a dissertation submitted to the faculty of the graduate school

RANGE-FREE LOCALIZATION AND TRACKING IN
WIRELESS SENSOR NETWORKS
A DISSERTATION
SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL
OF THE UNIVERSITY O F MINNESOTA
BY
ZIGUO ZHONG
IN PARTIAL FULFILLMENT OF THE REQUIREMENT S
FOR THE DEGREE O F
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 th em 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 ad vice 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 P rof. David Hung-Chang Du, who generously helped
me and s tr ongly supported my future career.
I would like to thank all my coauthors, labmates and colleagues in Minnesota, UVA and
UIUC including Pengp en g, Ting, Yongle, Paul, Shuo, Qingquan, Fulon g, 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. Tew-
fik’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, IE E E, USENIX, and the University of Minnesota MESS Group .
i
Abstract
Wireless sensor networks (WSN) have been considered as promising tools for many location-
dependent applications su ch 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: ran ge-based and range-free. Range-
based methods are accurate but costly for requiring per-node ranging hardware, careful sys-
tem 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 sens ing 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 range-
free 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 f urther extend the discovery to
mobile target tracking. By converting the tracking problem from sequential localization to
a maximum likelihood shortest path searching in a gr aph, 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 u s e of low-cost range-free solutions in real world app lications.
ii
Contents
Acknowledgements i
Abstract ii
List of Tables vii
List of Figures viii
List of Abbreviations xiii
1 Introduction 1
1.1 Localization and Its Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Objectives and Contributions . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Uncontrolled Event-driven Localization . . . . . . . . . . . . . . . . 3
1.2.2 Localization and Tracking using Signature Distance . . . . . . . . . 5
1.3 Organization of the Manuscript . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Background and Related Work 7
2.1 Range-based Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Signal Strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.1.1 Directly Infer Distance fr om RSS Measurements . . . . . . 8
2.1.1.2 RF Profi ling and Fingerprint Matching . . . . . . . . . . . 10
2.1.2 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
2.2 Range-free Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.1 Anchor Proximity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 Network Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.2.1 Centralized Methods . . . . . . . . . . . . . . . . . . . . . . 23
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
3 Uncontrolled Event-driven Lo calization 34
3.1 Chapter Introd uction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Localization without Time Syn chronization . . . . . . . . 49
3.2.6.3 Sequence Flip and Protection Band . . . . . . . . . . . . . 50
3.2.7 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 Sum mary . . . . . . . . . . . . . . . . . . . . . 60
3.2.8 Test-bed E valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2.8.1 Indoor System Evaluation . . . . . . . . . . . . . . . . . . . 61
3.2.8.2 Outdoor System Evaluation . . . . . . . . . . . . . . . . . . 64
3.2.9 Summary and Remarks on MSP . . . . . . . . . . . . . . . . . . . . 67
3.3 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
3.3.2 LUE Basic Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.3.2.1 Event Generation Parameter Estimation . . . . . . . . . . 71
3.3.2.2 Location Ar ea Estimation . . . . . . . . . . . . . . . . . . . 73
iv
3.3.2.3 Localization Algorithm . . . . . . . . . . . . . . . . . . . . 76
3.3.3 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
3.3.6 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 Sum mary . . . . . . . . . . . . . . . . . . . . . 89
3.3.7 Test-bed E valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Performan ce . . . . . . . . . . . 91
3.3.8 Summary and Remarks on LUE . . . . . . . . . . . . . . . . . . . . 91
4 Localization and Tracking with Signature Distance 93
4.1 Chapter Introd uction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
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
4.2.2 Design: a Relative Distance . . . . . . . . . . . . . . . . . . . . . . . 98
4.2.2.1 Neighborhood Ordering as a Signature . . . . . . . . . . . 98
4.2.2.2 SD: S ignature Distance . . . . . . . . . . . . . . . . . . . . 99
4.2.2.3 RSD: Regulated Signature Distance . . . . . . . . . . . . . 104
4.2.3 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 E x perimentation . . . . . . . . . . . . . . . . . . . . . . . . 109
4.2.5.1 Experiment I: Linear Network . . . . . . . . . . . . . . . . 110
4.2.5.2 Experiment I I: Regular 2D Network . . . . . . . . . . . . . 114
4.2.5.3 Test-bed Evaluation Summary . . . . . . . . . . . . . . . . 117

