also referred to as LPL (Low Power Listening) [3,7], is one of the best methods for energy saving in low traffic conditions [8, 4]. In preamble sampling (see Fig. 1), nodes keep their radios off most of the time to reduce idle listening and periodically wake up for a short time to check whether there is an ongoing transmission on the channel. If a node detects a transmission, it keeps its radio on to receive a data frame sent after the preamble. To avoid deafness, nodes precede each data frame with a preamble long enough to make sure that all the nodes will wake up at least once during the preamble. The initial idea of preamble sampling has inspired the design of many variants such as: WiSeMAC [4], XMAC [9], MFP (Micro Frame Preamble) [1], DFP (Data Frame Preamble) [1], CSMA-MPS [10], STEM [6], WOR (Wake On Radio) [5], and SCP [11]. However, their authors only considered the energy consumption of the MAC layer and neglected the effect of transmission errors. We analyze several representative protocols of preamble sampling schemes with non-persistent receivers: LPL, MFP, DFP, XMAC, and WOR. Our analysis A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 36–47, 2008. c IFIP International Federation for Information Processing 2008 

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Fig. 1. Preamble sampling technique

Fig. 2. Preamble composed of control frames—MFP

takes into account bit error rate and traffic load to compute energy consumption and link reliability. To obtain numerical comparisons we assume physical layer characteristics of the most efficient low power radio chips available on the market. Our analysis show that two access methods obtain the longest normalized lifetime for a wide range of bit error rates: MFP and DFP. The rest of the paper is organized as follows. In Section 2, we describe the operation of variants of the basic preamble sampling protocol with non-persistent receivers. In Section 3, we define the metrics to compare the representative protocols and derive expressions for each specific variant in Section 4. In Section 5, we present numerical comparisons for various input parameters and conclude in Section 6.

2

Preamble Sampling Protocols

In basic preamble sampling protocols, the preamble consists of a specific pattern of bits to let the receiver know that a data frame will be transmitted. As the transmission of the preamble consumes energy, many other protocols proposed to transform the preamble into a series of frames, which we call preamble-frames in this paper. The frames transmitted in the preamble may be small control frames such as in MFP (Micro Frame Preamble) and in XMAC, or copies of the forthcoming data frame such as in DFP (Data Frame Preamble) and in WOR. In MFP, the preamble is composed of a series of small control frames, referred to as micro-frames. Each micro-frame carries information on the forthcoming data frame: its contents and the instant at which it will be transmitted. Thus,

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Fig. 3. Preamble composed of control frames—XMAC

when the receiver wakes up to sample the channel, it receives a micro-frame from which it learns when the forthcoming data will be transmitted. In the meantime, after the reception of the micro-frame and before the arrival of the data frame, the receiver goes to sleep mode to save energy. The receiver wakes up again only to receive the data frame. XMAC uses a similar approach to MFP, however it inserts gaps between each two consecutive control frames called XMAC-frames so that the receiver can send an ACK-frame back to the transmitter, which in turn stops transmitting the preamble and sends the data frame. The main advantage of XMAC-frames is to avoid the transmission of a full-length preamble, thereby reducing energy at both the transmitter and the receiver. However, this technique applies only to unicast frames since broadcast frames are not acknowledged. In DFP, the preamble frames are copies of the forthcoming data frame. The advantage of DFP is that the node that wakes up to check the channel can immediately receive the data frame. DFP also presents another advantage: duplicating the data in preamble frames increases transmission reliability. However, the node cannot avoid receiving irrelevant data, which may consume non-negligible energy if data frames are large or when they are transmitted at low bit rates. WOR uses a similar approach to DFP: preamble frames called WOR-frames are copies of the forthcoming data frame. However, in contrast to DFP, WORframes are not transmitted in a contiguous way, but they are separated by gaps to let the receiver send an ACK-frame. As in XMAC, when the transmitter receives an ACK-frame, it stops the transmission of WOR-frames and considers the whole transmission successful. In this paper, we consider the variant of the above protocols in which the receiver is non-persistent: it goes back to sleep mode if it fails to receive the first preamble frame it samples.

3

Performance Evaluation Metrics

We consider that two nodes communicate over a wireless link modeled as the Binary Symmetric Channel (BSC) in which a bit has constant and independent

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Table 1. Notation for the analysis p m d a x pm pd pa px Tm Td Ta Tx τ TCS TCI Pr Pt Ps rm rd rw rx

bit error probability micro-frame size in bits data frame size in bits ack-frame size in bits xmac-frame size in bits probability that a micro-frame is corrupted, pm = 1 − (1 − p)m probability that a data frame is corrupted, pd = 1 − (1 − p)d probability that an ack-frame is corrupted, pa = 1 − (1 − p)a probability that an xmac-frame is corrupted, px = 1 − (1 − p)x transmission time of one micro-frame transmission time of one data frame transmission time of one ack-frame transmission time of one xmac-frame transition time from sleep mode to active mode the carrier sense duration the check interval duration power drained in receive mode power drained in transmit mode power drained in sampling mode number of frames transmitted in MFP, rm = TCI /Tm  number of frames transmitted in DFP, rd = TCI /Td  number of frames transmitted in WOR, rw = TCI /(Tw + Ta ) number of frames transmitted in XMAC, rx = TCI /(Tx + Ta )

error probability p. We want to find the energy cost of transmitting a data frame over the link (the amount of energy drained both at the transmitter and the receiver) and estimate its reliability (the probability that the receiver correctly decodes the data frame). Although BSC is a simple error model, our results provide an interesting insight into the main properties of preamble sampling protocols. Table 1 presents the notation used in our analysis. We assume that a data frame can be retransmitted in case of errors. If pf is the probability of a failed transmission, we define the reliability pR as the probability of a successful delivery in n attempts pR = 1 − pnf . There are no retransmissions for broadcasts, thus n is equal to 1. 3.1

Sampling Cost

The energy drained in sampling is E s = Ts Ps , where Ps is the power needed for listening to the channel and Ts is the time required to decide whether the channel is free or there is an ongoing valid transmission. The detection of the validity of a transmission depends on the protocol variant. 3.2

Transmission Cost

We distinguish between a successful transmission and a single transmission. A single transmission involves only the preamble and the data, whereas a sucessful

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transmission may include several (up to n) single transmissions. We use etsucc (resp. etfail ) to refer to the energy drained in the case of a successful (resp. failed) single transmission. We call E t the average energy drained in n transmission attempts. We have: n−1     1 − pnf t i t t E =(1− pf ) pf [iefail + esucc ] + pnf netfail = pf etfail + (1− pf )etsucc . 1 − pf i=0 3.3

Reception Cost

Similarly, we derive the average energy drained in reception. Let ersucc (resp. erfail ) express the energy the receiver drains in receive mode in the case of a successful (resp. failed) single transmission. We thus obtain:   1 − pnf r r r E = pf efail + (1 − pf )esucc . (1) 1 − pf 3.4

Normalized Lifetime

We define the lifetime duration of a node as L◦ =

Einitial , P◦

(2)

where P◦ (joule/sec) is the average power a sensor node consumes and Einitial (joule) is its initial energy. Symbol ’◦’ denotes a given protocol. For the sake of conciseness and simplicity, we only consider the power consumed by the radio— we assume that the overhead of the microcontroller is very small. Therefore, we have P◦ = P◦t + P◦r + P◦s ,

(3)

where P◦t (resp. P◦r and P◦s ) is the average power drained in transmission (resp. reception and sampling). The average power drained during preamble sampling is P◦s =

E◦s . TCI

(4)

Similarly, we compute the average power drained during transmission P◦t = E◦t · Ttraffic

(5)

and the average power drained during reception P◦r = E◦r · Ttraffic . where Ttraffic is the average number of messages transmitted per unit time.

(6)

Preamble MAC Protocols with Non-persistent Receivers

4

41

Evaluation of Preamble Protocols with Non-persistent Receivers

In this section, we evaluate the reliability and the energy consumption of preamble sampling MAC protocols with non-persistent receivers: LPL, MFP, DFP, WOR, and XMAC. 4.1

LPL (Low Power Listening)

To perform channel sampling, a node running LPL goes from sleep mode to active mode, which requires duration τ . In active mode, the node needs duration TCS to determine whether it is receiving a signal. Therefore, the energy drained in channel sampling is: es = (τ + TCS )Ps The energy drained in channel sampling is the same whether the expected frame is broadcast or unicast. However, the other parameters differ depending on communications patterns: broadcast or unicast. In the rest of the paper, we compute these parameters only for unicast cases. Those for broadcast can be easily derived using a similar methodology while ignoring the ACK-frames. A single transmission fails when either the data frame or the ACK-frame are corrupted, therefore, the probability of failure is pf = 1 − (1 − pd )(1 − pa ). The reception starts when a node detects a preamble. As the receiver may wake up at any time during the preamble, it receives on the average the half of the preamble plus the data frame. In the case of unicast, the energy drained in reception is: ersucc = (τ + TCI /2 + Td )Pr + Ta Pt erfail = (τ + TCI /2 + Td )Pr + (1 − pd )Ta Pt Before transmitting, the transmitter checks whether the channel is free, goes from carrier sensing mode to transmit mode, and transmits the full-length preamble followed by the data frame. The transition time from carrier sensing mode to transmit mode is short and thus can be neglected. Note that the energy drained in transmission is the same whether the single transmission succeeds or fails. In both cases, the transmitter goes to receive mode after transmission to wait for the ACK-frame. Thus: etsucc = etfail = es + (TCI + Td )Pt + Ta Pr 4.2

MFP (Micro Frame Preamble)

In MFP, the energy drained during channel sampling is the same as for LPL, i.e. es = (τ + TCS )Ps . A single transmission succeeds when the receiver correctly

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decodes both a micro-frame and the forthcoming data frame, and the transmitter correctly decodes the ACK-frame. Therefore, the failure probability is: pf = 1 − (1 − pm )(1 − pd )(1 − pa ). As the preamble in MFP is composed of micro-frames, the transmitter needs to transmit rm micro-frames to cover the duration of the check interval thus draining the energy etsucc = etfail = es + (rm Tm + Td )Pt + Ta Pr . As the receiver does not necessarily wake up at the beginning of a micro-frame, then it misses the half of a micro-frame on the average before it starts correctly receiving the subsequent micro-frame. Thus, the time needed to receive a complete micro-frame is Tm /2 + Tm = 3Tm /2. After that, the receiver goes back sleeping and then wakes up again to receive the data frame. The receiver sends an ACK-frame only if it correctly receives the data frame. Therefore, we obtain two different formula. For a successful single transmission, we obtain: ersucc = (τ + 3Tm /2 + τ + Td )Pr + Ta Pt . A single transmission may fail due to a corrupted micro-frame or the error in the data frame. In the case of a corrupted micro-frame, the receiver listens for duration 3Tm /2 on the average. Thus, we have: erfail = (τ + 3Tm /2 + τ + Td )Pr + (1 − pd )Ta Pt . 4.3

DFP (Data Frame Preamble)

The energy drained in channel sampling is the same as for MFP and LPL: es = (τ + TCS )Ps . Using the same reasoning as for MFP, we obtain: pf = 1 − (1 − pd )(1 − pa ) etsucc = etfail = es + (rd Td + Td )Pt + Ta Pr ersucc = (τ + 3Td /2)Pr + (τ + Ta )Pt erfail = (τ + 3Td /2)Pr + (1 − pd )(τ + Ta )Pt . 4.4

(7)

WOR (Wake On Radio)

As the receiver may wake up during the gaps separating WOR-frames, the sampling time should last for TCS + Ta , where Ta is the gap duration. Thus es = (τ + Ta + TCS )Ps With non-persistent receivers, the probability of a failed single transmission is equal to the probability that either a data-frame or an ACK-frame are corrupted: pf = 1 − (1 − pd )(1 − pa ).

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The receiver that wakes up to sample the channel receives a WOR-frame, which is a copy of the data frame. Then, the receiver sends an ACK-frame that stops the transmission of WOR-frames. As the wakeup time of the receiver is random, the transmitter transmits the half of the WOR-frames on the average in the case of a successful single transmission. Thus etsucc = es +

rw + 1 (Td Pt + Ta Pr ) + (Td Pt + Ta Pr ). 2

However, in the case of a failed single transmission, the transmitter transmits all WOR-frames. So, we have etfail = es + rw (Td Pt + Ta Pr ) + (Td Pt + Ta Pr ). The energy drained in reception of a successful single transmission is:   ersucc = τ + (Ta + Td )/2 + Td Pr + Ta Pt . In case of a failed transmission the energy is different, because the receiver does not transmit an ACK-frame if it does not correctly decode a data frame. Therefore, we have:   erfail = τ + (Ta + Td )/2 + Td Pr + (1 − pd )Ta Pt . 4.5

XMAC

As in WOR, the inter preamble-frame gaps cause the energy drained in channel sampling to become: es = (τ + Ta + TCS )Ps . A single unicast transmission with XMAC succeeds when the receiver correctly receives a XMAC-frame, the data frame, and the transmitter correctly receives the ACK-frame after the data frame. Note that a single transmission may be successful even if the acknowledgment of the XMAC-frame is not correctly received by the transmitter. In this case, the transmitter does not cut its preamble transmission, so that the receiver goes back to sleep and wakes up again to receive the data. Therefore, the probability of failure is: pf = 1 − (1 − px )(1 − pd )(1 − pa ). The energy drained in successful reception also depends on whether the acknowledgment of the XMAC-frame is correctly received by the transmitter or not. Therefore, in pa of cases, the node goes back to sleep and wakes up again to receive the data frame, hence the presence of τ preceding Td in the following expression:   ersucc = τ + (Ta + Tx )/2 + Tx Pr + Ta Pt + (pa τ + Td )Pr + Ta Pt .

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The energy drained in failed reception is the following:     erfail = τ + (Ta + Tx )/2 + Tx Pr +(1 − px ) Ta Pt +(pa τ + Td )Pr +(1 − pd )Ta Pt . In the case of failure, the transmitter sends a series of XMAC-frames and waits for the acknowledgment after transmitting the data frame. Therefore, we obtain: etfail = rx (Tx Pt + Ta Pr ) + Td Pt + Ta Pr . In the case of success, either the transmitter receives an acknowledgment during the transmission of XMAC-frames, which cuts their transmissions, or it does not receive such an acknowledgment so it continues to transmit XMAC-frames. Therefore, we have:

rx + 1 t esucc = (1 − pa ) (Tx Pt + Ta Pr ) + Tx Pt + Ta Pr + Td Pt 2

+pa rx (Tx Pt + Ta Pr ) + Td Pt + Ta Pr .

5

Performance Comparisons

For the evaluation of preamble sampling protocols, we compute the numerical values of pf , erfail , ersucc and use them in the formula derived in Section 3 to find pR , E t , E r , and L for each protocol. We consider micro-frames of 18 bytes, ACKframes and XMAC-frames of 16 bytes, data frames and WOR-frames of 138 bytes. We use the characteristics of the CC 2500 chip for radio parameters [5]. 5.1

Transmission and Reception Costs

Fig. 5.1 presents the mean energy drained in transmission. It shows that LPL, MFP, and DFP consume almost the same amount of energy during a transmission. Recall that in these protocols, all nodes transmit a full-length preamble followed by a data frame. Small differences in the figure are due to the differences in the preamble length: for example MFP and DFP use preambles a little bit longer than that of LPL, because they are formed of an integer number of preamble frames, the sum of which should be at least as long as the check interval. We notice that all the protocols converge to the same value when the bit error rate increases, specifically when the bit error rate is larger than 0.04. In such conditions, reliability is extremely low (see Fig. 6) so that all single transmissions fail. Our comparisons considered the maximal number of retransmissions n = 3, so the energy drained is three times greater than the energy drained in a single transmission. When the bit error rate is low, the energy drained in transmission with WOR or XMAC is less than that for the other protocols, because of the inter preamble-time expected for the reception of ACK-frames.

Preamble MAC Protocols with Non-persistent Receivers

0.04

0.02 0.018 0.016

0.03

Reception cost (Joule)

Transmission cost (Joule)

0.035

0.025

0.02

0.015

LPL MFP DFP WOR XMAC

0.01

0.005 −5 10

45

−4

10

−3

10

Bit Error Rate (p)

−2

10

0.014

LPL MFP DFP WOR XMAC

0.012 0.01 0.008 0.006 0.004 0.002

−1

10

0 −5 10

−4

10

−3

10

−2

10

−1

10

Bit Error Rate (p)

Fig. 4. Mean energy drained in transmission Fig. 5. Mean energy drained in reception

However, when the bit error rate is important (e.g. larger than 10−2 ), all the protocols consume the same amount of energy in transmission, because receivers cannot correctly decode a preamble-frame and thus they do not interrupt the transmission of full-length preambles. Under high bit error rate all protocols use full-length preambles. Fig. 5 presents the mean energy drained in reception. We can see that for LPL it is far larger than that drained in the case of the other protocols, because the preamble used by LPL does not carry any information on the forthcoming data transmission time, therefore, the receiver remains in active mode until it receives the data. WOR and DFP consume more energy in reception than MFP and XMAC, because the time needed to decide whether a preambleframe is correctly received or not, is shorter in MFP and XMAC than in DFP and WOR. The same reason also makes the energy drained in receive mode in WOR larger than that of DFP, because of the inter WOR-frames expected in WOR. We can also see that the energy drained in reception by MFP and XMAC decreases when the bit error rate is high (e.g. above 10−2 ). For high bit error rates, a non-persistent receiver running XMAC or MFP does not correctly decode a preamble-frame, therefore, it does not wake up later to receive the forthcoming data frame. Note that this behavior of XMAC and MFP allows the receiver not to waste energy by waking up again to receive a data frame when the probability of reception is low. This results in a bell-like shape of the energy drained in reception for XMAC and MFP (cf. Fig. 5). The energy drained by all protocols increases when the bit error rate is high, because of multiple retransmissions: each time a single transmission fails, the energy drained by the receiver increases as it wakes up again to receive retransmissions. We can see that the amount of energy drained by the receiver converges to an asymptotic value corresponding to the energy drained when all the retransmissions have been performed.

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1

4000

Optimal Normalized Lifetime (sec)

0.9 0.8

Reliability

0.7 0.6 0.5 0.4 0.3 LPL MFP DFP WOR XMAC

0.2 0.1 0 −5 10

−4

10

−3

10

−2

10

Fig. 6. Communication reliability

5.2

−1

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Bit Error Rate (p)

LPL MFP DFP WOR XMAC

3500

3000

2500

2000

1500

1000 −5 10

−4

−3

10

10

−2

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−1

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Fig. 7. Maximum Normalized Lifetime

Reliability and Optimal Normalized Lifetimes

Fig. 6 presents reliability, the probability of a successful transmission for different variants of protocols. It shows that reliability drastically decreases for bit error rates over 10−2 . We can also see that reliability of MFP and XMAC is very close and that of LPL, DFP, and WOR is the same, because successful transmission in the latter protocols depends only on the correct reception of a data frame. Note that LPL, DFP, and WOR are slightly more reliable than MFP and XMAC, because successful transmission does not depend on the correct reception of preamble frames. Fig. 7 presents the optimal maximum lifetimes for each protocol variant for different bit error rates. The lifetimes are normalized, i.e. obtained with the initial energy of 1 Joule. Each point corresponds to the maximum lifetime obtained with the optimal value for TCI and for traffic load of 1 message per minute. The lifetime takes into account the energy drained in sampling: we can see that MFP, DFP, and LPL have longer lifetimes than XMAC and WOR, because of the dominant energy drained in sampling.

6

Conclusions

In this paper, we have compared several representative protocols of preamble sampling schemes (LPL, MFP, DFP, XMAC, and WOR) with non-persistent receivers. Our goal was to take into account imperfect channel conditions as well as traffic load and find which protocol results in the longest network lifetime. Although our analysis builds on a simple channel model (BSC), the numerical results provide an instructive insight into the main properties of preamble sampling protocols. In particular, our results show that the channel sampling costs have a significant impact on energy consumption along with retransmissions. Two preamble protocols (MFP and DFP) achieve the overall best performance. If we consider the case of persistent receivers [2], i.e. receivers that keep receiving a preamble until it is correctly received or the channel is back to idle, our companion analysis of the same representative protocols (LPL, MFP, DFP, XMAC,

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and WOR) shows that there are not clear winners among them—different protocols perform better than the others for a given transmission error rate [2]. We can conclude that non-persistent access methods should be used over channels with high error rates. In such conditions, non-persistent methods save the energy of a node that does not continue to receive the preamble, because the probability of correct reception is low. On the opposite, persistent methods are better for channels with low error rates. In this case, persisting in preamble reception increases the probability of correct reception and thus saves the transmitter the cost of retransmitting.

References 1. Bachir, A., Barthel, D., Heusse, M., Duda, A.: Micro-Frame Preamble MAC for Multihop Wireless Sensor Networks. In: Proceedings of IEEE ICC, Istanbul, Turkey (June 2006) 2. Bachir, A., Heusse, M., Duda, A.: Preamble MAC Protocols with Persistent Receivers in Wireless Sensor Networks (Submitted, 2007) 3. El-Hoiydi, A.: Aloha with Preamble Sampling for Sporadic Traffic in Ad Hoc Wireless Sensor Networks. In: Proceedings of IEEE ICC, New York (April 2002) 4. Enz, C., El-Hoiydi, A., Decotignie, J., Peiris, V.: WiseNET: An Ultralow-Power Wireless Sensor Network Solution. IEEE Computer 37(8), 62–70 (2004) 5. Chipcon Corporation. CC2500 Single Chip Low Cost Low Power RF Transceiver, Data Sheet (2005) 6. Schurgers, C., et al.: Optimizing Sensor Networks in the Energy-Latency-Density Design Space. IEEE Transactions on Mobile Computing 1(1), 70–80 (2002) 7. Hill, J., Culler, D.: Mica: a wireless plateform for deeply embedded systems. IEEE Micro. 22(6) (2002) 8. Polastre, J., Hill, J., Culler, D.: Versatile Low Power Media Access for Wireless Sensor Networks. In: Proceedings of ACM SenSys (2004) 9. Buettner, M., et al.: X-MAC: A Short Preamble MAC Protocol For DutyCycledWireless Networks. In: Proceedings of ACM SenSys, Boulder, CO (November 2006) 10. Mahlknecht, S., Boeck, M.: CSMA-MPS: A Minimum Preamble Sampling MAC Protocol for Low Power Wireless Sensor Networks. In: Proceedings of IEEE Workshop on Factory Communication Systems, Vienna, Austria (September 2004) 11. Ye, W., Silva, F., Heidemann, J.: Ultra-Low Duty Cycle MAC with Scheduled Channel Polling. In: Proceedings of ACM SenSys, Boulder, CO (November 2006)

An Overlay Maintenance Protocol for Overlay Routing on Top of Ad Hoc Networks Sandrine Calomme and Guy Leduc Research Unit in Networking Electrical Engineering and Computer Science Department University of Li`ege, Belgium

Abstract. The protocol described in this paper builds and maintains an overlay topology on top of an ad hoc network. The overlay is intended to be used by a routing application. As flooding is a key component of many route discovery mechanisms in MANETs, we evaluate the delivery percentage, bandwidth consumption and time duration of flooding a message on the overlay. We also consider the overlay path stretch as an indicator for the data transfer transmission time. The protocol does not require any information from the underlay routing protocol, nor cooperation from the nodes that do not belong to the overlay. Each overlay node maintains a set of nearest overlay nodes and exchanges its neighbourhood information with them in order to select useful overlay links. Resilience is afforded by setting a minimum number of overlay neighbours. The performance observed over OLSR are good, for all overlay densities and mobility level studied.

1

Introduction

The overlay technique consists in building a virtual topology over the network physical topology of a network. At the transport level, the overlay links are tunnels that can be laid over several physical links. Layer 4 overlays have been successfully proposed in the Internet for improving the basic routing service provided by IP for a variety of applications. They can afford for example a multicast functionality, object location, secure data dissemination, content-distribution, quality of service or fault-tolerant routing. They have also been successful for insitu testing of new networking solutions, a field that recently attracted a growing interest [1]. Many overlay applications have been proposed, and many with their own proprietary functionality support. Several overlay functionalities are covered by different works. These are for example the overlay topology discovery, routing path selection, fault detection and tolerance, overlay link performance estimation or resource allocation. Hence, the idea of designing a general unified framework for various application-specific overlays emerged. A generic overlay solution allows the designer of an overlay application to concentrate on its application goal and needs rather than on the maintenance of the overlay [2, 3, 4]. A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 48–59, 2008. c IFIP International Federation for Information Processing 2008 

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There are several incentives for deploying overlays on MANETs. Firstly, the ad hoc users are likely to desire the same services as the one offered when they are connected through wired or infrastructure mode wireless access to the Internet. Secondly, ad hoc networks and overlay applications are faced with the same fundamental challenge of providing connectivity in a decentralised, dynamic environment. In both domains, the user devices work simultaneously as end-system and routing nodes. This is an advantage compared to the Internet, where core routers that take an active role in an overlay cannot benefit from useful information own by the user application, and where end-host based overlays suffer from performance limitations. However, the maintenance of an overlay on top of an ad hoc network is challenging because of mobility and low bandwidth. In a wired context, a complete decoupling of overlay topology and underlying data network topology may not be completely desirable because it leads to inefficiencies. In ad hoc networks, the maintenance of many long tunnels is not even conceivable. Mobility makes long routes very unreliable. The cost of rebuilding long tunnels may be important, in terms of delay and of bandwidth. Hence, the benefits of the overlay application could be completely lost if overlay neighbours were not close to each other. We thus present in this paper a protocol that is able to build and maintain a locality-aware overlay on top of an ad hoc network. We do not specify its application domain and evaluate the virtual structure obtained by an indirect performance evaluation based on overlay flooding. The diffusion of message can be seen as a worst-case scenario for group communication. It is also a key component of many unicast route discovery mechanisms in MANETs.

2

Related Work

The deployment of layer 4 overlays in MANETs has not yet received a lot of attention. In [5], an overlay approach is proposed for service discovery in ad hoc networks. A new layer is inserted above the transport layer in order to build, maintain, and offer an overlay structure that optimally serves the mechanisms of service trading and efficiently adapts to the changing physical topology. The maintenance protocol consists of an adaptation of CAN that lightens its structure. It selects short overlay links and reduces the control traffic. The adaptation of application-layer multicast to the ad hoc environment was proposed by the AMRoute protocol [6], which establishes a mesh between the multicast users and then a tree for data distribution over the mesh. More proposals are available for P2P content distribution in MANETs. Many of them exploit cross-layer interactions with the routing protocol [7, 8, 9], or the node position knowledge [10] or even introduce the necessary p2p functions at the network routing layer [11, 12]. An elegant p2p design for ad hoc networks is proposed in [13]. However, the authors do not take into account mobility as they are mostly interested in evaluating the feasibility of DHT lookup in ad-hoc networks. In all above cited works, the use of logical long-range neighbours is avoided because of their prohibitive maintenance cost. Another common point is that

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the overlay quality is evaluated with the level of performance obtained by its user application.

3 3.1

The Overlay Topology Control Protocol Protocol Overview and Assumptions

We denote our protocol by OTC, standing for Overlay Topology Control protocol. The algorithm is fully distributed and local, i.e. each overlay node exchanges only a few messages with a limited number of nearest overlay nodes. It builds connected overlay topologies, at least with a high probability. We consider a connected underlay and assume that a routing protocol is available to all ad hoc nodes. The underlay routing protocol is supposed to provide short paths, but not necessarily the shortest ones and may build asymmetric paths. In order to work properly, the OTC protocol must obtain the distance between overlay nodes. We however do not assume that the underlay routing protocol is able to inform the above layer about the length of the available paths. We make instead the following, weaker, assumption: When a node receives a packet, it is able to know how many hops the packet has traversed since its emission. Each overlay node U : 1. Collects in its neighbour candidate list LU the identifier and the shortest distance to its K closest overlay nodes. 2. Also inserts in LU any overlay node V such that U ∈ LV (thus turning the neighbourhood relation into a symmetric relation). 3. Selects its overlay neighbours in LU by applying the pruning rule described below (in Sec. 3.5). 4. For resilience, does not prune: – its 3 nearest overlay nodes, nor – any overlay node located only at one hop. For all simulations, we distributed the overlay nodes randomly and uniformly on the set of ad hoc nodes. The overlay density is defined as the proportion of overlay nodes. We observed in an extensive set of simulations that setting K to the value of 8 was sufficient to guarantee connectivity of overlay topologies with a probability higher than 95% for up to 1000 underlay nodes and overlay densities ranging from 10 to 90% [14]. Each overlay node must update its neighbour candidates list as soon as feasible when nodes move. Possible updates are adding, sorting and deleting elements. The overlay nodes must also determine if a neighbour candidate must be selected as neighbour or pruned. We differentiate broadcast and unicast overlay neighbours. The former are also physical neighbours, i.e. there exists a direct radio communication link between them, while the distance between the latter is at least of two hops. We begin the OTC protocol description with the simple maintenance procedure of broadcast neighbours.

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The Discovery and Maintenance of Broadcast Neighbours

Each overlay node regularly emits an OTC HELLO message, encapsulated in a broadcast packet with the Time To Live (TTL) field set to one. If a node U receives an OTC HELLO message from a node V , it adds V to its neighbour candidates list LU . Every broadcast neighbour is automatically selected as overlay neighbour. Broadcast neighbours are purged if no hello message has been received during a given time interval. If a node U has less than K broadcast overlay neighbours, its neighbour candidates list must be supplemented by overlay neighbours located further. The necessary unicast overlay neighbours will be selected among them. The rest of this section is devoted to this more complicated part of the OTC algorithm. 3.3

The Discovery of New Unicast Neighbour Candidates

As soon as a node enters the overlay, it regularly emits an OTC REQUEST in broadcast packets. The Time To Live (TTL) field of these packets is set to increasing values, beginning from 2, until LU gets sufficiently long (at least K neighbour candidates). An overlay node V that receives an OTC REQUEST from U responds with a unicast packet containing an OTC REPLY if and only if node U is not already in LV . The neighbour candidates list is sorted by increasing distance and, when distances are equal, by increasing identifier. When node U receives the OTC REPLY from node V , it calculates at which position it would insert V in LU , using the number of hops the OTC REPLY has passed through and V ’s identifier. If the position is less than or equal to K, it inserts V in its neighbour candidates list, sets its monitoring state for V on, and sends a unique OTC ADVERTISE message to V . At this point of the protocol, this message is used by U for forcing V to create the (U, V ) pair of neighbour candidates. This ensures the symmetry of the neighbourhood relationship. Node U maintains a monitoring state for each of its neighbour candidates. If node U monitors node V (i.e. its monitoring state for node V is on), this means that node U is responsible for estimating the distance d(U, V ), and communicating any change to V . The complete distance update process is described below, on Section 3.4. If U ∈ / LV when node V receives the first advertisement from U , node V inserts U in its neighbour candidates list, and registers the distance d(U, V ) announced in the advertisement. It sets its monitoring state for U off and begins to send OTC ADVERTISE messages to U at regular intervals. The reception of these frequent advertisements by node U makes it capable of monitoring the distance between U and V , by observing the number of hops they traversed. At stability, there is one and only one end node per candidate overlay link that monitors its length. A simple discovery process is illustrated on Fig. 1. The overlay nodes – U , V and W – are grey-shaded. At the beginning, LU = ((W, 1, m)), which means

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that U only knows one broadcast neighbour W and monitors it1 . At the end, U and V have been inserted in the neighbour candidates list of each other, with distance d(U, V ) = 2. Node U monitors node V , V does not monitor U (indicated in the Fig. 1(b) by the expression ¬m). Note that the distances registered in LU and LV are equal because it corresponds to the number of hops traversed by the reply, even if the request or the first advertisement has followed an underlay path of different length. The general rule is that the distance recorded for any overlay link equals the number of hops observed by the monitoring node at reception of an OTC message. In our example, these are the reply and subsequent advertisements flowing from node V to node U . Node U (W, 1, m)

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The Maintenance of the Neighbour Candidates List

Consider an overlay node U . In a mobile context, the content of its ordered neighbour candidates list, LU , evolves continuously. The set of K closest overlay nodes of U and the distance between U and a member of LU may change. Moreover, overlay node U may enter or leave the set of K closest overlay nodes of other overlay nodes. New unicast overlay neighbour approaching.. Consider a node U that already knows at least K neighbour candidates. Let us define its current range RU by the distance registered for the Kth element of LU . If RU ≤ 2, interesting new neighbours are at most one hop away, and their discovery will be done through the reception of their hello messages. When RU > 2, in order to spot 1

The monitoring state for broadcast neighbours is not used by the algorithm and set on by default.

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approaching unicast overlay nodes, node U regularly emits new OTC REQUEST messages. The TTL field of the broadcast packet containing these requests is set to RU − 1. As U owns a sufficient number of neighbour candidates, new overlay nodes located RU hops or farther are not considered of better quality than the elements of LU and must not be sought. The process induced by the reception of these requests is the same as described above. Distance update. The updates are made possible by the regular emission of OTC ADVERTISE messages. An overlay node U sets an advertisement timer for each of its neighbour candidates. The advertisement timer set for a node V is reset every time U sends an advertisement to V or receives an advertisement from V . An overlay node assigns a longer expiration time to candidates it monitors than to candidates that it does not monitor. The difference of expiration time is such that, if there is no advertisement loss nor distance change, an overlay node only receives regular advertisements from neighbour candidates that it is monitoring. If it notices a distance modification, it sends a unique OTC ADVERTISE to the corresponding, unmonitoring, peer. The overlay nodes thus also receive asynchronous advertisements from neighbour candidates that they are not monitoring, for being informed of any modification of the corresponding candidate overlay links. If there are losses or if both nodes of a neighbour candidates pair are in the monitoring state advertisements are still received. The latter case may appear in transient scenarios, caused by mobility or at set up. For example when overlay nodes U and V discover the existence of each other in a short interval of time, by the reception of two OTC REQUEST messages sent in opposite direction. When an overlay node U receives an advertisement from V on a path of h hops, it first checks its monitoring state for V and updates d(U, V ): – If node U is not monitoring V , it sets its local d(U, V ) variable to the value indicated in LV . – If node U is monitoring V , it first verifies in the advertisement that V is not also monitoring U . If U and V are monitoring each other, a tie function is applied on their identifiers in order to elect the monitoring overlay node. If U stops monitoring V , it reacts as described above. If it continues to monitor V after the check, it sets d(U, V ) to the value h. If d(U, V ) has changed, U corrects the position of V in LU . If it is monitoring V , it also directly sends a new advertisement to V in order to inform it about the distance update (or, as explained in next section, a delete message indicating that the neighbour relation is no more useful). Old unicast overlay neighbour leaving. Once the distance update process is completed, node U calculates the new position it would occupy in LV consequently to the distance update. On the basis of the updated lists LU and LV , it can then detect if V is still required or not in LU . If the position of V in LU and of U in LV are both greater than K, then the neighbourhood relationship

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between U and V is no more necessary. Node U deletes V from LU and cancels its advertisement timer for V . It also sends an OTC DELETE message to V . If the delete message is lost, it will be sent again at reception of the next advertisement from V . If node V remains in LU , node U can then determine if it must be selected as a neighbour or pruned. 3.5

The Selection of Neighbours in the List

A node U selects or prune a neighbour candidate V on the basis of the updated lists LV and LU . The pruning rule has a real parameter α ∈ [1, 2] that we call the pruning selectivity. An overlay link (U, V ) is pruned if and only if there exists a third overlay node W that appears before V in LU and before U in LV , and such that d(U, W )+ d(W, V ) ≤ αd(U, V ). The pruned neighbours are not deleted from the candidates list. OTC control messages are continuously exchanged with pruned neighbour candidates as well as with selected ones. Oppositely, the overlay data messages will only flow on selected overlay links. W

U

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Fig. 2. Diffusion consumes less bandwidth on the pruned overlay topology

An example is given on Fig. 2. The underlay paths U → W and V → W are both shorter than U → V . Nodes U and V thus prefer to select W than each other as neighbour. The idea is, for every pair of candidate overlay neighbours (U, V ) to prune each other if they share a common better candidate, under the condition that the remaining overlay path that links them is not stretched by more than a factor α2 . Notice that the pruning rule does not affect the overlay connectivity and that both overlay nodes U and V take locally the same pruning decision about each other. In this example topology, the number of packets emitted when U sends an overlay broadcast message is reduced from 7 packets on the initial topology to 4 on the pruned topology. However, the time needed for V to receive the first copy of the message and the path followed by this copy from U to V may be longer. We observed on larger topologies that the gain in bandwidth and its resulting decrease of contention were significant, while the mentioned increase of path stretch and diffusion time stayed acceptable. 2

Note however that the final maximal overlay stretch may be higher than α because the pruning rule is also applied to (U, W ) and (W, V ), and so on.

