RANGE-FREE LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY ZIGUO ZHONG IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY TIAN HE, ADVISOR SEPTEMBER, 2010 c ZIGUO ZHONG 2010 ALL RIGHTS RESERVED Acknowledgements Over the last four years, I have had the privilege to work with a number of people who have made my time at the University of Minnesota enjoyable and rewarding I’d like to thank all of them Without them this dissertation would not be possible I am deeply grateful to my advisor, Prof Tian He Tian is an outstanding computer scientist with broad knowledge, sharp intuition, and grand vision Tian is also a great mentor He is very patient and gives me lots of freedom to explore the field by myself Under his guidance, I was able to learn the fundamental lessons of being a researcher: finding valuable problems, investigating innovative ideas and presenting meaningful results His inspiration and warm personality have won my highest respect and trust I am extremely thankful for the time and invaluable advice from Prof Ahmed H Tewfik, Prof Ibrahim Volkan Isler and Prof Stergios I Roumeliotis, as well as from Prof John A Stankovic, Prof Zhi-Li Zhang and Prof David Hung-Chang Du, who generously helped me and strongly supported my future career I would like to thank all my coauthors, labmates and colleagues in Minnesota, UVA and UIUC including Pengpeng, Ting, Yongle, Paul, Shuo, Qingquan, Fulong, Liangyin, Jason, Yaohua, Shan, Hengchang, Jiakang, Qing, Hongyang, with whom I have shared hours of discussion, work and laughter It has always been enjoyable and fruitful to work with them Life in graduate school was not only about sensor nodes I am glad for the happy times spent with some of the greatest friends Special thanks to Guojin He and Yu Wang Gratitude to Weijia, Weikang, Hao, Jing and Yingchun In addition, thanks to Prof Tewfik’s group on the 6th floor, Prof Isler’s group next door, Prof Zhang’s group and Prof Roumeliotis’ group both at DTC, with whom I really enjoyed discussion and parties Most importantly, none of this would have been possible without the unwavering support from my family In spite of being separated by the vast Pacific Ocean, my parents (and parents-in-law) have always inspired me with courage, strength and love My dearest wife, Dana, has shared with me all the sweets and bitters of life here as a grad student, and has never failed to believe in me I feel exceptionally favored to have you Last but not the least, I gratefully acknowledge financial support from the National Science Foundation, ACM, IEEE, USENIX, and the University of Minnesota MESS Group i Abstract Wireless sensor networks (WSN) have been considered as promising tools for many locationdependent applications such as area surveillance, search and rescue, mobile tracking and navigation, etc In addition, the geographic information of sensor nodes can be critical for improving network management, topology planning, packet routing and security Although localization plays an important role in all those systems, itself is a challenging problem due to extremely limited resources available at each low-cost sensor node Previous research generally divides into two groups: range-based and range-free Rangebased methods are accurate but costly for requiring per-node ranging hardware, careful system calibration, or extensive environment profiling Range-free approaches feature reduced overhead at the resource constrained sensor node side, nevertheless, with less accuracy depending on anchor density, network connectivity, event distribution, etc This thesis offers novel solutions to bridge the gap between low cost and high accuracy for range-free localization In the first part, we explore uncontrolled event-driven localization that advances the state-of-the-art an important step towards a usable system As the first to apply the concept of sequence to localize nodes, our designs significantly improve system flexibility by providing a trade-off between physical cost (anchors) and soft cost (events), a useful layer of abstraction that adopts different sensing modalities, and a potential