4.2.6 Simulation Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 117
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 Robustn ess of RSD . . . . . . . . . . . . . . . . . . . . 122
4.2.6.5 Simulation Sum mary . . . . . . . . . . . . . . . . . . . . . 124
4.2.7 Summary and Remarks on LBC . . . . . . . . . . . . . . . . . . . . 125
4.3 SBT: Sequence-based Tracking Un der 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 O ptimal Path Matching . . . . . . . . . . . . . 133
4.3.2.6 Algorithm and Complexity Analysis . . . . . . . . . . . . . 135
4.3.3 Multi-dimensional S moothing . . . . . . . . . . . . . . . . . . . . . . 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
4.3.4 Issues in Practical Applications . . . . . . . . . . . . . . . . . . . . . 138
4.3.4.1 Issue on System Scalability . . . . . . . . . . . . . . . . . . 138
4.3.4.2 Issue on Multiple Targets . . . . . . . . . . . . . . . . . . . 139
4.3.4.3 Issues on Time S y nchronization and Energy Efficiency . . . 139
4.3.5 Simulation Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.3.5.1 Noise Mod els . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Sum mary . . . . . . . . . . . . . . . . . . . . . 146
4.3.6 Test-bed E x perimentation . . . . . . . . . . . . . . . . . . . . . . . . 147
4.3.7 A Brief Discussion on Mob ile Tracking . . . . . . . . . . . . . . . . . 148
4.3.8 Summary and Remarks on SBT . . . . . . . . . . . . . . . . . . . . 149
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 L ocalization in WSN . . . . . . . . . . . . . . . . . 33
3.1 Default S imulation Configurations for MSP . . . . . . . . . . . . . . . . . . 53
3.2 Default S imulation Configurations for LUE . . . . . . . . . . . . . . . . . . 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 S imulation Configurations for LBC . . . . . . . . . . . . . . . . . . 118
4.5 Default S imulation Configurations for SBT . . . . . . . . . . . . . . . . . . 140
vii
List of Figures
1.1 Localize the Th esis in the State-of-the-art . . . . . . . . . . . . . . . . . . . 3
2.1 Round-trip Time of Fly Measurements . . . . . . . . . . . . . . . . . . . . . 13
2.2 Example Patterns of the Received UWB Signal . . . . . . . . . . . . . . . . 14
2.3 Ranging with Radio Interferometry . . . . . . . . . . . . . . . . . . . . . . . 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 O verview . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Obtaining Multiple Node Sequences . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Elimination Rule in Sequ en ce-based MSP . . . . . . . . . . . . . . . . . . . 39
3.4 Sequence-based MSP Example . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5 Iterative MSP: Reprocessing the Node Sequence fr om Scan 1 . . . . . . . . 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 3 at Anch or 1 . . . . . . . . . . . 45
3.11 Example of the Wave Propagation Situation . . . . . . . . . . . . . . . . . . 48
3.12 Node Sequence without Time Synchronization . . . . . . . . . . . . . . . . . 49
3.13 The Problem of Sequence Flip . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.14 The Application of P rotection Band . . . . . . . . . . . . . . . . . . . . . . 51
3.15 Basic MSP: Error vs. Number of Anch ors . . . . . . . . . . . . . . . . . . . 53
3.16 Basic MSP: Error vs. Number of Scans . . . . . . . . . . . . . . . . . . . . 54
3.17 Basic MSP: Error vs. Number of Target Nodes . . . . . . . . . . . . . . . . 54
3.18 Sequence-based MSP: Error vs. Number of Anchors . . . . . . . . . . . . . 55
3.19 Sequence-based MSP: Error vs. Number of Scans . . . . . . . . . . . . . . . 56
3.20 Sequence-based MSP: Error vs. Number of Target Nodes . . . . . . . . . . 56
viii
3.21 Improvements of Iterative MSP . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.22 Improvements of DBE MSP . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.23 Adaptive MSP for a 200 by 200 Field . . . . . . . . . . . . . . . . . . . . . . 58
3.24 Adaptive MSP for a 500 by 80 Field . . . . . . . . . . . . . . . . . . . . . . 59
3.25 Impact of the Number of Candidate Events . . . . . . . . . . . . . . . . . . 59
3.26 The 360-node Mirage Test-bed (Light Beam Scan) . . . . . . . . . . . . . . 60

3.27 The 20-node Outd oor Experiments (Sound Wave Propagation) . . . . . . . 60
3.28 Number of Flips for Different Scan Speed . . . . . . . . . . . . . . . . . . . 61
3.29 Scanning Speed and Protection Band: Number of Unlocalized Nodes . . . . 62
3.30 Scanning Speed and Protection Band: Mean Localization Error . . . . . . . 62
3.31 Scanning Speed and Protection Band: Maximum Localization Error . . . . 62
3.32 MSP Methods and Protection Band: Number of Unlocalized Nodes . . . . . 64
3.33 MSP Methods and Protection Band: Mean Localization Error . . . . . . . . 64
3.34 MSP Methods and Protection Band: Maximum Localization Error . . . . . 64
3.35 Number of Anchors and Scans: Number of Unlocalized Nodes . . . . . . . . 65
3.36 Number of Anchors and Scans: Mean Localization Error . . . . . . . . . . . 65
3.37 Number of Anchors and Scans: Maximum Localization Error . . . . . . . . 65
3.38 The Experiment of Wave Detection . . . . . . . . . . . . . . . . . . . . . . . 66
3.39 Wave Detection: Ranks vs. Distances . . . . . . . . . . . . . . . . . . . . . 66
3.40 Localization Error (Sound Wave Propagation) . . . . . . . . . . . . . . . . . 67
3.41 LUE System Overview I: Node Sequence and An chor Subsequence . . . . . 69
3.42 LUE System Overview II: Map Partition and Location Area Es timation . . 70
3.43 Estimate Angle Range by Intuition . . . . . . . . . . . . . . . . . . . . . . . 71
3.44 Extract the Joint Part of Es timations . . . . . . . . . . . . . . . . . . . . . 72
3.45 Example of Redundant Estimation Units . . . . . . . . . . . . . . . . . . . . 72
3.46 Example of Area Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.47 Example Joint Location Ar ea for Node 3 . . . . . . . . . . . . . . . . . . . . 74
3.48 Example of Location Area Finding . . . . . . . . . . . . . . . . . . . . . . . 75
3.49 Comparison Between Two Possible Scan Angles . . . . . . . . . . . . . . . . 78
3.50 MLE for Final Position Selection . . . . . . . . . . . . . . . . . . . . . . . . 80
3.51 Basic LUE Design with Wave Propagation Events . . . . . . . . . . . . . . 84
3.52 Event Generation Parameter MLE with Wave Propagation Events . . . . . 85
3.53 Final L ocation MLE with Wave Propagation Events . . . . . . . . . . . . . 85
3.54 Impact of the Number of Anchors for Basic LUE Design . . . . . . . . . . . 86
3.55 Impact of the Number of Events for Basic LUE Design . . . . . . . . . . . . 86
3.56 Effectiveness of Event Generation Parameter MLE . . . . . . . . . . . . . . 87