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Evaluation Performance Criteria and Flooding Method

The objective of OTC is to offer a logical communication structure between the overlay nodes which allows the deployment of efficient overlay routing protocols and services. The quality of the overlay structure is thus strongly linked to desired properties of overlay routing protocols. We translate this in terms of the following objectives. 1. Diffusion bandwidth: As routing control traffic is often generated by flooding, the bandwidth necessary to send a message from one overlay nodes to all other ones, with a simple flooding procedure, must be as low as possible. 2. Diffusion time: In order to quickly compute valid routes, the overlay control traffic must be flooded rapidly. 3. Diffusion delivery percentage: In order to find routes, the overlay control traffic must be received by all overlay nodes. 4. Path stretch: The average cost of the shortest overlay path between any pair of overlay nodes must be as close as possible to the cost of the direct underlay path between them. In order to spare bandwidth, an overlay node employs the following flooding technique: 1. For all overlay neighbours located only one hop away, it emits a single overlay message, which is actually broadcast in the underlay with a Time To Live (TTL) field set to one. 2. For every overlay neighbour located further away, an individual overlay message is created, which will be unicast to it by the underlay routing protocol. 4.2

Simulations Description

We use the ns-2.29 simulator. There are 100 ad hoc nodes, randomly and uniformly distributed on a square field. The length of the field is of 1200 meters and the radio transmission range of the nodes equals 250 meters. All experiments are conducted for overlay densities of 10, 50 and 90%. The simulation duration is of 150 seconds. The OTC protocol starts on every overlay node at the beginning of the simulation and uses a pruning selectivity α = 1.5. In the static case, the maximal pruning selectivity provide the best results [14]. However, we observed with the mobile scenarios that some more resilience, afforded by a lower selectivity, is preferable when the nodes move in order to preserve the overlay structure connectivity. After 30 seconds, a source overlay node starts to emit 100 overlay messages of 64 bytes, at the average rate of one message per second, and the performance log also commences. We test static and mobile scenarios. The latter are generated by the random waypoint scenarios generator provided with the ns distribution and grouped in two sets, slow and fast (as in [7]). Slow scenarios are defined by a pause time

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uniformly distributed in [0, 10] seconds and a speed in [1, 5] meters per second. The pause time of fast scenarios is uniformly distributed in [0, 5] seconds and their speed in [5, 15] meters per second. We present the performance of OTC over the Optimised Link State Routing protocol (OLSR) [15], a proactive routing protocol. We also ran simulations over the Ad-hoc On-demand Distance Vector routing protocol (AODV) [16]. Results proved that the OTC protocol can be applied over reactive as well as over proactive routing protocols. However, we observed that the performance obtained over AODV rapidly degrades when node move, except for the lowest overlay density. The reason is that AODV cannot sustain the maintenance of many paths in a mobile context. Proactive routing protocols are less efficient than AODV when the traffic pattern is light, because the paths between each pair of ad hoc nodes is maintained even if only of few of them are required by the users. However, their routing load is far less sensitive to the number of user flows. When these are numerous, proactive routing protocols outperform reactive ones. 4.3

Results

The average performance obtained when flooding an overlay message on the topologies built by OTC over OLSR are shown on Fig. 3. The 95%-confidence intervals are specified. As a reference, we also indicate, on each sub-figure, the performance obtained when flooding a broadcast packet on the underlay. 1.4

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Diffusion delivery percentage. For all overlay nodes, except the overlay broadcast messages source, we determined how many overlay messages out of the hundred sent were received before the end of the simulation. The diffusion delivery percentage over OLSR is very good in the static and slow case, for all overlay densities. When nodes move a lot and rapidly, the diffusion delivery percentage is also very good (above 94%), except for the lowest overlay density (only 83% of the overlay nodes receive the flooded message). Noticeably, in the latter case, we obtained very good performance over AODV because the number of overlay links is low. In such situation, AODV resists better to mobility than OLSR [17]. Hence, we assume that the performance drop is due to OLSR and not to OTC. Bandwidth consumption. We logged the number of packets emitted during the simulation. We classified them into three categories: the packets containing the 64 bytes of data, the OLSR control messages and the OTC control messages. The control bandwidth includes both OLSR and OTC messages. The metric used on Fig. 3(b) is the number of packets emitted per flood and per ad hoc node. 800

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The total bandwidth amount increases reasonably with the degree of mobility. At very low and intermediate overlay densities, the flooding of a message on the overlay, which is able to propagate useful information for overlay routing, costs less than one packet per node. For all cases studied, we observed that the OTC traffic occupies less than the third of the total amount of control traffic. Figure 4 shows that maintaining an overlay with OTC over OLSR costs less than half the bandwidth needed by OLSR for maintaining underlay routes. The metric used is the number of bytes emitted per flood and per ad hoc node. Diffusion time. For each overlay message flooded on the overlay, we logged the interval of time elapsed between its emission and the moment at which its first copy was received by the last overlay node. This measures the maximum time that would be needed for an overlay route request to reach any destination. The overlay diffusion time raises with the mobility degree. The sharper increase is observed at the middle overlay density because of our flooding policy,

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where each overlay node must emit a new message for each of its unicast neighbours. At a high overlay density, most neighbours are located at one hop (they are broadcast neighbours). At low density, there are a few overlay links, thus the time required for accessing the media is lower. Path stretch. Each time an overlay message was received, we also computed the ratio of the number of hops it has passed through since its emission and of the shortest path length from the source. This defines the overlay path stretch. Its average value is under 1.6 for all cases studied. Note that the path stretch of broadcast packets flooded on the whole ad hoc network is also greater than 1.0 because of collisions.

5

Conclusions

We described and evaluated a protocol for building and maintaining a generic service overlay over an ad hoc network. The OTC protocol keeps, in a mobile environment, a set of overlay links as close as possible to a target overlay topology defined in previous work [14]. While service overlays have proved their utility in the Internet, there are currently only a few proposals for ad hoc overlays. We used a general approach. The overlay maintenance protocol is not application-specific, avoids as much as possible cross-layer optimisations and assumptions about the underlay topology or routing protocol. The performance evaluation utilises generic performance criteria based on overlay flooding. On top of OLSR, the maintenance of the overlay generates an acceptable volume of OTC messages. The flooding of a message on overlay topologies built by OTC always shows up good performance, except when the overlay density is very low and the mobility degree high. Note that the OTC protocol is not proposed as a final, ready-to-use solution. The performance study mainly shows the feasibility of maintaining the overlay structure while using a minimal amount of information. Our proposition thus leaves a large open space for optimisations.

Acknowledgements This work has been partially supported by the European Union under the ANA FET project (FP6-IST-27489).

References 1. Bavier, A., Feamster, N., Huang, M., Peterson, L., Rexford, J.: In vini veritas: realistic and controlled network experimentation. In: Proc. of ACM SIGCOMM (2006) 2. Li, Z., Mohapatra, P.: Qron: Qos-aware routing in overlay networks. IEEE Journal on Selected Areas in Communications 22(1) (2004)

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3. Subramanian, L., Stoica, I., Balakrishnan, H., Katz, R.H.: Overqos: offering internet qos using overlays. SIGCOMM Comput. Commun. Rev. 33(1) (2003) 4. Touch, J.: Dynamic internet overlay deployment and management using the x-bone. Computer Networks 36(2-3) (2001) 5. Klein, M., Konig-Ries, B., Obreiter, P.: A lightweight overlay for service discovery in mobile ad hoc networks. In: Proc. of 3rd Workshop on Applications and Services in Wireless Networks (ASWN 2003) (July 2003) 6. Xie, J., Talpade, R.R., McAuley, A., Liu, M.: Amroute: Ad hoc multicast routing protocol. Mobile Networks and Applications 7(6) (2002) 7. Conti, M., Gregori, E., Turi, G.: A cross-layer optimization of gnutella for mobile ad hoc networks. In: Proc. of ACM MobiHoc 2005, Urbana-Champaign, IL (2005) 8. Delmastro, F.: From pastry to crossroad: Cross-layer ring overlay for ad hoc networks. In: Proc. of 2nd IEEE International Workshop on Mobile Peer-to-Peer Computing (MP2P 2005) (March 2005) 9. Pucha, H., Das, S.M., Hu, Y.C.: How to implement dhts in mobile ad hoc networks? In: Proc. of MobiCom 2004, Philadelphia, PA (2004) 10. Cramer, C., Fuhrmann, T.: Proximity neighbor selection for a dht in wireless multihop networks. In: Proc. of 5th IEEE International Conference on Peer-to-Peer Computing (P2P 2005) (August 2005) 11. Zahn, T., Schiller, J.H.: Madpastry: A dht substrate for practicably sized manets. In: Proc. of 5th Workshop on Applications and Services in Wireless Networks (ASWN 2005) (June 2005) 12. Pucha, H., Das, S.M., Hu, Y.C.: Ekta: An efficient dht substrate for distributed applications in mobile ad hoc networks. In: Proc. of 6th IEEE Workshop on Mobile Computing Systems and Applications (WMCSA 2004) (December 2004) 13. Kummer, R., Kropf, P., Felber, P.: Distributed lookup in structured peer-to-peer ad-hoc networks. In: Proc. of International Conference on Distributed Objects and Applications (DOA 2006) (October 2006) 14. Calomme, S., Leduc, G.: Efficient and resilient overlay topologies over ad hoc networks. In: Hutchison, D., Katz, R.H. (eds.) IWSOS 2007. LNCS, vol. 4725, Springer, Heidelberg (2007) 15. Clausen, T., Jacquet, P.: Optimized link state routing protocol (olsr). 16. Perkins, C., Royer, E.M.: Ad hoc on-demand distance vector routing. In: Proc. of IEEE Workshop on Mobile Computing Systems and Applications(WMCSA 1999) (1999) 17. Clausen, T., Jacquet, P., Viennot, L.: Comparative study of routing protocols for mobile ad-hoc networks. In: Proc. of 1st Annual Mediterranean Ad Hoc Networking Workshop (Medhocnet 2002) (2002)

An Overlay Architecture for Vehicular Networks Luigi Liquori1, Diego Borsetti2 , Claudio Casetti2 , and Carla-Fabiana Chiasserini2 1

2

INRIA Sophia Antipolis M´editerran´ee, France [email protected] Dipartimento di Elettronica, Politecnico di Torino, Italy [email protected]

Abstract. We propose and discuss an overlay architecture relying on a mobile ad hoc network, called Arigatoni on wheels (Ariwheels for short). More specifically, Ariwheels is a virtual network organization that is designed for a vehicular network underlay environment. It provides efficient and transparent service advertising and retrieves services carried by on-board and roadside nodes. The paper outlines application scenarios for Ariwheels and evaluates them through simulation in a realistic vehicular environment.

1 Introduction The explosive growth of the information technology fosters the design of large programmable overlay networks connecting virtual organizations of computers. These are capable of providing a rich spectrum of services through the use of aggregated computational power, storage and services. The main idea of programmable overlay networks is to realize computation, storage and information retrieval via a seamless, geographically distributed, open-ended network of bounded services owned by “Agents” and “Brokers”, acting with partial knowledge and no central coordination. To make these services accessible to all Agents, an efficient and scalable communication protocol among them needs to be devised. Arigatoni [1] is a structured multi-layer overlay network which provides service discovery with variable guarantees in a virtual organization, where peers can dynamically appear, disappear, and self-organize. To the best of our knowledge, Arigatoni is the first fully–programmable overlay architecture. It dictates how and where services are declared and discovered in the overlay, allowing peers to make secure use of global services. Thanks to such features, Arigatoni appears to be the natural choice for information delivery and sharing in a urban vehicular environment. In the context of mobile ad hoc networks, solutions previously proposed in the literature have addressed service discovery [2] and subscription for mobile users. In particular, the works in [3,4] present service discovery protocols that are based on the deployment of a virtual backbone of directories within an infrastructure-less network. Each node composing the backbone acts as a service Broker by performing service 

This work is supported by AEOLUS FP6-IST-FET Proactive, INRIA Sophia M´editerran´ee through the project Colors-Ariwheels, University of Nice Sophia Antipolis, and by the Regione Piemonte through the project VICSUM.

A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 60–71, 2008. c IFIP International Federation for Information Processing 2008 

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discovery in its proximity, while global service discovery is provided by the cooperative action of the directories. In [5], service publishing and subscribing, i.e., the way clients and servers are matched together, is addressed: a cross-layer approach is applied, which leverages some routing-specific metrics, such as hop count or node traffic load. Also, a general discussion on the use of Brokers for publish/subscribe services in vehicular networks is presented in [6], while in [7] Broker entities are exploited in a publish/subscribe protocol combined with a content-based routing scheme. In this paper, we design a new network architecture called Ariwheels which is a smooth integration of the Arigatoni programmable overlay network and a mobile ad hoc network. We consider both vehicle-to-vehicle and vehicle-to-infrastructure communication, since the number of vehicles properly equipped will realistically remain limited in the not too distant future, thus hampering a vehicular network relying only on mobile nodes. Pedestrian users, equipped with small wireless devices, are naturally considered as mobile. We define the semantics and the interaction among logical entities in Ariwheels, and investigate some performance metrics that validate our design. Therefore, the main contributions of this paper are: – to define the Ariwheels overlay architecture units; – to provide insight of the overlay architecture over a vehicular underlay network; – to set up and simulate various scenarios, via the Omnet++ [8] event-based simulator. The rest of the paper is structured as follows: after Section 2 describing an introduction of the Arigatoni programmable overlay network, Section 3 introduces the Ariwheels’s architecture and its logical units. Section 4 shows our simulation results, while Section 5 provides some concluding remarks.

2 Arigatoni in a Nutshell What follows is a short overview of the activity of the main entities and of the protocols involved (the interested reader can refer to [9,10,1]). 2.1 Functional Units Two main logical entities (the Agent and the Broker) and two basic protocols (a registration and a service discovery protocol) are the core of the Arigatoni architecture. The Agent is a computing device with wired/wireless connectivity capabilities. It does not necessarily need to be a high-end device, such as a supercomputer; on the contrary, it may have limited storage and computation capabilities, and few basic installed applications (a simple editor, one or two compilers, an email client, a mini browser). Agents are organized in colonies, lead by a Broker. Unlike the Agent, though, the Broker is required to be a mid- to high-end device, equipped with a high–speed wired/ wireless connection and a service table, crucial to perform the publish/subscribe content-based routing. Given the hierarchical architecture, colonies may recursively be embedded into super-colonies, each lead by a super-Broker.

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The Agent should be able to work in local mode for all the tasks that it can manage locally or in colony mode, by first registering itself to one or many colonies of the overlay, and then by asking and serving colony-originated requests via the Brokers. The tasks of an Agent can thus be summarized as: – discovering the address of one or more Brokers, acting as colony leaders, upon its arrival in a “connected area”; – registering to one or many Brokers, thus entering the virtual organization; – requesting and offering services to other Agents, through its own Broker; – connecting directly to other Agents in a peer-to-peer fashion, and exchanging services between each others. Note that an Agent can also be a service provider. This symmetry is one of the key features of Arigatoni. The Broker requires higher capabilities than an Agent to store and manage the contentbased routing table of the colony it leads. Such table is essential to route queries, and the Broker must also efficiently match and filter the routing table against a received query. The tasks of a Broker are: – discovering the address of another Broker, and possibly embedding its colony into the other Broker’s; – registering/unregistering Agents in its colony and updating the internal contentbased routing table accordingly (who offers what within the colony, its address, and other geographical information); – receiving Agents service requests, discovering the services that satisfy an Agent request in its local colony, according to its content-based routing table, or delegating the request to its direct super-Broker; – in case the Agent request can be satisfied, forwarding, in a service response, all the information necessary to allow the requesting agent to communicate directly with the agent offering the service; – in case the agent request cannot be satisfied, notifying the requesting agent, after a fixed timeout period, that its service request could not be served. 2.2 Arigatoni Service Discovery and Intermittent Protocols There are mostly two mechanisms of service discovery, namely: – the process of a Broker finding and negotiating services to serve an Agent request in its own colony; – the process of an Agent discovering a Broker, upon physical/logical insertion in a colony. The first discovery is processed by Arigatoni service discovery protocol, while the second is processed out of the Arigatoni overlay, using well-known network protocols like DHCP, SLP in Bluetooth, or Active/Passive Scanning in Wi-Fi. The Service Discovery Protocol (SDP). It is used by a Broker to find and negotiate services to serve agent requests in its own colony. SDP allows the request for multiple

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services and service conjunctions (i.e., each agent may offer several services at the same time). The SDP protocol allows Agents to: – ask to a Broker a request for a service set S; – reply to a Broker the availability to offer a service set S . The colony’s Broker handles the service request received through SDP and looks up the service set in its routing table, filtering S against the offered set S . If a match is found, the Broker returns to the requesting agent the address of the agent matching, partly or fully, the request. From then on, the two agents interact in a peer-to-peer fashion, without further intervention of the Broker. Each Broker maintains a content-based routing table locating the services that are registered in its colony. The table carries one entry for each member matching the ID of the Agent with the set of services it can offer. The Virtual Intermittent Protocol (VIP). It manages peers’ participation in Arigatoni colonies. The protocol deals with the dynamic topology of the overlay, by allowing individuals to login/logout to/from a colony. Registration is the act through which an Agent becomes member of a colony. An Agent is allowed to unregister when it has no pending service requests or offers. Agents that abruptly unregister or behave as “free riders” (using other Agents’ services without offering or giving theirs in return) are tagged as unfair and may be forcefully unregistered from a colony by its Broker. An Agent registers to a colony with a list of services. If a Broker accepts an individual in its colony, then it sends a service update to its direct super-Broker in order to communicate the availability of the new services in its colony. This message is then propagated from Broker to Broker until the root (if any) of the multi-layer overlay is reached. This means a high node churn forces routing tables to be faulty until all service updates are properly propagated. As such, service registration in an overlay network computer is an activity that must be taken seriously into account.

3 The Ariwheels Vehicular Overlay Network Ariwheels is an overlay architecture for vehicular networks, based on Arigatoni. Overlay networks in a vehicular setting are problematic because of the highly dynamic nature of mobile nodes, and the limited number of vehicles that, at first, will be equipped with the necessary communication devices. For this reason, we envision an architecture encompassing both mobile users and infrastructure nodes. The most challenging problem in Ariwheels design is an efficient mapping between physical devices in the vehicular underlay network and virtual entities in the Arigatoni overlay network. 3.1 Ariwheels Architectural Overview We consider an urban area in which a Mobile Ad hoc Network (MANET) is deployed. Such MANET is populated by both mobile users, e.g., pedestrians with hand-held devices, cars equipped with browsing/computational capabilities, public-transportation

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vehicles and roadside infrastructures such as bus stops. All devices are supposed to have a wireless interface. Depending on their mobility, they may also be equipped with a wired interface. Such is the case of wireless Access Points (APs), which are installed at a bus stop, in order to provide connectivity either to users waiting for a bus or to the bus itself (hence to its passengers). In such settings, devices carried by cars and pedestrians play the role of Mobile Agents; roadside infrastructures (APs) and public transportation vehicles (buses, trams, cabs. . . ) act as Brokers, although some distinctive behaviors have to be introduced. A Broker in Ariwheels is logistically represented by a bus stop, and its colony is composed by Mobile Agents that have registered to it when they were within radio range of the AP installed at the bus stop. Therefore, a colony is a logical entity, whose members may be physically localized anywhere within the area where Ariwheels is deployed (i.e., they have to be within the Broker radio range only while registering to it). However, to take into account the high mobility of the scenario and enhance its performance in terms of load balancing and service response time, we introduce an additional, Ariwheels-specific entity, the Mobile Broker (mB). This unit is a public transport vehicle equipped with a scaled-down Broker-like wireless device. Every Mobile Broker is associated to (i.e., it has the same identity of) a single Broker. Such association exists at the overlay level and holds throughout its bus route. Clearly, at the underlay level, connectivity between the Mobile Broker and the associated Broker may at times be severed. The main aim of the Mobile Broker is to introduce the novel concept of colony– room: a small subset of Mobile Agents with a wireless connection to the Mobile Broker (pedestrian users on the bus, or pedestrian/vehicles around the bus or travelling along the same bus direction during a traffic jam. . . ). In addition, thanks to its mobility, the Mobile Broker can collect registrations from Mobile Agents that were too far from the AP of the associated Broker, and, therefore, might have never had the chance to register to it. As such, every Mobile Broker associated with a Broker may enrich the colony with new members, and with the contents they own, that are not within the Broker’s radio range. The Mobile Broker collects (un)registrations, service requests and service offers from the Agents within the colony–room. When a wireless connection has been established between the Mobile Broker and a roadside AP (not necessarily corresponding to the associated Broker), the data path to the associated Broker is again available and an information exchange takes place resulting in the updating of each other’s data. Specifically, the following actions occur. Firstly, the associated Broker merges the Mobile Broker’s routing table with the one it currently carries. Then, the associated Broker handles the registration/discovery information and generates the appropriate responses. Finally, depending on the response time, the responses are returned to the Mobile Broker before it leaves the wireless AP coverage, or the next time it connects to an AP: this will normally happen at the next bus stop. Figure 1 illustrates the relationships among overlay and underlay entities in Ariwheels. A central coordination entity is located at a headquarter (HQ), in our case corresponding to the local transportation authority building. The coordination entity plays

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HQ

B1

B4

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mB1

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overlay wireless coverage Fig. 1. An Ariwheels scenario

the role of a super-Broker and it is provided with a wired connection to each of the 4 roadside AP at bus stops (B1 to B4). Mobile Brokers (mB1 and mB3) shuttle between bus stops, each carrying a different Broker association (to B1 and B3), while Mobile Agents (portable devices or on-board car devices in the figure) are either connected to Brokers or Mobile Brokers, depending on their mobility. 3.2 Behaviors of Ariwheels Entities We can now summarize the different activities of the three main entities in Ariwheels, i.e., Mobile Agents, Brokers and Mobile Brokers. Mobile Agents. Their activity is carried out through the following main operations. – Broker Discovery: the reception of a HELLO message from one or more Brokers (and/or Mobile Brokers) by an Agent that is not already registered to a Broker; the choice of the Broker to which to register is performed using information stored in the HELLO message. – (Un)Registration: the Agent sends a service registration (SREG) message of the VIP protocol trying to register to a new Broker or to unregister from the current Broker. – Service Request/Response: these tasks require that the Agent is already registered to a Broker and that it is part of a colony. It can send a service request (SREQ) of the SDP protocol to its Broker or a service response (SRESP) offering some services. The service will be then exchanged in peer-to-peer fashion. Brokers. Their activity is carried out through the following main operations. – Colony Health: periodically broadcasting of HELLO messages in order to establish the liveliness property of Mobile Agents;

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– Colony (Un)Registration: to and from a higher-level Broker; – Colony Management: the Broker interacts with colony members, (un)registering and handling service requests through the VIP and SDP protocols. Mobile Brokers. The activity of a Mobile Broker is carried out through the following three main operations. – Broker Association: the process of associating to a specific Broker; the Broker for which the Mobile Broker acts as colony-room is chosen according to some policy (see below) and the association is held throughout the Mobile Broker’s route. – Colony-Room Advertising: periodical broadcasting of HELLO messages along its whole route; HELLO messages forward information about the associated Broker for which they are acting as colony-room. – Relaying: relaying VIP and SDP messages from (to) Agents inside the colonyroom to (from) the associated Broker. Relaying may occur at once if a wireless connection to an AP exists, or it may be deferred until the wireless connection is re-established. 3.3 Membership Policies As a byproduct of the Ariwheels architecture, members of a colony will be geographically distributed, although this distribution should be carefully planned by a Broker (accepting or refusing registration requests) for load balancing purposes. VIP registration policies are usually not specified in the protocol itself; thus every Broker is free to choose its acceptance policy. Different self-organization policies may be used to address issues such as load-balancing and colony specialization. Possible policies therefore are: (i) mono-thematic: a Broker accepts an Agent in its colony only if it offers services that the colony already owns in large quantities, so as to increase its specialization; (ii) balanced: a Broker accepts an Agent in its colony only if it offers services that the colony lacks, with the aim of evening out its service offer; (iii) unbalanced: a Broker unconditionally accepts all Agent registration. Membership to a colony is also affected by the Mobile Broker association. The choice of which Broker is associated to a Mobile Broker can be performed by a specific load balancing algorithm that is run periodically, i.e., when the public transportation vehicle leaves the deposit and sets off on its route. One possible aim of the load balancing algorithm is to let the Mobile Broker collect Agents for a Broker with a scarcely populated colony at the time of the Mobile Broker departure; other aims, such as specializing the colony population, can be also envisaged. 3.4 Service Discovery in Ariwheels Service discovery over a MANET, hence in Ariwheels, plays a crucial role in the successful retrieval of information. There are two main concerns regarding the issue of service discovery. The first one is to expedite the selection of the Mobile Agent providing the service, given that Ariwheels Agents are nominally free to roam in and out of their own Broker’s underlay reach. Therefore, if a service is advertized by more than one Agent, and a request for that service is pending, a Broker should be given the opportunity to hand the service request over to the Agent that is more likely to be within

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its reach. The second concern is the suitability of the match that the Broker is about to create. Indeed, finding a “good” Agent carrying the requested service must also account for its ability to establish an effective communication channel with the Agent requesting the service. The mobility of both Agents (the requesting one and the potential provider) must be accounted for, e.g., by selecting Agents that are either within radio range of each other, or that are likely to remain within some AP coverage for enough time. From a practical standpoint, service discovery at an Arigatoni Broker is carried out through a table that mainly records colony member IDs and their service lists. In Ariwheels, however, the table information for each Agent is integrated by a liveliness field, indicating the time elapsed since the last contact from that Agent, and by a mobility field, that can be used to pinpoint the position of the Agent and to infer the direction of its movement (the latter information is provided by the Agent in its last message sent to its Broker). In order for these additional table parameters to be maintained up-to-date, Agents are required to interact with their own Broker on a regular basis. Such interaction, in the form of a refresh SREG, should not be limited to the period when the Agent is within its own Broker radio range. Rather, it should also be promoted when the Agent is within any Broker range (whence it will be relayed to the Agent’s Broker). The refresh SREG will therefore be issued by the Agent upon hearing a HELLO message from a Broker1 . The rationale is to let the Agent’s Broker know that the Agent is within coverage of whatever wireless technology is used by any Ariwheels Brokers, hence it is readily accessible if a service is requested. For each Ariwheels Broker, the content-based routing table has the form: Agent ID Services Liveliness Mobility A {Si }i=1...n t (x, y, θ, v) ··· ··· ··· ··· where {Si }i=1...n is the set of services it can offer, t is the time passed since the Agent has sent an Ariwheels message, and (x, y, θ, v) is a quadruple denoting physical position, direction and speed (all those information easily retrievable by a GPS module, or computed through it). The table is updated according to the dynamic registration and unregistration of Agents in the overlay. When an Agent asks for a service, then the query is filtered against the routing tables of its own Broker; in case of a filter-failure, the Broker forwards the query to its direct super-Broker.

4 Simulation Scenario We implemented Ariwheels in the Omnet++ [8] simulator, coding the overlay part and exploiting the existing wireless underlay network modules. In the underlay we used IEEE 802.11 at the MAC layer and the DYMO routing protocol (an AODV-like reactive routing protocol). We tested the performance of Ariwheels in a vehicular environment. We used a realistic mobility model generated by VanetMobiSim [11], whose output (mobility traces) 1

Broadcast storms are prevented by forcing a latency period between consecutive the same Agent.

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was fed to the Omnet++ simulator. Vehicles travel in a 1 km2 city section over a set of urban roads, which include several road intersections regulated by traffic lights or stop signs. In particular, we adopted the IDM-IM microscopic car-following model [12], which allows us to reproduce real-world traffic dynamics as queues of vehicles decelerating and/or coming to a full stop near crowded intersections. We assumed that 60 vehicles enter the city section from one of the border entry/exit points, randomly choose another border entry/exit point as their destination, compute the fastest path to it and then cross the city section accordingly. A vehicle entering the topology is assigned a top speed of v m/s, that it tries to reach and maintain, as long as traffic conditions and road signs allow it to. When a vehicle reaches its destination, it stops for a random amount of time, uniformly distributed between 0 and 60 s, then it re-enters the city section. In our simulations, we tested two different top speeds v: 9 m/s (approx. 32 km/s) and 15 m/s (approx. 54 km/s). Upon entering the topology, a vehicle acting as Mobile Agent owns a set of 12 unitary services (e.g., files, traffic informations, point of interests) randomly chosen from a set of 20 services. A Mobile Agent issues a (SREQ) for a service it is missing and the inter-request time is supposed to be exponentially distributed with parameter λ = 0.05 [req./s]. As typical in the publish/subscribe paradigm, where peers are not slaves, upon receiving a SREQ for a service it owns, a Mobile Agent sends back a positive response with a certain probability, which is set to 0.9 in our simulations. The simulated city topology, shown in Figure 2, features 6 bus stops with APs, each corresponding to a Broker. Furthermore, 3 buses acting as Mobile Brokers weave their own routes across the topology, among a population of as many as 60 vehicles acting as Mobile Agents. Each bus carries 10 passengers equipped with Mobile Agent capabilities, and it associates to the Broker with the smallest colony at the time of departure

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from the bus station. Brokers apply the unbalanced acceptance policy and filter the routing table against a received query by using the liveliness information only.

5 Performance Evaluation Although the number of parameters that can potentially be analyzed is quite high, we focus on few selected sets of results for the sake of brevity. Therefore, we compare results for different radio ranges, different vehicle speeds, scenarios with and without Mobile Brokers (i.e., buses), and we evaluate the impact of a liveliness-based filtering. We initially address the success probability of a service request, i.e., the probability that a service request is positively answered by a Mobile Agent either in the requester’s With buses 1

liveliness - v=9m/s liveliness - v=15m/s random - v=9m/s random - v=15m/s

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without buses

v = 15 m/s

v = 9 m/s

v = 15 m/s

Range Liveliness Random Liveliness Random Liveliness Random Liveliness Random 90

3.42

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v = 15 m/s

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0.13

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180

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colony or in another colony (after delegation to the super-Broker). Figure 3 carries a comparison of success probabilities for an Ariwheels system in which Brokers either use randomly-ordered routing tables, or list Agents in increasing order of time elapsed since their last contact, i.e., of their “liveliness”. In the latter case, a service request is more likely to be routed toward an Agent that is still within radio coverage of a Broker. The top plot refers to the scenario with roaming buses acting as Mobile Brokers, while the bottom plot refers to the scenario without any buses. Different vehicles speeds are considered. It can be seen that the liveliness-based filtering yields the best results in almost all cases, and its prominence is especially clear when vehicles are slowly moving (hence lingering longer within radio coverage of a Broker). The gap between liveliness-based and random filtering is reduced when the radio coverage increases, bringing more vehicles within extended radio coverage. The price to pay, however, is the increase in 802.11 channel congestion, and consequently almost no gain as the coverage grows from 180 m to 270 m. This trend is confirmed by the scenario without buses, although the success probability is generally smaller due to the lack of the “colony-room” effect introduced by buses. We point out that, with a widespread content distribution as the one we simulated (namely, each Agent owns 12 out of 20 services), a fully wired static scenario would gave more than 90% successful service requests. In a MANET, instead, several underlayrelated events are responsible for missing responses: for example, a miscue from the routing protocol may lead an Agent to wrongly believing that it is within radio range of a Broker (or that a Broker may be reachable in multihop fashion through buses or other vehicles); an SREQ will therefore be issued but never delivered to the Broker, negatively affecting the success probability. Another metric of interest is the average time after which a response to an SREQ is returned to the requesting Agent (Table 1). As can be expected, the trends already observed for the success probability are found in the response times as well. Finally,

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Table 2 reports the probability that an SREQ cannot be solved within the requester’s colony and is thus sent to the super-Broker, which is in charge of delegating it to another Broker. Again, the lower delegation probability exhibited by the liveliness-based filtering in the scenario featuring Mobile Brokers confirms that keeping tabs on which Agents are promptly available dramatically increases Ariwheels performance.

6 Conclusions and Future Work This work presented Ariwheels, a structured overlay architecture for vehicular networks that aimed at facilitating the offering and retrieving of services by mobile users. Simulation results in a realistic environment have shown that Ariwheels provides good performance in service localization and have highlighted critical issues such as the need for proper content-routing table filtering and the role played by mobile Brokers (public transportation vehicles) in supporting and promoting the information exchange. Future work will focus on further specializing the routing table filtering, including localization information as a means to select a good peer match to the requesting Agent, as well as reputation and trustfulness mechanisms to maximize the response rate and fire “free riders”.

References 1. Liquori, L., Cosnard, M.: Logical Networks: Towards Foundations for Programmable Overlay Networks and Overlay Computing Systems. In: TGC, Trustworthy Global Computing. LNCS, Springer, Heidelberg (to appear, 2008) 2. Fiore, M., Casetti, C., Chiasserini, C.-F.: Efficient Retrieval of User Contents in MANETs. In: IEEE INFOCOM., pp. 10–18. IEEE, Los Alamitos (2007) 3. Kozat, U.C., Tassiulas, L.: Network Layer Support for Service Discovery in Mobile Ad Hoc Networks. In: IEEE INFOCOM., pp. 1965–1975. IEEE, Los Alamitos (2003) 4. Sailhan, F., Issarny, V.: Scalable Service Discovery for MANET. In: IEEE PerComm, International Conference on Pervasive Computing and Communications, pp. 235–244. IEEE, Los Alamitos (2005) 5. Varshavsky, A., Reid, B., de Lara, E.: A Cross-Layer Approach to Service Discovery and Selection in MANETs. In: IEEE MASS, International Conference on Mobile Ad-hoc and Sensor Systems, mettere numero pagina, pp. 8–12. IEEE, Los Alamitos (2005) 6. Iftode, L., Borcea, C., Ravi, N., Nadeem, T.: Exploring the Design and Implementation of Vehicular Networked Systems. Technical Report DCS-TR-585, Rutgers 7. Lundquist, D., Ouksel, A.: An Efficient Demand-driven and Density-controlled Publish/Subscribe Protocol for Mobile Environments. In: ACM International Conference on Distributed Event-based Systems, Toronto, Canada, pp. 26–37 (2007) 8. OMNET++ Discrete Events Simulator, http://www.omnetpp.org 9. Benza, D., Cosnard, M., Liquori, L., Vesin, M.: Arigatoni: Overlaying Internet via Low Level Network Protocols. In: IEEE JVA, John Vincent Atanasoff International Symposium on Modern Computing, pp. 82–91. IEEE, Los Alamitos (2006) 10. Chand, R., Cosnard, M., Liquori, L.: Powerful Resource Discovery for Arigatoni Overlay Network. Future Generation Computer Systems 24(1), 31–38 (2008) 11. VanetMobiSim. http://vanet.eurecom.fr 12. Fiore, M., Haerri, J., Filali, F., Bonnet, C.: Vehicular Mobility Simulation for VANETs. In: IEEE ANSS, Annual Simulation Symposium, pp. 301–309. IEEE, Los Alamitos (2007)

DBR: Depth-Based Routing for Underwater Sensor Networks Hai Yan, Zhijie Jerry Shi, and Jun-Hong Cui Department of Computer Science and Engineering University of Connecticut, Storrs, CT 06269-2155 {hay04002,zshi,jcui}@engr.uconn.edu

Abstract. Providing scalable and efficient routing services in underwater sensor networks (UWSNs) is very challenging due to the unique characteristics of UWSNs. Firstly, UWSNs often employ acoustic channels for communications because radio signals do not work well in water. Compared with radio-frequency channels, acoustic channels feature much lower bandwidths and several orders of magnitudes longer propagation delays. Secondly, UWSNs usually have very dynamic topology as sensors move passively with water currents. Some routing protocols have been proposed to address the challenging problem in UWSNs. However, most of them assume that the full-dimensional location information of all sensor nodes in a network is known in prior through a localization process, which is yet another challenging issue to be solved in UWSNs. In this paper, we propose a depth-based routing (DBR) protocol. DBR does not require full-dimensional location information of sensor nodes. Instead, it needs only local depth information, which can be easily obtained with an inexpensive depth sensor that can be equipped in every underwater sensor node. A key advantage of our protocol is that it can handle network dynamics efficiently without the assistance of a localization service. Moreover, our routing protocol can take advantage of a multiple-sink underwater sensor network architecture without introducing extra cost. We conduct extensive simulations. The results show that DBR can achieve very high packet delivery ratios (at least 95%) for dense networks with only small communication cost.