option of achieving node positioning via natural ambient events In the second part, we focus on the challenging problem of localization with merely rangefree sensing results Different from binary proximity, we invent the signature distance as a metric that, for the first time, enables quantifying distance relationships among neighboring nodes with sub-hop resolution in a range-free manner With little overhead, this metric can be conveniently applied for enhanced system accuracy We further extend the discovery to mobile target tracking By converting the tracking problem from sequential localization to a maximum likelihood shortest path searching in a graph, we demonstrate robust tracking under unreliable sensing and without complex movement modeling By investigating into two important branches of range-free localization − event-driven localization, and localization with local sensing − the research presented in this thesis aims at promoting the use of low-cost range-free solutions in real world applications ii Contents Acknowledgements i Abstract ii List of Tables vii List of Figures viii List of Abbreviations xiii Introduction 1.1 Localization and Its Challenges 1.2 Research Objectives and Contributions 1.2.1 Uncontrolled Event-driven Localization 1.2.2 Localization and Tracking using Signature Distance Organization of the Manuscript Background and Related Work 2.1 Range-based Localization 7 1.3 2.1.1 Signal Strength 2.1.1.1 Directly Infer Distance from RSS Measurements 2.1.1.2 RF Profiling and Fingerprint Matching 10 Time of Fly 11 2.1.2.1 Time of Fly of Acoustic Signals 11 2.1.2.2 Time of Fly of RF Signals 13 2.1.3 Angle of Arrival 15 2.1.4 Radio Interferometry 17 2.1.5 Remarks on Range-based Localization 19 Range-free Localization 20 2.2.1 Anchor Proximity 21 2.2.2 Network Connectivity 23 2.2.2.1 23 2.1.2 2.2 Centralized Methods iii 2.2.2.2 Distributed Methods 26 2.2.2.3 Dealing with “Complex Shapes” and “Holes” 27 2.2.3 Localization Events 30 2.2.4 Remarks on Range-free Localization 33 Uncontrolled Event-driven Localization 34 3.1 Chapter Introduction 34 3.2 MSP: Multi-sequence Positioning 34 3.2.1 System Overview 36 3.2.2 Basic MSP 37 3.2.3 Advanced MSP 39 3.2.3.1 Sequence-based MSP 39 3.2.3.2 Iterative MSP 41 3.2.3.3 Distribution-based Estimation (DBE MSP) 42 3.2.3.4 Adaptive MSP 44 3.2.4 Overhead and Complexity Analysis 46 3.2.5 Wave Propagation Example 47 3.2.6 Practical Deployment Issues 48 3.2.6.1 Incomplete Node Sequence 48 3.2.6.2 3.2.6.3 Localization without Time Synchronization Sequence Flip and Protection Band 49 50 Simulation Evaluation 52 3.2.7.1 Performance of the Basic MSP 53 3.2.7.2 Improvements of Sequence-based MSP over Basic MSP 55 3.2.7.3 Improvements of Iterative MSP over Sequence-based MSP 57 3.2.7.4 Distribution-based Estimation over Iterative MSP 57 3.2.7.5 Improvements of Adaptive MSP 58 3.2.7.6 Simulation Summary 60 Test-bed Evaluation 60 3.2.8.1 Indoor System Evaluation 61 3.2.8.2 Outdoor System Evaluation 64 Summary and Remarks on MSP 67 LUE: Localization with Uncontrolled Events 68 3.3.1 System Overview 68 3.3.1.1 Concepts in Event-driven Localization 68 3.3.1.2 Localization with Uncontrolled Events 69 LUE Basic Design 71 3.3.2.1 Event Generation Parameter Estimation 71 3.3.2.2 Location Area Estimation 73 3.2.7 3.2.8 3.2.9 3.3 3.3.2 iv 3.3.2.3 Localization Algorithm 76 LUE Advanced Design 77 3.3.3.1 Event Generation Parameter MLE 77 3.3.3.2 Final Position MLE 79 3.3.4 Overhead and Complexity Analysis 81 3.3.5 Discussion on Wave Propagation Events 83 3.3.5.1 Basic Design with Wave-based Events 83 3.3.5.2 Advanced Design with Wave-based Events 84 Simulation Evaluation 85 3.3.6.1 Simulation for the Basic LUE Design 86 3.3.6.2 Event Generation Parameter MLE 87 3.3.6.3 Final Position MLE 88 3.3.6.4 Simulation Summary 89 Test-bed Evaluation 89 3.3.7.1 Localization Results 89 3.3.7.2 Discussion on Node Pair Flip 90 3.3.7.3 Discussion on Localization Performance 91 Summary and Remarks on LUE 91 Localization and Tracking with Signature Distance 4.1 Chapter Introduction 93 93 3.3.3 3.3.6 3.3.7 3.3.8 4.2 LBC: Range-free Localization Beyond