3.57 Effectiveness of Final Position MLE . . . . . . . . . . . . . . . . . . . . . . 88
3.58 Testbed LUE Result Illustration . . . . . . . . . . . . . . . . . . . . . . . . 90
3.59 Time Gap vs. Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
ix
3.60 Node Pair Flip vs. Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.1 Experimental Results: RS S vs. Distance . . . . . . . . . . . . . . . . . . . . 95
4.2 Empirical Date for System Level RSS vs. Physical Distance . . . . . . . . . 96
4.3 Empirical Date for the Monotonicity from Each Node’s Point of View . . . 97
4.4 Neighborhood Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.5 1 Explicit Node-Pair Flip . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.6 10 Implicit Node-Pair Flips . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.7 2 Possible No de-Pair Flips . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.8 The Physical Meaning of Node-Pair Flips . . . . . . . . . . . . . . . . . . . 102
4.9 Physical Distance vs. Bisector Lines Passing . . . . . . . . . . . . . . . . . . 103
4.10 Far-away Node Pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.11 Motivation for SD Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.12 Bisector Lines and Small Regions . . . . . . . . . . . . . . . . . . . . . . . . 106
4.13 Correlation with Physical Distance − S D vs. RSD . . . . . . . . . . . . . . 107
4.14 RSD for Non-neighboring Nodes . . . . . . . . . . . . . . . . . . . . . . . . 107
4.15 RSD Design Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.16 Test-bed Ex periments I: Linear Network . . . . . . . . . . . . . . . . . . . . 110
4.17 Distance Correlation Comparison: RSD vs. Hop (Linear Network) . . . . . 111
4.18 Localization in Linear Networks: DV-Hop vs. DV-RSD . . . . . . . . . . . 112
4.19 Localization in Linear Networks: RPA-Hop vs. RPA-RSD . . . . . . . . . . 112
4.20 Localization in Linear Networks: MDS-Hop vs. MDS-RSD . . . . . . . . . . 113
4.21 Comparison: RSD vs. Hop Distance . . . . . . . . . . . . . . . . . . . . . . 113
4.22 Test-bed Ex periments II: Regular 2D Network . . . . . . . . . . . . . . . . 114
4.23 Network Layout and Neighborhood Size . . . . . . . . . . . . . . . . . . . . 114
4.24 Distance Correlation Comparison: RSD vs. Hop (Regular 2D Network) . . 115
4.25 Localization Results of MDS-RSD and MDS-Hop . . . . . . . . . . . . . . . 116

4.26 The Correlation Coefficient between RSD and Physical Distance . . . . . . 118
4.27 Impact of Different σ
x
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.28 Impact of Different β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.29 Impact of Different Numbers of Anchors . . . . . . . . . . . . . . . . . . . . 121
4.30 Impact of Different Node Densities . . . . . . . . . . . . . . . . . . . . . . . 121
4.31 Different System Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4.32 Example Spatial Distribution of the Radio Path Loss β . . . . . . . . . . . 123
4.33 Robustness of RSD for Spatially Unbalanced Radio Path Loss β . . . . . . 124
4.34 SBT System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.35 Examples for Map Division after WSN Deployment . . . . . . . . . . . . . . 128
4.36 Detection Sequences v.s. Signature Sequences . . . . . . . . . . . . . . . . . 129
4.37 Examples for the Sequence Distance . . . . . . . . . . . . . . . . . . . . . . 130
4.38 Sequence Distance vs. Geographic Distance . . . . . . . . . . . . . . . . . . 130
x
4.39 Examples for SeqD Calculation with Wildcard Matching . . . . . . . . . . . 131
4.40 Neighbor Faces and Neighborhood Graph Building . . . . . . . . . . . . . . 133
4.41 Neighborhood Graph for Randomly Deployed 4, 8, 12 and 16 Nodes . . . . 134
4.42 Converting Optimal Path Matching to Shortest Path Searching . . . . . . . 134
4.43 SBT Allows Multi-modality Integration at the Sequ en ce Layer . . . . . . . 136
4.44 Issues of System Scalability and Multiple Targets Tracking . . . . . . . . . 138
4.45 Demo of Reduced Candidate Path Graph H . . . . . . . . . . . . . . . . . . 139
4.46 A Tracking Example from Simulation . . . . . . . . . . . . . . . . . . . . . 142
4.47 Impact of the Sensing Noise to Trackin g Error . . . . . . . . . . . . . . . . 143
4.48 Impact of the Number of Sensor Nodes . . . . . . . . . . . . . . . . . . . . . 144
4.49 Impact of the Number of Starting Faces . . . . . . . . . . . . . . . . . . . . 144
4.50 The Effectiveness of Smoothing . . . . . . . . . . . . . . . . . . . . . . . . 145
4.51 Regular Deployment: Matrix Shape . . . . . . . . . . . . . . . . . . . . . . 146
4.52 Regular Deployment: Cross Shape . . . . . . . . . . . . . . . . . . . . . . . 146