1

Introduction

Wireless sensor networks have been used extensively in many land-based applications. Recent several years have also seen a rapidly growing trend towards the application of sensor networks in underwater environments, i.e., building underwater sensor networks (UWSNs) [1] [4] [6] [13] [14] [16]. Among many research issues in this new and promising area, delivering packets from a source node to a destination, namely routing, is one of the fundamental problems that need to be studied for constructing the UWSN protocol stack. In fact, providing scalable and efficient routing service in UWSNs is very challenging due to the unique characteristics of underwater sensor networks. A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 72–86, 2008. c IFIP International Federation for Information Processing 2008 

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First of all, radio does not work well in water because of its rapid attenuation. Thus acoustic communications are usually adopted in underwater environments. Acoustic channels often feature low bandwidths and long propagation delays. Thus a routing protocol with long end-to-end delays or high bandwidth requirements is not a good choice. Secondly, most nodes in a UWSN can move passively with water currents (except that some gateway nodes are fixed at the water surface or anchored at the bottom), resulting in highly dynamic network topology. To handle dynamic networks, existing routing protocols for land-based (static) sensor networks need to update routing information periodically, which introduces significant communication overhead. Thirdly, similar to land-based sensor nodes, underwater sensor nodes are usually powered by batteries, which are even harder to recharge or replace in hash underwater environments. Thus, energy efficiency is another important concern for UWSN routing. In short, a routing protocol designed for UWSNs should consider all these factors: long propagation delays, low communication bandwidths, dynamic topology, and energy efficiency. Recently, there are a couple of proposals for UWSN routing, such as VBF [17] and HHVBF [11]. All of them belong to geographic-based routing protocols and thus require full-dimensional location information of all sensor nodes. This is a tough requirement since localization in UWSNs is another challenging research issue yet to be solved. In this paper, we propose a novel protocol, called depth-based routing (DBR), for underwater sensor networks. DBR well utilizes the general underwater sensor network architecture: data sinks are usually situated at the water surface. Thus based on the depth information of each sensor, DBR forwards data packets greedily towards the water surface (i.e., the plane of data sinks). In DBR, a data packet has a field that records the depth information of its recent forwarder and is updated at every hop. The basic idea of DBR is as follows. When a node receives a packet, it forwards the packet if its depth is smaller than that embedded in the packet. Otherwise, it discards the packet. Obviously, if there are multiple data sinks deployed at the water surface, as in the multiplesink underwater sensor architecture [4] [15], DBR can naturally take advantage of them. Packets reach any of the sinks are treated as successfully delivered to the final destination since these water-surface sinks can communicate with each other efficiently through radio channels, which have much higher bandwidths and much lower propagation delays. To summarize, the main advantages of DBR are as follows. 1) It does not require full-dimensional location information. 2) It can handle dynamic networks with good energy efficiency. 3) It takes advantage of multiple-sink network architecture without introducing extra cost. We will show the performance of DBR using extensive simulations. The rest of the paper is organized as follows. Section 2 briefly reviews some related work. Section 3 presents the DBR protocol in detail. Section 4 evaluates the performance of DBR through simulations. Finally, Section 5 concludes the paper and discusses some future work.

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Related Work

In this section, we review several existing routing protocols for wireless sensor networks and underwater sensor networks, which are categorized roughly into two classes. Routing Protocols for Land-based Sensor Networks. Many routing protocols [3] [7] [8] [18] [19] have been proposed for land-based sensor networks. However, most of these protocols are proposed for static or low dynamic networks. For example, in Directed Diffusion [8], routing is initialized by a sink. A sink first floods its interest for data across the network. Upon receiving an interest, a sensor node responds with data along all possible paths. Then an optimal path is enforced from the sensor node to the sink. In SPIN [7], routing is initialized by sensor nodes. When a node wants to send data, it first broadcasts a description of the data. A neighboring node decides whether to request the data based on its local resource. Clearly, a direct application of these routing protocols proposed for static sensor networks in UWSNs with dynamic topologies requires frequent routing information updates, which lead to significant communiation overhead, low energy efficiency, and high end-to-end delays. Geographic-based Routing Protocols. Geographic-based routing protocols [2][5][9] leverage the location information of sensor nodes to forward packets from a source node to a destination node. For example, GPSR [9], is a typical geographic-based routing protocol for land-based sensor networks. The basic idea is that given the location information of all nodes, a sensor node can forward packets to a proper next hop node. A GPSR protocol has two components: a greedy algorithm, and a recovery algorithm. In the greedy algorithm, a forwarding node tries to forward packets to one of its one-hop neighboring nodes which has the shortest distance to the destination node. It is possible that a forwarding node itself has the shortest distance to the destination, resulting in a “void” zone between the forwarder and the destination in which none of the forwarders’ neighbors resides. In such cases, a recovery algorithm is performed to route the packet around the “void” zone until the routing can continue in the greedy mode. VBF [17] is a geographic-based routing protocol designed for underwater sensor networks. In VBF, the packet delivery is guided by the vector from the source to the destination. Only those sensor nodes that are within a range R of the vector will forward packets, where R is a system parameter that can be tuned. The forwarding process of VBF can be seen as to build a routing pipe between the source node and the destination node so that packets are delivered through the nodes in the pipe. To achieve high energy efficiency, multi-path elimination based on a desirableness factor is considered. Namely, an intermediate node only forwards a packet to one of its neighboring nodes, which is the closest to the vector. VBF works well for underwater sensor networks. However, it makes the assumption that the location information of each sensor node can be obtained through a localization service, which is another difficult issue in UWSNs.

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The main difference between our proposal, DBR, and geographic-based routing protocols is that DBR does not need full-dimensional location information. Instead, only local depth information of each node is required in packet forwarding.

3

Depth-Based Routing Protocol

In this section, we present our DBR protocol in detail. 3.1

Network Architecture

As mentioned earlier, DBR can naturally take advantage of the multiple-sink underwater sensor network architecture [4] [15]. An example of such networks is illustrated in Fig. 1. In the network, multiple sinks equipped with both radiofrequency (RF) and acoustic modems are deployed at the water surface. Underwater sensor nodes with acoustic modems are distributed in the interested 3-D area, with each likely to be a data source. They can collect data and also help relay data to the sinks. Since all the sinks have RF modems, they can communicate with each other very efficiently via radio channels. Hence, if a data packet arrives at any sink, we assume it can be delivered to other sinks or remote data centers efficiently. This assumption can be easily validated by the fact that sound propagates (at a speed of 1.5 × 103 m/s in water) five orders of magnitudes slower than radio (with a propagation speed of 3 × 108 m/s in air). To be more focused, we do not consider communications between surface sinks in this paper. Instead, we assume that a packet reaches the destination as long as it is successfully delivered to one of the sinks. Furthermore, we assume that each underwater node knows its depth information, namely the vertical distance from itself to the water surface. In practice, depth information can be obtained easily with a depth sensor. In comparison, obtaining full-dimensional location information is much more difficult. 3.2

Protocol Overview

DBR is a greedy algorithm that tries to deliver a packet from a source node to sinks. During the course, the depth of forwarding nodes decreases while the packet approaches the destination. If we reduce the depth of the forwarding node in each step, a packet can be delivered to the water surface (if no “void” zone is present). In DBR, a sensor node distributively makes its decision on packet forwarding, based on its own depth and the depth of the previous sender. This is the key idea of DBR. In DBR, upon receiving a packet, a node first retrieves the depth dp of the packet’s previous hop, which is embedded in the packet. The receiving node then compares its own depth dc with dp . If the node is closer to the water surface, i.e., dc < dp , it will consider itself as a qualified candidate to forward the packet. Otherwise, it just simply drops the packet because the packet comes from a

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Water surface

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Fig. 1. Multiple-sink underwater sensor network architecture

(better) node which is closer to the surface. It is not desirable for the receiving node to forward the packet. It is very likely that multiple neighboring nodes of a forwarding node are qualified candidates to forward a packet at the next hop. If all these qualified nodes try to broadcast the packet, high collision and high energy consumption will result. Therefore, to reduce collision as well as energy consumption, the number of forwarding nodes needs to be controlled. Moreover, due to the inherited multiple-path feature of DBR (in which each sensor node forwards packets in a broadcasting fashion using an omnidirectional acoustic channel), a node may receive the same packet multiple times. Consequently, it may send the packet multiple times. To improve energy efficiency, ideally a node needs to send the same packet only once. We will address the techniques of suppressing redundant packets in the next section. 3.3

Protocol Design

Packet Format. The packet format in DBR is illustrated in Fig. 2. The packet header consists of three fields: Sender ID, Packet Sequence Number, and Depth. “Sender ID” is the identifier of the source node. “Packet Sequence Number” is a unique sequence number assigned by the source node to the packet. Together with Sender ID, Packet Sequence Number is used to differentiate packets in later data forwarding. “Depth” is the depth information of the recent forwarder, which is updated hop-by-hop when the packet is forwarded. Redundant Packet Suppression. To save energy as well as reduce collision, redundant packets need to be suppressed. There are two major reasons for redundant packets. One is that multiple paths are naturally used to forward packets. The other is that a node may send a packet many times. Although multiple paths in DBR can not be completely eliminated, we use a priority queue to reduce the number of forwarding nodes, and thus control the number of forwarding paths. To solve the second problem, a packet history buffer is used in DBR to ensures that a node forwards the same packet only once in a certain time interval.

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Fig. 2. DBR packet format

In DBR, each node maintains a priority queue Q1 and a packet history buffer Q2. An item in Q2 is a unique packet ID, which is composed of Sender ID and Packet Sequence Number. When a node successfully sends out a packet, it inserts the unique ID of the packet into Q2. When Q2 is full, the new item will replace the Least Recently Accessed (LRA) item. In other words, Q2 maintains a recent history of the packets the node has sent. An item in Q1 includes two components: a packet and the scheduled sending time for the packet. The priority of an item in Q1 is represented by the scheduled sending time. More specifically, an item with earlier sending time has a higher priority. When a node receives a packet, instead of sending the packet immediately, it first holds the packet for a certain amount of time, called holding time. The scheduled sending time of a packet is computed based on the time when the packet is received and the holding time for the packet. At a node, an incoming packet is inserted into Q1 if it has not been sent by the node before (i.e., its unique ID is not in Q2) and it was sent from a lower node (i.e., a node with a larger depth, dp > dc ). If a packet currently in Q1 is received again during the holding time, the packet will be removed from Q1 if the new copy is from a node with a smaller or similar depth (dp ≤ dc ), or its scheduled sending time will be updated if the new copy is from a lower node (dp > dc ). After a node sends out a packet as scheduled, the packet is removed from Q1 and its unique ID inserted into Q2. Holding Time Calculation. As mentioned earlier, a node uses holding time to schedule packet forwarding. At a node, the holding time for a packet is calculated based on d, the difference between the depth of the packet’s previous hop and the depth of the current node. Nodes with different depths will have different holding times even for the same packet. In order to reduce the number of hops along the forwarding paths to the water surface, DBR tries to select the neighboring node with the minimal depth to be the first one to forward a packet. It also tries to prevent other neighboring nodes from forwarding the same packet to reduce energy consumption. Fig. 3 shows an example. Node S is the sender, and nodes n1 , n2 , and n3 are all its one-hop neighboring nodes. The solid line circle represents the transmission range of node S. When node S broadcasts a packet, all neighboring nodes will receive this packet. Node n3 is below S so it discards the packet. Although nodes n1 and n2 are both qualified forwarding nodes, node n1 is preferred to forward the packet. The forwarding of node n2 is prevented if it receives the packet from n1 before its own scheduled sending time for the packet.

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n1 n2 d1 d2 S n3

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Based on the above analysis, we can see that the holding time must satisfy two conditions: (1) the holding time should decrease with the increase of d; and (2) the difference between the holding times of two neighboring nodes should be long enough so that the forwarding of the node with the smaller depth can be heard by the other node timely (before the lower node starts its own packet forwarding). Let us still take Fig. 3 as an example to show how we calculate the holding time. We express the holding time using a linear function of d as follows, where d is the depth difference of the current node and the previous one. f (d) = α · d + β

(1)

Let d1 and d2 be the depth difference at nodes n1 and n2 , respectively. Let us assume that n1 receives a packet from S at time t1 , n2 receives the packet at time t2 , and t12 is the propagation delay between n1 and n2 . Then the two conditions can be represented by the following inequalities: f (d1 ) < f (d2 ),

(2)

t1 + f (d1 ) + t12 ≤ t2 + f (d2 ).

(3)

and Substituting f (d) with our linear expression, we have α≤

(t2 − t1 ) − t12 , (α < 0). d1 − d2

2 )+t12 Here α is negative. As long as |α| ≥ (t1 −t , both conditions can be met. d1 −d2 Considering the worst positions for n1 and n2 , we can choose |α| = d12τ −d2 , where τ = R/v0 is the maximal propagation delay of one hop (R is the maximal

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transmission range of a sensor node and v0 is the sound propagation speed in water). The value of α depends on (d1 − d2 ), the depth difference of nodes n1 and n2 . For a node’s one-hop neighbors, α can vary between 0 and R, the maximal transmission range of a sensor node. When (d1 − d2 ) approaches 0, α → −∞. It shows that we cannot find a constant α to make condition (2) always satisfied. Instead, we use a global parameter δ to replace (d1 − d2 ) for the holding time calculation. Therefore α = − 2τ δ . We guarantee that node n1 will forward a packet first and prevent the forwarding of node n2 if d1 − d2 ≥ δ. Let the node with the minimal depth have holding time 0. We can compute β by solving the following equation: −

2τ R+β =0 δ

Substituding α and β in the linear function (1), we have the definition of f (d) as follows: 2τ f (d) = · (R − d), δ ∈ (0, R]. (4) δ When a small δ is chosen, nodes have longer holding times (if their depth differences with the previous forwarder are not exactly R). This may result in longer end-to-end delays. At the same time, the forwarding at these nodes is more likely to be suppressed by the forwarding from a neighboring node closer to the water surface, which results in lower energy consumption. In Section 4, we will study the impact of δ on DBR’s performance through simulations. Depth Threshold. In order to further control the number of nodes involved in packet forwarding, we introduce another global parameter, Depth Threshold dth , in DBR. A node will forward a packet only if the difference between the depth of the packet’s previous hop dp and the depth of the current node dc is larger than the threshold dth . dth can be positive, 0, or negative. If dth is set to 0, all the nodes with smaller depths than the current node are qualified forwarding candidates. If dth is set to −R, where R is the maximal transmission range of a sensor node, DBR becomes the flooding protocol. Clearly, the depth threshold represents the tradeoff between packet delivery ratio and energy consumption. With a larger threshold, less nodes will be involved in packet forwarding, and thus less energy is consumed; but the packet delivery ratio will be lower. On the other hand, with a smaller threshold, more nodes will participate in packet delivery, and thus a higher packet delivery ratio can be achieved; but the energy consumption will be higher. Summary. We summarize the packet forwarding algorithm in Fig. 4. Upon receiving a packet, a node first checks if it is a qualified forwarder for the packet based on the depth information and the depth threshold dth . If it is not a qualified forwarder, it searches the packet in Q1 and removes the packet with the same packet ID since another (better) node has already forwarded the packet. If the node is a qualified forwarder, it searches the packet in the packet history

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Algorithm 1 Algorithm for packet forwarding in DBR ForwardPacket(p) 1: Get previous depth dp from p 2: Get node’s current depth dc 3: Compute d = (dp – dc) 4: IF d < Depth Threshold dth THEN 5: IF p is in Q1 THEN 6: Remove p from Q1 7: ENDIF 8: Drop p 9: return 10: ENDIF 11: IF p is in Q2 THEN 12: Drop p 13: return 14: ENDIF 15: Update p with current depth dc 16: Compute holding time HT 17: Compute sending time ST 18: IF p is in Q1 THEN 19: Get previous sending time of p STp 20: Update p’s sending time with min(ST, STp) 21: ELSE 22: Add the item into Q1 23: ENDIF

Fig. 4. DBR packet forwarding algorithm

buffer Q2. If the packet is found in Q2, it is dropped as it has been forwarded recently. Otherwise, the node calculates the sending time for the packet based on the current system time and the holding time and inserts the packet into the priority queue Q1. Note that if the packet is already in Q1, the sending time is updated to the earlier time. Later, the packets enqueued in Q1 will be sent out according to their scheduled sending times.

4

Performance Evaluation

In this section, we evaluate the performance of DBR and compare it with the Vector Based Forwarding (VBF) protocol[17]. 4.1

Simulation Settings

All simulations are performed using the Network Simulator (ns2) [12] with an underwater sensor network simulation package (called Aqua-Sim) extension. In our simulations, sensor nodes are randomly deployed in a 500m × 500m × 500m

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3-D area. Multiple sinks are randomly deployed at the water surface. While we assume that the sinks are stationary once deployed, the sensor nodes follow the random-walk mobility pattern. Each sensor node randomly selects a direction and moves to the new position with a random speed between the minimal speed and maximal speed, which are 1 m/s and 5 m/s respectively unless specified otherwise. Although the source node can be anywhere in the network, for easy simulations, we place it at a random position at the bottom layer in our experiment. The data generating rate at the source node is one packet per second, with a packet size of 50 bytes. The communication parameters are similar to those on a commercial acoustic modem, LinkQuest UWM1000 [10]: the bit rate is 10k bps; the maximal transmission range is 100 meters (in all directions); and the power consumption in sending, receiving, and idling mode are 2w, 0.1w, and 10mw, respectively. The same broadcast Media Access Control (MAC) protocol as in [17] is used in our simulations. In this MAC protocol, when a node has a packet to send, it first senses the channel. If the channel is free, it continues to broadcast the packet. Otherwise, it backs off. The packet will be dropped if the maximal number of backoffs have been reached. We use the following metrics to evaluate the performance of routing protocols. – Packet Delivery Ratio is defined as the ratio of the number of distinct packets received successfully at the sinks to the total number of packets generated at the source node. Although a packet may reach the sinks multiple times, these redundant packets are considered as only one distinct packet. – Average End-to-end Delay represents the average time taken by a packet to travel from the source node to any of the sinks. – Total Energy Consumption represents the total energy consumed in packet delivery, including transmitting, receiving, and idling energy consumption of all nodes in the network. 4.2

Impact of Design Parameters

Now we examine how the performance of DBR is affected by algorithm parameters and network settings: parameter δ, depth threshold dth , node mobility, and number of sinks. Parameter δ. The parameter δ decides the holding time of packets at each node. With a larger δ, each node has a shorter holding time and hence the average end-to-end delay will be reduced; but more nodes will forward the same packet, which results in more energy consumption. On the other hand, with a smaller δ, a node will have longer holding time and hence the average end-to-end delay is increased; but less nodes will forward the same packet, as helps to reduce energy consumption. Fig. 5 shows how the energy consumption and average end-to-end delay change with three different values of δ: R/4, R/2, and R, where R is the maximal transmission range. In this set of simulations, the number of sinks is set to 5 and the depth threshold is set to 0. From Fig. 5(a), we observe that the average

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end-to-end delay when δ = R4 is about three times of the delay when δ = R. Fig. 5(b) shows that the energy consumption does increase as δ is lifted, but not significantly (less than 5%). Hence, we choose δ = R in later simulations. Depth Threshold. Fig. 6 shows how the depth threshold, dth , affects the packet delivery ratio and energy consumption. In this set of simulations, the number of sinks is set to 5. From Fig. 6, we see that when the depth threshold increases, both the packet delivery ratio and the total energy consumption decrease. This is because increasing the depth threshold has a similar effect to reducing the number of available nodes in the network. Therefore, the number of forwarding nodes will decrease. Consequently, the packet delivery ratio decreases and less energy is consumed. Node Mobility. To evaluate how node mobility affects the performance of DBR, we simulate DBR with different fixed node speeds at 1 m/s, 5 m/s, and 10 m/s. The depth threshold is set to 0 and the total number of sinks deployed is 5. The simulation results are plotted in Fig. 7. From Fig. 7, we observe that the packet delivery ratio, total energy consumption, and average delay do not change much with node speed. The reason is that all routing decisions in DBR are made locally based on a node’s depth information. No topology or route information needs to be exchanged among neighboring

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nodes. Therefore, DBR can handle dynamic network topologies well. For example, Fig. 7(a) shows that DBR successfully delivers at least 88% of the packets in all cases. When the total number of nodes is larger than 400, almost all packets can be successfully delivered. From Fig. 7 we can also see how node density affects the performance of DBR. The figure shows that the packet delivery ratio and the energy consumption increase as the number of nodes increases. The average end-to-end delay, however, decreases. This is because when the number of nodes increases, the number of forwarding nodes also increases, which leads to a higher packet delivery ratio and higher energy consumption. At the same time, the probability that a forwarding node has a shorter distance to the sinks increases as more nodes participate in packet forwarding. As a result, the average end-to-end delay decreases. Number of Sinks. Fig. 8(a) shows how the packet delivery ratio changes with different number of sinks. DBR with multiple sinks has a better packet delivery ratio than DBR with only one sink. Since DBR is a greedy algorithm trying to deliver data packets to the water surface, increasing the number of sinks at the water surface will increase the chance that a packet is received by a sink. This explains the higher delivery ratio when multiple sinks are deployed. The total energy consumption for different number of sinks is shown in Fig. 8(b). We observe that the energy consumption is almost the same for different number of sinks. The reason is that in DBR, the number of sinks does not affect the forwarding process. Therefore, all different settings have almost the same energy consumption. From Fig. 8(c), we observe that DBR with multiple sinks has a slightly better average end-to-end delay than DBR with one sink. This is because in the multiple-sink case, a packet is considered successfully delivered whenever it reaches any of the sinks. 4.3

Comparison with VBF

Now we compare DBR with VBF [17]. In this set of simulations, we consider two settings for DBR: one sink and multiple sinks. For one-sink DBR, the depth threshold is set to 0. In multiple-sink DBR, 5 sinks are randomly deployed at

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the water surface and the depth threshold is set to 20 meters. For VBF, we set the routing pipe radius to 100 meters, which is the maximal transmission range. Fig. 9 compares DBR and VBF with respect to the three metrics. Fig. 9(a) shows that for the one-sink setting, DBR achieves a similar packet delivery ratio to VBF. With the multi-sink setting, however, DBR can achieve a much better delivery ratio, especially for sparse networks. For example, in a network with 200 nodes, DBR with five sinks has a packet delivery ratio of around 70%, which is more than four times larger than 15%, the delivery ratio of VBF. Fig. 9(b) shows that DBR has better energy efficiency compared with VBF. In all cases, the total energy consumption of DBR is about half that of VBF. This is mainly due to the the redundant packet suppression techniques adopted by DBR. The two-queue mechanism in DBR has greatly reduced the redundant packet transmissions in the network. It should be noted, however, that this comparison is made based on the basic settings of VBF. VBF may get better energy efficiency when advanced adaptation algorithms are applied and optimal parameters are chosen. Fig. 9(c) shows that the best end-to-end delay is achieved in multiple-sink DBR while VBF has a better end-to-end delay than one-sink DBR. This is because VBF tries to find the shortest path from the source node to the sink along the virtual vector between them. Thus the delay in VBF is shorter than that in one-sink DBR. In multiple-sink DBR, however, packets can be delivered to any sink, instead of a fixed sink as in VBF. It should be noted that DBR and VBF target different network settings and have quite different network assumptions. For example, VBF is designed for networks with a single sink. Although DBR can work in one-sink networks, it has better performance in multiple-sink settings. 4.4

Summary

The simulation results show that DBR protocol works well for dense networks. However, the delivery ratio in sparse networks is relatively low. The reason is that DBR has only a greedy mode. The greedy method alone is not able to achieve high delivery ratios in sparse networks. We will investigate recovery algorithms for DBR in the future.

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The DBR protocol requires more memory in sensor nodes to maintain two buffers. Since the underwater sensor nodes normally are equipped with more resources than land-based sensor nodes, the memory overhead is not significant in most systems. Moreover, the applications for underwater sensor networks have relatively low data rate so only small buffers need to be maintained. For example, we observed in our simulations that the average number of packets in the sending queue of each node is less than 10. As a result, the cost of extra memory is affordable for underwater sensor nodes.

5

Conclusions and Future Work

In this paper, we presented Depth Based Routing (DBR), a routing protocol based on the depth information of nodes, for underwater sensor networks. DBR uses a greedy approach to deliver packets to the sinks at the water surface. Different from other geographical-based routing protocols that require full-dimensional location information of nodes, DBR only needs the depth information, which can be easily obtained locally at each sensor node. Further, DBR can naturally takes advantages of the multiple-sink underwater sensor network architecture without introducing extra cost. Our simulation results have shown that DBR can achieve high packet delivery ratios (at least 95%) for dense networks, with reasonable energy consumption. To achieve better performance for sparse networks, recovery algorithms need to be explored to avoid the “void” areas where the greedy strategy fails. Advanced recovery algorithms should be developed for higher energy efficiency and delivery ratio. DBR provides a distributed routing protocol based on local information of the sensor nodes. Besides the depth information, other information, such as the residual energy level and estimated distance to neighboring nodes, could also be useful in making routing decisions that can further reduce energy consumption and extend the network’s life time. For multiple-sink network settings, we only consider some simple cases in which the sinks are randomly, uniformly deployed on the water surface. Given the routing protocol and the node deployment model, we may find better deployment locations for the multiple sinks to achieve better performance.

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References 1. Akyildiz, I.F., Pompili, D., Melodia, T.: Challenges for efficient communication in underwater acoustic sensor networks. ACM SIGBED Review 1(1) (July 2004) 2. Basagni, S., Chlamtac, I., Syrotiuk, V.R., Woodward, B.A.: A distance routing effect algorithm for mobility (dream). In: MOBICOM 1998 (1998) 3. Braginsky, D., Estrin, D.: Rumor routing algorithm for sensor networks. In: WSNA 2002 (September 2002) 4. Cui, J.H., Kong, J., Gerla, M., Zhou, S.: Challenges: Building scalable mobile underwater wireless sensor networks for aquatic applications. Special Issue of IEEE Network on Wireless Sensor Networking (May 2006) 5. Grossglauser, M., Vetterli, M.: Locating nodes with ease: Mobility diffusion of last encounters in ad hoc networks. In: IEEE INFOCOM 2003 (March 2003) 6. Heidemann, J., Ye, W., Wills, J., Syed, A., Li, Y.: Research challenges and applications for underwater sensor networking. In: IEEE Wireless Communications and Networking Conference (April 2006) 7. Heinzelman, W.R., Kulik, J., Balakrishnan, H.: Adaptive protocols for information dissemination in wireless sensor networks. In: Mobicom 1999 (August 1999) 8. Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and roust communication paradigm for sensor networks. In: MOBICOM 2000 (August 2000) 9. Karp, B., Kung, H.T.: Gpsr: Greedy perimeter stateless routing for wireless networks. In: Proc. Mobicom (2000) 10. Linkquest, http://www.link-quest.com/ 11. Nicolaou, N., See, A., Xie, P., Cui, J.-H., Maggiorini, D.: Improving the robustness of location-based routing for underwater sensor networks. In: Proceedings of IEEE OCEANS 2007 (June 2007) 12. The ns mannual (2002), http://www.isi.edu/nsnam/ns/doc/index.html 13. Partan, J., Kurose, J., Levine, B.N.: A survey of practical issues in underwater networks. In: Proc. of ACM International Workshop on UnderWater Networks (WUWNet), September 2006, pp. 17–24 (2006) 14. Proakis, J., Sozer, E.M., Rice, J.A., Stojanovic, M.: Shallow water acoustic networks. IEEE Communications Magazines, 114–119 (2001) 15. Seach, W.K., Tan, H.X.: Multipath virtual sink architecture for underwater sensor networks. In: OCEANS (May 2006) 16. Xie, G.G., Gibson, J.: A networking protocol for underwater acoustic networks. Technical Report TR-CS-00-02 (2000) 17. Xie, P., Cui, J.-H., Lao, L.: Vbf: Vector-based forwarding protocol for underwater sensor networks. In: Proceedings of IFIP Networking (May 2006) 18. Ye, F., Luo, H., Cheng, J., Lu, S., Zhang, L.: A two-tier data dissemination model for large-scale wireless sensor networks. In: MOBICOM 2002 (September 2002) 19. Ye, F., Zhong, G., lu, S., Zhang, L.: Gradient broadcast: A robust data delivery protocol for large scale sensor networks. ACM Wireless Networks(WINET) 11(2) (2005)

Channel Allocation for Multiple Channels Multiple Interfaces Communication in Wireless Ad Hoc Networks Trung-Tuan Luong, Bu-Sung Lee, and C.K. Yeo School of Computer Engineering Nanyang Technological University, Nanyang Avenue, Singapore 639798 {Y050089,bslee,asckyeo}@ntu.edu.sg

Abstract. A major drawback of multi-hop communication in ad hoc network is the bandwidth scarcity along the forwarding path of data packet. The main reason for the lack of bandwidth or the exponential reduction in the bandwidth is the contention for bandwidth between nodes along the path. In this paper we will demonstrate one way to overcome this problem through the use of multiple channels multiple interfaces (MCMI). We investigate different forwarding channel selection schemes for MCMI communication with Destination Sequenced Distance Vector protocol: same channel, random and round robin. Based on our analysis and simulation results we have shown that MCMI protocol significantly improves the channel capacity while maintaining low end-to-end delay. Forwarding channel selection policies play an important role in determining the performance of the MCMI. Among the channel assignment policies under study, round robin policy provides the best performance. The results provide a baseline for evaluating bandwidth of multiple channels multiple interfaces networks. Keywords: Multiple channel communication, channel interference, channel assignment, bandwidth.

1

Introduction

Wireless communication has become an indispensable part of modern day-to-day life. Research in ad hoc networks has focused on single channel networks, in which a common channel is desired for simple routing control in multi-hop networks. A well known fact that is affecting the performance of ad hoc networks is the significant throughput degradation along the multi-hop path [1]. When a single channel is used both for incoming and outgoing traffic, throughput is halved as they use the same radio channel. That means when one node is transmitting, its neighbors nodes must all be in listening mode otherwise collision will occur. This problem is amplified across the network, and after a few hops the bandwidth is reduced significantly. To overcome the throughput degradation problem, a natural approach is to use multiple channels in which nodes work on different channels in an interference area. A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 87–98, 2008. c IFIP International Federation for Information Processing 2008 

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Recent research and commercial efforts in the wireless networking industry have focused on a new class of ad hoc networks: multiple channels multiple interfaces ad hoc networks [1]. These networks are characterized by a set of fixed nodes with multiple wireless interfaces utilizing multiple orthogonal channels at the same time over a given swath of spectrum such as IEEE 802.11 a/b/g. Simultaneous operation of multiple channels provides the following advantages: – Capacity enhancement: A node with two interfaces can send packets on two channels simultaneously. More importantly, forwarding nodes can both send and receive at the same time. Using appropriate protocols for channel selection and assignment, such a system can provide significantly greater capacity than a single interface system – Load sharing: Load sharing occurs where a traffic flow is distributed among the available connections to achieve lower latency and increase robustness to network. – Channel failure recovery: Graceful degradation and robustness against channel errors is possible by employing frequency diversity. Frequency diversity can be achieved by using multiple interfaces and operating each on different channel frequencies. In this paper, we implement the Destination Sequenced Distance Vector (DSDV) for multiple channels multiple interfaces wireless ad hoc networks (DSDV-MCMI) to reduce the bandwidth degradation problem. In DSDV-MCMI, we investigated three different policies for forwarding channel selection: same channel, random channel and round robin channel to evaluate the channel interference along the multi-hop path. Our simulation results show that DSDVMCMI protocol significantly improves the network goodput while maintaining low end-to-end delay. Among the three different channels selection policies for MCMI, round robin policy, where the packets are forwarded based on the packet incoming channel, is the best at preventing channel interference; which results in shortest end-to-end delay time. Our paper outline is as follows. Section 2 briefly discusses the related work. In Section 3, we present the multiple channels multiple interfaces protocol with DSDV-MCMI. The simulation and results analysis are given in Section 4. Finally, Section 5 concludes the paper.

2

Related Work

We can categorize strategies for using multiple channels into two groups depending on the number of interfaces used: single interface and multiple interfaces. The first strategy is to enable a single wireless interface card to access multiple channels [2,3]. For example, So and Vaidya [3] propose a scheme that allows wireless devices to communicate on multiple channels using a single interface card. The scheme requires frequent channel switching, which incurs considerable overhead on the current hardware. The other strategy is to use multiple interface cards; Nasipuri et al. [4,5,6] propose a class of multi-channel carrier sense multiple access

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MAC protocols where all nodes have an interface on each channel. These proposed protocols use different metrics to choose the channel for communication between nodes. The metric may simply use the idle channel [4], or the signal power observed at the sender [5], or the received signal power at the receiver [6]. Wu et al [7] propose a MAC layer solution that requires two interfaces. One interface is assigned to common channel for control purposes, and the second interface is switched between the remaining available channels for transmitting data. Lee [8] proposed a class of proactive routing protocol for multi channels, which uses one control channel and N data channels. The nodes exchange control packages on the control channel to negotiate the best channel for the receiver’s data channel. Most past research in the use of multiple channels require either modification of the MAC layer protocol or changes to IEEE 802.11. Compared to previous research, our proposed protocol focus on exploit the multiple channels by using concurrent multiple interfaces. The protocol does not require channel synchronization among nodes. Although there is some additional control overhead, the simulation results show that the proposed protocol significantly increase network goodput while decreasing end-to-end delay efficiently.

