Mobility Models for Next Generation Wireless Networks

Dr. Paolo Santi, Istituto di Informatica e Telematica del CNR, ItalyDr. Santi received the Laura Degree and Ph.D. degree in computer science from the University of Pisa in 1994 and 2000, respectively. He is part of the research staff at the Istituto di Informatica e Telematica del CNR in Pisa, Italy, since 2001, first as a Researcher and now as a Senior Researcher.During his career, he visited Georgia Institute of Technology in 2001 and Carnegie Mellon University in 2003. His research interests include fault-tolerant computing in multiprocessor systems (during PhD studies), and, more recently, the investigation of fundamental properties of wireless multihop networks such as connectivity, topology control, lifetime, capacity, mobility modelling, and cooperation issues.

• List of Figures xv List of Tables xxiiiAbout the Author xxvPreface xxviiAcknowledgments xxxiiiList of Abbreviations xxxvPart I INTRODUCTION1 Next Generation Wireless Networks 31.1 WLAN and Mesh Networks 51.2 Ad Hoc Networks 81.3 Vehicular Networks 101.4 Wireless Sensor Networks 131.5 Opportunistic Networks 142 Modeling Next Generation Wireless Networks 192.1 Radio Channel Models 202.2 The Communication Graph 262.3 The Energy Model 313 Mobility Models for Next Generation Wireless Networks 333.1 Motivation 333.2 Cellular vs. Next Generation Wireless Network Mobility Models 353.3 A Taxonomy of Existing Mobility Models 383.4 Mobility Models and Real-World Traces: The CRAWDAD Resource 433.5 Basic Definitions 45Part II “GENERAL-PURPOSE” MOBILITY MODELS4 Random Walk Models 514.1 Discrete Random Walks 524.2 Continuous Random Walks 554.3 Other Random Walk Models 574.4 Theoretical Properties of Random Walk Models 585 The Random Waypoint Model 615.1 The RWP Model 625.2 The Node Spatial Distribution of the RWP Model 645.3 The Average Nodal Speed of the RWP Model 695.4 Variants of the RWP Model 736 Group Mobility and Other Synthetic Mobility Models 756.1 The RPGM Model 766.2 Other Synthetic Mobility Models 837 Random Trip Models 897.1 The Class of Random Trip Models 897.2 Stationarity of Random Trip Models 937.3 Examples of Random Trip Models 94Part III MOBILITY MODELS FOR WLAN AND MESH NETWORKS8 WLAN and Mesh Networks 1018.1 WLAN and Mesh Networks: State of the Art 1018.2 WLAN and Mesh Networks: User Scenarios 1078.3 WLAN and Mesh Networks: Perspectives 1098.4 Further Reading 1119 Real-World WLAN Mobility 1139.1 Real-World WLAN Traces 1139.2 Features of WLAN Mobility 11610 WLAN Mobility Models 12110.1 The LH Mobility Model 12210.2 The KKK Mobility Model 12910.3 Final Considerations and Further Reading 137Part IV MOBILITY MODELS FOR VEHICULAR NETWORKS11 Vehicular Networks 14111.1 Vehicular Networks: State of the Art 14111.2 Vehicular Networks: User Scenarios 14611.3 Vehicular Networks: Perspectives 15011.4 Further Reading 15112 Vehicular Networks: Macroscopic and Microscopic Mobility Models 15312.1 Vehicular Mobility Models: The Macroscopic View 15412.2 Vehicular Mobility Models: The Microscopic View 15612.3 Further Reading 15713 Microscopic Vehicular Mobility Models 15913.1 Simple Microscopic Mobility Models 15913.2 The SUMO Mobility Model 16413.3 Integrating Vehicular Mobility and Wireless Network Simulation 168Part V MOBILITY MODELS FOR WIRELESS SENSOR NETWORKS14 Wireless Sensor Networks 17514.1 Wireless Sensor Networks: State of the Art 17514.2 Wireless Sensor Networks: User Scenarios 18014.3 WSNs: Perspectives 18314.4 Further Reading 18415 Wireless Sensor Networks: Passive Mobility Models 18515.1 Passive Mobility in WSNs 18615.2 Mobility Models for Wildlife Tracking Applications 18715.3 Modeling Movement Caused by External Forces 19116 Wireless Sensor Networks: Active Mobility Models 19716.1 Active Mobility of Sensor Nodes 19816.2 Active Mobility of Sink Nodes 208Part VI MOBILITY MODELS FOR OPPORTUNISTIC NETWORKS17 Opportunistic Networks 21717.1 Opportunistic Networks: State of the Art 21717.2 Opportunistic Networks: User Scenarios 21917.3 Opportunistic Networks: Perspectives 22217.4 Further Reading 22318 Routing in Opportunistic Networks 22518.1 Mobility-Assisted Routing in Opportunistic Networks 22518.2 Opportunistic Network Mobility Metrics 23119 Mobile Social Network Analysis 23719.1 The Social Network Graph 23819.2 Centrality and Clustering Metrics 23919.3 Characterizations of Human Mobility 24419.4 Further Reading 25020 Social-Based Mobility Models 25120.1 The Weighted Random Waypoint Mobility Model 25220.2 The Time-Variant Community Mobility Model 25420.3 The Community-Based Mobility Model 25620.4 The SWIM Mobility Model 25920.5 The Self-Similar Least Action Walk Model 26420.6 The Home-MEG Model 26720.7 Further Reading 270Part VII CASE STUDIES21 Random Waypoint Model and Wireless Network Simulation 27521.1 RWP Model and Simulation Accuracy 27621.2 Removing the Border Effect 27821.3 Removing Speed Decay 28521.4 The RWP Model and “Perfect Simulation” 28722 Mobility Modeling and Opportunistic Network Performance Analysis 29322.1 Unicast in Opportunistic Networks 29322.2 Broadcast in Opportunistic Networks 299Appendix A Elements of Probability Theory 309A.1 Basic Notions of Probability Theory 309A.2 Probability Distributions 313A.3 Markov Chains 317Appendix B Elements of Graph Theory, Asymptotic Notation, and Miscellaneous Notions 323B.1 Asymptotic Notation 323B.2 Elements of Graph Theory 326B.3 Miscellaneous Notions 330References 333Index 335