4.53 An Example of Random Deployment . . . . . . . . . . . . . . . . . . . . . . 146
4.54 Number of Faces in Different Placements . . . . . . . . . . . . . . . . . . . . 147
4.55 Error Distributions for Different Placements . . . . . . . . . . . . . . . . . . 147
4.56 Outdoor System Evaluation: Tracking A Mobile Robot . . . . . . . . . . . . 148
4.57 RF Signal Strength Based Tracking Results . . . . . . . . . . . . . . . . . . 148
xi
List of Abbreviations
AOA Angle of Arrival
AP Access Point
CCA Curvilinear Component Analysis
CCR Corner Cube Retro-Reflector
COG Center of Gravity
CRB Cram´er-Rao bound
DOA Direction of Arrival
DSSS Direct-Sequence Spread Spectrum
ESPRIT Estimation of Signal Parameters by Rotational Invariance Techniques
ETOA Elapsed Time of Arrival
FPGA Field-Programmable Gate Array
GeoD Geographical Distance
GPS Global Position System
KTD Kendall Tau Distance
LBC Localization Beyond Con nectivity
LLE Locally Linear Embedding
LOS Line of Sight
LP Linear Program
LS Least Squares
LUE Localization with Un controlled Events
MDS Multidimensional Scaling
ML Maximum Likelihood
MLE Maximum Likelihood Estimation

MSE Mean Squared Error
xii
MSP Multi-Sequence Positioning
MTT Multiple Target Tracking
MUSIC Multiple Signal Classification
PLM Positioning using Local Maps
PM Path Matching
PN Pseudo Noise
RF Radio Frequency
RFID Radio-Frequency Identification
RIM Radio Interferometry Measurement
RSD Regulated Signature Distance
RSS Radio Signal Strength
RT Ray Tracing
RWP Random Way-Point Model
SA Simulated Annealing
SBT Sequence Based Tracking
SD Signature Distance
SDP Semidefinite Programming
SeqD Sequence Distance
SVD Singular Value Decomposition
TDOA Time Difference of Arrival
TOA Time of Arrival
TOF Time of Flight
UDG Unit Disk Graph
UWB Ultra Wide Band
WSN Wireless Sensor Networks
xiii
Chapter 1
Introduction

Recent advancements in micro electronics, wireless communication, and low-cost sensor
technologies have enabled the emergence and evolution of wireless sensor networks (WSN)
as a new paradigm of computer networking [
1]. A wireless sensor network is composed of
a number of low-cost, tiny sensor nodes that are capable of sensing, data processing, short
range wireless communication, and even actuation [
2, 3]. Sensor nodes are deployed in areas
of interest to cooperatively monitor physical or environmental cond itions, such as sound,
vibration, temperature, pressu re, motion, electromagnetic disturbance, etc. Wireless sensor
networks have shown more and m ore popularity for both military tasks [
4, 5, 6, 7, 8, 9] and
civil applications, including industrial process monitoring and control [
10, 11, 12, 13, 14],
structure health monitoring [
15, 16, 17, 18, 19], habitat and environment monitoring [20,
21, 22, 23, 24, 25], health-care applications [26, 27, 28, 29, 30, 31], home automation [32,
33, 34, 35, 36], veh icle networks and intelligent transportation systems [37, 38, 39, 40, 41].
1.1 Localization and Its Challenges
In many aforementioned applications, the lo cation information of each sensor node in the
network is critical for the service. This is because users normally need to know not only
what happens, but also where interested events happen or where the target is. For example,
in battlefield surveillance [
6, 7, 8], the knowledge of where the enemy comes from can be
much more important than only knowing the appearance of the enemy; in a disaster relief
operation using WSN to locate survivors in a collapsed building, it is critical that sensors
report monitoring information along with their location [
23, 48, 52, 56 ]. On the other hand,
the position parameters of sensor nodes are assumed to be available in m any operations for
network management, such as routing where a family of geographical algorithms have been
proposed [