3 3.1

Multiple Channels Multiple Interfaces Routing with DSDV Model Description

Module based node for ns-2. Wireless nodes are becoming multimodal. They are equipped with multiple interfaces possibly using different technologies, including wired access, and may use them concurrently. Paquereau [9] designed module based wireless node (MW-Node), a more flexible and better integrated wireless and mobile support in ns-2[10]. It is not a new implementation but primarily a reorganization of already existing components. The MW-Node enables new features to be supported by the simulator. For instance, true support for multiple wireless interfaces on a single node, each may have its own routing protocol. DSDV-MCMI. Based on the MW-Node model, we developed destination sequenced distance vector for multiple channels multiple interfaces, DSDV-MCMI, to demonstrate the multiple channels multiple interfaces routing scheme. Nodes can have more than one network interfaces as shown in Fig. 1. These interfaces are assigned to orthogonal channels so that they will not interfere with each other. Every interface has one DSDV [11] routing agent for generating control messages and forwarding data packets. Destination-Sequenced Distance-Vector Routing (DSDV) is a table-driven routing protocol based on the Bellman-Ford algorithm. Each mobile node maintains a route to every other node in a routing table. Route entry contains destination address, next hop address and number of host to reach the destination. In order to prevent routing loop, each entry is marked with a sequence number assigned by the destination node. The sequence numbers enable node to

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Fig. 1. Multi-interface node

distinguish stale routes from new ones. The DSDV routing agent at each interface maintains its’ own routing table for all available destinations. To maintain the consistency of routing tables in a dynamically varying topology, each node’s interface periodically transmits full updates or immediately transmits incremental updates when significant new information is available, such as a topology changes. We design a channel assignment module on top of the network interfaces. This module is in charge of selecting the network interface for forwarding each data packets. 3.2

Channel Assignment Policy

The purpose of channel assignment is to determine the forwarding channel for packet to minimize interference with other nodes. We apply the static approach [12] that assigning a common set of channels for node’s interfaces. Every node has N interfaces, which are assigned to the same set of N orthogonal channels. At the source node, data packet is sent out on an interface which is randomly selected. When an intermediate node along the path receives a data packet on incoming channel Chin ; the node uses one of the following three policies to select the outgoing channel Chout for relaying data packet: – Policy 1 Same channel: node will relay the packet on the same channel as the packet was received. This approach incurs high channel interference in multi-hop route, which results in long delay and packet drops that degrade the performance significantly. Chout = Chin – Policy 2 Random channel: node relays data packet on a randomly selected channel. The probability that the forwarding channel is the same as the received channel is N1 . When a node has more interfaces, the less chance that the forwarding channel is the same as the received channel. Chout = Random(Ch0 ,Ch1 ,..,ChN −1) – Policy 3 Round robin channel: when a node receive data packet need to be forwarded on ith channel it will forward that packet on the (i+1)th channel. Chout = (Chin + 1) mod (N)

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91

Channel Interference

Assuming that nodes are ideally placed along the route with hop distance ’d’ as in Fig.2, all the node has transmission range ’T’ and carrier sensing range ’C’. The ideal placement of a node is d ≤ T < 2∗d

(1)

The transmissions of a node interfere with transmissions of other nodes within its’ sensing range. Thus sensing range is also called as interference range. The interference distance in term of number of hops along a path is:     M = Cd hops where Cd is floor of Cd This means that transmission of a node may interfere with transmission of the next M nodes and transmission of the previous M nodes along the path. We will prove the throughput by using multiple channels for transmission; the minimum number of channels required to eliminate the channel interference among forwarding nodes along the path is (M+2). As shown in Fig. 2, nodes A1 , A2 ,..,

Fig. 2. Multi-hop path

AM+1 are within interference range of each other, because the distance between A1 and AM+1 is M hops. Therefore the (M+1) data transmissions from Ai to Ai+1 (i=1,2,..,M+1) can interfere with each other. Due to the exposed node problem, data transmission from node AM+1 to node AM+2 can also interfere with CTS/ACK packet sent from node A1 to node S. Thus we can see that there are (M+2) data transmissions interfering with each other. Hence we need at least (M+2) channels so that every data transmission can work on different channel to ensure these communications will not interfere with each other. In Fig. 3, we apply policy 3 for forwarding channel selection among the set of (M+2) channels { Ch0 , Ch1 ,..,ChM+1 }. We can see that two transmissions use the same channel, such as link S-A1 and link AM+2 -AM+3 , are (M+1) hops apart along the path, hence they will not interfere with each other. Thus with (M+2) available channels, we can use policy 3 for channel assignment for data transmission so that we can eliminate channel interference

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Fig. 3. Channel assignment for communication link in multi-hop path

3.4

Capacity of a Chain of Multiple Channels Multiple Interfaces Nodes

In this subsection, we evaluate the capacity of a single chain of nodes where packets are originated at first node and forwarded along the chain to the last node. We investigate chain of nodes under saturated condition that every node along the chain always has packet to forward. When node A select channel to forward packet, we denote PI (A) the probability that the selected channel will be interfered. Let S be the set of N available channels; B is channel bandwidth; B(A) is bandwidth experienced by node A. BChain is the maximum bandwidth can be achieved for transmission from source to destination of the chain. M is the interference distance in term of number of hops and L is hops length of the chain. Along the chain, we can see that the middle node such as node AM+1 in Fig. 2 is the bottleneck node, because its’ data transmission may share the same channel with most number of other transmissions. Bandwidth of chain is reduced significantly because of those nodes. Thus we will evaluate the bandwidth can be experienced by the middle node A in the chain. Policy 1. we have Chout = Chin , therefore two consecutive data transmissions along the path always interfere with each other. Thus channel interference always happens, PI (A) =1. If the chain is long enough, which is equal or more than (2*M+2) hops long. We examine the middle node AM+1 as in Fig. 2; AM+1 will share the same channel with the next M nodes AM+2 ,.., A2M+1 and the previous M nodes A1 ,.., AM along the path. However due to the exposed node problem, transmission from node AM+1 to node AM+2 will also interfere with CTS/ACK packet sent from node A1 to node S. Therefore we can say that node AM+1 shares the same B channel with (2*M+1) other node.; this makes B(AM+1 ) = 2∗M+2 . Thus the B chain of nodes can achieve a maximum bandwidth of 2∗M+2 . If hops length L < (2*M+2); the middle node will have carrier sensing range cover the whole chain, thus its’ data transmission will share the same channel with all the other (L-1) transmissions along the path. So its’ experienced bandwidth B will be B L . Therefore the chain can achieve a maximum bandwidth of L .  B if L ≥ (2 ∗ M + 2) BChain = 2∗M+2 (2) B if L < (2 ∗ M + 2) L Policy 2. we denote PN I (A) the probability that the selected channel of A will not be interfered. PN I (A) = 1 - PI (A). Let K be number of forwarding nodes

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along the chain excluding A stay inside carrier sensing range of A. As discussed in policy 1, K is (2*M+1) if L ≥ (2*M+2); otherwise K is (L-1). We can see that PN I (A) equals to the probability that node A selects channel Chi and the other K nodes select among the rest (N-1) channels (S\ {Chi }) multiply by N.  K *N = NN−1  K PI (A) = 1- PN I (A) = 1- NN−1

PN I (A) =

1 N

*

 N −1 K N

we denote PI (A,J) the probability that the selected channel of A will be shared with other J nodes  J+1  N −1 K−J  J  N −1 K−J PI (A,J) = N1 ∗ N ∗ N = N1 ∗ N J=0..K  where  1 K  K K (N −1)K−J B B(A) = J=0 PI (A, J) ∗ J+1 =B ∗ N J=0 J+1

BChain = B ∗

1 N

K   K (N − 1)K−J (2 ∗ M + 1) if L ≥ (2 ∗ M + 2) whereK = L−1 if L < (2 ∗ M + 2) J + 1 J =0 (3)

Policy 3. We first evaluate the long chain of nodes that L ≥ (2*M+2). If N ≥ (M+2), policy 3 can help to eliminate channel interference; thus PI (A) = 0 and B(A)= B. Else if N < (M+2), PI (A) = 1 and node A will share the same M channel with next M along the chain. Node A N nodes and previous N nodes   may share the same channel with the exposed node if M+1 = M+1 N N . Therefore B(A) = M + BM +1 +1 . N   N  Then we evaluate the short chain that L< (2*M+2). If L ≤ N then all the transmissions use different channel thus PI (A) = 0 and B(A)= B. Else if L  > L N, data transmission of node A will share the same channel with other N -1 L L transmission. Therefore PI (A) = 1 and B(A)= B where N is ceiling of N L N  ⎧ B if L ≥ (2 ∗ M + 2)andN ≥ (M + 2) ⎪ ⎪ B ⎪ ⎨ M if L ≥ (2 ∗ M + 2)andN < (M + 2)  N + MN+1 +1 BChain = (4) B if N < L < (2 ∗ M + 2) ⎪ L ⎪   ⎪ N ⎩ B if L ≤ N

4 4.1

Simulation and Results Verification of Chain Results

First we simulate MCMI in a chain of nodes setup, where nodes are placed ideally 150m apart from each other. The transmission range is 250m and carrier sensing range is 550m. This is the worst-case placement, where node is placed at just above half of transmission range of each other. In this scenario, interference distance M is 3 hops. Packet is originated at source node with the rate of 2Mbps, which equals to channel bandwidth; so that data transmission of the chain is

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T.-T. Luong, B.-S. Lee, and and C.K. Yeo Table 1. Bandwidth drop rate when chain’s length increases Chain length (hops) 1 2 3 4 5 5

Interface Interface Interface Interface Interface Interface

with with with with with

Policy Policy Policy Policy Policy

3 3 3 2 3

1 2

3

4

5

6

7

8

9

10

1 2

1 3 1 2

1 4 1 2 1 2

1 5 1 3 1 2 1 2

1 6 1 3 1 2 1 2

1 7 1 4 1 3 1 2

1 8 1 4 1 3 1 2

1 8 1 4 1 3 1 2

1 8 1 4 1 3 1 2

1 1 1 1 1 1

1 1 1 0.9 1

1 1 1 0.73 0.59 0.47 0.38 0.30 0.24 0.24 0.24 1 1 1 1 1 1 1 1

under saturated condition. We will measure the bandwidth can be achieved when the chain is 1-hop length and measure how the bandwidth drops when chain is longer. We increases numbers of interfaces equipped by nodes and apply different channel selection policy to verify our analysis. We calculate the drop rate of maximum bandwidth achieved of chain of Nhops length compared to 1-hop length chain according to equation (2), (3), (4). The result is summarized in Table 1 Fig. 4 shows the comparison results of our

Fig. 4. Bandwidth drop rate when number of hops increases

simulation and analysis. We can see that our simulation’s results are very close to the analysis results shown in Table 1. When the chain gets longer, packet must traverse more hops. Packets’ end-to-end delay increases while bandwidth decreases. 4.2

Simulation of MCMI in Two Dimensions Multihops Network Environment

We evaluated the performance of MCMI in static ad hoc network of map size 1200mx800m with network node density of 80 nodes. This setting helps to ensure that network is crowded enough so that we can have a path between any nodes; and network is also scarce enough so that the path length is long for evaluating effects of different channel allocation policies. The average hop counts of our

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Table 2. Simulation Setting Parameter

Setting

Traffic model Packet size Packet sending rate Mac Transmission range Carrier sensing range Map size Node number Simulation time

20CBR connections 512 bytes 2pkt/s-12pkts/s 802.11 2Mbps 250m 550m 1200mx800m 80 300s

simulation are 3.5 hops. We simulate 20 CBR connections with 512bytes packet size in 300 seconds. The packet rate increases from 2 packets/s to 12 packets/s to vary the traffic load. A summary of the simulation setting is given as shown in Table 2. The following performance metrics are used to evaluate the simulation results: – Goodput: the number of useful bits per unit of time forwarded by the network from a certain source address to a certain destination, excluding protocol overhead, and excluding retransmitted data packets – End-to-end delay: the average time it takes for a packet to traverse the network from its source to destination. 4.3

Result Analysis

Goodput Experiment 1: Effects of number of Interfaces,N. Fig. 5 shows the goodput results at different traffic load for different number of channels. The goodput performance of N-interfaces is normalized against the performance of 1-interface. With increasing number of node’s interface, nodes can receive packet on one interface and send packet on other interface at the same time. Thus with more interfaces, node can share the traffic load better, which help to reduce the congestion. Therefore as expected when number of interfaces increases, the goodput increases. We do not expect the goodput to increase linearly as the number of interfaces increases, because communication paths can crossover and interfere each others. This causes bandwidth reduction in all the crossover communication path. Experiment 2: Effects of Forwarding Channel Assignment Policy. The normalized goodput performances of three channel assignment policies is compared against policy 1 in three-interface network are shown in Fig. 6. Policy 3 is the best as it diversifies channel usages, which results in greatest goodput achieved. Policy 1 does nothing to prevent channel interference, thus its’ goopdut performance is much less than that of policy 3. Policy 2 does a little effort to prevent

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Fig. 5. Normalized goodput regarding number of interfaces

Fig. 6. Normalized goodput regarding forwarding channel selection policy

channel interference by selecting forwarding channel randomly, which still has probability of channel interference. Thus policy 2 performs slightly worse than policy 3. End-to-End Delay Experiment 1: Effects of number of Interfaces,N. Fig. 7 shows the corresponding results of end-to-end delay for different number of network interface used for transmission, as the traffic load is increased. A key observation is that, in a congested network, packets will be kept in queue for very long, waiting for channel availability; which resulted in either long end to end delay or packet dropped. When node has more interfaces, it will be able to forward packets through all the interfaces. Thus packets need not wait long in queue; as the result, with more interface the end-to-end delay decreases considerably. For example at packet sending rate of 10pkt/s, the end to end delay can be reduced by 2, 4, 6 times that of a single interface when node has 2, 3 and 4 interfaces respectively.

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Fig. 7. End-to-end delay regarding number of interfaces

Experiment 2: Effects of Forwarding Channel Assignment Policy. Fig. 8 presents the end to end delay performance of three channel assignment policies in three-interface node configuration with varying traffic load. Forwarding data packets on the same channel as the receiving channel incurs the greatest channel interference, thus policy 1 has the highest end-to-end delay. Policy 3 is the best as it diversifies channel usages, thus it can help reduce channel interference, which results in shortest end-to-end delay. The performance of policy 2 is slightly worse than policy 3.

Fig. 8. End-to-end delay regarding forwarding channel selection policy

5

Conclusion

We presented a multiple channels multiple interfaces ad hoc network routing protocol, DSDV-MCMI, which utilizes multiple channels to enable simultaneous multiple transmission to improve network capacity. This proposed scheme does not require modification to the current IEEE 802.11 MAC protocol. Simulation

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results shows that DSDV-MCMI exploits multiple channels multiple interfaces to improve network capacity and reduce channel interference. With more equipped network interfaces, we can achieve higher goodput and shorter end-to-end delay time. We also suggested some policies for forwarding channel selection: same channel, random channel and round robin channel; these policies play an important role in determining performance of MCMI. However, there is also an issue of crossover interference among the communication path and one needs a deeper understanding before a satisfactory solution is found.

References 1. Bahl, P., Adya, A., Padhye, J., Walman, A.: Reconsidering wireless systems with multiple radios. SIGCOMM Comput. Commun. Rev. 34(5), 39–46 (2004) 2. Chandra, R., Bahl, P., et al.: Multinet: Connecting to multiple ieee 802.11 networks using a single wireless card. In: Proceedings of IEEE Infocom 2004, Hongkong (March 2004) 3. So, J., Vaidya, N.H.: Multi-channel mac for ad hoc networks: handling multichannel hidden terminals using a single transceiver. In: MobiHoc 2004: Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing, pp. 222–233. ACM Press, New York (2004) 4. Nasipuri, A., Zhuang, J., Das, S.R.: A multichannel csma mac protocol for multihop wireless networks. In: Wireless Communications and Networking Conference. WCNC, vol. 3, pp. 1402–1406. IEEE, Los Alamitos (1999) 5. Nasipuri, A., Das, S.: Multichannel csma with signal power-based channel selection for multihop wireless networks. In: Vehicular Technology Conference, 2000. IEEE VTS-Fall VTC 2000. 52nd, vol. 1(1), pp. 211–218 (2000) 6. Jain, N., Das, S., Nasipuri, A.: A multichannel csma mac protocol with receiverbased channel selection for multihop wireless networks. In: Proc. IEEE IC3N, Phoenix (October 2001) 7. Wu, S.-L., Lin, C.-Y., Tseng, Y.-C., Sheu, J.-P.: A new multi-channel mac protocol with on-demand channel assignment for multi-hop mobile ad hoc networks. In: ISPAN ’00: Proceedings of the 2000 International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN 2000), p. 232. IEEE Computer Society Press, Washington (2000) 8. Lee, U., Midkiff, S.F., Park, J.S.: A proactive routing protocol for multi-channel wireless ad-hoc networks (dsdv-mc). In: International Conference on Information Technology: Coding and Computing (ITCC 2005), vol. 2, pp. 710–715 (2005) 9. Paquereau, L., Helvik, B.E.: A module-based wireless node for ns-2. In: WNS2 2006: Proceeding from the 2006 workshop on ns-2: the IP network simulator, p. 4. ACM Press, New York (2006) 10. network simulator version 2 (ns 2): http://www.isi.edu/nsnam/ns/ 11. Perkins, C.E., Bhagwat, P.: Highly dynamic destination-sequenced distance-vector routing (dsdv) for mobile computers. Computer Communications Review 24(4), 234–244 (1994) 12. Katzela, I., Naghshineh, M.: Channel assignment schemes for cellular mobile telecommunication systems: a comprehensive survey. Personal Communications, IEEE [see also IEEE Wireless Communications] 3(3), 10–31 (1996)

Resilience to Dropping Nodes in Mobile Ad Hoc Networks with Link-State Routing Ignacy Gawędzki and Khaldoun Al Agha Laboratoire de Recherche en Informatique Université Paris-Sud 11, CNRS F-91405 Orsay, France [email protected], [email protected]

Abstract. Currently emerging standard routing protocols for MANETs do not perform well in presence of malicious nodes that intentionally drop data traffic but otherwise behave correctly with respect to control messages of the routing protocol. In this paper, we address the problem of coping with the presence of such nodes in networks that use a linkstate routing protocol. The solution, based on the verification of the principle of flow conservation, is purely protocolar and does not rely on any specific underlying OSI layer 1 or 2 technical feature. In addition, it is well suited for integration into existing link-state routing protocols for ad hoc networks, by using existing periodic control messages and by not requiring any clock synchronization between the nodes. The method, once implemented in an actual routing protocol, proves to increase the ratio of successfully delivered data packets significantly. Keywords: ad hoc networks, link-state routing, security, resilience.

1

Introduction

Routing protocols for mobile ad hoc networks (MANETs) have received much attention in the past decade, but currently the most mature solutions still rely on the full cooperation of the participating nodes. The presence of one or more malicious nodes may have negative consequences on the proper operation of the network. In the case of proactive protocols, nodes exchange control messages allowing them to maintain their routing table and data packet forwarding is performed on a hop-by-hop basis, using a traditional protocol stack (most commonly TCP/IP). This makes these protocols easy to implement as userspace processes while the operating system’s kernel remains unchanged. The downside is that securing the protocol itself is not enough to make the network resilient to malicious nodes, since they can always play nice with control packets, while not as much with data packets. The presented solution aims to reduce the impact of malicious nodes as much as possible, allowing others to route data packets around nodes that tend to lose traffic, provided that malicious nodes are not colluding. A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 99–111, 2008. c IFIP International Federation for Information Processing 2008 

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After related work in the field is summarized in Sect. 2, the solution is detailed in Sect. 3. Performance evaluation is presented in Sect. 4, followed finally by a conclusion and some perspectives in Sect. 5.

2

Related Work

The issue of Byzantine robustness of network operations has received attention for as long as the network administration of the Internet has become more decentralized and thus more exposed to attacks. The problem reemerged for MANETs, since the direct application of solutions for wired networks appeared to be either impossible or ineffective. In the family of reactive routing protocols — of which DSR [9] and AODV [13] are notorious examples — various solutions have been proposed [2,7,1] to provide Byzantine robustness. Since route discovery and maintenance as well as data packet forwarding is taken care of by the protocol, cryptographic methods can be used to ensure that every packet has been forwarded to the destination, without being corrupted, misrouted nor forged. As for proactive routing protocols, the OLSR [5] protocol has received attention from Raffo et al. [14] who proposed to secure the content of control messages to prevent nodes from advertising false or expired information. Fourati et al. also proposed a way to secure control messages [6] by requiring neighbors to validate and sign TC messages using threshold cryptography. While these propositions aim to guarantee the correctness of the protocol in presence of malicious nodes, they do not protect against data packet dropping. Protecting data packets in the same (intrusive) way as proposed for reactive protocols would break the principle of separate table-driven hop-by-hop routing, so it is not desirable in this case. Additional cryptographic tools such as key distribution systems to allow sender authentication should be used, but they still do not enable the prevention or detection of intentional packet dropping. In order to make packet dropping detectable, Marti et al. proposed [12] to exploit the ability of nodes to overhear the transmissions of their neighbors to check whether they have retransmitted the packets as requested. Unfortunately, such an approach does not work anymore when directional antennae or multiple network interfaces (or simply different communication channels) are used on nodes. Other approaches, based on active probing of nodes to detect droppers, have also been proposed [10,2,11]. They rely on the inability of nodes to either distinguish probes from plain data packets or identify their originator. It is worth noting that many solutions to encourage cooperation [4,15] have been proposed as well. Although they may be very effective to discourage selfishness, they do not protect against malicious nodes that drop data traffic, be it at their own expense. In the context of static wired networks, Bradley et al. proposed [3] that nodes should periodically verify that each of their neighbors satisfies the principle of flow conservation based on their advertised packet counters. Nodes that fail to pass these tests are flagged as bad and excluded from the network. Their

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assumptions about the network have later been criticized as being too strong and a few possible attacks have been exposed by Hughes et al. [8]. However, this solution is also hardly applicable to MANETs, because of mobility and the difficulty to maintain tightly synchronized clocks between nodes.

3 3.1

Proposed Solution Motivations and Assumptions

Our experience in implementation of MANET routing protocols taught us that the less a solution is intrusive in the existing framework the easier it is to implement, maintain and port. Besides, relying only on basic features of drivers allows wider hardware support. With a protocolar approach, we sought a solution easy to implement by requiring little or no change at all in the OS kernel while no cross-layer requirement eases support for a wide range of network interface cards. The solution assumes that cryptographic mechanisms allow any node, be it the destination host or an intermediate router, to check a packet’s integrity and authenticate its source. The network is assumed to be running a linkstate routing protocol that exchanges periodic control packets. The bound P on the time separating two successive control packets is assumed to be finite and known. 3.2

Solution Outline

The solution is composed of three distinct phases, that are performed continuously during the operation of the network. First, perform unicast packet accounting, exchange counters periodically and verify the principle of flow conservation. Second, maintain a local degree of distrust in each other node, based on how often it fails the flow conservation tests and diffuse that value in the network. Third, combine local degrees of distrust from other nodes into a single, uniform metric (i.e. every other node comes up with the same values) and use it for routing table calculation. Note that intentional and unintentional packet droppings are not distinguished, which is desirable since in the third phase, intentional as well as unintentional packet droppers are simply avoided. This should have a positive effect on the overall operation of the network. 3.3

Checking Flow Conservation

In this section, the method used to check whether nodes’ counter values do not satisfy the flow conservation principle is presented. Definition 1. Let the network be a graph G = (V, E) with V the set of vertices (the nodes) and E : R+ → V × V a function mapping instants in time t to sets of arcs E(t) (the one-way links) existing at these instants.

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j i Definition 2. Let Iij (t) and Iij (t) be respectively i’s and j’s counter of the amount of bytes that flowed on link (i, j) up to instant t in packets which desj i tination was not j. Similarly, let Oij (t) and Oij (t) be respectively i’s and j’s counter of what flowed on (i, j) up to t in packets which source was not i.

The flow conservation principle states that   i i ∀t, ∀i, Iji (t) − Oij (t) = 0 .

(1)

j i i If i advertised its counters Iji (t) and Oij (t) periodically, its neighbors could check (1) for each instant t at which i sent an advertisement. But since that simple check is based on i’s counters only, i could as well have advertised false values. Additional checks are thus needed to ensure that these are correct, namely:

∀t, ∀i, j,

j j i i Iji (t) = Iji (t) and Oij (t) = Oij (t)

and Tiji (t) = Tijj (t) ,

(2)

where Tiji (t) and Tijj (t) are respectively i’s and j’s counter of the total amount of bytes that flowed on (i, j) up to t. Because of mobility, the neighborhoods are changing and so absolute values of counters are inconvenient. Differential values are better advertised instead: a differential value is simply the difference between the current absolute value of a counter and its absolute value at the time of last advertisement. Definition 3. Let Si (t) be the value of i’s sequence counter at instant t and let Ti (n) be the instant at which the sequence counter is incremented from n − 1 to n and i’s advertisement set with sequence number n is transmitted. In the following, X is used to represent either I, O or T , to avoid repeating three times each formula that holds for each type of counter. j i i Definition 4. For each counter in the form Xij (t) or Xij (t), let X˙ ij (t) and j ˙ Xij (t) be their differential values.

∀t, ∀i, j, ∀k ∈ {i, j},

k k k X˙ ij (t) = Xij (t) − Xij (Tk (Sk (t)))

(3)

Definition 5. Let T− i (n) be the instant right before i’s nth advertisement set is sent. This notation is used only with differential values to avoid ambiguity.   ∀t, ∀i, Si T− (4) i (Si (t)) = Si (t) − 1 Using periodic messages of the routing protocol, nodes advertise successive differential values of their counters. Definition 6. An advertised values of counters (AVC) tuple from i for neighbor j with sequence number n has the following form:  i  −   −    i I˙ij Ti (n) , O˙ ij Ti (n) , T˙iji T− i (n) , i Vj (n) = i  −  . (5)  −    i I˙ji T (n) , O˙ ji T (n) , T˙jii T− (n) i

i

i

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P Ti (n)

Ti (n + 1)

103

P

Ti (n + 2)

Ti (n + 3) from i time from j

Tj (m − 2)

Tj (m − 1) P

Tj (m) Tj (m + 1) P

P

Fig. 1. Desynchronized advertisements: differential counter values from i and j are not advertised at the same time and thus do not represent flow on the same interval. Vertical arrows indicate instants at which advertisements from i (above) and j (below) are sent. Upper bound on the advertisement interval P is shown as well.

Between two successive transmissions of an AVC tuple for some neighbor j, a node i may receive zero or more AVC tuples regarding itself from j. Definition 7. Let Sij (n) be the (possibly empty) set of sequence numbers of j’s AVC tuples received by i between its (n − 1)th and nth advertisements.   ∀i, j, ∀n, Sij (n) = m : Ti (n − 1) ≤ Tj (m) < Ti (n) ∧ (j, i) ∈ E (Tj (m)) (6) To allow neighbors of i to verify (2), i has to retransmit, along with each AVC tuple about a neighbor j, a set of reverse AVC tuples. In all, values of counters are diffused to the whole 2-hop neighborhood. Definition 8. Node i’s nth advertisement set contains nth link advertisements about each of its neighbor j. ⎧⎛  ⎞ ⎫ ⎬   ⎨ ⎝n, Vji (n), Ai (n) = m, Vij (m) ⎠ (7) ⎩ ⎭ i j

m∈Sj (n)

Given that advertisements are sent asynchronously, (1) can be performed directly, whereas (2) most probably cannot. The question is then how to decide that those equations are not satisfied, based on desynchronized counters, i.e. differential counter values over different intervals of time (see Fig. 1). The idea is to consider what can be called desynchronized link balances. Definition 9. A desynchronized link balance in terms of counter X for the link (k, l) (either (i, j) or (j, i)), computed from advertisement number n from node i about node j has the following form:  −   j  −  i ∀i, j, ∀n, ∀(k, l), BX ikl (n) = X˙ kl Ti (n) − X˙ kl Tj (m) . (8) m∈Sij (n)

When a node k receives the nth advertisement set from i, it can compute BX iij (n), i i i ˙j − since X˙ ij (T− i (n)) and every Xij (Tj (m)) with m ∈ Sj (n) are contained in A (n).

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Obviously, BX iij (n) and BX iji (n) are seldom zero, unless the counters in Ai (n) are zero themselves. Instead of looking at desynchronized link balances for individual advertisements, let us consider the sum of several desynchronized link balances for successive advertisements. It appears that these accumulated link balances have interesting properties. Definition 10. Let Δij (n) be the difference in time between the instant Ai (n) is sent and last j’s advertisement set was received by i.

∀i, j, ∀n,

Δij (n) =

⎧ ⎨Ti (n) − max Tj (m) i

if Sij (n)  =∅ ,

⎩T (n) − T (n − 1) + Δi (n − 1) i i j

otherwise.

m∈Sj (n)

(9)

Suppose that k receives M successive advertisement sets from i, calculates the desynchronized link balances and sums them. That sum is expressed quite simply in terms of absolute counters. The intuitive argument can be seen on Fig. 1: the summed desynchronized link balance (the gray rectangles) from i’s advertisements n + 2 to n + 3 (i’s (n + 2)th advertisement contains j’s (m − 1)th counters and i’s (n + 3)th advertisement contains j’s mth and (m + 1)th counters), is clearly equal to the difference between the amount of bytes that flowed on interval [Tj (m + 1), Ti (n + 3)) and on interval [Tj (m − 2), Ti (n + 1)). Definition 11. Let F X iij (n) (resp. F X iji (n)) be i’s end flow on link (i, j) (resp. (j, i)), i.e. the quantity expressed by: ∀i, j, ∀n, ∀(k, l),

  i i F X ikl (n) = Xkl (Ti (n)) − Xkl Ti (n) − Δij (n) .

(10)

j i Theorem 1. If Xkl (t) = Xkl (t) (for any i, j, (k, l) = (i, j) or (k, l) = (j, i) and any t), the summed values of desynchronized link balances over an interval of sequence numbers [n, n + M ] are exactly:

∀i, j, ∀n, ∀M ≥ 0, ∀(k, l),

n+M 

BX ikl (p) = F X ikl (n + M ) − F X ikl (n − 1) . (11)

p=n

Proof. By induction on M ≥ 0. The case of M = 0 is trivially shown:     i i ∀i, j, ∀n, ∀(k, l), BX ikl (n) = Xkl Ti (n) − Xkl Ti (n) −Δij (n) (12)   j  j  i − Xkl Ti (n−1) + Xkl Ti (n−1) −Δj (n−1) . j i The induction hypothesis holds since we have that Xkl (t) = Xkl (t).



From the expression of (11), bounds on the accumulated desynchronized link balances can be expressed as follows.

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j i Theorem 2. If Xkl (t) = Xkl (t) (for any i, j, (k, l) = (i, j) or (k, l) = (j, i) and any t), then the accumulated desynchronized link balances are bound in the  q  n+M following way:  i  X Xi  ∀i, j, ∀n, ∀M ≥ 0, ∀(k, l), max B kl (p) − F kl (q) ≤ BX ikl (p) q∈[n,n+M ]

p=n

 ≤

min

q 

q∈[n,n+M ]

p=n

 BX ikl (p)

+ F X ikl (n + M ) .

(13)

p=n

The proof of Theorem 2 is straightforward but omitted for brevity. Definition 12. Let Γji (n), be the minimum difference in time between the instant Ai (n) is sent and first j’s advertisement set was received by i since Ai (n−1) was sent. ∀i, j, ∀n : Sij (n)  = ∅, Γji (n) = Ti (n) − min Tj (m) (14) i m∈Sj (n)

i ˙i − In Ai (n), the quantities X˙ ij (T− i (n)) and Xji (Ti (n)) are i’s claim about how much has flowed on links (i, j) and (j, i) respectively for each class X, during interval [Ti (n−1), Ti (n)). If that advertisement also contains at least one reverse j i AVC tuple for the links (i.e. Sij (n)  = ∅), then the quantities X˙ ji (T− i (n) − Γj (n)) j − i ˙ and Xij (Ti (n) − Γj (n)) are j’s corresponding claim on interval [Ti (n − 1) − Δij (n − 1), Ti (n) − Γji (n)).

Definition 13. Let Lij (n) and Nij (n) be the sequence numbers defined as follows.  ∀i, j, ∀n,

  Lij (n) = max m : m ≤ n ∧ Sij (m)  =∅   Nij (n) = min m : m > n ∧ Sij (m)  =∅

(15)

It appears that we have the following properties: ∀i, j, ∀n,       Ti (n) − Δij (n), Ti (n) ⊆ Ti Lij (n) − 1 , Ti (n)        Ti (n) − Δij (n), Ti (n) ⊆ Ti (n) − Δij (n), Ti Nij (n) − Γji Nij (n) .

(16)

It immediately follows that bounds for the end flow can be expressed in terms of differential values of counters contained in advertisements. ⎞ ∀i, j, ∀n, ∀(k, l), ⎛ n           − i i i i ˙j ⎠ (17) F X ikl (n) ≤ min ⎝ X˙ kl T− i (p) , Xkl Ti Nj (n) − Γj Nj (n) p=Lij (n)

If there is no discrepancy between i and j’s counters about link (k, l) (either (i, j) or (j, i)), then the bounds on the accumulated desynchronized link balances of (13) are verified. Even more so if upper bounds of the end flows from (17)

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are used instead of their actual values. In the following we call this verification the link coherence check. When such a check fails, the absolute value of the excess of accumulated desynchronized link balances with respect to the (loose) bounds is itself a lower bound on the amount of actual discrepancy, which is used directly in the second phase of the solution (detailed in Sect. 3.4). Then the corresponding accumulated values have to be reset to zero and the bounds to be initialized anew upon reception of the next advertisement. 3.4

Distrust System

A failed check is a proof of counter discrepancy, caused by dropped data traffic or false AVCs. Since usually some data packet loss is to be legitimately expected, a check failure should not be a direct reason for the exclusion of nodes. Nodes that drop data traffic should thus be avoided if possible, but not excluded. In the second phase of the solution, tests from the first phase are used to maintain a distrust metric on nodes which is used for path calculation. Unlike in usual trust or reputation systems, this metric cannot be individually maintained by each node, based on first-hand observations combined with second-hand observations from neighbors. The metric has to be uniform across all the nodes, so that every node tends to know of the same value for any given node in the topology, otherwise hop-by-hop routing may not be loop-free. Therefore, the metric is a combination of all the nodes’ local observations, diffused in the network. The local observations (failures to pass checks from the first phase) are used to maintain a local degree of distrust. This value is initially zero and each time a check fails, it is incremented as follows:   Di ← max(1, Di ) · 1 + Li · r+ , (18) where Di , Li and r+ are respectively the degree of distrust in i, the lower bound on the amount of actual discrepancy and the ratio used as a parameter of the system set to how fast a degree has to increase. The value recovers over time in the following way:   Di ← max 0, Di − Δt · r− , (19) where Di , Δt and r− are respectively the degree of distrust in i, the interval of time elapsed since last update and the ratio used as a parameter of the system set to how quickly a degree has to decrease with time. The local degree of distrust is diffused to other nodes, to allow them to compute the distrust metric for each other node. To make the calculation resilient to slander, we propose to combine the different degrees by calculating a kind of median value, which is insensitive to pathological samples that deviate too much. Using the local degrees of distrust directly is too simplistic and we require nodes to advertise a confidence factor along. That factor is used to enable each node to say to what extent its degree has to be taken into account in the calculation of the median. It is based on the amount of traffic that the observed

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node supposedly transmitted, according to its counters. The confidence factor increases with traffic: ci ← ci + Ti · r+ (20) (with ci , Ti and r+ being respectively the confidence factor for Di , the amount of traffic of i and the ratio used as a parameter of the system set to how quickly the confidence has to grow) and decreases with time passing: ci ← max(0, ci − Δt · r− )

(21)

(with ci , Δt and r− being respectively the confidence factor of Di , the interval of time elapsed since last update and the ratio used as a parameter of the system set to how quickly the confidence has to decrease with time). This way, the more (alleged) traffic goes through a node, the higher the confidence factor of its neighbors in their degree of distrust in that node. The value of the confidence factor that is actually advertised is required to be in the interval [0, 1), Ci = 1 −

1 , 1 + ci · ra

(22)

where Ci , ci and ra are respectively the advertised confidence factor of Di , the (internal) confidence factor of Di and the ratio used as a parameter of the system set to how quickly an advertised confidence factor has to come close to 1. The actual calculation of the metric based on individual couples (Dij , Cij ) (distrust and associated confidence in i diffused by j) is a weighted median: ∀i,



  Di1 , Ci1 , . . . , (Din , Cin ) ,  j 1  j Ci ≤ · C 2 j i j j:Di <Wi

and



Cij ≤

j:Dij >Wi

1  j · C , (23) 2 j i

where Wi is the combined metric for node i. A simple route calculation using a shortest path algorithm on the global topology graph is to set the weight wij of each link (i, j) to 1 + Wi (instead of 1 for a simple hop-count metric).