Connectivity 94 4.2.1 Empirical Data as Motivation 94 4.2.1.1 Preliminary Experiments 95 4.2.1.2 Large-scale Experiments 95 4.2.1.3 Analysis and Discussion 97 Design: a Relative Distance 98 4.2.2.1 Neighborhood Ordering as a Signature 98 4.2.2.2 SD: Signature Distance 99 4.2.2.3 RSD: Regulated Signature Distance 104 Design as a Supporting Layer 107 4.2.3.1 Connectivity-Based Schemes 107 4.2.3.2 Design Embedding 109 4.2.4 Complexity of RSD Embedding 109 4.2.5 Test-bed Experimentation 109 4.2.5.1 Experiment I: Linear Network 110 4.2.5.2 Experiment II: Regular 2D Network 114 4.2.5.3 Test-bed Evaluation Summary 117 Simulation Evaluation 117 4.2.2 4.2.3 4.2.6 v 4.2.6.1 The Noise Model 117 4.2.6.2 RSD as a Metric of Proximity 118 4.2.6.3 The Effectiveness of RSD 119 4.2.6.4 The Robustness of RSD 122 4.2.6.5 Simulation Summary 124 Summary and Remarks on LBC 125 SBT: Sequence-based Tracking Under Unreliable Sensing 125 4.3.1 System Overview 126 4.3.2 Main Design 127 4.3.2.1 Division of the Map 128 4.3.2.2 Unreliable Detection Node Sequence 129 4.3.2.3 The Sequence Distance 130 4.3.2.4 Neighborhood Graph 132 4.3.2.5 Tracking as Optimal Path Matching 133 4.3.2.6 Algorithm and Complexity Analysis 135 Multi-dimensional Smoothing 136 4.3.3.1 Modality Domain Smoothing 136 4.3.3.2 Time Domain Smoothing 137 4.3.3.3 Space Domain Smoothing 137 Issues in Practical Applications 138 4.3.4.1 4.3.4.2 Issue on System Scalability Issue on Multiple Targets 138 139 4.3.4.3 Issues on Time Synchronization and Energy Efficiency 139 Simulation Evaluation 140 4.3.5.1 Noise Models 140 4.3.5.2 An Example by Figures 141 4.3.5.3 SBT Performance Evaluation 141 4.3.5.4 Effectiveness of Smoothing 144 4.3.5.5 Impact of the Node Placement 144 4.3.5.6 Simulation Summary 146 4.3.6 Test-bed Experimentation 147 4.3.7 A Brief Discussion on Mobile Tracking 148 4.3.8 Summary and Remarks on SBT 149 4.2.7 4.3 4.3.3 4.3.4 4.3.5 Concluding Remarks 150 5.1 Summary of Contributions 150 5.2 Future Research Directions 151 Bibliography 153 vi List of Tables 2.1 Summary of Range-based Localization in WSN 20 2.2 Summary of Range-free Localization in WSN 33 3.1 3.2 Default Simulation Configurations for MSP Default Simulation Configurations for LUE 53 85 3.3 Comparison of Event-driven Localization Methods 91 4.1 Major Factors Affecting RSS Sensing 97 4.2 Statistics of the Linear Network 110 4.3 Statistics of the 2D Network 115 4.4 Default Simulation Configurations for LBC 118 4.5 Default Simulation Configurations for SBT 140 vii List of Figures 1.1 Localize the Thesis in the State-of-the-art 2.1 Round-trip Time of Fly Measurements 13 2.2 2.3 Example Patterns of the Received UWB Signal Ranging with Radio Interferometry 14 18 2.4 APIT: Triangular Coverage Based on Proximity 22 2.5 Estimate Inter-node Distance with Hop Count 23 2.6 Examples of Anisotropic Network Topology 28 2.7 The Ideas of REP 29 2.8 The Asymmetric Architecture of the Spotlight System 31 2.9 The design of StarDust 32 3.1 The MSP System Overview 36 3.2 Obtaining Multiple Node Sequences 37 3.3 Elimination Rule in Sequence-based MSP 39 3.4 Sequence-based MSP Example 41 3.5 Iterative MSP: Reprocessing the Node Sequence from Scan 42 3.6 An Example of Joint Distribution Estimation 43 3.7 The Idea of DBE MSP for Each Node 43 3.8 Four Cases for Each Node in the DBE Process 43 3.9 Basic Architecture of Adaptive MSP 44 3.10 Adaptive MSP: Candidate Slops for Node at Anchor 45 3.11 Example of the Wave Propagation Situation 48 3.12 Node Sequence without Time Synchronization 49 3.13 The Problem of 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device that estimates and analyzes angles of arrival of acoustic signals emitted from beacons on the ceiling The system achieves an accuracy... making it advance substantially towards a practical system Event-driven localization makes use of events (e.g ultrasound or air blast propagation, optical or laser beam scan), that are generated... spacial signatures of the target in the map of monitored area Instead of estimating each position point separately in a movement trace, we convert the original tracking problem to the problem of