42, 43, 44], topology control that uses location information as a priori knowledge
to adjust network connectivity for energy saving [
45, 46 , 47], and security maintenance
where location information can be used to pr event malicious attacks [
50, 52].
1
Localization, as one of the most fundamental and widely applied middle-ware service in
wireless sensor networks [
72, 160], basically allows every node in the network to obtain its
location information, either the absolute geographic coordinates, or a relative position that
can be transformed to the absolute counterpart when necessary. Localization plays a key role
in many s en sor network app lications, however, itself is a tough problem [
53, 54, 55], because
of the demanding requirements for low cost, high energy efficiency, and small footprint at
the resource constrained sensor nod e side, as well as practical issues associated with network
deployments. We list major difficulties that challenge accurate and efficient positioning in
wireless sensor networks in the following.
• Cost and energy constraints for every sensor node. The requirement for a low-cost and
low-energy design at each sensor node prohibits localization with additional hardware
support. For example, GPS (Global Position System [
58]), which is the most widely
used technique in localization, can hardly be applicable for every sensor node in the
network [
57, 72]. Similarly, extra ranging modules, such as directional antennas,
electronic compass, laser rangers, video cameras, etc, are severely limited due to their
incompatible size, considerable cost or excessive power consumption [2, 51, 55, 57].
This in dicates that a localization solution must be sensor node f riendly, where features
of low-cost, energy efficient, and small footpr int are necessary.
• Scalability of the Network. A wireless sensor network could potentially be composed
of a large number of n odes [1, 2, 8, 53]. For instance, ExScal [62] and GreenOrbs [24]

have employed more than one thousand sensor nodes in their deployed networks. It
is also projected th at future wireless sensor networks may include thousands or even
millions of nod es [
63, 64, 65]. In all those networks, traditional per-n ode location pa-
rameters configuration [
59, 60] could be extremely costly, if not impossible. Therefore,
a localization design must be network scalable, meaning that it should be cost-effective
with both small and large scale systems.
• Harsh working environments. Wireless sensor networks are likely to be randomly
deployed in inaccessib le terrains and environments [
1, 56, 61], such as battlefield and
conflict zone [
6, 7], as well as inhabitable areas [23, 66, 67], etc. Furthermore, there is
normally no infrastructure (e.g., radio signals from wireless AP towers or power line
radiations as coordinate references) that can be used for localization p urpose. In this
case, s elf-organized localization without close-in human interference and calibration is
essential. In other words, the localization mechanism is highly preferred to function
as an autonomous system that is free of in-field manual calibration and extensive
environment profiling.
Many ideas have been proposed for node localization in WSN [
69, 70, 71, 72]. Based on
2
whether accurate ranging is required, there are generally two typ es of methods: (i) ran ge-
based lo calization, and (ii) range-free localization. Range-based approaches could achieve
go od accuracy but costly for requiring either per-node ranging hardware [76, 84, 100, 103,
107, 112, 119, 121, 157], or careful system calibration an d environment profiling [81, 83 ,
97, 98], and thus are not appropriate for large-scale outd oor sensor networks. Range-free
designs localize senor nodes based on simple sen sing, such as wireless connectivity [
165,
166, 169, 170, 178, 179, 180, 190], anchor (beacon) proximity [158, 161, 162, 163, 164], or

localization events detection [
191, 192, 194, 195]. Those methods feature reduced system
cost at the resource constrained sen s or node side, however, with less accuracy depending
on network topology, anchor density, and event d istribution.
Realizing the limitations of existing work for large-scale outdoor environments, we tried
to investigate practical solutions to bridge the gap between low cost and high accuracy for
range-free localization. In the following, we give an overview about objectives, designs and
contributions of this thesis.
1.2 Research Objectives and Contributions
Our work contributes two new types of range-free localization methods (i) uncontrolled
event-driven localization, and (ii) localization and tracking using signature distance. Fig.
1.1
gives an overview about the work of this thesis respect to the state-of-the-art, where filled
patches illustrate objectives and contribu tions of the above two methods .
Figure 1.1: Localize the Thesis in the State-of-the-art
1.2.1 Uncontrolled Event-driven Localization
The first contribution of this thesis is the releasing of a key precondition of range-free event-
driven localization. We evolve the event-driven localization from using precisely-controlled
3
events, through semi-controlled events, and finally to uncontrolled events, making it advance
substantially towards a practical system.
Event-dr iven localization makes use of events (e.g. ultrasound or air blast propagation,
optical or laser beam scan), that are generated and propagate across the network area.
With known time-spatial relationship embed ded in the event distribution, the location of
each sensor node can be obtained by mapping the time of event detection with the event
position at that time instance. Traditional event-dr iven solutions (e.g., Spotlight [192]
and Lighthouse [
191]) d emonstrated that long range and h ighly accurate localization can
be achieved simultaneously with little additional cost at sensor nodes. These benefi ts,
however, come along with an implicit assumption that localization events can be precisely