4 4.1

Performance Evaluation Simulation Model

We have implemented our solution on top of a model of the OLSR protocol for the OPNET 12.0 simulator. Mobile nodes move on an area of 1000 m × 1000 m, with constant velocity (chosen uniformly on [0, 1] m/s) and constant direction (chosen uniformly on [0, 2π)) during an interval of time chosen exponentially with rate 1/600 per second. Data packets are generated exponentially with rate 5 per second, source and destination are chosen uniformly and data length is chosen uniformly between 40 and 1500 bytes. Cheaters, also chosen uniformly among the

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nodes, drop data packets with a parametrized probability. A IEEE 802.11 MAC layer is used on all nodes with 11 Mbps carrier, 50 mW transmission power, flow control (RTS/CTS) and default power thresholds (-85 dBm for carrier sense and -70 dBm for reception, resulting in ranges of respectively 1200 m and 214 m). The method presented in Sect. 3.3 and 3.4 has been implemented, along with a simple retransmission protocol for lost advertisements (omitted here for brevity), with r+ = 1/50, r+ = 1, r− = r− = 1/10 and ra = 10−6 . The MPR selection heuristic has been adapted so as to prefer neighbors with lower local distrust (details omitted here as well). MPRs diffuse local degrees of distrust and confidence factors of their selectors in TC messages. Cheaters do slander by always advertising a very high degree of distrust and a confidence factor of 1. The data packet delivery rate (total received data packets over total generated data packets) has been measured in each run and compared to several reference runs: in the No Cheater run, plain OLSR is used and the drop probability is forced to zero to see what OLSR achieves at best without cheaters; in the Plain run, plain OLSR is faced with non-zero drop probability to see how bad it does alone; in the Known run, OLSR has an oracle telling which node is a cheater and routing table calculation is performed as suggested at the end of Sect. 3.4 with Wi = 9 for each cheater i. That last reference run is used to see what performance could be achieved at best. Cheaters that intentionally drop data packets have many possible ways in which they can manipulate their counters. Among possible strategies, we have chosen four different ones: increment counters as though the packets were forwarded indeed; increment counters on all outgoing links so that the total added sum is the amount of lost data bytes; do not increment counters on outgoing links; increment counters only for every second packet. 4.2

Simulation Results

In the first batch of simulations, there were 100 nodes with 10% of cheaters. We have checked that the system stabilizes pretty quickly and after a few dozen seconds, the delivery ratio of our method is stable (though we checked for up to 10 1.0 0.9 delivery ratio

delivery ratio

0.9

0.8 Known Average Plain

0.7

0.8 0.7 0.6

No Cheater Known

Average Plain

0.5 0.25

0.5

0.75

drop probability

1.0

25 (3)

50 (5) 75 (8) total nodes (cheaters)

100 (10)

Fig. 2. Delivery ratio: with fixed density of 100 nodes and variable drop probability (left); with variable density and fixed drop probability of 1.0 (right)

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1.0

delivery ratio

0.8 0.6 Known Average Plain

0.4 0.2 0

5

10

15

20

25

30

35

40

45

50

55

60

percentage of cheaters

Fig. 3. Delivery ratio vs. cheaters: 1.0 drop probability and a total of 100 nodes

hours) and much closer to the Known case than to the Plain one. Lowering the drop probability, apart from raising the Plain curve, only lengthened by a small factor the time required for the delivery ratio to be as close to the Known curve (approximately 3 minutes for a drop probability of 0.25). The average delivery ratios of the Plain, Known and the average of the four cheating strategies (which results turned out to be very similar) after one hour of simulation, with 100 nodes, 10% cheaters with 1.0 drop probability, are presented in Fig. 2 (left). It appears clearly that in dense networks, the performance of our method sticks to the Known reference. To evaluate the performance of our method with respect to varying network density, we have run a batch of simulations with a different total number of nodes but a (almost) constant ratio of 10% of cheaters, each dropping with a probability of 1.0. The results presented in Fig. 2 (right) show that while the delivery ratio degrades with decreasing density, the performance of our method is still very close to the Known reference. The reason why maximum achievable performance decreases with the density is that there are fewer and fewer alternative paths for a given destination, hence fewer ways to avoid the cheaters. A last batch of simulations has been run with a duration of 15 minutes, a total number of 100 nodes but a varying number of cheaters with 1.0 drop probability. Results show (see Fig. 3) that our method improves the delivery ratio significantly, but less and less than the Known reference as the number of cheaters increases. 4.3

Discussion

Hughes et al. strongly criticized [8] the use of the principle of flow conservation as a way to detect malicious nodes in a network. They presented a number of attack scenarios which make simplistic approaches fail anyway. In our solution, we require all packets to be checked for integrity and their source address to be authenticated by all intermediate nodes, to exclude the possibility of packet modification or forgery going unnoticed. Some attacks, like “ghost routers” or the ones based on the reactivity of link-state routing protocols,

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are targeted at the routing protocol itself, which we assume here to be already protected. Note that the “kamikaze” attack, in which a malicious node intentionally advertises false counter values in order to be excluded along with some of its neighbors is simply handled by our solution, in which nodes are never excluded.

5

Conclusion

We have presented a way to augment a link-state routing protocol to make it resilient to the presence of nodes intentionally dropping data traffic. The method consists in three parallel operations: detect flow non-conservation on neighboring nodes, maintain a degree of distrust in all checked nodes, combine all degrees of distrust into a metric for routing table calculation. The method is very little intrusive into the system, provided that necessary cryptographic tools are available and that the routing protocol itself is protected. It is independent of OSI Data Link and Physical layers which makes it easier to implement and workable with different hardware setups (unidirectional antennae, multiple interfaces, etc). The method showed satisfactory performance, very close to an ideal case in which all cheating nodes are known to an oracle. Among the possible future works, we intend to explore the possibility of making the method work even in the presence of colluding cheaters. The interaction with some intrusion detection system can also be studied, in order to counter the “premature aging” attack.

References 1. Avramopoulos, I., Kobayashi, H., Wang, R., Krishnamurthy, A.: Highly secure and efficient routing. In: IEEE (ed.) INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, March 2004, vol. 1, pp. 208–220. IEEE, Los Alamitos (2004) 2. Awerbuch, B., Holmer, D., Nita-Rotaru, C., Rubens, H.: An on-demand secure routing protocol resilient to byzantine failures. In: WiSE 2002: Proceedings of the 3rd ACM workshop on Wireless security, pp. 21–30. ACM Press, New York (2002) 3. Bradley, K.A., Cheung, S., Puketza, N., Mukherjee, B., Olsson, R.A.: Detecting disruptive routers: a distributed network monitoring approach. Network, IEEE 12(5), 50–60 (1998) 4. Buttyán, L., Hubaux, J.-P.: Stimulating cooperation in self-organizing mobile ad hoc networks. MONET 8(5), 579–592 (2003) 5. Clausen, T., Jacquet, P.: Optimized link state routing (OLSR) protocol. In: RFC 3626, IETF (October 2003) 6. Fourati, A., Agha, K.A.: A shared secret-based algorithm for securing the OLSR routing protocol. Telecommunication Systems 31(2–3), 213–226 (2006) 7. Hu, Y.-C., Perrig, A., Johnson, D.B.: Ariadne: a secure on-demand routing protocol for ad hoc networks. In: MobiCom 2002: Proceedings of the 8th annual international conference on Mobile computing and networking, pp. 12–23. ACM Press, New York (2002)

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8. Hughes, J.R., Aura, T., Bishop, M.: Using conservation of flow as a security mechanism in network protocols. In: IEEE Symposium on Security and Privacy, pp. 132–141 (2000) 9. Johnson, D.B., Maltz, D.A., Hu, Y.-C.: The dynamic source routing protocol (DSR) for mobile ad hoc networks for IPv4. RFC 4728, IETF (February 2007) 10. Just, M., Kranakis, E., Wan, T.: Resisting malicious packet dropping in wireless ad hoc networks. In: Pierre, S., Barbeau, M., Kranakis, E. (eds.) ADHOC-NOW 2003. LNCS, vol. 2865, pp. 151–163. Springer, Heidelberg (2003) 11. Mahajan, R., Rodrig, M., Wetherall, D., Zahorjan, J.: Sustaining cooperation in multi-hop wireless networks. In: 2nd Symposium on Networked System Design and Implementation, Boston, MA, USA (May 2005) 12. Marti, S., Giuli, T.J., Lai, K., Baker, M.: Mitigating routing misbehavior in mobile ad hoc networks. In: MobiCom 2000: Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 255–265. ACM Press, New York (2000) 13. Perkins, C., Belding-Royer, E., Das, S.: Ad hoc on-demand distance vector (AODV) routing. RFC 3561, IETF (July 2003) 14. Raffo, D., Adjih, C., Clausen, T., Mühlethaler, P.: An advanced signature system for olsr. In: SASN 2004: Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks, pp. 10–16. ACM Press, New York (2004) 15. Zhong, S., Chen, J., Yang, Y.R.: Sprite: A simple, cheat-proof, credit-based system for mobile ad-hoc networks. In: INFOCOM (2003)

3-D Localization Schemes of RFID Tags with Static and Mobile Readers Mathieu Bouet and Guy Pujolle Université Pierre et Marie Curie (UPMC) – Laboratoire d’Informatique de Paris 6 (LIP6) 104 avenue du Président Kennedy, 75016 Paris, France {Mathieu.Bouet,Guy.Pujolle}@lip6.fr

Abstract. RFID is an automatic identification technology that enables tracking of people and objects. We propose a scalable and robust 3-D localization method of RFID tags, derived in two schemes, that uses the diversity of pervasive environments to improve its accuracy. RFID readers are placed on the floor and ceiling of a room. In the first scheme, static and mobile tags are localized using only connectivity information obtained through this structure. The second scheme involves in addition mobile RFID readers. They are used here to refine the localization process by adding more inclusive constraints to the calculation. We study the impact of the number of static and mobile readers on the accuracy. We then show that the first scheme constitutes an upper bound for the second one. Furthermore, we observe that the second scheme does not require a lot of mobile readers and that it considerably improves accuracy. Keywords: RFID, localization, mobility, 3 dimensions, tracking.

1 Introduction Radio Frenquency IDentification (RFID) has recently become widespread for automatic identification and tracking. A RFID system is composed of two types of entities: RFID tags (or transponders) that store identity, and RFID readers (or detectors) that remotely retrieve these data. Tags can be passive: communications are supplied by the reader generally by backscattering its carrier wave, semi-passive: the tags embed a power source for their internal processes and communicate with readers like passive tags, and active: they embed a battery that supplies communications with readers and functionalities such as cryptography or sensor. Active tags are more expensive than passive tags but their communication range is far larger and they can have more advanced utilizations [1]. RFID is a key technology in future pervasive systems and services. Indeed, it provides essential context-aware functionalities such as automatic identification, tracking, and real-time inventory. By building RFID in networks, new management concepts could be conceived so as to fully integrate real-time status of items and people. However, all the benefits of this technology could be hugely increased if identification information was linked to positions. It would add another dimension to the context-awareness: real-time location and consequently mobility. For example, it could be used in home-networking and libraries to retrieve specific objects, control A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 112–123, 2008. © IFIP International Federation for Information Processing 2008

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access or monitor events. Matching identities with their locations would also allow the development of new strategies in autonomic systems for mobility control, resource allocation, security, service discovery. Classic RFID systems only provide coarse-grained localization. In fact, they are generally composed of readers placed in strategic locations; their purpose is to identify all the tags that pass in their read range. As a matter of fact, the precision of the position corresponds to the size of the cell formed by their read range. In this paper, our major contribution is to design a 3-D localization algorithm for mobile and static RFID tags which, contrary to other related works, is based only on connectivity information and which takes into account mobile RFID readers. Our algorithm offers fine-grained position estimation and is conceived so as to be cheap, robust, fast, and thus scalable. Moreover mobile readers are used to refine and improve the precision. Our model is simple: we consider a room, a warehouse, or a container (modeled by a hexahedron) and we deploy readers on the floor and ceiling. No assumption is done on the equipments and their quantity is reduced to the minimum in order to have a low-cost system. In the first scheme, an estimated position is computed for each mobile or static tag by using only connectivity information (which reader can detect the tag and which cannot) with a semi-infinitely constrained multivariable nonlinear method. Then, we present a second method, derived from the first one, to increase the localization precision when there are mobile readers. They are used to refine the first method by adding more constraints. This calculation requires more time and can only be applied on static RFID tags. First, we evaluate the impact of the number of readers on the precision of the localization, and we show that the accuracy of our method is very high when they are densely deployed. Then, we study the impact of the number of mobile readers on the performance of the second method, we underline the existence of an upper bound for the precision, and we point out a quick and impressive reduction of the localization error. The rest of the paper is organized as follows. First we discuss related work in Section 2. Then we explain the basic localization method and the improved localization method with mobile readers in Section 3. Simulation methodology and results are discussed in Section 4. Finally, Section 5 concludes the paper.

2 Related Work Localization is often considered as a widely studied domain. However, few methods have been proposed for RFIDs. In fact, this area has specific constraints and requirements. Contrary to localization in Mobile Ad-hoc NETworks (MANETs) or in Wireless Sensor Networks (WSNs) for which a lot of algorithms have been conceived, a RFID system is centralized because the computation capabilities of RFID tags are very limited. Besides they are composed of tens of thousands of tags that need to be treated quasi simultaneously. Moreover, they are usually deployed in indoor environments and their signal is harshly impaired. For example, UHF tags’ radio waves do not bounce off metal and have difficulties to penetrate water. The most famous indoor localization sensing method is called LANDMARC (LocAtioN iDentification based on dynaMic Active Rfid Calibration) [2]. It only

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localizes active RFID. The system is calibrated with the help of reference tags (additional tags that are fixed and whose positions are known by the system): a map including the Received Signal Strength (RSS) of each reader is built. Then, the system has to retrieve the RSS information from each tag to the readers and checks the map to find out its estimated position. Another well-known RFID localization system is SpotON [3]. It is also based on RSS measurements and uses them to estimate the distance between readers and tags. A central server aggregates the data and performs a trilateration to obtain the position of the tags. In [4], the authors also estimate the distance between tags and readers. However, this estimation is done through the Time Difference Of Arrival (TDOA) technique: the system measures the time-of-flight and deduces the distance according to the frequency and the inter-delay. This scheme is designed for fixed passive SAW RFID tags. Finally, in [5] the authors propose a 3-D localization scheme. It is founded on the assumption that readers have numerous power levels and on the deployment of reference tags. The read range of each reader is increased or decreased until the tag is detected. Then, thanks to the reference tags, an upper and a lower bound are obtained. The localization is done by mixing the bounds of all the readers and by applying a time-costly computation on the obtained area. Distance estimation through RSS, TOA or AOA is often very complicated in indoor environments because of signal absorption, indirect path, and interferences. These techniques can lead to large errors and require specific and thus expensive equipments. Moreover, the use of reference tags considerably increases the whole cost of the system and necessitates manual installation. Our localization method is based on very simple principles in order to be cost-effective, robust in harsh environments, and fast enough to be scalable when thousands of items need to be treated simultaneously. Indeed, we consider readers with basic capabilities, we do not use any additional equipment, and we localize tags using only connectivity information, no distance estimation is performed. General indoor localization systems have also been studied for a long time. The three more famous are Cricket [6], Active Bat [7], and Active Badge [8]. In the last system, Active Badges transmit unique infra-red signals every 10 seconds. When they pass close to a reader, they are detected. The position of the badge corresponds to the one of the reader that detected it, and the precision is the size of the cell. In Active Bat, ultrasonic receivers (or sensors) are placed on the ceiling. The system measures the time-of-flight from the node to each receiver and applies a classic trilateration. Finally, Cricket is also based on TDOA measurements but the difference is that the sensors send their coordinates to the node and it is the node that performs the trilateration to estimate its position. Several location methods for geographic routing and security mechanisms have also been proposed for MANETs and WSNs. The vast majority of them measures inter-node distance through the RSS, TDOA, or Angle of Arrival (AOA) technique. The others are based on hop count or inclusive and exclusive constraints. The readers are referred to [9] for more details. As explained earlier, those approaches address different problems than the one treated in this article but they offer a good comprehension and a solid basis for our approach. This also goes for the numerous methods that use reference RFID tags to localize mobile robots.

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3 Our Algorithm 3.1 System Setup First of all, we place RFID readers on the floor and ceiling of a room, a warehouse, or a container. These are modelized by a hexahedron whose length, width, and height are L, W, and H respectively as shown in Fig. 1. The readers are spaced from each other according to a certain gap S. The exact position of each reader is known by the system. Furthermore, they are supposed to be as basic as possible, that is, they have no different power levels, no directional antenna, no multiple antennas, and no synchronization. They can also have different read ranges. For easier comprehension, we consider they all have the same capacities and thus the same read range R, and we assume the signal transmission between readers and tags forms a sphere. Both active and passive tags can be located with this system.

Fig. 1. Our model

3.2 Basic Position Estimation This first method can locate both static and mobile tags and is based on the architecture presented in Section 3.1. The localization of tags only requires connectivity information: which readers are able to detect a given tag and which cannot. No distance estimation is performed. A mean position is calculated for each combination of intersection of readers as shown in Fig. 2. These mean positions correspond to the barycenter (also known as the center of gravity or the center of mass) of the different volumes formed by the intersection of the readers’ detection areas. Tags’ positions are estimated as follows. First, we define an error objective function δ i (x, y, z ) for each combination of readers: nj

δ i ( x, y , z ) = ∑ j =1

(x − x ) + (y − y ) + (z − z ) 2

j

2

j

2

j

.

(1)

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Fig. 2. A 2 dimensional intersection of readers

Where n j is the number of readers that form the combination,

( x, y , z )

are the

unknown coordinates of the barycenter (that will be the estimated position), and x j , y j , z j the coordinates of the reader j .

(

)

The error objective function is defined as the sum of the distance between the tag whose position is unknown and every reader able to read it. Then, as the number of readers on the floor is equal to the one on the ceiling, we can easily deduce if the tag is close to the floor, in the middle zone, or close to the ceiling. Indeed, if the tag is near the floor, more readers on the floor than on the ceiling will be able to read it. In that case, we can add extra information to the error objective function in equation (2). The same is done if more readers on the ceiling than on the floor are activated in equation (3). Nothing is done if the quantities are equal.

δ i ( x, y , z ) = δ i ( x , y , z ) +

(z − z

floor

)

δ i ( x, y , z ) = δ i ( x, y , z ) +

(z − z

ceiling

)

2

2

,

(2)

.

(3)

Finally, we use a semi-infinitely constrained multivariable nonlinear method to find the minimum of the error objective function presented in equation (1). It takes as parameter non linear functions, linear inequalities (the difference between the maximum read range R and the distance reader-unknown estimated position), and lower and upper bounds (the dimensions of the hexahedron). This well-known algorithm uses cubic and quadratic interpolation techniques to estimate peak values in the semi-infinite constraints. The error objective function has been defined so that its minimum corresponds to the barycenter of the volume: the calculated point will minimize the sum of the distances with all the readers plus the distance with the floor or the ceiling. The problem is stated as follows:

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min δ i (x, y, z ) subject to

( x, y , z )

⎧0 ≤ x ≤ L ⎪0 ≤ y ≤ W ⎪ ⎪0 ≤ z ≤ H ⎪ ⎨ ( x − x )2 + ( y − y )2 + ( z − z )2 − R ≤ 0 1 1 1 ⎪ ⎪... ⎪ ⎪ x − xn 2 + y − y n 2 + z − z n 2 − R ≤ 0 j j j ⎩

(

(

) (

Where (x1 , y1 , z1 ) , …, x n j , y n j , z n j

) (

(4)

)

) are the coordinates of the readers that formed

the volume. It is obvious that if the result for a given combination has already been computed, it is not necessary to recompute it. Consequently, this localization method can require a few calculations at the beginning but will be very fast when all the cases are treated. Actually, a solution would be to calibrate the room at the start, i.e. compute the corresponding mean position of every combination of reader. The localization would then be very fast: check the built map and retrieve the tag’s position. This process can be easily applied both on static and mobile tags since only connectivity information is used. 3.3 Position Estimation Enhanced by Mobile Readers In realistic pervasive environments, RFID readers can be mobile. We propose to use this diversity to improve our localization scheme. We lean this method on the first one: the room is equipped in the same way and the positions are estimated through the same process if no mobile reader is present. The mobile readers do not need to be complex or expensive since, once again, we deduce connectivity information from their location and their maximum read range. Their positions are known by the system. The interaction between the static reader structure and the mobile readers is shown in Fig. 3.

Fig. 3. A 2 dimensional intersection of static and mobile readers

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Consequently, the equation (4) becomes:

min δ i ( x, y, z ) subject to

(x, y , z )

⎧0 ≤ x ≤ L ⎪0 ≤ y ≤ W ⎪ ⎪0 ≤ z ≤ H ⎪ ⎪ ⎪ 2 2 2 ⎨ (x − x1 ) + ( y − y1 ) + ( z − z1 ) − R ≤ 0 ⎪ ⎪... ⎪ 2 2 2 ⎪ x − xn j + y − y n j + z − z n j − R ≤ 0 ⎪ ⎪ x − x mobile 2 + y − y mobile 2 + z − z mobile k k k ⎩

(

) (

(

(

) (

) (

Where x mobilek , y mobilek , z mobilek

)

) (

)

(5)

)

2

−R≤0

are the coordinates of the mobile reader that

detected the tag. Each time a mobile reader performs the reading of a tag, an inclusive constraint is added. Obviously, if the tag is static, this scheme can be easily applied and the zone is reduced quickly or not depending on the number of mobile readers. If the tag is moving slowly, it can also be localized with this variant but it is necessary to put timestamp on the constraints defined by the mobile readers and to remove the oldest ones. If the tag is moving quickly, the first localization method has to be applied.

4 Simulation 4.1 Simulation Methodology and Settings We have run extensive simulations in order to study the accuracy of our methods. The semi-infinitely constrained multivariable nonlinear algorithm has been implemented in several mathematic programs. We chose the one in the Optimization Toolbox available in Matlab [10]. We have defined different error measurements so as to evaluate the accuracy: • The mean error in 2 dimensions:

ε 2D =

(xreal − xestimated )2 + ( y real − yestimated )2

(6)

,

And its standard deviation σ 2 D . • The mean error in 3 dimensions:

ε 3D =

(xreal − xestimated )2 + ( y real − yestimated )2 + (z real − z estimated )2

And its standard deviation σ 3 D .

,

(7)

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• The mean error regarding the estimated height:

εh =

(z real − z estimated )2

,

(8)

And its standard deviation σ h . We dimensioned the hexahedron as follows:

L = 100m W = 100m . H = 3m

(9)

Where L, W, and H are the length, the width, and the height respectively. These values have voluntarily been chosen bigger than the dimensions of a typical room so as to take into account all the possible shapes. We choose realistic values for the read range of the static and mobile RFID readers:

R = 5m .

(10)

Finally, we implemented the Random WayPoint Mobility Model (RWP) [11] for the mobile readers. This model is used in many works since it is relevant for simulation of people displacements. The mobile readers randomly pick a destination in the hexahedron, go to it with a constant speed, when they reach it they pause, and then start the same process. It would be better if the mobile readers were not all at the same height but for an easier analysis of the performances, we chose to place them at 1 meter from the floor and to make them move with the speed of someone who is walking (5 kilometers/hour). Since the related works presented in Section 2 do not provide enough accurate hypotheses or use different configurations and different equipments (such as reference tags) from our model, we are not able to propose any comparison. 4.2 Results Fig. 4 shows the mean error in two dimensions (on x and y coordinates) after 300 seconds for different quantities of mobile readers. The accuracy of our basic localization scheme is below 1 meter when the static readers are spaced until 6 meters. A local peak is observed at 4.5 meters. This is due to the shape of the intersections of readers’ areas. Indeed, this value is just below the one of the radius. The readers on the floor and ceiling thus form very irregular zones, hence a higher error. This data constitutes an upper bound for the second localization method. After 300 seconds, with only one mobile reader the 2-D error is reduced until 40 centimeters compared to the static results. In the same time, if 5 mobile readers are used, the estimated error falls between 18 and 57 centimeters. Furthermore, the use of 10 or 20 mobile readers almost cancels the impact of the inter-reader spacing since after 300 seconds the error is bounded between 17 and 23 centimeters.

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1.4

0 mobile reader 1 mobile reader 5 mobile readers 10 mobile readers 20 mobile readers

1.2

Accuracy (m)

1 0.8 0.6 0.4 0.2 0 2

2.5

3

3.5 4 4.5 5 Inter-Reader Spacing (m)

5.5

6

6.5

6

6.5

Fig. 4. 2-D accuracy after 300 seconds 1.6

0 mobile reader 1 mobile reader 5 mobile readers 10 mobile readers 20 mobile readers

Accuracy (m)

1.4 1.2 1 0.8 0.6 0.4 2

2.5

3

3.5 4 4.5 5 Inter-Reader Spacing (m)

5.5

Fig. 5. 3-D accuracy after 300 seconds

Fig. 6 shows the mean error on the estimated height. It is limited between 39 and 61 centimeters. When the readers are densely deployed, the shapes of the intersections formed by the readers’ areas are not as regular as the ones when the inter-spacing reader is around 3 or 3.5 meters. The same phenomenon occurs in the cases where they are far one another. Consequently, the accuracy is better around 3 – 3.5 meters. The results for the second localization scheme are roughly the same. However, we can notice an improvement of the accuracy when mobile readers are used with static readers spaced of at least 4 meters. The impact of the mobile readers could be hugely increased if they were not all placed at the same height and if their read range were not all equal. For easier comparison between the different scenarios we chose identical mobile readers. Fig. 5 shows the 3-D mean error. The curbs have almost the same shapes than the ones for the 2-D results. This can be explained by the fact that our second localization method has only a light impact on the improvement of the height accuracy. Therefore,

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0.65

0 mobile reader 1 mobile reader 5 mobile readers 10 mobile readers 20 mobile readers

0.6

Accuracy (m)

121

0.55 0.5 0.45 0.4 0.35 2

2.5

3

3.5 4 4.5 5 Inter-Reader Spacing (m)

5.5

6

6.5

Fig. 6. Accuracy on height after 300 seconds

the evolution of the 3-D accuracy tends to follow the one in 2-D. The error is bounded between 57 and 159 centimeters for the first localization method. When 20 mobile readers are used, after 300 seconds the error falls between 48 and 61 centimeters (until 62% of reduction). The evolution of the accuracy through time is illustrated in Fig. 7. The impact of the mobile readers is visible since the beginning. After 30 seconds, the precision has been greatly increased and it goes on over time. Nevertheless, we can notice that improvement of the accuracy tends to decrease since the difference between the results at 240 seconds and at 300 seconds is only a few centimeters. Finally, in Fig. 8 we compare the impact of the number of mobile readers on the accuracy. Since for the first localization scheme there is no mobile reader, the error remains unchanged through time. In addition, two interesting remarks can be done. First, it is not necessary to use a lot of mobile readers. Indeed, the difference between 1.4

0s 1s 30 s 60 s 120 s 180 s 240 s 300 s

1.2

Accuracy (m)

1 0.8 0.6 0.4 0.2 0 2

2.5

3

3.5 4 4.5 5 Inter-Reader Spacing (m)

5.5

6

6.5

Fig. 7. Evolution of the 2-D accuracy with 10 mobile readers

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0.6

Accuracy (m)

0.5 0.4 0.3 0.2 0 mobile reader 1 mobile reader 5 mobile readers 10 mobile readers 20 mobile readers

0.1 0 0

100

200

300

400 500 Time (s)

600

700

800

900

Fig. 8. Evolution of the 2-D accuracy for an inter-spacing of 5 meters

the results for 5 mobile readers and the ones for 10 and 20 is not as important as expected. Secondly, as noticed in Fig. 7, the mean error in each scenario tends to rapidly converge over time. A residual error remains because of the inter-spacing between the static readers. To sum up, the results for the first localization scheme constitute an upper bound for the mean error of the second scheme. The use of mobile readers improves the accuracy. It depends on the number of mobile readers, the space between the static readers, and the execution time. For example, when inter-space is 5.5 meters and when 10 mobile readers are involved, the 3-D error falls from 92 to 59 centimeters (a reduction of 36%) after 300 seconds. Moreover, the results show that the error reduction converges quickly over time and that it does not require a lot of mobile readers.

5 Conclusion We have proposed a 3 dimensional localization method for static and mobile RFID tags and we have shown how to use diversity of pervasive environments, that is to say mobile RFID readers, to improve the positioning accuracy. We have chosen very simple properties so as to obtain a robust, fast, cost-efficient, and thus scalable method. Static RFID readers are placed on the floor and ceiling of a room. Tags’ positions are estimated by using only connectivity information in a semi-infinitely constrained multivariable nonlinear method. We then derive this scheme: mobile readers are used to refine the inclusive constraints and thus to improve the accuracy of static tags’ estimated location. Our results show that the precision of the first scheme is very satisfactory compared to the requirements. Furthermore, the use of mobile readers hugely reduces the localization error. The analysis also shows that it is not necessary to involve a lot of mobile readers and that the error reduction rapidly converges making the second localization scheme not only very quick but also very precise.

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References 1. Want, R.: An introduction to RFID technology. IEEE Pervasive Computing 5(1), 25–33 (2006) 2. Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. In: First IEEE International Conference on Pervasive Computing and Communications, pp. 407–415 (2003) 3. Hightower, J., Borriello, G., Want, R.: SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength. UW CSE Technical Report (2000) 4. Bechteler, T.F., Yenigun, H.: 2-D localization and identification based on SAW ID-tags at 2.5 GHz. IEEE Transactions on Microwave Theory and Techniques 51(5), 1584–1590 (2003) 5. Wang, C., Wu, H., Tzeng, N.-F.: RFID-Based 3-D Positioning Schemes. In: 26th IEEE International Conference on Computer Communications (INFOCOM), pp. 1235–1243 (2007) 6. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: 6th Annual ACM International Conference on Mobile Computing and Networking (MOBICOM), pp. 32–43 (2000) 7. Ward, A., Jones, A., Hopper, A.: A New Location Technique for the Active Office. IEEE Personal Communications 4(5), 42–47 (1997) 8. Want, R., Hopper, A., Falcao, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems 10(1), 91–102 (1992) 9. Gustafsson, F., Gunnarsson, F.: Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements. IEEE Signal Processing Magazine 22(4), 41–53 (2005) 10. Matlab, http://www.mathworks.com/ 11. Hyytia, E., Lassila, P., Virtamo, J.: Spatial node distribution of the random waypoint mobility model with applications. IEEE Transactions on Mobile Computing 5(6), 680–694 (2006)

Performance Evaluation and Enhancement of Surface Coverage Relay Protocol Antoine Gallais and Jean Carle IRCICA/LIFL, Univ. Lille 1, INRIA Futurs, France {antoine.gallais,jean.carle}@lifl.fr http://www.lifl.fr/POPS

Abstract. Area coverage protocols aim at turning off redundant sensor nodes while ensuring full coverage of the area by the remaining active nodes. Connectivity of the active nodes subset must also be provided so that monitoring reports can reach the sink stations. Existing solutions hardly address these two issues as a unified one and very few are robust to non ideal physical conditions. In this paper, we propose a deep analysis and some enhancements of a localized algorithm for area coverage, based on Surface Coverage Relays (SCR) and able to build connected active nodes sets that fully cover the area. We first enhanced the critical phase of our protocol (the relay selection) and show that the number of active nodes can be drastically reduced. We then raise the issue of the robustness of the protocol once a realistic physical layer is simulated. Our algorithm proved itself to be an interesting solution as it remained able to still ensure high coverage level under realistic physical layer conditions. We also added the possibility to finely tune the overall proportion of active nodes through a new parameter used during local relay selection phases. Keywords: Wireless sensor networks, area coverage, configurable localized algorithm, realistic physical layer.

1 Introduction Wireless sensor networks are made up of hundreds of devices in which a battery, a sensing module and a wireless communication device are embedded. They are deployed over hostile or remote environments, in which they become one-use-only since their batteries cannot easily be replaced or refilled. Energy consumption is therefore balanced by taking advantage of the redundancy induced by the random deployment of nodes; some nodes are active while others are in sleep mode, thus using less energy. Such a dynamic topology should not impact the monitoring activity. The ensuing issue consists in the active nodes to fully cover the area. Furthermore, as monitoring reports should at least reach the sink stations, the set of active nodes should be connected. This is also known as the connected area coverage problem. This work focuses on a localized algorithm, considering first that the availability of a deciding central entity is a strong assumption and also willing to show that such an organization can be obtained from networks without any infrastructure. We proposed in [1] a fully localized solution to the connected sensor area coverage issue. Both a neighbor A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 124–134, 2008. c IFIP International Federation for Information Processing 2008 

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detection phase and a relay selection phase are required to complete the activity decision process. In this article, we deeply study the relay selection phase, made from a new method described here. This latest solution allows us to strongly reduce the proportions of active nodes while not inducing any communication overhead nor any new information in control messages. Most of existing area coverage solutions rely on an ideal communication model (each node has a transmission threshold, beyond which no message may be directly sent). The radio channel randomness is hardly considered. We studied the robustness of our SCR protocol with such a new assumption. After showing that it was slightly impacted, we introduce a new parameter during the relay selection phase in order to vary the size of the relay set thus finely tuning the proportions of active nodes.

2 Preliminaries We assume that randomly deployed sensors are time-synchronized, static and positionaware. Several solutions have been studied to achieve these goals [2]. The location of a sensor node is used as its unique identifier. We also consider that sensors are able to determine whether an area is covered by a set of sensors or not. Various coverage evaluation schemes have already been used for that purpose [3,4]. 2.1 Communication and Sensing Models A wireless network is modeled by a graph G = (V, E), V being the set of vertices and E ⊆ V 2 the set of edges that the available communications. An edge between two vertices u and v exists if u is physically able to send a message to v. The neighborhood set of u, noted as N(u), is defined as: N(u) = {v ∈ V | v  = u ∧ (u, v) ∈ E}.

(1)

Each sensor has a communication range Rc and a sensing range Rs . We denote by S(u) the area covered by a node u and S(A) the area covered by a set of nodes A = {a1 , a2 , . . . , an } such that: 

i=|A|

S(A) =

S(ai ).

(2)

i=1

2.2 Radio Channel Model Given a graph G = (V, E), a communication range Rc and the Euclidean distance between nodes u and v dist(u, v), the unit disk graph model defines the set of edges E as: E = {(u, v) ∈ V 2 | u  = v ∧ dist(u, v) ≤ Rc },

(3)

Yet, in a practical context, transmission quality varies over the time and a node u may be able to communicate with node v at time t, but not at time t+1. Each communication

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link has a probability to exist which is influenced by a lot of factors such as the emitting power or the distance between the emitter and the receiver. Many models exist and some generators have already been proposed to allow model generation from parameters such as the kind of material used or the localization of the sensors themselves (see [5] for instance). To replace the unit disk graph, we opted for the lognormal shadowing model thus transforming G into a weighted graph, where the weight of each edge (u, v) ∈ E is equal to the probability of correct reception p(dist(u, v)) for the two nodes u and v. An approximated function P(x) is described by Kuruvila et al. [6]: ⎧ ⎪ 1− ⎪ ⎪ ⎪ ⎪ ⎨ P(x) =

⎪ ⎪ ⎪ ⎪ ⎪ ⎩

( Rxc )2α 2

if 0 < x ≤ Rc ,

( 2RRcc−x )2α 2

if Rc < x ≤ 2 × Rc ,

0

otherwise,

(4)

α being a factor that highly depends on the environment and x being the considered distance. This function assumes that the probability of correct reception for the range Rc is always equal to P(Rc ) = 0.5. Fig. 1 illustrates this function for α = 2, which is frequently encountered in the literature. Being aware of the approximation that is made here, we are not pretending to use a highly realistic model. Yet, we believe that considering radio channel randomness during protocol analysis and simulation constitutes a first step towards facilitating further developments on real material.

3 Related Work Many papers have already addressed the problem of connected area coverage in wireless sensor networks once sensors are allowed to self-schedule their activity. Some have assumed a given topology to start the study, such as a grid for instance [7]. In [8], authors study some deployment patterns in order to ensure both coverage and connectivity once wireless sensors are deployed over a sensing field. Meanwhile, the deployment might be random thus forcing sensor devices to make decisions based on non predictable information. Tian and Georganas [9] have proposed a node scheduling scheme in which time synchronized and randomly deployed sensor devices regularly detect their neighbors and listen for retreat messages for a given timeout. A node can get passive if remaining neighbors fully cover its area. To ensure connectivity of the set of active nodes, a strong assumption is made regarding the ratio between sensing and communication radii (Rs ≤ Rc ). Such an assumption had already been used and proved as preserving network connectivity as long as area coverage was provided [3,4]. Recently, a randomized algorithm was proposed in [10]. Based on a Markov model, this algorithm allows each node to probabilistically turn off while preserving both coverage and connectivity. Yet, the need for a new analysis when considering non homogeneous settings might be an obstacle in real deployments where neighbor information is required to ensure coherent node behavior once the neighborhood is modified for any reason (mobility, failure).

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Unit disk graph 1

Lognormal shadowing

Probability of correct reception

0.8

0.6

0.4

0.2

0

0

0.5

1 Distance

1.5

2

Fig. 1. The two considered physical models (Rc = 1, α = 2)

4 Base of the SCR-CADS Protocol While coverage might not be a strict requirement for wireless sensor networks, connectivity among the set of monitoring nodes is crucial as every collected piece of information should be able to reach one of the sink stations. This is why we propose a new look at our SCR-CADS protocol which has been proved to ensure both connectivity and coverage in an original manner [1]. We show how this algorithm could be configured and especially what its resistance to new communication models is. Our solution consists in three consecutive phases (a neighbor discovery mechanism, a relay selection phase and a decision making process), each being detailed in this section. Regular neighbor discovery. A classical neighbor discovery phase is first required. At the beginning of each round, a hello message is sent by each node. It contains information such as its geographical position and a priority, assumed to be unique among the set of nodes. Relay selection. When considering node coverage, Jacquet et al. [11] have defined the notion of multipoint relay (MPR). Each node selects a subset of its neighborhood; the set of relays. For any given node u, this set, denoted as M P R(u), enables multi-hop communications with every 2-hop neighbors: MPR : V → V u −→ M P R(u) ∀u, ∃ MPR | N (MPR(u))\N (u) = N (N (u))\N (u),

(5)

V being the whole set of nodes. We can observe that M P R(u) = N (u) satisfies the relation. Yet, trying to have a MPR set as little as possible leads to diminishing the size of the dominating set, denoted as MPR-DS. Each node must send its own relay set to its whole neighborhood so that a simple decision rule can be made. Authors have proved that some rules were providing MPR-DS that were connected.

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As we aim at providing area coverage by connected sets, we have extended this solution to the problem of area coverage in wireless sensor networks. The relay selection phase has been modified. Each node computes its own relay set; this set must cover as large an area as the whole set of neighbors. This set is called Surface Coverage Relay (SCR) set. We defined a SCR function for this purpose: SCR : V → V u −→ SCR(u) SCR(u) standing for the subset of N (u) for which the property holds: ∀u, ∃ SCR | S(SCR(u))\S(u) = S(N (u))\S(u).

(6)

Each node must be able to compute its SCR set. Finding the subset of minimal size is equivalent to the minimum set cover problem, which is NP-complete [12]. This is why heuristics have to be designed in order to compute SCR sets that are as optimal as possible considering the number of nodes involved. Initially, a heuristic based on centroid computation was used [1]. In this article, we propose a new heuristic to compute SCR sets. Each node starts with an empty SCR set. Then, step by step, a neighbor is added to the SCR set if and only if it covers a portion of area yet uncovered by the SCR set. To get interesting subsets, at each step, the neighbor that brings the more new coverage should be added. Ideally, the neighbors should therefore be ordered according to their coverage potential regarding previously observed nodes. The first heuristic was based on a simple idea; at each step, we try to find the node that should bring the more coverage. To avoid heavy computation, we opted for the one whose location is the furthest from the center of mass of already selected relays (every relay having the same mass). It turned out that still large sets were computed. We now propose a new heuristic for selecting the relays, based on a simple intuition; the furthest neighbor is the one that covers the largest portion of area outside the sensing zone of the selecting sensor. Neighbors are therefore ordered according to their distance to the selecting node. The furthest is added to SCR set, then the second furthest is evaluated and so on. Note that for any heuristic, considered neighbors have to be distinguished in case of equality when facing the choice criterion. Which node is considered first is determined with a simple random function. Once SCR sets have been computed, each node has to decide its own activity status. We now explain the decision rule used for this purpose. Making activity decision. In the MPR-DS protocol [11], nodes apply a simple rule to decide their status (dominant or not). This rule is based on a key, assumed to be unique among the set of nodes. Definition 1. Every node u whose key is the lowest among the neighborhood or which belongs to the relay set of the neighbor with the lowest key must be active. As already shown in [11], if relay sets are computed so that the property 5 holds, then the induced set of active nodes is connected. We have already proved that using this rule

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with SCR sets was providing a set covering as large an area as the whole set of nodes. It is also obvious that SCR sets are MPR sets once Rs = Rc since 2-hop neighbors are necessarily located within one or several sensing areas of the 1-hop neighbors. This means that the covering set is also connected once we every activity decision has been made. Now that we have presented SCR-CADS and a new heuristic to compute SCR sets, we are evaluating this protocol under several communication models. Indeed, it has already been largely spread that protocols evaluated under ideal communication assumptions (i.e. the unit disk model) were more prone to failures or to dysfunction once used in practice (see [13] for some instances among area coverage protocols). The remaining of this article is therefore focused on the analysis of SCR-CADS protocol and its performances with a realistic physical layer for communications.

5 Performance Evaluation We have simulated only homogeneous sensor networks. Experimental results were obtained from randomly generated connected networks with a discrete event simulator. Nodes are deployed over a 50 × 50 rectangle area, considering a Poisson point process of intensity λ > 0. We define the density d of the network to be the average number of nodes in a given communication area: we thus have d = λ × πRc2 . Both the communication range (Rc ) and the sensing radius (Rs ) are fixed at 10. Simulations were launched over densities varying from 20 to 90 nodes per communication zone, with a step of 10. For each density, the number of performed iterations is adjusted so that 95% of the results are in a sufficiently tight confidence interval. Each iteration consists of rounds. Each round starts with the neighbor discovery phase, followed by the SCR sets computation and the decision making process. A sensing area is modeled as a disk while the communciation model varies (unit disk model or a realistic physical layer). When nodes have to compute the area covered by a given neighbor, they use a coverage evaluation scheme based on a disks intersections theorem, already applied in [3,4]. A round ends with the sensing period which involves every node that has decided to be active. We essentially measure the percentage of active nodes and the percentage of preserved coverage of original area. 5.1 Under Ideal Communication Assumptions We give complementary results about the SCR protocol when simulated with ideal communication assumptions. By ideal, we mean that no message can ever be lost, neither due to message collisions nor to the communication environment itself. Area coverage and connectivity. As already mentioned, the algorithm has been proved as preserving both area coverage and connectivity of the set of active nodes. We therefore focus on the size of the active nodes sets in order to evaluate the impact of the heuristic used during the relay selection phase. Size of SCR sets. We have observed the average size of the SCR sets in order to compare the performances of the original heuristic with the ones the latest solution, based on distances. The size of the relay sets is a matter of prime importance first

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because the number of active nodes is strongly linked to these and second because they determine the communication overhead induced by the protocol. Concerning the first point,every node whose key is the lowest among its neighborhood is necessarily active. This number does not vary as long as the network density remains identical. Yet, the larger the relay sets, the higher the probability for a node to belong to the SCR set of the neighbor with the lowest key and so to become active. Concerning the second point, as relay sets must be sent to every 1-hop neighbor, the less large they are, the better for the communication overhead and thus the energy consumption. We could observed that the distance-based heuristic was actually performing better than the centroid-based one. As shown on Fig. 2(a), the average size of the SCR sets generated by the distance-based heuristic varies from roughly 8 neighbors to 11, respectively for densities 20 and 90, while the centroid-based method leads to 11 neighbors selected as relays at density 20 and nearly 24 at density 90, that is more than twice the size obtained by our distance-based heuristic. As already mentioned, the size of the relay sets straightly impacts the number of active nodes. As our new heuristic allows us to considerably reduce this size, we are now looking to what extent this improved relay selection impacts the average number of active nodes that are involved at every round. Active nodes. Figure 2(b) shows the average number of active nodes versus the network density. As expected, the percentage of active nodes decreases as the network density increases; the more nodes there are, the less the proportion of required sensors to achieve full coverage of the area. Interestingly, we can observe that the centroid-based heuristic leads to 24.7% of active nodes at density 90 while the distance-based method lowers this result to only 13% for the same density. We therefore obtained very interesting results with this new method. 5.2 Under Realistic Physical Layer Assumptions We now try to evaluate the SCR-CADS protocol with a realistic communication model. The goal for such an investigation is twofold. First, we aimed at testing the robustness

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of this algorithm and then some potential enhancements were to be implemented and also evaluated. Introducing a more realistic physical layer leads to more channel randomness, that is communication links that are probabilistic; two nodes can communicate with a given probability depending on the distance that separates them. Some area coverage protocols have already been studied under this new assumption and we aim at evaluating SCR protocol also. In our algorithm, once the relay sets have been computed, they are sent so that every node can make its activity decision from the rule previously described in 4. The so computed set of active nodes is connected and covers as large an area as the whole set of nodes once the unit disk model is used. When a realistic physical layer is used, we could observed some changes. Area coverage. Figure 3(a) shows the average percentage of covered area by the sets of active nodes computed by our SCR protocol. We can observe that while area coverage was fully preserved with an ideal communication model, this property does no longer holds under our new assumptions. Area coverage goes from a bit more than 95% at density 30 to less than 90% at density 80. Yet, this loss of coverage is not as important as we could have expected, and especially not as dramatic as the ones we could already observed with other protocols [13]. This shows an inherent robustness for our SCR protocol. Let us detail the reasons why the impact is that low. During the neighbor discovery phase, hello messages from some of the theoretical 1-hop neighbors may not be received by a given node u. Yet, this actually increases the probability for u to evaluate its own key as being the lowest among its neighbors. Then, the less neighbors are discovered, the higher the probability for u to get active, the worst case being no hello message ever received and u having an empty neighbor table, thus necessarily deciding to be active. In such a case, we would have all nodes being leaf and thus active, which is not the case here as some coverage losses have still been recorded. We may now focus on the relay selection phase. Once relays have been selected, SCR sets are sent to the neighbors. If some of these messages are never received by a given node u, then it results in some nodes being aware of only a subset of its real SCR selectors. If one of these potentially missed selectors is the neighbor with the 25 Distance−based heuristic Centroid−based heuristic 100 20

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lowest key so far, such a wrong reception leads to u not knowing it has been selected by its neighbor with the lowest key, which would have meant it had to be active. In such a case, u decides to be passive and therefore jeopardizes area coverage. This is why some coverage losses are observed on Fig. 3(a). However, note that if a relay message is received from a yet unknown node whose key is the lowest among already collected ones, then the receiving node must consider this message as a hello message. It must also check if it has been selected as a relay by this node. This demonstrates the inherent robustness of SCR protocol since only coverage losses may happen only in case the relay message of the neighbor with the lowest key (and whose previous hello message was received) was never received. This means that only this message is crucial while others can only bring some new information that could help nodes make the right activity decision. Active nodes. We also observed to what extent the average percentage of active nodes was impacted by our new physical layer assumptions. Generally, when some coverage losses occur, it is either caused by a decrease of the number of active nodes or by a spatial incoherence (as many active nodes but concentrated on a given region at the expense of another one which is left uncovered). The latter case can hardly take place thanks to the homogeneity of both our deployment and our random processes used during key computation. Indeed, fig. 3(b) shows that the percentage of active nodes is largely decreased (from less than 10% at density 30 to only 2% at density 90) when the distance-based heuristic is used. Although this tremendous drop of the percentage of active nodes could lead us to think that large coverage holes have occurred, we have already observed that the impact on area coverage was minor. The SCR-CADS protocol is inherently robust and does not much suffer from loss of active nodes since the remaining ones have self-elected with a coherent local process. 5.3 Tending to More Robustness and Customization of SCR Protocol Although the impact of a realistic physical layer is not important on area coverage, we could observe that the number of active nodes was largely decreased. As already mentioned, the percentage of active nodes is very much induced by the average size of the relay sets. We are now trying to tune the relay selection made by each node in order to be able to tune the global percentage of active nodes, and also the area coverage. We therefore introduced a coverage parameter, denoted as SCR COV , which stands for the number of relays that should cover any portion of the sensing area of a neighbor currently evaluated for potential selection. Every neighbor is now added to the set of relays if and only if its sensing area is not k-covered by already selected relays, with k being SCR COV . Several definitions of k-coverage exist in the literature. Our approach is to consider a k-covered piece of area as soon as k distinct sensor devices are able to sense it. This should help us to raise the proportions of active nodes as relay sets could be made larger. By this way, the resistance to some communication anomalies should be obtained thanks to the redundancy induced in the set of monitoring nodes. Note that this solution solely requires a modified coverage evaluation scheme.

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Area coverage. The prime goal of our SCR COV parameter is to be able to better resist to message loss. Figure 4(a) shows area coverage versus network density, with SCR COV ∈ {2, 3, 4} when the distance-based heuristic is used. As long as SCR COV > 1, area coverage is at least 95%. This shows that, even in presence of a realistic radio channel, our algorithm can provide satisfying results in terms of area coverage. Our new parameter may also help us to customize the relay selection more finely. Until now, we were only using our two selection heuristics. The problem with such a method is that dynamically modifying the way a relay selection is conducted by a node would require changing the code it actually runs. Given the hardware constraints of the target devices, it would be more convenient to interact with the algorithm by the means of a simple parameter. Active nodes. We observed to what extent the SCR COV value was able to impact the proportions of active nodes. The average percentage of active nodes is shown on Fig. 4(b), for several SCR COV values. When SCR COV = 2 (which is sufficient to ensure more than 95% of covered area), we have between 17% and 4% of active nodes. This shows that our SCR COV not only deserves to be used to improve area coverage but also to finely tune the proportions of active nodes.

6 Conclusion We have enhanced our SCR-CADS protocol in order to have a more efficient relay selection that fits in today’s requirements for wireless sensor networks algorithms. We have shown that our algorithm, originally designed under ideal communication assumptions, could work properly with a realistic physical layer, maintaining a high coverage level. We then introduced a new parameter during the relay selection, thus enabling full coverage in case this would be strongly required by the application. This allows to finely tune the proportions of active nodes as we tend to a more configurable protocol. Future work will focus on characterizing connectivity under more realistic communication assumptions. We also aim at observing the impact of both synchronization and

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localization errors which could be induced either due to the environment or to the used algorithm itself.

Acknowledgments This work was partially supported by a grant from CPER Nord-Pas-de-Calais/FEDER TAC COM’DOM, CNRS National platform RECAP.

References 1. Carle, J., Gallais, A., Simplot-Ryl, D.: Preserving area coverage in wireless sensor networks by using surface coverage relay dominating sets. In: Proceedings of IEEE Symposium on Computers and Communications (ISCC), Cartagena, Spain, pp. 347–352 (2005) 2. R¨omer, K.: Time Synchronization and Localization in Sensor Networks. PhD thesis, ETH Zurich, Switzerland (2005) 3. Zhang, H., Hou, J.C.: Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc and Sensor Wireless Networks journal (AHSWN) 1, 89–123 (2005) 4. Xing, G., Wang, X., Zhang, Y., Lu, C., Pless, R., Gill, C.: Integrated coverage and connectivity configuration for energy conservation in sensor networks. ACM Transactions on Sensor Networks (TOSN) 1(1), 36–72 (2005) 5. Cerpa, A., Wong, J.L., Kuang, L., Potkonjak, M., Estrin, D.: Statistical model of lossy links in wireless sensor networks. In: Proceedings of International Symposium on Information Processing in Sensor Networks (IPSN) (2005) 6. Kuruvila, J., Nayak, A., Stojmenovi´c, I.: Hop count optimal position based packet routing algorithms for ad hoc wireless networks with a realistic physical layer. In: Proceedings of IEEE Mobile Ad hoc and Sensor Systems (MASS), Fort Lauderdale, FL, USA (2004) 7. Shakkottai, S., Srikant, R., Shroff, N.: Unreliable sensor grids: Coverage, connectivity and diameter. In: Proceedings of IEEE International Conference on Computer Communications (INFOCOM), San Francisco, CA, USA (2003) 8. Liu, C., Xiao, Y.: Random coverage with guaranteed connectivity: Joint scheduling for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems (TPDS) 17(6) (2006) 9. Tian, D., Georganas, N.D.: Connectivity maintenance and coverage preservation in wireless sensor networks. AdHoc Networks Journal (Elsevier Science), 744–761 (2005) 10. Yener, B., Magdon-Ismail, M., Sivrikaya, F.: Joint problem of power optimal connectivity and coverage in wireless sensor networks. Wireless Networks 13(4), 537–550 (2007) 11. Adjih, C., Jacquet, P., Viennot, L.: Computing connected dominated sets with multipoint relays. Ad Hoc and Sensor Wireless Networks journal (AHSWN) 1(3), 27–39 (2005) 12. Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972) 13. Gallais, A., Ingelrest, F., Carle, J., Simplot-Ryl, D.: Preserving area coverage in sensor networks with a realistic physical layer. In: Proceedings of IEEE International Conference on Computer Communications (INFOCOM), Anchorage, AK, USA (short paper, 2007)

On Extending Coverage of UMTS Networks Using an Ad-Hoc Network with Weighted Fair Queueing R. El-Azouzi1 , R. El-Khoury1, A. Kobbane1,2 , and E. Sabir1,2 1

LIA/CERI, Universit´e d’Avignon, Agroparc BP 1228, Avignon, France 2 LIMIARF, Universit´e Mohammed V Rabat-Agdal, 4 Av. Ibn Battouta B.P. 1014, Maroc {rachid.elazouzi,ralph.elkhoury,abdellatif.kobbane, essaid.sabir}@univ-avignon.fr

Abstract. In this paper, we are interested in the interconnection between an ad-hoc network and UMTS system. In presence of two access technologies, new challenges arise and mobile host will have face multiple base stations or gateways with different utilizations. As a result, a user who needs to establish a voice call, prefers to be connected through UMTS and a user who needs to transmit a Best Effort traffic with high rate, prefers to be connected through ad-hoc network. The aim of that combination is to improve the coverage of cellular networks and increases the services which can be made available for mobiles. This combination also reduces transmission power for mobile hosts. Our main result is characterization of stability condition and the end-to-end throughput using the rate balance. Numerical results are given and support the results of the analysis. Finally, we perform extensive simulation and verify that the analytical results closely match the results obtained from simulations. Keywords: Interconnection, cross-layer, routing, ad-hoc, UMTS, stability.

1

Introduction

In order to meet the increasing in data and user mobility, a considered amount of effort is tunneled towards the convergence of multi-service networks of diverse technologies. Next generation wireless networks (NGWN) will be heterogeneous radio access technologies such UMTS, WiMAx, WLAN, etc, coexist. However, the NGWN will be a joint radio resource management among multiple operators of different radio resource technologies [3]. In this paper, we are interested in the planning of ad-hoc networks in providing an improved cellular coverage of UMTS cellular networks and different services can be made available for nodes. Indeed, there exist several types of traffic that require different QoS. In presence of several access technologies, new challenge arises and mobile host will 

This work was partially supported by ANR WINEM No. 06TCOM05 and Maroc Telecom R&D No. 10510005458.06 PI.

A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 135–148, 2008. c IFIP International Federation for Information Processing 2008 

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face multiple base stations or gateway with different utilization. This opens the possibility of using the most appropriate multiple access types for each type of call. For example, for a network with a low bandwidth and high reliability access as UMTS, it is preferable to be used by a voice call. As a result, a user prefers to be connected through UMTS network which can offer higher reliability for particular services, even at a high cost. A multi-hop wireless ad hoc network is a collection of nodes that communicate with each other without any established infrastructure or centralized control. Many factors interact with each other to make the communication possible like routing protocol and channel access. Recently, wireless ad-hoc networks have been envisioned of commercial applications such as providing Internet connectivity for nodes that are not in transmission range of a wireless access point. Cellular network and ad-hoc networks should be considered as complementary systems. Hence, it is possible to use ad-hoc networks as an extension of the cellular networks like UMTS. Thus it is possible for a multihomed node to use different access technologies at different time to assure a permanent connectivity. As a result, potential benefits can be envisaged for UMTS cellular as : extend the cellular coverage, reduce the power transmission which implies the reduction of intra-cell and extra-cell interference. In the ad-hoc networks, new services can be made available as VoIP and streaming video. In this work we are interested in the interconnection between a MANET and a 3G system. In order to be able to connect to more than one base station (or network), a terminal should have at least two network cards. In the ad-hoc network, we consider the random access mechanism for the wireless channel where the nodes having packets to transmit in their transmit buffers attempt transmissions by delaying the transmission by a random amount of time. This mechanism acts as a way to avoid collisions of transmissions of nearby nodes in the case where nodes can not sense the channel while transmitting (hence, are not aware of other ongoing transmissions). We assume that time is slotted into fixed length time frames. In any slot, a node having a packet to be transmitted to one of its neighboring devices decides with some fixed (possibly node dependent) probability in favor of a transmission attempt. If there is no other transmission by the other devices whose transmission can interfere with the node under consideration, the transmission is successful. As examples of this mechanism, we find Aloha-type [14] and IEEE 802.11 CSMA/CA-based mechanism. With these mechanisms, each node determines its transmission times [4,25]. In the UMTS network, the nodes communicate using Direct Sequence-Code Division Multiple Access (DD-CDMA). The node transmits the packet with fixed rate and fixed power [26]. We consider an automatic-repeat-request(ARQ) mechanism in which the node keeps retransmitting a packet until the packet is received at base station without any error. At any instant in time, a device may have two kinds of packets to be transmitted: 1. Packets generated by the device itself. This can be sensed data if we are considering a sensor network. 2. Packets from other neighboring devices that need to be forwarded.

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We consider two separate queues for these two types and do a weighted fair queueing (WFQ) for these two queues. This configuration allows nodes to have flexibility for managing at each node forwarded packets and its own packets differently. Two queues allow to model the selfish behavior or on the contrary to give higher priority to connections that traverse many hops that could otherwise suffer from large loss rates. The main contribution of this paper is to provide approximation expressions of stability. Our main result is concerned with the stability of the forwarding queues at the devices. It states that whether or not the forwarding queues can be stabilized (by appropriate choice of WFQ weights) depends only on the routing and the channel access rates of the devices. The end-to-end throughput achieved by the nodes are independent of the choice of the WFQ weight. The context of the stability that we study is new as it takes into account the possibility of a limited number of transmissions of a packet at each node after which it is dropped. Related works. Multihoming has been traditionally used by stub networks for improving network availability. Previous studies [5], have shown that ”Multihoming Route Control” can improve the Internet communication performance of the networks significantly. A comparison study of overlay source routing and multihoming is presented in [6]. The goal of this work is how much benefit does overlay routing provides over BGP, when multihoming and route control are considered. In wireless network, the usefulness of attaching to the Internet using different access technologies at different time, or even simultaneously, has been described in various works [7, 8]. A work devoted to IEEE 802.11 WLANs is [9], where users can choose between several access points and even split their traffic by using several of them. The network stability has been studied extensively both for networks with centralized scheduling [15, 17], Aloha protocol [10, 21] and UMTS system [19,20]. Among the most studied stability problems are scheduling [15,16] as well as for the Aloha protocol [27, 24]. Tassiulas and Ephremides [15] obtain a scheduling policy for the nodes that maximizes the stability region. Their approach inherently avoids collisions which allows to maximize the throughput. Radunovic and Le Boudec [11] suggest that considering the total throughput as a performance objective may not be a good objective. Moreover, most of the related studied do not consider the problem of forwarding and each flow is treated similarly (except for Radunovic and Le Boudec [11] or Tassiulas and Sarkar [23]). The rest of the paper is organized as: In section II, we formulate the problem. In section III, we study the stability of any given node in the system using the rate balance equation. We analyze the uplink case in section IV, valid our model and provide some numerical examples and some concluding remarks in section V.

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Background. Consider a geographical area which is not totally covered by any 3G system. The system is composed of three geographical classes (See fig.1); Class C1 contains terminals which communicate with each other using an ad-hoc network and some of them could be covered by the UMTS. The class C3 contains

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all terminals which are in the direct range of the 3G Node-B. We denote by C2 the set of mobiles covered by both the 3G system and the ad-hoc network, i.e. C2 = C1 ∩ C3 . Let N1 = card(C1 \C2 ) and N2 = card(C2 ). The 3G base station is denoted by B. The idea behind this work is to find a solution that allows non covered mobiles (C1 \C2 ) to use the 3G services besides they are not in the covered area, and allow to 3G devices to connect to Internet thanks to ad-hoc network. So we consider a scheme with special kind of terminals, each one disposes of two network cards (IEEE 802.11 and DS-CDMA), hence it could be connected to either the ad-hoc or the 3G system according to the coverage criterion. Moreover this mobile is assumed to be able to forward packets arriving from ad-hoc mobiles, and therefore transmits them to the 3G node-B or to another ad-hoc node; In other terms these type of mobiles can play the role of gateways that allow to access to the node-B and therefore to benefit from its services.

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Fig. 1. A single Cell 3G system combined with ad-hoc network. Access to UMTS and Internet without coverage constraint using an IEEE 802.11/UMTS gateway solution.

As long as a 3G node is far of the base station as its transmit power is higher, which could deteriorate the other nodes QoS. To avoid this, far nodes will prefer to use their ad-hoc neighbors to transmit and therefore save their energy. Moreover, for a 3G user who needs to connect to the Internet, it is very interesting to access to it via ad-hoc for high rate and low cost comparing to Internet via UMTS. Remark. (Case of a node j ∈ C2 ). Node j has neighbors transmitting using the IEEE 802.11 band and others using the UMTS band; When j transmits over the ad-hoc subsystem, it only collides with its neighbors using ad-hoc. And it is influenced only by users communicating with the node-B when it transmits using DS-CDMA. In others terms the two subsystems are separated and do not interfere with each other. Network layer. In order to insure the forwarding of a packet to a given destination (ad-hoc node or the Node-B), we assume that each terminal i possesses two queues Qi and Fi . Fi is selected with probability fi and receives packet to be forwarded to another destination whereas Qi represents the queue which carries the own packets of node i and is selected for transmission with probability 1 − fi . In this work we consider the saturated case where node i has always

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packets to transmit from Qi . These queues are assumed to have infinite capacity. The network layer handles these queues using a weighted fair queuing (W F Q) protocol. For notation, we denote by Rs,d the set of nodes between a source s and a destination d (s and d not included). MAC layer. The MAC layers of the two subsystems are different. We consider for ad-hoc subsystem a collision channel where no more than one node can transmit successfully in a slot; Denote by τ a the duration of one slot. A successful transmission is occurred when there is no collision on reception, in other terms neighbors of an intermediate node should not transmit when it receives a packet. Each mobile transmitting a packet either from Qi or Fi should access the channel by probability Pi . We can consider the aloha protocol, CSMA/CA or any other mechanism to access the channel. For instance, the attempt rate (for node i) in IEEE 802.11 DCF (see [25]) is, Pi =

2(1 − 2Pc ) (1 − 2Pc )(CWmin + 1) + Pc CWmin (1 − (2Pc )m )

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Pc denotes the conditional collision probability given that a transmission atmax tempt is made and CW is the contention window. m = log2 ( CW CWmin ) is the maximum of backoff stage. We assume that a packet is retransmitted (if needed) till success or dropping, Let Ki,s,d be the maximum number of transmissions allowed by a mobile i per packet. Yet expression (1) is valid only for IEEE 802.11 systems. In our case we have an hybrid network, so it does not hold anymore and we shall calculate it. Assume xi is the total proportion of UMTS cycles for a given node i. Let τiu be the average needed number of slots to send a UMTS packets. Proposition 1. Consider a heterogenous network composed of a UMTS and an Ad-hoc subsystems, then we have the following, 1) The proportion of UMTS traffic is, xi = Pi,B (1 − πi fi ) + fi



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Proof. Here we develop a cycle-based method to prove proposition 1. Let us a observe the system (in particular a node i) for Ci,t cycles. Ci,t is the number of th u Ad-hoc cycles until the t slot, and Ci,t is the corresponding number of cycles for the UMTS traffic. We denote by Tta the total number of transmission slots used by Ad-hoc connections till the tth slot. The proportion of UMTS cycles is xi =

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is the probability to choose a UMTS packet from Qi . We have = =

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a where Ti,t denotes the number of cycles having Ad-hoc packets till slot t. The proportion of UMTS cycles becomes xi = Pi,B (1 − πi fi ) + fi s πi,s,B The proof is complete for part 1. a Tta T a Ci,t Ci,t = lim ta · · t→∞ t t→∞ C C t i,t i,t

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limt→∞

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Pi u i limt→∞ Cti,t = =L Ci,t Pi (1 − xi ) + τi xi The result follows by substituting each term by its established expression.

For the UMTS subsystem, we assume that the base station communicates with 3G nodes using Direct Sequence Code Division Multiple Access (DS-CDMA). Each user i transmits to the base station with a transmission rate ρi,B (Uplink transmission rate), which is the bandwidth to the processing gain ratio. It depends on the mobile classes, the solicited service and the global load. The base station uses a downlink transmission rate ρB,i when sending to i. Another important parameter is the Packet Success Rate (PSR), which we denote by ξ(γi ), it is a function of the instantaneous signal to noise plus interference ratio (SINR). ξ(.) could be any increasing continuous, differentiable and S-sharped (sigmoidal) function with ξ(0) = 0 and ξ(+∞) = 1. Some properties and examples of S-sharped functions are studied with more details in [26] and references therein. Define μi,B as the uplink service rate, i.e. at which UMTS packets are served at node i. We denote by μB,i the arrival rate to node i from the node-B. Let 1 τi,B = μi,B be the average service time in number of ad-hoc slots. Then, it can be Ma written as: τi,B =  ρi,B ξ(γ a  where Ma is the length of one ad-hoc packet (in i ).τ a bit) and ρi,BMξ(γ is the average service time in second of a packet while allowing i) infinity retransmissions of a failure packet, see [26].

Cross-layer architecture. Nodes with two network cards are able to communicate with other nodes or with the 3G node-B. The system performance depends on more than one layer, this motivates us to use a cross-layer architecture (fig.2). This new network insight is more powerful and flexible thanks to communication and information exchange between different layers. Remark. It is interesting to note that a mobile is able to transmit on one interface and receive on the other and vice-versa. Moreover, it could get si-

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Fig. 2. Cross-layer architecture including the two subsystems

multaneously one ad-hoc and one UMTS reception. Whereas we do not allow simultaneous transmissions over the two network cards. This is due to the nature of scheduling mechanism that we consider (selection of one queue a time). If we allow parallel selections we could make two simultaneous transmissions possible.

3

Stability of Forwarding Queues

In this section we study stability properties of nodes in the system in particular. a node i ∈ C2 which we call gateways. Classically ,the forwarding queue Fi is stable if the departure rate of packets from Fi is at least equal to the arrival rate into it. This is expressed by a rate balance system. For any given nodes i, s, d ∈ C1 ∪ C3 ; i denotes an intermediate node, s is the source and d is the destination. Let ji,s,d denote the entry in the set Rs,d just after i and N (i) be the set of i’s neighbors. The probability that a transmission from node i on route from node s to node d is successful is,  Pi,s,d = (1 − P j ) (4) j∈ji,s,d ∪N (ji,s,d )\i

The expected number of attempts till success or dropping from i on route Rs,d is, 1 − (1 − Pi,s,d )Ki,s,d Li,s,d = Pi,s,d Let πi,s,d be the probability that the queue Fi has a packet at the first position ready to be forwarded to the path Rs,d . It follows that the probability that the  queue Fi has at least one packet to be forwarded is πi = s,d πi,s,d . If Pi,d denotes the probability that i transmits to d then the average of Li,s,d over all possible paths is,   Li = πi,s,d fi Li,s,d + (1 − πi fi )Pi,d Li,i,d (5) s,d:i∈Rs,d

d

In the following we classify connections onto three categories. – ad-hoc to ad-hoc connections: This corresponds to the case studied in [1] in which an ad-hoc node transmits to another ad-hoc node, i.e. s, d ∈ C1 .

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– ad-hoc to UMTS connections: This is the uplink case in which nodes transmit to the base station. For s ∈ C1 and d = B, note that a UMTS packet is transmitted until success from the gateway to the base station. – UMTS to ad-hoc connections: This illustrates downlink scheme, in other terms when the base station is transmitting to a given node so s = B and d ∈ C1 ∪ C3 . We note for transmissions to the base station, that an intermediate node i Pi forwards packets to a next hop at a rate L , whereas it forwards them to the i base station (if it is in the direct range of B) at a rate μi,B . For more generality we define μi,B as the service rate for the uplink scheme as following,  μi,B =

μi,B , Pi , L i

if

if i ∈ C3 i ∈ C1 \C2

(6)

The departure rate In order to compute the departure rate of a given node, we shall make differentiation between ad-hoc and UMTS connections. Also the geographical criterion is taken into consideration thanks to P i and μi,B . The long term departure rate from node i for the connection Rs,d is,

di,s,d

⎧ Pi ⎪ ⎨ πi,s,d fi Li , = πi,s,B fi μi,B , ⎪ ⎩π Pi i,B,d fi L , i

s, d ∈ C1 s ∈ C1 , d = B d ∈ C1 , s = B

(7)

The arrival rate Here, we compute the arrival rate for each node i. Let i, s, d ∈ C1 ∪ C3 . s sends a packet destined to d with probability Ps,d . When the UMTS node-B transmits to a node d, we denote by g (for gateway) the first hop on the route RB,d . i’s long term arrival rate is expressed, according to possible connections, as following:

ai,s,d

⎧ 

 Ps ⎪ ⎪ (1 − π f ) P 1 − (1 − Pk,s,d )Kk,s,d , ⎪ s s s,d ⎪ ⎪ Ls ⎪ k∈Rs,i ∪s ⎪ ⎪ ⎨ 

 Ps = (1 − πs fs ) Ps,B 1 − (1 − Pk,s,B )Kk,s,B , ⎪ Ls ⎪ k∈Rs,i ∪s ⎪ ⎪ 

 ⎪ ⎪μ ⎪ 1 − (1 − Pk,s,d )Kk,s,d , ⎪ ⎩ B,g

s, d ∈ C1 s ∈ C1 , d = B d ∈ C1 , s = B

k∈RB,d

(8) End to end throughput of a connection. The end to end throughput between a couple of nodes s and d is exactly the arrival rate to the destination d. Namely thps,d = ad,s,d . Recall that gateways keep transmission of a packet over the UMTS subsystem till success (correctly received by the base station).