generated and distrib uted to a specified location at a specific time instance. In practice,
accurate event control is difficult to achieve, especially in outdoor scenarios when the terrain
is uneven, or the event distribution device is not well calibrated and its position is difficult
to maintain (e.g., the helicopter-mounted case in [
192]). We consider those methods as the
first generation of event-driven localization based on precisely-controlled events.
To address limitations of prior work, the first attempt in this thesis is a method called
multi-sequence positioning (MSP), for large-scale stationary sensor node localization in de-
ployments where an event source has line-of-sight to all sensors. The novel idea behind MSP
is to estimate each sensor node’s two-dimensional location by processing multiple easy-to-
get one-dimensional node sequences obtained through loosely-guided event distribution. As
the first to apply the concept of node sequence for localization in wireless sensor network,
MSP offers s everal benefits. First, compared to a range-based approach, the design does
not require ad ditional costly hardware. It works using sensors typically used by sen s or net-
work applications such as light and acoustic detectors that we specifically consid ered in our
design. Second, compared to other range-free methods, MSP requires only a small number
of anchors (theoretically as few as two), so high accuracy can be achieved economically
by introducing more events instead of more anchors. In other words , it provides a nice
trade-off between physical costs (anchors) and soft cost (events), while maintaining th e de-
sired localization accuracy. Last but the most notable, compared to previous event-driven
approaches [
191, 192], MSP does not need precise and sophisticated event distribution by
bringing in a small number of anchor nod es, an advantage that signifi cantly simplifies the
system d esign and reduces calibration cost.
We define MS P as the second generation of event-driven localization which relaxes the
requirement from precisely-controlled events to semi-controlled events [196]. This is because
although MSP does not require pr ecise event distribution control, it assumes the kn owledge
of event generation. As an important step further, a followed project investigates node
localization with uncontrolled events, or L UE in short.
Localization with totally uncontrolled events has two obvious benefits. First of all,

simple event generation mechanisms can be applied to make the system very flexible and
4
convenient to work with. Secondly, non-artificial natur al events could possibly be utilized
for localization purpose. The design of LUE extends and generalizes the methodology de-
veloped in previou s MSP, by estimating both event generation parameters and the location
area of each sensor node via processing n ode sequences obtained from uncontrolled event
distribution [197]. Besides a basic design, this thesis also introduces two interesting tech-
niques to further extract statistic inf ormation embedded in node sequences collected under
two situations: (i) sensor no de density is high; and (ii) abundant events are available,
respectively. The LUE design demonstr ates the possibility of accomplishing event-driven
localization with uncontrolled events, and thus provides us a potential option of achieving
node positioning through long-term natural ambient events.
1.2.2 Localization and Tracking using Signature Distance
The second contribution of the thesis is the invention of signature distance (SD) to achieve
range-free localization beyond connectivity with sub-hop resolution.
Our work is motivated by the finding that localization by means of mere connectivity
may underutilize the proximity information available from neighborhood sensing [
198]. Al-
though radio signal strength (RSS) is considered irregular in many situations due to the
unknown radio propagation loss, multi-path fading effects, hardware discrepancy, antenna
issues and so forth [
97, 98, 216, 217, 218, 224], our empirical study shows that in the ou t-
door op en -air scenario, radio signal strength weakens approximately monotonically with the
physical distance (in a statistic sense), especially from the viewpoint of a single node, where
RSS might provide some useful distance-related information telling about which n eighborin g
node is closer and which is further.
Starting from this findin g, we propose the idea of signature distance (SD) and its en -
hanced version regulated signature distance (RSD), as metrics for describing the proximity
among 1-hop neighboring nodes. The design of signature distance nicely utilizes the fact that
common views (i.e., local sensing results) among different nodes imply geographic proximity.

It contributes a novel range-free approach to extracting relative distance information f rom
neighborhood orderings that can be obtained easily from simple sensing and serve as unique
high-dimensional location signatures for s en s or nodes in the network. By applying RSD, for
the first time, distance relationships among neighboring nodes get quantified with su b-hop
resolution in a range-free manner. And with little additional cost, RSD can be conveniently
applied as a transparent supporting layer for many state-of-the-art connectivity-based lo-
calization solutions to achieve better accuracy. Moreover, the embedding of RSD provides
an interesting feature of robustness for localization under unevenly distributed radio prop-
agation path loss.
We then extend the concept of localization with sequence processing and the idea of
signature distance to mobile tracking applications. One of the major challenges in tracking
5
systems using wireless s en sor networks is that nodes’ detections of the moving target could
be unreliable due to a combination of factors such as irregular signal patterns emitted from
the target, in-field environment noise, sensing irregularity and so on [219]. To address
this issue, this thesis proposes a new mobile target tracking mechanism that accomplishes
the tracking task by processing a series of detection node sequences that are utilized as
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 finding the sh ortest path in a graph [
220], which is equ ivalent to the optimal
matching of a series of node sequences, by applying the space and time domain con straints
that are universally appropriate for any moving object.
As a range-free approach, localization by processing node sequences provides two unique
benefits. First of all, the system is more robust to random sensing noise. On one hand,
as a range-free solution, ordering of nodes according to their detections effectively prevents
errors from common sensing bias among nodes; on the other hand, single node’s sensin g error
becomes less detrimental to the tracking system that depends on the statistical information
embedded in whole node sequence rath er than sensing results from a single node. Secondly,
tracking by node sequen ce processing provides a layer of abstraction [