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The Rate Balance System (RBS)

A queue Fi is stable while its corresponding departure rate is greater or equal than the arrival rate into it. In this subsection we consider the extreme case where we have strict equality. We will derive the rate balance equation for every node in the system. When the steady state is achieved, if all queues are stable, then for each i, s and d such that i ∈ Rs,d we get di,s,d = ai,s,d , this is the rate balance equation on the path Rs,d . Then for all i, s and d, we get a system of equations from which we can find a solution of the πi,s,d for all i, s and d. And then, find πi , for all i, which determines the load of the forwarding queues and the main condition of their stability which is πi < 1. Each node can forward packets for the three types of connections defined previously, and therefore we can explicit the rate balance equations using three generic equations: ⎧ 

 P s Li ⎪ ⎪ πi,s,d fi = (1 − πs fs ) Ps,d 1 − (1 − Pk,s,d )Kk,s,d , s, d ∈ C1 ⎪ ⎪ ⎪ P i Ls ⎪ k∈R ∪s ⎪ s,i ⎪ ⎪ 

⎨  (1 − πs fs ) P s πi,s,B fi = Ps,d 1 − (1 − Pk,s,d )Kk,s,d , s ∈ C1 , d = B μ Ls ⎪ i,B ⎪ k∈Rs,i ∪s ⎪ ⎪ ⎪ 

 ⎪ Li ⎪ ⎪ πi,B,d fi = μB,g 1 − (1 − Pk,s,d )Kk,s,d , d ∈ C1 , s = B ⎪ ⎩ P i k∈R B,d

(9) We sum over all sources s and destinations d for all connections written above and get the global rate balance equation which is useful to study some special cases. Theorem 1. In the steady state, if all the queues in the system (both ad-hoc and UMTS) are stable, then for each i, s, d ∈ C1 ∪ C3 , such that i ∈ Rs,d we have,   P i fi  πi,s,d fi + μi,B πi,s,B = Li s,d s d:i∈R

B,d

+

 s,d:i∈Rs,d

(1 − πs fs )

P s Ps,d Ls



μB,i



 1 − (1 − Pk,B,d )Kk,B,d k∈RB,i

 1 − (1 − Pk,s,d )Kk,s,d

(10)

k∈Rs,i ∪s

Let zi,s,d = πi,s,d fi , for all i, s and d, be the unknown of the rate balance system which is a non linear system because P i and Li usually depend on zi,s,d . We remark as in paper [1] that the solution is independent on the forwarding probability, and consequently the end to end throughput is also not impacted by the choice of fi as it appears when computing the end-to-end throughput thps,d = ad,s,d . This result holds only when the forwarding queues are stable. As a consequence, the forwarding capabilities of gateways do not affect their energy consumption.

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Another important remarks concern the interaction between the ad-hoc and the UMTS. Practically, the bit rate per node in the ad-hoc network is larger than the UMTS, but the size of this former network and its characteristics (as channel access and routing) influence drastically its capacity. Yet, connecting two heterogenous networks normally needs a scaling and an adaptation of the rates on each interface, if not, stability of the gateway nodes becomes a real issue. From the rate balance system, we find zi,s,d function of the service rate of the UMTS node μi,B and of the base station rate μB,i . So, the condition on zi,s,d (zi,s,d < 1) will be translated to a condition on the parameters of the UMTS, for example, we can find a minimum packets rate for UMTS nodes or a threshold of the SINR γ that insures stability on gateway nodes. Concerning the throughput of ad-hoc nodes connected to the base station, the throughput can be written in a simple form, in case of stability, as: thps,B = zg,s,B μg,B , where g is a gateway node. For the particular uplink case, the arrival rate (second equation of (8)) is independent of μi,B since πs fs , Li and Pi do not depend on it. Then when μi,B changes the load zi,s,B changes in the opposite sense, it follows that the throughput stays unchanged.

4

The Uplink Case Analysis

In the following we discuss and study with more details a scheme when the RBS is linear such the uplink case which is very important. There is indeed many asymmetric services which do not requires any request-response pattern (e.g. downloading files). In this case all sources has the same destination and each intermediate node forwards always to the same next hop. it follows that Li is independent of the unknown πi , Ls =



πs,s ,d fs Ls,s ,d + (1 − πs fs )Ps,d Ls,s,d = πs fs Ls,s,d + (1 − πs fs )Ls,s,d = Ls,s,d

s

The RBS is reduced to the second equation when d = B, we sum over all s, 

zi,s,B =

 ys P s Ps,B

s

μi,B Ls

s



 1 − (1 − Pk,s,d )Kk,s,d

k∈Rs,i ∪s

Let yi = 1 − πi fi , later we will refer to yi as the stability region of Fi . We have, yi +



ys ωi,s = 1

(11)

s

where ωi,s =



P s Ps,d d μi,B Ls

k∈Rs,i ∪s

 1 − (1 − Pk,s,d )Kk,s,d . (11) can be written in

a matrix form and then will be resolved more easily. Y (I + W ) = 1

(12)

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W is the N ×N matrix whose (s, i)th entry is ωs,i , Y is the unknown N −dimensional row vector, i.e. Y (i) = yi . In the following we will restrict to symmetric ad-hoc network. The transmit and forwarding probabilities are the same for all users, i.e. Pi = P and fi = f . Assume that every ad-hoc node i has the same number of neighbors ni = n (such a grid or a mesh), it follows that Pi,s,B = (1 − P )n . For simplification and without loss of generality we will be assuming that Ki,s,d ≡ 1. Let denote by d(i, s, B) the number of intermediate nodes on the route Rs,B . Since we only consider uplink connections we shall put μB,g = 0. Proposition 2. A necessary condition for stability of Fi , for any given node in the system, for the uplink scheme is that, f≥ where ω i,s,B =

 s

ωi,s,B ωi,s,B + μi,B

s.t.

f ≤1

(13)

P (1 − P )n(d(i,s,B)+1)

Proof. For any given routing, the input ratio into the forwarding queue Fi is, ai =

 

 (1 − f πs )P Ps,s,B 1 − (1 − Pk,s,B )Kk,s,B s

k∈Rs,i

 ≥ (1 − f )P (1 − P )n(d(i,s,B)+1) = amin i s

The departure rate is di = μi,B f πi ≤ μi,B f = dmax i Since Fi is stable if and only if di ≥ ai , then the result follows. The next corollary provides a condition related to UMTS subsystem. Corollary. Considering the uplink case, a necessary condition that insures stability of queue Fi for any given gateway i, is that, μi,B ≥

1−f · ωi,s,B f

(14)

A more useful form is to convert it onto a condition on instantaneous SINR as, γi ≥ ξ −1

1 − f  Ma · · ωi,s,B a f ρi,B τ

(15)

 where ωi,s,B = s P (1−P )n(d(i,s,B)+1) . An interesting result of proposition (13) is that UMTS parameters do not affect stability of non covered nodes.

5

Numerical Results and Simulations

Consider in fig. (3) one single UMTS cell, covered by base station B, and extended with an Ad-Hoc network which is itself related to Internet via AP 9.

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Connection a: 4-3-2-1-B Connection b: 6-5-1-B Connection c: 10-1-7-8-AP

1O

a

3

2

a c 4

Internet

b

b

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5 1

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7 8

Fig. 3. Cellular and Ad-Hoc networks 0.076

1 node node node node

0.9 0.8

1 2 3 5

0.074

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0.072

π

i

0.6 0.5 0.4

a b

0.07

0.068

0.3 0.2

0.066

0.1 0 0

0.2

0.4

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0.6

0.8

0.064 0

1

0.2

0.4

μ

0.6

0.8

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i,B

Fig. 4. Queues load for K = 4 and f = 0.8 Fig. 5. Throughput of connections a and b 1

0.08

node node node node

0.9 0.8

1 2 3 5

0.07 0.06

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0.7

πi

0.6 0.5 0.4 0.3

0.05 0.04 0.03

0.2 0.02

a b

0.1 0 0

0.2

0.4

μi,B

0.6

0.8

1

Fig. 6. Queues load πi from simulation

0.01 0

0.2

0.4

μ

0.6

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1

i,B

Fig. 7. Throughput from simulation

Both nodes in the UMTS cell and the Ad-Hoc network can access to the services of the two networks. For that, gateway nodes play an important relay role on forwarding packets for both technologies. For that we are interested to show, at first, numerical results concerning the load of these latter nodes and then the end -to-end throughput of connections that traverse these nodes. We use a discrete time simulator that reflects the behavior of nodes in our model described in section 2, to validate our formulation results. In fig. (3), nodes 4 and 6 access the UMTS system with the base station B via connections a and b. Node 10 which is in the UMTS cell also reaches the access point AP (node 9) via the Ad-Hoc network with a connection c.

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We fixe P3 = P9 = 0.2, P4 = 0.4 and all other Pi to 0.3. The number of transmissions is set to Ki,s,d ≡ K = 4 and the forwarding probability to fi ≡ f = 0.8. Fig. (4) and (5) consider only the uplink case where Ad-Hoc nodes communicate with the base station. Fig. 4 shows the load variation function of the UMTS service rate μ1,B . It is clear that increasing μ1,B would ameliorate the stability of node 1. Here, we see that we need μ1,B > 0.19 to get π1 < 1. For an Ad-Hoc node rate of 2Mbps and an Ad-Hoc packet size of 1000 Bytes, the slot size becomes τ a = 4ms, then a minimum UMTS rate needed must be at least 47.5 packet/s (ad-hoc packet). Concerning the throughput, fig. (5) shows the insensitivity of the throughput with the service rate of the UMTS. Note that only for π1 < 1 the figure of the throughput make sense. Elsewhere it will be an increasing function of μ1,B as it is shown in the simulation fig. (7). A validation with simulations of the numerical results is shown in fig. (7,6). Conclusion. In this paper we propose extending the coverage of UMTS using an ad-hoc network, our main result is characterization of stability condition and the end-to-end throughput, we also validate the analytical result using a discrete simulator. An ongoing work consists on studying the impact of transmit power over the 3G subsystem on the stability of the forwarding queues. We also consider a joint power and rate control game to seek the equilibrium points under some QoS constraints.

References 1. El Khoury, R., Elazouzi, R.: Stability-throughput analysis in a multi-hop ad hoc networks with weighted fair queueing. In: The Proc. 45th Annual Allerton Conf. on Communication, Control, and Computing (Allerton 2007), Monticello, IL (September 2007) 2. Kherani, A., ElAzouzi, R., Altman, E.: Stability-Throughput tradeoff and routing in multi-hop wireless ad-hoc networks. In: The Proceeding of Networking Conference 2006, Coimbra, Portugal, MAY 19, vol. 15 (2006) (Best paper Award) 3. Bria, A.: Joint resource management of cellular and broadcasting systems-research challenges. In: RVK 2005 Conference (2005) 4. Bianchi, G.: Performance analysis of the IEEE 802.11 distribute coordination function. IEEE Journal on Selected Areas in Communications (March 2000) 5. Smith, P.: Bgp multihoming techniques. In: NANOG, vol. 23 (2001) 6. Shaikh, A., Sitaraman, R., Akella, A., Maggs, B.: A measurement-based analysis of multihoming. In: SIGCOMM (2003) 7. Stemm, R., Katz, R.: Vertical handoffs in wireless overlay networks. Journal on Mobile Networks and applications 3(4), 335–350 (1998) 8. Wakikawa, R., Paik, E.K., Ng, C.W., Ernst, T., Montavont, N., Noel, T.: Goal and benefits of multihoming. IETF Internet Draft (July 2005) 9. Altman, E., Shakkottai, S., Kumar, A.: The case for non-cooperative multihoming of users to access points in ieee 802.11 wlans. In: IEEE INFOCOM 2006, Barcelona (2006) 10. Rao, R., Ephremides, A.: On the stability of ineracting queues in a multi-access system. IEEE Tran. Inform. Theory 34, 918–930 (1988)

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11. Radunovic, B., Le Boudec, J.Y.: Joint Scheduling, Power Control and Routing in Symmetric, One-dimensional, Multi-hop Wireless Networks. In: WiOpt 2003: Modeling and Optimization in Mobile, Ad-Hoc and Wireless Networks, SophiaAntipolis, France (2003) 12. Clausen, T., Jacquet, P., Laouiti, A., Muhlethaler, P., Qayyum, A., Viennot, L.: Optimized Link State Routing Protocol. In: IEEE INMIC Pakistan (2001) 13. Murthy, S., Garcia-Luna-Aceves, J.J.: An Efficient Routing Protocol for Wireless Networks. ACM/Baltzer Journal on Mobile Networks and Applications, Special Issue on Routing in Mobile Communication Networks 1 (1996) 14. Roberts, L.G.: Aloha packet system with and without slots and capture, Tech. Rep. Ass Notes stanford Research Institute advance Recharch Projects Agency Network Infomation Center (1972) 15. Tassiulas, L., Ephremides, A.: Stability properties of constrained queuing systems and scheduling for maximum throughput in multihop radio network. IEEE Trans. Automat. Contr. 37(12), 1936–1949 (1992) 16. Tassiulas, L.: Linear complexity algorithm for maximum throughput in radio networks and input queued switches. In: IEEE Infocom 1998, pp. 533–539 (1998) 17. Tassiulas, L.: Scheduling and perfomance limits of networks with contantly changing topology. IEEE TRans. Inform. Theory 43(3), 1067–1073 (1997) 18. Altman, E., Borkar, V.S., Kherani, A.A., Michiardi, P., Molva, R.: Some game theoretic problems in wireless ad-hoc networks. In: Kotsis, G., Spaniol, O. (eds.) Euro-NGI 2004. LNCS, vol. 3427, pp. 82–104. Springer, Heidelberg (2005) 19. Schr¨ oder, B., Weller, A.: Prediction of the Connection Stability of UMTS Services in the downlink- an Analytical Approach. In: Vehicular Technology Conference (2002) 20. Kumar, A., Patil, D.: Stability and throughput analysis of unslotted CDMAALOHA with finite number of users and code sharing. Telecommunication Systems 8 (December (2-4), 1997) 21. Luo, W., Ephremides, A.: Stability of N interacting queues in multiqueue distributed systems: buffered random acess system. IEEE Trans. Inform. Theory 45, 1579–1587 (1999) 22. Tsybakov, B., Mikhailov, W.: Ergodicity of slotted Aloha system. Prob. Inform. Transmission 15(4), 301–312 (1979) 23. Tassiulas, L., Sarkar, S.: Max-Min fair scheduling in wireless networks. In: Proceeding of Infocom 2002 (2002) 24. Tsybakov, B.S., Bakirov, V.L.: Packet transmission in radio networks. Probl Infom. Transmission 21(1), 60–76 (1985) 25. Yang, Y., Hou, J.C., Kung, L.-C.: Modeling the effect of transmit power and physical carrier sense in multi-hop Wireless networks. In: Infocom 2007, Alaska (2007) 26. Meshkati, F., Poor, H.V., Schwartz, S.C., Mandayam, N.B.: An energy efficient approach to power control and receiver design in wireless data networks. IEEE transactions on communications 53(11), 1885–1894 (2005) 27. Anantharam, V.: The stability region of the finite-user slotted Alloha protocol. III Trans. Inform. Theory 37(3), 535–540 (1991)

A Performance Analysis of Authentication Using Covert Timing Channels Reed Newman and Raheem Beyah Communications Assurance and Performance Group, Computer Science Department, Georgia State University Atlanta, GA, USA [email protected], [email protected]

Abstract. Authentication over a network is an important and difficult problem. Accurately determining the authenticity of a node or user is critical in maintaining the security of a network. Our proposed technique covertly embeds a watermark, or identifying tag, within a data stream. By implementing this model on a LAN and WLAN we show that this method is easily adaptable to a variety of networking technologies, and easily scalable. While our technique increases the time required for data to be transferred, we show that the throughput of the link during the brief authentication window is decreased by no more than 8% in a switched LAN and 11% in a WLAN. During our empirical analysis we were able to detect the watermark with 100% accuracy in both a LAN and WLAN environment.

1 Introduction With the large amount of network applications in existence today, it is becoming increasingly difficult for network administrators to police the traffic on their networks. Additionally, these network applications are increasingly carrying sensitive information; as the sensitivity of the information traversing the network increases, so too must the level of security within the network. A key step in securing a network is the authentication of all nodes1 on that network. Authentication of nodes allow perimeter devices the ability to determine if a node's requests should be granted. Reliable node authentication helps to ensure the basic elements of network security: positively identifying a node, allowing that node specific access privileges, and holding that node accountable should it compromise the security or productivity of the network. Leading industry approaches, such as Cisco’s AAA Server and Cisco's NAC model [1], rely on user name/password, S/Key, Token Cards or system profiling to authenticate users. While all of these methods provide a reasonable level of security, each are expensive and have been shown to have flaws [2,3]. Additionally, simple access control lists have proven to be ineffective as spoofing IP and MAC addresses is a trivial task, allowing attackers to easily gain access to systems and networks. 1

Authentication relating to nodes can easily be extended to users. When referencing nodes, unless otherwise noted, the statement applies to nodes and users.

A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 149–161, 2008. © IFIP International Federation for Information Processing 2008

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By not requiring strict node authentication, joining a network is a trivial matter. An attacker may obtain a physical connection to a network, enter the network through a wireless access point, circumvent 802.1x via phishing or other known exploits, or access the network via a VPN connection. By requiring each node joining the network to authenticate itself, the overall security of the network is increased making systems within the network harder to breach. We propose to embed a watermark, or signature specific to a node, within a data flow using inter-packet delay. The watermark will be embedded between select packets in a covert manner, such that it is difficult to detect and does not decrease the throughput of the link significantly. Our model can be used to supplement the above methods or be used as a standalone method of authentication. Our technique does not require synchronization between the nodes' clocks, and can easily be adapted for user authentication. We acknowledge that our proposed method, a method advocating security by obscurity, is not an impervious solution but assert that the additional layer of security increases the difficulty for an attacker. We believe that providing authentication in a covert manner adds the same level of benefit one achieves when changing an internal server’s (sftp, ssh, etc.) port from a well-known port to a random port. The attacker would require not only a technique by which to break into the server (e.g., a buffer overflow attack, etc.), but must also find the server, via port scanning, over a range of 64,000 ports without being detected. Further, this work evaluates performance of using timing channels in general to transmit data. Our specific application of this method uses this channel to communicate authentication information. The remainder of this paper is organized as follows: Section 2 reviews related work, Section 3 details the covert timing channel model, Section 4 outlines the experimental setup and procedure, Section 5 provides analysis on the model, Section 6 provides experimental data, and Section 7 contains our conclusions and future work.

2 Related Work The idea of using inter-packet delay (IPD) for node identification is not necessarily new. There have been several different applications regarding the use of IPD in recent academic research. The majority of the work focuses on the detection of stepping stone connections (i.e., intermediate systems attackers use to launch attacks and insulate themselves from detection), with most solely considering interactive (SSH) traffic. The authors of [4] extend the work done in [5,6] to detect a correlation between stepping stones. In [4], the authors use a binning technique to partition the data stream allowing the encoded watermark a greater level of robustness against timing perturbations and repacketization within the stepping stone links. In [7], the authors extend the work of [8] in an effort to defeat chaff packets (e.g., structurally correct packets inserted within the data flow to obfuscate a pattern) that may be inserted by a node within the stepping stone chain. This is done by decoding watermarks from all possible subsequences of a downstream flow, and choosing the “best” watermark (defined as the watermark with the least hamming distance from the original watermark).

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Peng, et al. [9] investigate the secrecy of active watermarking. The authors develop an attack technique that infers important parameters of the watermarks, and also recovers and duplicates watermarks through the stepping stones. The authors of [10] attempt to detect stepping stone connection correlations of SSH traffic by creating a logical partition between ON and OFF periods of usage. By examining the IPD of connections and determining if the OFF period transitions to an ON period within a certain threshold, it can be reasonably stated that these connections are related. It is interesting to note that this is an entirely passive technique. In [11], provable upper bounds are set on the number of packets required to confidently detect encrypted stepping stone streams with proven guarantees of a low false positive rate. The methods in [11] also take into consideration the usage of chaff packets, and provide bounds on the amount of chaff needed by the attacker to evade detection. The model proposed by [12] is similar to that of [11], with the addition of wavelets. In [12], the authors attempt to differentiate between the short term behavior of the stepping stone streams, where timing perturbations and chaff packets can mask the correlation between connections, and the long term behavior of stepping stone streams. Wang and Reeves [5] propose a watermark-based scheme that can detect correlation between streams of encrypted traffic. However, the assumption is made that the attackers timing perturbations are independent and evenly distributed. Should the traffic be disturbed in another fashion, such as with the insertion of chaff packets, their method will show a decrease in accuracy. Zhang, et al. [13] provide an upper bound on the number of packets required to detect attackers in stepping stone networks when chaff packets and timing perturbations exist simultaneously. The authors of [14] show that VoIP encoding scheme can easily contain a covert channel by altering the least significant bit. They provide analysis on the bandwidth and the amount of data transferred. Wang, et al. [6] show that encrypted VoIP calls hidden through an anonymizing network can still be traced, using a technique similar to that of Paxson [10]. Only tangentially related to our work, the authors of [15] studied the loss of anonymity in a flow-based wireless mix network under flow marking attacks, in which an adversary embeds a pattern of marks into wireless traffic flows by electromagnetic interference. They asserted that traditional mix technologies are not effective in defeating flow marking attacks in wireless networks. They proposed a new countermeasure based on digital filtering technology. The authors of [16] introduce a covert timing channel model based on the presence or absence of packets arriving during a specific time period. The authors attempt to detect this covert timing channel over IP using a similarity comparison between packet inter-arrival times. The concept of covert channels was first introduced by B. Lampson in 1973 [17]. Covert channels at the network and transport layers in TCP/IP were first investigated by Rowland [18] and Fisk, et al. [19]. Currently software such as Covert_TCP [19] and Nushu [20] are available to hide data within TCP headers. Project Loki [21] has also shown that ICMP packets are capable of carrying covert information within their headers. By moving the covert channel to the application layer, detection of covert channels becomes even more difficult. It was discussed in RFC 3205 [22] that HTTP be used as a carrier for other protocols; this was obviously meant for the covert encapsulation of the protocols. Several tools are also available to tunnel protocols

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through HTTP, like Lars Brinkhoff's httptunnel [23], primarily for the purpose of evading firewalls. The predominate focus of these works, with the exception of [6,14-19,21-22], are on detection of stepping stones using SSH style traffic. SSH traffic can be classified as high-latency traffic, caused by user pauses in commands being issued. In [14], data in the least significant bit of the VoIP stream is altered to create a covert channel. Wang et al. [6] use a technique similar to that of Paxson [10] to correlate between VoIP flows hidden by an anonymizing network. Additionally, all of the works discussed above, with the exception of [15-19,21-22], focus on wired networks. In [17-18,21-22], the covert channel is actually embedded or encapsulated within another protocol. We propose a general model (protocol independent) for node authentication that is able to take advantage of low-latency traffic and be effective on both wired and wireless networks. We evaluate this model experimentally and address the performance and accuracy of our model. This model does not require a large amount of overhead during the brief authentication window, using no more than 8% of the available bandwidth during testing in a switched LAN and no more than 11% in a WLAN.

3 Covert Timing Channel Model Through the application of Steganography to a network flow, we achieve Covert Timing Channels, which are defined as parasitic communication channels that draw bandwidth from other channels, via the disruption of event timing relative to other events, in order to transmit information [24-25]. Traditionally Covert Timing Channels are used for the transmission of messages, and likewise, to provide authentication, we transfer a watermark or signature specific to a node, through this channel. Particularly, we disrupt the inter-packet timing within a data flow over a network (specifically using the TCP and UDP protocols). By adding minimal amounts of delay to select packets within the flow, we add a watermark to the flow in such a way that it is unique to a specific node. By delaying packets within a data stream, even minimally, we detract from the bandwidth of the network and thus increase the time required for a given task. We will show that bandwidth degrades proportional to the amount of delay needed to accurately transfer a watermark, the length of the watermark, and the rate at which the node must be re-authenticated. Disrupting the inter-packet timing within a data flow is inherently volatile, especially when considering outside influences such as the overhead incurred from the use of TCP. Given occasionally network volatility we must assume that sending one watermark may not provide adequate authentication. Additionally to provide a greater degree of robustness, in certain cases it may be necessary to increase the frequency of watermark transmissions. For instance, a university system may not require the node to re-authenticate itself often whereas a military institution, which places a higher priority on security, may. Resending the watermark increases the security provided by the model, allowing the host network to ensure that the node it authenticated originally is still that node. In Section 4 we show that the increase per

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additional watermark is small, resulting in a minimal decrease of network throughput. Additionally, we provide analysis on the amount of delay required to accurately detect the watermark, versus the percentage error of false negatives. The degree of natural delay varies from one network type to another. For instance, assuming that network delay is the only factor, the time required to transfer a file over the Internet or VPN connection is greater than the time required to transfer that same file over a wireless network (from one local computer to another); the time required for the transfer of that file over the wireless network is greater than the time required to transfer the file over a 100Mbps LAN connection. With the addition of network congestion, packet loss, etc., the distinction between network types becomes even clearer. As such, the addition of delay required by our model dynamically varies depending on what type of network the user is using. Our model calculates the minimal delay required to accurately embed a watermark into the data flow by using the roundtrip time (RTT) of the first ten data packets received per connection over a TCP connection. This approach will not work over a UDP flow, as there are no acknowledgment packets being returned to the sender. If UDP is being used, the client will ping the server several times and calculate the delay based on the RTT provided. An overview of our model, graphically represented in Fig. 1, is as follows. Initially a bootstrap phase is entered. During this phase, a kernel module, called the Encoder, is loaded and an initial watermark is created. The Encoder associates itself with an application at the transport layer and acts as a filter allowing for the queuing of packets, so that delay may be added. An additional kernel hook, called the Detector, registers itself to determine the RTT of the first ten data packets sent, which will determine the amount of delay added to the data flow. The initial watermark is currently generated from a password, but can be altered to be generated from another input; for example, using a serial number unique to a node (or other identifying information) in addition to a password would allow for the authentication of the user/machine pair. To generate a watermark, our password is hashed using the SHA-1 hashing algorithm, translated from hex to binary, and truncated to the desired length of the watermark. Two delay values are used within the watermark. A high value is used to represent a binary 1, and a low value used to represent a binary 0; the true high (λ) and low (ω) values are set by the Detector with λ > ω and ω being greater than the average delay between packets. When delaying a packet that is part of the watermarked sequence, the binary watermark is consulted to determine if the delay to be added is λ or ω; if the binary value of the delay is a 0, the value represented by ω is used, with the value of λ being used if the binary value of the delay is a 1. As data is sent from the application, it is registered with the Encoder. The Encoder tracks the number of packets being sent, as well as the start time of the data flow. From a predetermined level of security configured by the user, the Encoder will embed the watermark within the data stream after every β number of packets, or after γ units of time have elapsed. Additionally, the user may configure the watermark to be rehashed using the current watermark as the key to create a new watermark after δ packets have been transferred, or to create a new watermark after ζ units of time have elapsed. From this point, the data is transferred over the network to the receiver where another module, a kernel hook, decodes the signal; it is not required that a kernel hook be used in this instance, the application itself could provide this service. Abstractly,

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Fig. 1.

Fig. 2.

Fig. 3.

Fig. 1. A work flow depicting the Covert Timing Channel Model. Fig. 2. Depiction of the Experimental Testbed. Fig. 3 Watermarks in UDP traffic transmitted over a WLAN link. Here λ = 12 ms and ω = 8 ms, with the Watermark = {1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0}.

we shall refer to this entity as the Decoder. Prior to decoding the watermark, the data flows' inter-packet timing sequence is subjected to a high-pass filter which filters out the majority of the un-encoded network traffic. Two methods of decoding the watermarks were investigated. These methods are as follows: 1. The Simple Threshold Method – A cutoff point is determined by taking the mean of the IPD not set to zero by the high-pass filter. If a packet has been delayed by an amount greater than the cutoff, it is considered to represent a 1, otherwise it is considered to represent a 0. 2. The Multi-Threshold Method – Using a moving window of two, the values are multiplied together and stored in a separate array. Doing this places the number pairs into three distinctly separate categories, 00, 01/10, and 11. It is difficult to determine the order of the pairing in the 01/10 category; as a solution, the original stream is reviewed, and the greater value is determined to be the 1 value. Due to space limitations, only the Simple Threshold Method was used during testing. Should the flow be found to not contain the watermark after a certain threshold, as determined by the level of security required, the Decoder can be configured to signal a firewall, or other external device, in an effort to alert a user or disallow continued traffic from the source. There are many additional steps that could be taken, each specific to an individual organization's needs, and beyond the scope of this work.

4 Experimental Setup and Procedure To test the Covert Timing Channel Model in a controlled environment, an experimental testbed was constructed. The testbed was comprised of a Lenovo 3000 C100 laptop running Fedora Core 4, kernel ver. 2.6.16-1.2111_FC4 with 512 MB RAM as the client sending the watermark. One server, called Server 1, used in the

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testbed was a custom build desktop computer running Fedora Core 6, kernel ver. 2.6.20 with a 3.0 GHz processor and 1 GB RAM connected on a local network with the client by both a wired and wireless connection. An Airlink101 wireless card was used in the Lenovo client, and a D-Link wireless USB adapter was used in Server 1 for the wireless testing. A Netgear 802.11b router was used for the local wired and wireless testing; encryption was left on during the wireless testing to more accurately simulate a real-world environment. The majority of network traffic was generated by the client to the server, with several other computers generating traffic through the router on the local network, but not to the client or server. This is an appropriate setup considering most institutions use a switched network, possibly a wireless network as well, with multiple users active at any given time. In our experiments there were two different scenarios: 1. 2.

Switched LAN – The data transfer occurs entirely over a 10/100 Ethernet network. 802.11b WLAN – The data transfer occurs entirely over an 802.11b wireless network.

Several different configurations were used with multiple trials being run for each configuration. Packets were queued using a Netfilter kernel module [26]. The packets were passed to user space if they were being sent out of a predetermined port, and placed into a separate queue with an appropriate release time set. If the packets were part of a watermark, the release time was set according to the watermark; if the packets were not part of a watermark, their release time was set to the current time. The head of the queue was continually checked to determine if the packet had reached its release time, and the packet was released if the current time was greater than the release time. All network traffic was monitored using Tcpdump [27]. Traffic was generated by transferring a 2.97 MB text file using a simple sockets program written in C, resulting in the transfer of 2035 packets over the network for Figures 4 and 5. For Figures 6-9, the same sockets program was used to transfer 88,000 packets over the network. Each set of captured data was parsed to a flat file using a pcap parser. The files were then processed in Matlab to detect the watermark embedded within the data flow. Five hundred tests per network type were run without using the watermark to provide a baseline.2 For each set of parameters used in Figures 6-9, ten trials were run per configuration, resulting in approximately 400 trials.

5 Analysis The balance of performance and security within large networks is crucial in maintaining the viability of that network. Corporate, university, government and military networks all require different levels of security and minimum tolerable levels of performance, leading to the need for scalable and adaptable solutions that do not prohibit performance. Using the Covert Channel Model, each network type receives a configurable and robust method to authenticate nodes for a minimal cost to the 2

UDP traffic was generated as a CBR data flow (35Mbps).

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network. The cost of using these methods varies depending on the frequency of authentication, size of the watermark, and the amount of delay added. This cost is minimal over switched LAN and WLAN connections. When adding a watermark, the majority of the data flow is uninterrupted (Unwatermarked Delay). Only the portion of the data flow containing the watermark adds delay (Watermarked Delay). Additionally, the number of times the watermark repeats increases the delay (Watermarked Repetition Delay), leading to the delay model (Total Delay). unwatermarked_transmission_time = ( packets_total – packets_watermarked ) x (RTT/2) high_delay = packets_watermarked @ λ x λ low_delay = packets_watermarked @ ω x ω watermarked_delay = high_delay + low_delay watermarked_repititions_delay = total_repititions x watermarked_delay transmission_time = unwatermarked_transmission_time + watermarked_repitions_delay

The delay added by the watermark depends on 4 variables: (1) The value of λ; (2) the value of ω; (3) the length of the watermark; and (4) the number of times the watermark is repeated. Both (1) and (2) are configurable and by minimizing these parameters the performance of the network is preserved. Both (3) and (4) are a function of the security required by the network. The Detector attempts to minimize the values in (1) and (2). By using the values of first ten data packets sent, the Detector determines which type of network is being used and sets the rates accordingly. It determines the connection type based on the RTT time of the first ten packets. The distinction between a LAN and a WLAN is made based on the variance within the first ten packets. If a LAN is in use, the Detector sets λ= (φHigh x ( RTT / 2 )) and sets ω = (φLow x (RTT/2)) with φHigh = 46.5 and φLow = 15.5. If a WLAN connection is in use the Detector sets the Encoder to “spike” the interface. When using a WLAN connection φHigh = 1.84 and φLow = 1.0; due to the comparatively high bandwidth of the LAN connection, larger normalizing parameters (φHigh, φLow) are required. During testing, it was initially observed that large delays were required on WLAN networks to ensure that the entire watermark was received. When using limited bandwidth, such as that on a WLAN, packets may be queued within the interface before being transferred to the network. By adding a large delay (“a spike”) prior to sending the watermark, the interface is allowed to clear its queue and thus transmit the watermark with greater accuracy and lower delays. Empirical testing shows that by “spiking” the interface, bandwidth over a WLAN connection is preserved far better than by simply increasing λ and ω. This will be discussed in more detail in Section 6. While testing was done using both TCP and UDP traffic, we will focus our analysis on TCP. The primary reason for this is that UDP traffic is relatively well behaved (with no outside influences unlike TCP) and easily carries the watermark. This is shown in Fig. 3 where UDP traffic is sent over a WLAN connection (which is more volatile than a LAN connection) at a rate well below the rate determined to be optimal. During this trial, watermarks were detected at 100% over the entire duration. UDP traffic over a LAN connection showed similar results. Ideally, after minimizing the delay, the watermark is transfered over the network and detected immediately. While this may be true with λ and ω set to a high value, it is not always the case. Network perturbations may cause alterations within the watermark

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leading to a false negative (no false positives were observed). In this case, multiple watermarks may be sent to authenticate the node. These incomplete watermarks are examined to determine their proximity to the correct watermark. Should they be within a tolerance level against false negatives (a predetermined value from the user) they will be considered the same as an ideal watermark. Additionally, care is taken such that delay values are not set too high (i.e., above the TCP timeout values), since packet retransmissions could also cause an increased number of false negatives. Optimal values for encoding differ per network. To maximize throughput the desired value for ω is slightly above the normal sending rate. Desired values for λ are slightly but distinguishably above ω. While this varies depending on what network type is in use, testing within our testbed has shown that optimal values in our LAN are λ = 6ms and ω = 2ms and in our WLAN are λ = 22ms and ω = 16ms. This was determined empirically and is illustrated in Fig. 4 and Fig. 5. These values lead to a highly detectable solution that is still covert and minimizes the number of watermarks required for accurate detection. Network performance is not greatly affected if λ and ω are set to relatively low values (but not the lowest possible values) and the number of watermarks contained within the authentication cycle is small. An authentication cycle represents the transmission of one or more watermarks. The transmission of several watermarks may be required to achieve an acceptable degree of detection accuracy. While it is certainly possible for a greater degree of performance to be achieved by setting λ and ω to lower values, doing so decreases the probability that the watermark will be accurately detected requiring a larger number of watermarks to be included within an authentication cycle. The computational power required and the speeds at which the watermarks can be detected are also important factors. The Simple Threshold Method is a powerful and easily implementable method to detect the watermark. It only requires enough memory to buffer the current watermark, and minimal computations; as such, the Simple Threshold Method is faster than the Multi-Threshold Method. The drawback to using this method is that it has minimal intelligence, and can create a higher false positive rating if network conditions change. The Multi-Threshold Method provides a greater degree of robustness with lower false positive rates. However, it does require that the original time stamps be held in memory as well as the current watermark, and a slightly greater amount of computation to be done. Comparatively, the MultiThreshold Method provides better protection against changes within network conditions. Due to space limitations our experimental data only provides analysis using the Simple Threshold method. At the monitoring node each watermark is individually detected and compared to the true watermark to determine its accuracy. Each of these accuracy ratings are stored until enough watermarks have been received (or time has elapsed) to complete one authentication cycle. The maximum value of the stored accuracy ratings is selected and compared to the configured threshold. If this maximum is greater than the threshold the user is considered to be authenticated, otherwise the maximum value of the stored accuracy ratings is averaged with all previously detected maximum rates and this value is considered to be the user’s current authentication value. The authentication value is compared to the configured threshold again to determine if the user is authenticated.

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Fig. 4.

Fig. 5.

Fig. 6

Fig. 7.

Fig. 8.

Fig. 9

Fig. 4. contains the results from trials used to empirically determine the optimal values for λ and ω, as well as the number of watermarks required per authentication cycle for different levels of detection accuracy in a LAN. Fig. 5 contains the results from trials used to empirically determine the optimal values for λ and ω, as well as the number of watermarks required per authentication cycle for different levels of detection accuracy in a WLAN. Figs. 6 and 8 show the percentage decrease in network throughput, compared to the baseline throughput, when the number of authentication cycles is increased in a switched LAN and WLAN respectively. Figs. 7 and 9 show the percentage decrease in network throughput, compared to the baseline throughput, when the length of a single watermark is increased in a switched LAN and WLAN respectively.

6 Performance Analysis RTT values differ per network. During testing it was observed that RTT values for a switched LAN had a median value of 0.258ms and a mean value of 0.247ms. Within a WLAN the RTT median value was 23.90ms and a mean value of 42.43ms. Using the Detector, the appropriate λ / ω delay times for a LAN and WLAN network are 6ms/2ms and 22ms/16ms respectively. As previously mentioned these values were determined empirically and are fixed in order to provide the performance analysis. While certain institutions may require the highest level of security, the needs of other institutions may not be as stringent. The Covert Timing Channel method is designed to be a general method of authentication for institutions requiring various levels of security. As such, performance testing was done for several different levels of accuracy using different watermarks lengths and various numbers of watermarks included within a single authentication cycle. By using shorter watermark lengths and/or fewer watermarks per authentication cycle, institutions (such as educational facilities that do not require high levels of security) may authenticate nodes with a

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lower degree of accuracy without seriously impacting the performance of the network. Conversely, institutions requiring a greater degree of security may increase the length of the watermark and/or the number of watermarks per authentication cycle. Doing so allows nodes to be authenticated at a higher degree of accuracy while increasing the complexity required for watermarks to be detected at high levels of accuracy. Testing on the switched LAN differed from that of the WLAN network in that the addition of a watermark was either fully detectable or not detectable at all up to 95% accuracy using an authentication cycle containing one watermark, after which an increase in the authentication cycle size (increased to three watermarks) was required to achieve 100% accuracy at lower rates. This is the result of extremely low latency between hosts. The “authentication watermarks” were fully detectable until 6ms/2ms, after which no watermarks were detected at all. As long as λ > 5ms and 1ms < ω ≤ λ2ms, watermarks were generally 95% detectable with 0% error within the detection. The values of 6ms/2ms were chosen for the LAN because of their consistent ability to provide a great degree of detectability with minimal performance degradation (Fig. 4). Our primary consideration during testing was the amount of overhead this model requires for accurate detection. Two configurable parameters were tested to provide a performance versus security comparison: (1) the cost to network throughput for different watermark lengths; and (2) the cost to network throughput for additional redundancy using authentication cycles. By increasing the length of the watermark and by adding additional authentication cycles, the security of the model is increased at a cost to the network throughput. Fig. 6 represents the cost of increasing the number of watermarks contained within one authentication cycle. This figure provides results for 1 watermark, 20 bits (or packets) long, per authentication cycle, with one to ten authentication cycles being sent over the authentication period of 88,000 packets for 95% detection rate. To achieve 100% detection, 3 watermarks were used per authentication cycle using the same λ and ω values. The throughput of the watermarked flows are compared to the average of several hundred baseline transmissions in the same environment. Transmission of more authentication cycles caused a decrease in the throughput of the network. In our experiments it was noted that there was an average of 0.54% decrease in throughput per additional authentication cycle transmitted, with a maximum performance penalty of 7.32% over our trials. Fig. 7 represents the cost of increasing the watermark length, tested using 6 watermarks per authentication cycle for 100% detection, and 1 watermark per authentication cycle for 95% detection. The cost of increasing the watermark from a length of 5 to 40 is a total decrease in throughput of 7.5%, with each additional bit costing, on average, a 0.036% decrease in throughput. Performance on a WLAN network had a greater degree of variance, primarily due to the nature of the network. Due to the bursty nature of a WLAN network, a higher proportional delay value was required for accurate detection. Testing was done at a detection accuracy of 90% (λ = 20ms), 95% (λ = 20ms), and 100% (λ = 22ms), with ω = 12ms (the optimal low value for our tested network) for each.

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Fig. 8 represents the cost of increasing the amount of authentication cycles per authentication period on a WLAN network. Each authentication cycle contains 6/3/2 watermarks for 100%/95%/90% detection respectively, consisting of 20 bits (or packets). In our experiments it was noted that there was, on average, a 0.63% decrease in network throughput per additional authentication cycle added, with a maximum decrease of 7.17%. Fig. 9 represents the cost of increasing the watermark length in a WLAN network. This figure contains a large degree of variance due to the nature of WLAN networks, but shows a general decrease in network throughput as the length of the watermarks are increased; with a larger amount of test data, we believe that this trend will smooth over time. On average adding an additional bit in a watermark on a WLAN network caused a 0.0435% decrease in throughput with, generally, an average maximum performance penalty of 8.54% when disregarding a single outlying value. All figures, while showing the general trend expected, are comprised of a limited set of trials. It is our belief that the anomalies within each figure will smooth over a larger number of trials.

7 Conclusion and Future Work While our specific application uses the Covert Timing Channel model to communicate authentication information, we investigate the use of the Covert Timing Channel model to transmit data in general. We show that the Covert Timing Channel method provides a secure, reliable, and cost effective process for which nodes may be authenticated within a network. Our model is highly scalable, easily adaptable and backwards compatible with existing technology, and provides excellent performance on switched LAN and WLAN networks. While the performance of this model is readily visible from the LAN and WLAN test results, we believe that this model could be extended for use over WAN networks as well; in our future work, we intend to show that this model will perform well over VPN connections. Additionally, we intend to investigate the effects of chaff packets on our method. Lastly, we shall apply our method to node authentication using real world traffic.

References 1. Cisco, NAC. http://www.cisco.com/en/US/netsol/ns466/ networking_solutions_package.html 2. Thumann, M., Roecher, D.: NAC@ACK: Hacking the Cisco Nac Framework. In: The Proceedings of Black Hat Europe (2007) 3. Cisco Security Response: AAA Command Authorization By-Pass. 0125-aaatcl.pdf (2006), http://www.cisco.com/warp/public/707/cisco-sr4. Pyun, Y.J., Park, Y.H., Wang, X., Reeves, D.S., Ning, P.: Tracing Traffic through Intermediate Hosts that Repacketize Flows. In: INFOCOM 2007. 26th IEEE International Conference on Computer Communications (2007) 5. Wang, X., Reeves, D.S.: Robust Correlation of Encrypted Attack Traffic through Stepping Stones by Manipulation of Interpacket Delays. In: Proc. of the 10th ACM conference on Computer and Communications Security (CCS), October 2003, pp. 20–29 (2003)

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6. Wang, X., Chen, S., Jajodia, S.: Tracking Anonymous Peer-to-Peer VoIP Calls on the Internet. In: Proc. of the 12th ACM conference on Computer and Communications Security (CCS), November 2005, pp. 81–91 (2005) 7. Peng, P., Ning, P., Reeve, D.S., Wang, X.: Active Timing-Based Correlation of Perturbed Traffic Flows with Chaff Packets. In: Proc. Of the 2nd International Workshop on Security in Distributed Computing Systems (SDCS), June 2005, pp. 107–113 (2005) 8. Want, X., Reeves, D.S., Ning, P., Feng, F.: Robust Network-Based Attack Attribution through Probabilistic Watermarking of Packet Flows. Technical Report TR-2005-10, Department of Computer Science, NC State Univ. (2005) 9. Peng, P., Ning, P., Reeves, D.S.: On the Secrecy of Timing-Based Active Watermarking Trace-Back Techniques. In: Proc. of the 2006 IEEE Symposium on Security and Privacy (S&P), May 2006, pp. 334–349 (2006) 10. Zhang, Y., Paxson, V.: Detecting Stepping Stones. In: Proc. of the 9th USENIX Security Symposium, August 2000, pp. 171–184 (2000) 11. Blum, A., Song, D.X., Venkataraman, S.: Detection of Interactive Stepping Stones: Algorithms and Confidence Bounds. In: Proc. of the 7th International Symposium on Recent Advances in Intrusion Detection (RAID), Octeber 2004, pp. 258–277 (2004) 12. Donoho, D.L., Flesia, A.G., Shankar, U., Paxson, V., Coit, J., Staniford, S.: Multiscale Stepping-Stone Detection: Detecting Pairs of Jittered Interactive Streams by Exploiting Maximum Tolerable Delay. In: Proc. of the 5th International Symposium on Recent Advances in Intrusion Detection (RAID), October 2002, pp. 17–35 (2002) 13. Zhang, L., Persaud, A., Johnson, A., Guan, Y.: Stepping Stone Attack Attribution in NonCooperative IP Networks. Iowa State University, Tech. Rep. TR-2005-02-1 (February 2005) 14. Takahashi, T., Lee, W.: An Assessment of VoIP Covert Channel Threats. In: Proc. of SecureComm 2007, 3rd International Conference on Security and Privacy in Communication Networks (2007) 15. Fu, X., Zhu, Y., Graham, B., Bettati, R., Zhao, W.: On Flow Marking Attacks in Wireless Anonymous Communication Networks. In: Proceedings of the 25th International Conference on Distributed Computing Systems (ICDCS) (2005) 16. Cabuk, S., Brodley, C., Shields, C.: IP Covert Timing Channels: Design and Detection. In: the Proceedings of the 11th ACM conference on Computer and Communications Security (October 2004) 17. Lampson, B.W.: A Note on the Confinement Problem. Communications of the ACM 16, 613–615 (1973) 18. Rowland, C.H.: Covert Channels in the TCP/IP Protocol Suite. First Monday 2.5 (May 1997) 19. Fisk, G., Fisk, M., Papadopoulos, C., Neil, J.: Eliminating Stenagraphy in Internet Traffic with Active Wardens. In: Information Hiding 2002, pp. 18–35. Springer, Heidelberg (2002) 20. Rutkowska, J.: The Implementation of Passive Covert Channels in the Linux Kernel. In: Chaos Communication Congress, Chaos Computer Club e.V. (2004) 21. route, alhambra. Project Loki. Phrack 7(49) (November 1996) 22. Moore, K.: On the Use of HTTP as a Substrate. Tech. Rep. In: Ternet Engineering Task Force, RFC 3205 (February 2002) 23. Brinkhoff, L.G.: httptunnel, http://www.nocrew.org/software/httptunnel.html 24. Covert Channels Definition, http://en.wikipedia.org/wiki/Covert_channel 25. Stenography Definition, http://en.wikipedia.org/wiki/Stenography 26. Netfilter / IPTables, http://www.netfilter.org/ 27. Tcpdump, http://www.tcpdump.org/

PassPattern System (PPS): A Pattern-Based User Authentication Scheme T. Rakesh Kumar and S.V. Raghavan Network Systems Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras Chennai-600036, India {rakeshk,svr}@cs.iitm.ernet.in

Abstract. Authenticating a user online, without compromising the user comfort is an important issue. The most popular approach to authenticate a user online is Password-based authentication. Studies show that, users (always) choose very simple passwords which are often easy to guess. On the contrary, randomly generated strings are difficult to remember, especially if the user is having many passwords. In this paper we present a dynamic password scheme based on patterns, called PassPattern System (PPS), which works using the existing infrastructure. PPS is an Adaptive Authentication System, where the strength of the system can be changed depending on the need of the application without compromising the user comfort. Keywords: User Authentication, Security.

1 Introduction "Open, Sesame!" is a classical example of using a SecretWord/Password in the famous tale Ali Baba and the Forty Thieves [1] to open the door of the magical cave. Since then SecretWord/Password has been used to authenticate the user before accessing any resource. With the advancement in computers, Internet and electronic commerce, authentication has become increasingly important. One of the main problems with the username-password scheme is ‘selection of the password’ itself. Studies show that users will always pick passwords which are short and easy to remember [2]. Often it is very easy to break the password of the user, if the personal information about him/her is known and more often than not, it is widely known. With technological advancement, alternative authentication schemes are reported in literature, which are more secure than the conventional Password-based scheme using Biometrics, RSA SecurID® [3], and Graphical passwords [4]. Each mechanism has its own advantages and disadvantages. One of the main reasons why password is still widely used is that newer technologies entail significant changes in the infrastructure of the system. Besides, the vulnerabilities of Graphical Passwords are still not fully understood and is an active area of research [4]. It appears that the need of the hour is to design an authentication scheme that is easy and intuitive to use, strong and robust against all possible attacks, and works using the existing hardware/software infrastructure. A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 162–169, 2008. © IFIP International Federation for Information Processing 2008

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In this paper we present the PPS, which is a dynamic password authentication system, where the user will prove to the system that he/she knows the secret instead of communicating it directly. PPS can be visualized as a challenge-response system, where in the response to a challenge is hardly reusable, there by rendering the replay attack or its variant ineffective. 1.1 Related Work There are several attempts reported in literature about authentication schemes in lieu of the traditional Password-based system. While each attempt is successful in increasing the strength of the system against some of the known attacks, they are either computationally intensive or they require additional hardware/software in the infrastructure. In this section we review the contemporary attempts, identify the gaps, and highlight the motivation for developing PPS. Password-based authentication. Password system is the oldest and the most popular authentication scheme used in the modern world. But users tend to choose simple passwords, which are easy to remember but they are very easy to break [2,5]. One way of preventing this is to provide users with a random password. While it is difficult for the user to remember, with contemporary computing power even a computer generated random password of length 8 characters is not strong enough for bruteforce attack; With Blue Gene/L1, it will take 1 hour to break password of length 10 characters. According to Microsoft “A strong password should appear to be a random string of characters to an attacker. It should be 14 characters or longer, (eight characters or longer at a minimum)” [6]. While it is recommended that user should not use same password for more than one account, it is quite common that the users have more than one online profile. Besides, users have to maintain a separate account for e-mail, social networking, e-banking, e-commerce, blogging, multimedia sharing, etc. Remembering a 14 character password for each account is difficult. The other way of increasing the strength of the password system is to make the password dynamic, i.e. the user has to enter a different password every time he/she does a login. But existing dynamic password or One-Time Password schemes either require extra hardware or complex mathematical operations. Biometrics. Biometrics comes under the category of authentication systems which use ‘What the user is’. The main disadvantage with any biometric system is the need for a change in the infrastructure of the entire system. For example if Yahoo wants to replace its authentication system from password to fingerprint based authentication, all the users have to install fingerprint readers on their computers and Yahoo should maintain a registry. This kind of change in infrastructure may not be feasible always. Moreover, biometrics are not often secret [7], as people publicly expose their voice and fingers in various ways on a regular basis, creating the possibility of biometric spoofing. For biometrics like Retina-based authentication and Vein-based authentication, spoofing is not possible but the installation cost is very high. Graphical passwords. The main idea of Graphical passwords [4] is that users find it easy to remember Graphical passwords than text-based passwords. Graphical 1

IBM Blue Gene is the current fastest super computer. [www.research.ibm.com/bluegene/]

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passwords are more secure than text-based passwords from various attacks like dictionary attack, bruteforce attack and spyware. Some of the problems with the graphical passwords are that the users tend to choose the same sequence of images as their password making it easy to guess; Graphical passwords require much more storage than conventional text-based passwords. RSA SecurID®. RSA SecurID® is a two-factor authentication. It is based on something you know (a password or PIN) and something you have (an authenticator). RSA SecurID® is one of the dynamic password systems, where the user password will change for every login. Issues that cause concern are the cost and the possible of loss of physical devices. Virtual keyboard. Virtual keyboard is yet another user authentication system, where the user has to enter his/her password by clicking the characters on a virtual keyboard instead of typing the password on the conventional keyboard. While such a system is good against keyloggers and spywares, it is susceptible to other attacks like Shoulder surfing, guessing, and Social Engineering. Motivation for PPS. The discussions hitherto underline the need for an authentication system, that is simple, easy-to-use, inexpensive, incorporates the essence of challenge-response system, dynamically changing, robust against attacks such as bruteforce, dictionary, shoulder surfing, spyware, social engineering, ManIn-The-Middle attack and all above doesn’t demand any special hardware/software.

2 PassPattern System PassPattern System is a challenge-response system and is based on the premise that ‘humans are good at identifying, remembering and recollecting graphical patterns than text patterns’ [8]. The core idea of PassPattern system is that, ‘Instead of remembering a sequence of characters as the secret, users have to remember a shape (which is stored internally as a sequence of positions in hash form) as the secret’. Whenever the user wants to get authenticated, the PPS displays an NxN matrix of cells, which is known as PatternSquare. Each cell in the PatternSquare is an image, which represents a character as shown in Fig. 1(a). The character can be an alphabet, number or even special characters. The PatternSquare is the challenge that is sent by the server to the user. The PatternSquare will be generated dynamically upon each user request. Hence the characters in each cell of the PatternSquare may change or their position may change or both may change, for every authentication request. At the time of registration the user is asked to choose a sequence of positions as shown in Fig. 1(b), by typing the characters in those positions. The sequence of positions then becomes the user’s PassPattern. The cell sequence of the PassPattern so chosen will remain the secret between the user and the system. The user has to remember only the shape (cell sequence in the PatternSquare) as that is the only secret. For the user to get authenticated, he/she has to type the characters that are present in the chosen shape (PassPattern) in the PatternSquare. The cells in the PatternSquare are colored2 in such a way that, the user can see smaller 2

To enhance user convenience; no relationship to security of the PPS.

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(b)

Fig. 1. (a) A sample PatternSquare. This is the sample of the image that the user will see, whenever he/she wants to login to the system. (b) A sample user choice of a PassPattern. A user can choose any sequence of positions as the PassPattern.

subsections of the PatternSquare (3 x 3 in our example), which makes it easy for him to remember his PassPattern. Whenever the user wants to login to the system, the user has to type the sequence of characters which appear in the user’s PassPattern presented by the system as a challenge (This sequence of characters is referred to as SecretWord). During every presentation of the PatternSquare, the contents change and hence the user has to type a different SecretWord as shown in Fig. 2, during every authentication process such as login.

Fig. 2. The above figure shows PatternSquares that are displayed to the users at different login attempts. SecretWords corresponding to the PassPattern in Fig. 1(b) will be “t?X4”, “@?7-” and “b-97”.

2.1 Design Issues PPS allows users to choose any set of characters as their PassPattern. The user can select the PassPattern based on some sequences familiar to him/her, for example knight’s moves on a chess board or some symmetric positions. Because of the coloring2 of the cells, remembering the PassPattern will be easy. The PPS comprises of 4 parts - PPS software, Pseudo-Random number generator, Image generator, and PassPattern database. PPS software controls all activities in the system, Pseudo-Random number generator and Image generator are used to create dynamic challenges and PassPattern database stores all the PassPatterns of the users. Internally the cells of the PatternSquare are indexed in row-major order (and left to

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right), starting from 0 to N2-1 (where N is the size of the matrix). Hence the PassPattern of a user will be stored as a string of numbers. Both PassPattern and usernames will be stored in the database as Hash, using MD5 hash function. Registration phase. Whenever a user sends a request for new registration, the server will create two S-seeds based on the “current clock time” (This seeds are valid only for that session at the server). Using S-seeds as the seed values, the Pseudo-Random generator will (randomly) pick two sets of 49 characters (for a 7x7 matrix) out of 94 printable characters in ASCII. The Image generator takes these characters and creates two sets of 49 images and sends the images to PPS Software. PPS Software creates two PatternSquares using the images and sends them to user as challenges. These two challenges are similar to the process of retyping the password in traditional password systems at the time of registration. The S-seed values are preserved as the session variable (in physical memory) for the verification phase.

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Fig. 3. (a) Registration phase in the PPS. (b) Validation and Creation of the user account.

Once the user gets these two challenges, the user has to type the username and SecretWords. The SecretWords will be based on the user selection of the PassPattern. The PPS checks both the PassPatterns and if they are same, then the PPS calculates the MD5 Hash of the username and PassPattern and stores them in the PassPattern database and acknowledges the successful registration to the user. The Authentication phase. Whenever a user makes a login or authentication request, the PPS Software creates the PatternSquare (similar to the registration phase) and sends it to the user as a challenge. Once the user gets the challenge, the user has to type the username and the SecretWord which is based on his/her PassPattern and the PatternSquare (Secret and Challenge). The PPS software calculates the MD5 Hash of the username and PassPattern and compares the result with the value stored in the PassPattern database. Based on the comparison results, the user will be either allowed or rejected.

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Fig. 4. (a) Challenge creation phase in the PPS. (b) Response validation phase in the PPS.

Other Design Issues. PPS is an adaptive user-authentication system. The strength of PPS can be adapted for various situations. The strength of the PPS can be varied by changing two parameters – the size of the PatternSquare and the type of image. The first method is to change the size of the PatternSquare, i.e. the more the size, more is the security. Number of possible patterns of length n in an NxN matrix will be (N2)n. Thus, as the size of PatternSquare (N) increases, the total possible number of PassPatterns will increase, which in turn increases the strength of the system. The second method of increasing the security is by changing the type of the image in the PatternSquare. The strength of the system can be drastically improved by changing the type of the image. Each character in the PatternSquare can be represented with different coding, compression, noise, distortion, and even as a CAPTCHA3 image.

3 Security Strength of PPS In general, several attacks are possible on an authentication system. For any authentication system, the hacker can attack at least at three places; they are server, client, and the communication link. The different attacks on sever includes Bruteforce attack, Dictionary attack, attack on PassPattern database, and compromising the server as a whole. At the client, the possible attacks are keylogging and shoulder surfing. Finally on the communication link, the possible attack is Man-In-The-Middle attack, which can be done using packet sniffers. In terms of the data being passed from the user to the server and the data which is being stored in the server PPS is comparable with the classical Password-based authentication system. In both cases, user sends the username and a SecretWord and this will be compared with the registry in the database. But because of the dynamic nature of the challenge-response system, PPS is more secure than Password-based scheme to attacks such as Bruteforce, Dictionary attack, Keylogger, and Shoulder surfing. For the rest of the attacks, PPS is as vulnerable as Password-based schemes 3

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to determine whether the user is human. For more information on the CAPTCHA refer [http://www.captcha.net].

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(e.g. Server database compromise attack). The best known solution for such attacks is to use cryptography protocols at the server or on the communication link. In this section we analyze the impact of the four attacks mentioned here on PPS. Bruteforce attack. The hacker can try two kinds of Bruteforce attacks on this system. The first way of attacking the system is to ignore the PatternSquare and try with some random string. For a user PassPattern of length 4, there will be a unique SecretWord for the given session. If the hacker wants to guess that SecretWord, the probability of success will be 1/(944) = 1.28x10-8 (Since there are 94 printable characters). If the guess is wrong the probability of success will remain same for the next guess, it is because the SecretWord will change with every attempt. Hence, The probability of success for every attempt =

1 94n

(1)

The other way of doing Bruteforce search is to try all combinations of positions. For example, if we consider a 7x7 PatternSquare there will be 49n (if selection of PassPattern includes reuse of positions) or 49Pn (without reuse of positions) different patterns of length n. ⎧( N 2 ) n ⎪ Number of possible PassPatterns = ⎨ N 2! ⎪ ( N 2 − n)! ⎩

(2)

To break the system, the hacker on an average has to break (nx49n)/2 images (with reuse) or (nx49Pn)/2 (without reuse). ⎧ n × ( N 2 )n ⎪ 2 ⎪ Number of images that are to be broken = ⎨ (3) n × N 2! ⎪ ⎪⎩ 2 × ( N − n)! N represents the size of the PatternSquare and n represents the length of the PassPattern. Dictionary attack. Dictionary attack is one of the most commonly used techniques to break a Password-based system. In the case of PPS commonly used shapes, sequences can be possible candidates in a dictionary. However, as the PatternSquare changes randomly on every presentation, it approaches the behavior of one-time pad. Keyloggers. Keylogger is a program, which captures the user’s keystrokes and sends this information to the hacker. The natural protection for an authentication system from the keylogger is to have a one-time password (or Dynamic password). PPS being a dynamic password system, is not vulnerable to keyloggers. Even if the hacker gets the SecretWord of the user of a PPS system, this SecretWord cannot be reused by the hacker to login to the system, because of the dynamic nature of the PatternSquare. Shoulder surfing. Shoulder surfing is looking over someone’s shoulder when they enter a password or a PIN code. It is an effective way to get information in crowded places because it is relatively easy to stand next to someone and watch as they fill out a form, enter a PIN number at an ATM machine, or use a calling card at a public pay

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phone. Shoulder surfing can also be done at a distance with the aid of binoculars or other vision-enhancing devices to know the password. Shoulder surfing can be done easily on the password system, just by seeing the keys that the user is typing. But to decode the PassPattern in the PPS, the hacker has to see both the key sequence and the PatternSquare and do a mapping before the user submits the page. So shoulder surfing is of little or no use in PPS as compared to a Password-based system.

4 Conclusion In this paper we presented PPS, a user authentication system which can be a potential replacement to classical Password-based authentication system. The strength of the system can be changed by varying the size of the PatternSquare and the type of image associated witch each cell of the PatternSquare without increasing the length of the PassPattern. PPS is very easy for the user to use and at the same time it is more secure than conventional Password-based system. An existing Password-based system can be migrated to PPS, without any change in the infrastructure. Some of the open problems in this area are: What is the ideal length of the PassPattern? What is the ideal size of the PatternSquare? What is the general user behavior while choosing a PassPattern? Prototype of the PPS is available at http://netlab.cs.iitm.ernet.in/pps

References 1. McCaughrean, G.: One Thousand and One Arabian Nights, USA. Oxford University Press, Oxford (1999) 2. Adams, M.A., Sasse, M.S.: Users are not the enemy: why users compromise computing security mechanisms and how to take remedial measure. Communications of the ACM 42(12), 40–46 (1999) 3. RSA Security Inc. RSA SecurID® authenticators, http://www.rsasecurity.com 4. Suo, X., Owen, Y.Z.: Graphical Passwords: A Survey. In: 21st Annual Computer Security Applications Conference (ACSAC 2005), pp. 463–472 (2005) 5. Gilhooly, K.: Biometrics: Getting Back to Business. Computerworld (May 09, 2005) 6. Strong passwords: How to create and use them, http://www.microsoft.com/ protect/yourself/password/create.mspx 7. Brainard, J., Juels, A., Rivest, R.L., Szydlo, M., Yung, M.: Fourth-Factor Authentication: Somebody You Know. In: Proceedings of the 13th ACM conference on Computer and communications security, pp. 168–178 (2006) 8. Shepard, R.N.: Recognition memory for words, sentences and pictures. Journal of verbal Learning and verbal Behavior 6, 153–163 (1967)

A Secure and Efficient Three-Pass Authenticated Key Agreement Protocol Based on Elliptic Curves Meng-Hui Lim1 , Chee-Min Yeoh1 , Sanggon Lee2 , Hyotaek Lim2 , and Hoonjae Lee2 1

Department of Ubiquitous IT, Graduate School of Design & IT, Dongseo University, Busan 617-716, Korea {menghui.lim,yeohcm}@gmail.com 2 Division of Computer and Information Engineering, Dongseo University, Busan 617-716, Korea {nok60,htlim,hjlee}@dongseo.ac.kr

Abstract. Key agreement protocol is of fundamental importance in providing data confidentiality and integrity between two or more parties over an insecure network. In 2004, Popescu [14] proposed an authenticated key agreement protocol in which its security is claimed. However, Yoon and Yoo [19] discovered its vulnerabilities two years later and proposed an improved variant of it. In this paper, we highlight the vulnerability of this improved variant under the LaMacchia et al.’s extended Canetti-Krawczyk security model [12]. With this, we propose another enhanced version of Popescu’s protocol which offers stronger security features and appears to be significantly more efficient than Yoon-Yoo’s scheme. In order to justify our claims, we present a thorough heuristic security analysis on our scheme and compare the computational cost and security attributes with the surveyed schemes.

1

Introduction

Authenticated key agreement protocol is essential for two or more specific entities to communicate securely over an open network (e.g. the Internet), in which the communication can be fully controlled by adversaries. The protocol must be properly designed in such a way that at the end of the protocol execution, both communicating parties can be assured of each other’s identity and agreed on an unique shared secret key (also known as session key) based on the information contributed equally by each party. Once the key agreement is successfully carried out, the derived session key can be used subsequently to create a confidential channel particularly for preserving data confidentiality and integrity. Therefore, an authenticated key agreement protocol should strictly prohibit other unauthenticated parties aside from the intended protocol participants from gaining access to the shared secret under any circumstances. 

This work was supported by MIC of Korea, under ITRC support program supervised by IITA (IITA-2007-C1090- 0701-0026).

A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 170–182, 2008. c IFIP International Federation for Information Processing 2008 

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Key agreement protocols can employ symmetric or/and asymmetric cryptography in its construction. Generally, symmetric protocols require a common secret key to be shared in prior between the communicating parties whereas asymmetric or public key-based protocols require the communicating entities to learn only specific authenticated public information of their peer (such as identity, public key, etc.), which is usually exchanged via certificates. This paper is concerned with two-party authenticated key agreement protocols in the asymmetric settings. Over the years, there has been overwhelming interest in the use of public keybased cryptography, mainly due to the employment of elliptic curve operations in constructing cryptographic key agreement protocols. However, as designing a secure and efficient protocol is far from being a simple task, many protocols have been proven to be fallible. Strangio [16], in 2005, has proposed an efficient two-party elliptic curve based key agreement protocol, namely ECKE1, which is believed to be secure at the first glance since the security attributes are well discussed in his work. Unfortunately, Wang et al. [18] have revealed a weakness in Strangio’s protocol recently. Specifically, they have presented a valid key compromise impersonation attack on it. Besides, Duraisamy et al. [8] have also pointed out a similar attack independently. To defeat the vulnerability of ECKE1, Wang et al. [18] and Strangio [17] have proposed their revised version of ECKE1, namely ECKE1N and ECKE1R respectively at the expense of a higher computational workload. In 2004, Popescu [14] has proposed another notable key agreement scheme, in which the author claimed such protocol to be secure and more efficient than the exisitng ones due to its low computational cost required at both communicating entities. However, in 2006, Yoon-Yoo [19] have subverted Popescu’s claim by exploring a series of cryptographic attacks which encompass a key-compromise impersonation attack, a reflection attack and a replay attack. Subsequently, Yoon-Yoo have proposed another improved scheme [19] in order to defeat these vulnerabilities. To justify their claims on the security of their protocol, they have demonstrated a detailed heuristic security analysis on it. In this paper, we point out that Yoon-Yoo’s improved scheme [19] does not seem secure when both ephemeral keys of a specific completed session are exposed to a passive adversary who only eavesdropped on the session. This compromise of session-specific secret information is regarded as one of the essential attacks captured by the extended Canetti-Krawczyk security model [12]. Based on the shortcoming, we propose another improved variant of Popescu’s protocol [14] and we claim that our protocol is able to achieve a stronger and higher sense of security and efficiency as compared to Yoon-Yoo’s scheme. The structure of this paper is organized as follows. In Section 2, we illustrate the basic concepts needed to evaluate the security properties of a key agreement protocol and define the elliptic curve as well as several hard cryptographic problems. In section 3, we revisit Yoon-Yoo’s improved scheme and illustrate its insecurity. We propose an efficient three-pass authenticated key agreement protocol and evaluate its security in sections 4 and 5 successively. In Section 6, we compare our scheme with

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the other existing key agreement schemes in terms of computational efficiency and security. Lastly, we conclude this paper in Section 7.

2 2.1

Preliminaries Security Properties of Key Agreement Protocol

There are numerous security attributes that have been defined and discussed, specifically for the analysis of the key agreement protocols. The most fundamental property of a key agreement protocol is the passive attack resilience, that is, the protocol should be able to prevent the adversary from obtaining the session key or any other useful information (such as ephemeral keys) by merely eavesdropping on the protocol. On top of that, we also assume that the adversary has full control of the communications on the open network, such that the adversary is capable of intercepting, deleting, injecting, altering and replaying messages in any online instance of the key agreement protocol. These potential attacks has in fact led to the additional security properties: known session key security, perfect forward secrecy, key compromise impersonation resilience, unknown key-share resilience and key control resilience. Please refer to Blake-Wilson and Menezes’s definition of these security attributes [4,3] for an excellent overview. 2.2

Elliptic Curve Cryptography

The notion of elliptic curve cryptography was first introduced independently by Miller [13] and Koblitz [10]. Since then, numerous elliptic curve cryptosystems have been proposed and employed. The main attraction of it is that it allows much smaller parameters (e.g. key size) to be employed in order to achieve an equivalent level of security as compared to the traditional public-key cryptosystems such as RSA and DSA. Since an elliptic curve cryptosystem, that is mainly based on the intractability of Elliptic Curve Discrete Logarithm Problem (ECDLP), takes full exponential time, its resistance against the sub-exponential attack offers potential reductions in processing power and memory size which is essential in applications on constrained devices [15]. Let E(Fq ) be an elliptic curve of defined over a finite field Fq of characteristic p. The public elliptic curve domain parameters over Fq is defined as a 8-tuple (q, F R, S, a, b, P, n, h), where q is the prime order of the field, F R (field representation) indicates the representation used for the elements of Fq , S is the random seed for elliptic curve generation, the coefficients a, b ∈R Fq define the equation of elliptic curve E over Fq (y 2 = x3 + ax + b for p = q > 3, where 4a3 + 27b2  = 0), the base point P = (xp , yp ) in E(FQ ), the prime n is the order of P (n > 2160 ) and the cofactor h = E(FQ )/n, where E(FQ ) denotes the number of FQ -rational points on E. These parameters should be chosen appropriately to prevent the employment of any efficient algorithm from solving the Discrete Logarithm Problem (DLP) or the computational Diffie-Hellman Problem (CDHP) in the cyclic subgroup P  [16].

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Since many cryptographic primitives base their security on the underlying assumptions in which the DLP and CDHP on some cyclic groups are intractable, our proposed key agreement protocol would not be exceptional. Our protocol also rests upon a few related conjectures before its security can be claimed. Now, we define several cryptographic problems which we will assume their hardness throughout this paper. Conjecture 1 (ECDLP). Let E(Fq ) and P be defined as above. The Elliptic Curve Discrete Logarithm Problem is said to be intractable if for any probablilistic polynomial time Turing Machine A with the knowledge of Y = xP , where Y ∈ P , the probability of success in computing logP Y = x ∈R ecdlp [1, n − 1], denoted as SuccP,E(Fq ) (A) is negligible: ⎡

⎤ x ∈R [1, n − 1]; ecdlp ⎦  ε, Y = xP ; SuccP,E(Fq ) (A) = P r ⎣ A(n, P, Y ) = logP Y where the probability is taken over the coin tosses of A for any random choice of x. Conjecture 2. (ECCDHP). Let E(Fq ) and P be defined as above. The Elliptic Curve Computational Diffie-Hellman Problem is said to