198]. As long as the
node sequences obtained reflect the relative distance relationships among the target and the
sensor nodes with known positions, specific format of the physical s en s ing modality (e.g.,
infrared, isotope and radio radiation, acoustic or seismic wave) is irrelevant to the tracking
algorithm. Therefore, the design is quite generic, flexible, and compatible with different
sensing modalities.
1.3 Organization of the Manuscript
The rest of the thesis is organized as follows. Chapter
2 provides a survey about local-
ization in wireless sensor networks. Chapter
3 concentrates on the topic of event-driven
localization, and presents designs of (i) multi-sequence positin g (MSP ), and (ii) localization
with uncontrolled events (LUE). Their superior accuracy and flexibility over traditional
event-driven solutions are demonstrated through multiple test-bed experiments as well as
extensive simulation. Chapter
4 introduces the idea and app lication of signature distance
by p resenting designs of (i) localization beyond connectivity (LBC), and (ii) sequ en ce-based
tracking with unreliable sen sing results (SBT). Results from simulation and system evalu-
ation validate the performance gain of our design comparing with previous work. Finally,
Chapter
5 provides concluding remarks, limitation discussion and an outlook on future
research directions.
6
Chapter 2
Background and Related Work
Localization in wireless sensor networks has attracted a lot of research efforts in recent
years [
68, 69, 70, 71 , 72]. The early common ground achieved is that GPS [58] is not an
almighty solution for sensor network based applications, because of its expensive cost, high
energy consump tion, and rigid deployment constraints [57, 68, 71, 72, 158]. As a result,

researchers have continued investigating innovative ideas to realize practical, inexpensive,
flexible and robust localization in wireless sensor networks.
Most of the proposed localization solutions for WSN can be generally categorized into
two classes: (i) range-based localization and (ii) r ange-free localization. Their major dif-
ference lies in whether ranging-efforts are required at sensor nodes in the network. In
the following, we give a survey about techniques developed by range-based and range-free
positioning in Section
2.1 and Section 2.2, respectively.
2.1 Range-based Localization
The methodology of range-based localization, such as Cricket [100], Rad ar [81], APS [135]
PinPoint [
121], TPS [103], RIPS [152], BeepBeep [108], SpinLoc [157], etc, depends on
accurate ranging results among in-field sensor nod es. In other words, most of those designs
are based on fine-grained point-to-point d istance, angle, or relative velocity measurements to
identify n odes’ coordinates. After obtaining ranging results, geographical calculations such
as triangulation [53, 79, 81, 179], bilateration [73], multilateration [68, 76, 77, 78], and convex
optimization (e.g., Semidefinite Programming (SDP) [
74, 75]) are applied to compu te the
best-effort position estimations of sensor nodes in the network. In the following subsections,
we explain range-b ased methods from the perspective of four types of elementary ranging
modalities, includ ing (i) signal strength, (ii) time of fly, (iii) angle of arrival, and (iv)
radio interferometry. Note that this classification does not prevent designs using hybrid
measurements for better accuracy performance and system flexibility [
83, 157, 145].
7
2.1.1 Signal Strength
In many ways, radio s ignal strength (RSS) is considered as an appealing modality for range
estimation in w ireless (sensor) networks, mostly because RSS inf ormation can be obtained
at almost no additional cost with each radio message sent and received [
96, 98]. The major

challenge is that radio signal str en gth is so unpredictable [
80, 216, 217, 218, 224, 225],
where reflecting and attenuating caused by objects in the environment can have much
larger effects on RSS than distance, making it difficult to infer distance from RSS without
a detailed model of the physical environment [
96, 97, 98].
To effectively utilize RSS for localization, two directions have been investigated: (i)
directly infer distance from RSS m easurements [
82, 83, 85, 86], and (ii) radio profiling and
radio-frequency (RF) fingerprint matching [
81, 87, 88, 89, 90, 93 , 94]. In the following, we
summarize basic ideas for typical examples of the above two types of methods .
2.1.1.1 Directly Infer Distance from RSS Measurements
As a pioneering work of RSS-based localization, SpotOn [
82] demonstrates mobile sensor
node (RFID tags) localization with simple RSS sensing results. This work considers that
the received signal strength (RSS) is a function of the physical distance (d) between th e
mobile sensor and the powerful base station (r adio readers) as
RSS(d) = 0.0236 ·d
2
− 0.629 ·d + 4.781 (2.1)
which is derived from empir ical data, and RSS in Eq.
2.1 is measured in an abstract unit [82].
Given RSS measurements and corresponding mapped distance estimations for multiple base
stations, a central server then triangulates the precise position of th e tagged obj ect. Spo-
tOn [
82] provides a simple solution for indoor mobile localization with sub-meter accuracy.
As an early system, this system suffers from multiple problems such as requiring environ-
ment profi ling, depending on a large number of base stations, and being sensitive to errors
caused by radio irregularity [

80].
To overcome the uncertainty of RSS and reduce the system cost, Patwari, et al made
dual effectors in [
86]. First of all, unlike Eq. 2.1, which is a deterministic model for RSS
under different distance, [
86] applies a widely observed statistic model to describe radio
propagation. The expected received signal strength, denoted as
¯
P in [
86], is related to the
distance d with
¯
P (d) = Π
0
− 10 · n
p
· log
10

d
∆
0

(2.2)
where n
p
is the radio path-loss factor (also called the fading factor or attenuation factor [
221,
222, 225]), typically between 2 and 6 [86, 221, 225], and Π
0

is the received power (in dBm)
at a short reference distance ∆
0
. Staring from Eq. 2.2, [86] derives a bias-corrected pseu do
8
maximum likelihood estimator (pseudo-MLE) for the distance as
δ
BC
i,j
=
∆
0
C
· 10
n
0
−P
i,j
10·n
p
, where C = exp



1
2 ·

10·n
p
σ

dB
·log10

2



(2.3)
In Eq.
2.3, P
i,j
is the measured RSS with zero mean Gaussian noise of variance σ
2
dB
.
The second contribution of this work is the application and comparison among three
manifold learning algorithms for sensor node localization [165, 170], including Isomap [165],
Laplacian Eigenmap [
173] and dwMDS [85]. Those algorithms require less numb er of anchor
nodes that are used for coordin ates rotation and scaling [
86], and achieve better localization
accuracy throughout the network because of using aggregated information . Nevertheless,
those m ethods can not automatically recognize and remove measurement outliers during
processing, resulting in a degraded positioning performance.
To overcome the non-robustness to significant n oise of previous designs, a recent work
SISR [
95] proposes an error-tolerant method to automatically identify “bad nodes” and
“bad links” arising from these errors , so that they receive less weight in the least-square
localization process [
83, 84]. The basic idea is to apply a residual shaping fu nction to de-

emphasize the impact of measurement outliers in the overall cost function. Specifically,
instead of op timizing the sum of squared residu es F, namely,
F =

i,j
r(i, j)
2
, where r(i, j) =
ˆ
d
i,j
− d
i,j
(2.4)
where
ˆ
d
i,j
is the distance estimated by a least-squares estimator and d
i,j
is the direct RSS-
to-distance measurement b etween node i and node j. SISR solves an optimization problem
F =

i,j
s(i, j), where s(i, j) =

αr(i, j)
2
if |r(i, j)| < τ

ln(|r(i, j)| −u) −v otherwise
(2.5)
In the above, u = τ −
1
2ατ
, v = ln(
1
2ατ
) − ατ
2
. α and τ are parameters to be configur ed to
control the overall shape of the cost function. Based on Eq.
2.5, SISR [95] is able to suppress
and even discount the influence of measurement outliers and achieve notable performance
gain while adding little ultra cost at the sensor node side.
To determine values of α and τ in Eq.
2.5, simulations in [95] suggest iterative refinement
that is relatively costly in terms of computation. In addition, as most of previous work,
SISR depends on in-field calibration to determine environmental parameters for converting
RSS values to actual physical distance for unknown radio fading factor a and bias b in the
following Eq.
2.6 [95]
RSSI(d) = 10 ·log
10
(d
a
) + b (2.6)
9
2.1.1.2 RF Profiling and F ingerprint Matching
Motivated by the fact th at direct distance estimation from received signal stren gth is found

to be ineffective in the indoor s cenario [
81], many localization solutions use RSS f or po-
sitioning by employing a technique called RF profiling [
81, 87, 88, 89 , 90, 93, 94]. Those
methods work by constructing a map of signal strength about the overage area during the
deployment phase of the network. The RSS values recorded at each position in the area
are collected from all available anchor nodes. The record for a particular position is called
the RF fingerprint of that position. At a later time, a node with unknown location can be
localized by matching the detected RF fingerprint at its current position to the profiles of
the positions recorded in the map.
When localizing a target node, RADAR [
81] searches the map of RSS profiles to pick
the location that best matches the observed signal strength of the target node. The metric
used for RF fi ngerprint comp arison is th e Euclidean distance in a special signal space. For
example, for a candidate position j in the map, the distance between the measured RSS
values from N anchor nodes (i.e., {RSS
′
i
} | i = 1, 2, ···N) and the RF profile for position
j in the map (i.e., {RSS
j
i
} | i = 1, 2, ···N), denoted as D
j
, can be computed with
D
j
=
N


i=1

(RSS
j
i
− RSS
′
i
)
2
(2.7)
where N is the number of in-filed anchors available at j. By applying the empirical method,
RADAR can provide a good localization performance with the median distance err or ranging
from 2 to 3 meters [
81].
Building an empirical map can be tedious and costly. RADAR provides an altern a-
tive of constructing a virtual map by applying carefully derived radio propagation models.
Specifically, a Wall Attenuation Factor (WAF) model is proposed in [
81] as follows
P (d) = P (d
0
) − 10 ·n ·log

d
d
0

−

nW ·W AF nW < C

C ·WAF nW ≥ C
(2.8)
where n is the attenuation factor; P(d
0
) is the signal power at a reference distance d
0
; d
is the transmitter-receiver separation distance; C is the maximum number of obstructions
(walls) up to which the attenuation factor makes a difference; nW is the number of obstruc-
tions (walls) between the transmitter and the receiver; and W AF is the wall attenuation
factor [137, 222]. Unfortunately, results in [81] reported that the empirical method out-
performed the use of virtual map. The key weakness of the alternative strategy is that
the estimated map from proposed radio propagation model may not fit well the actual
environment.
To overcome d ifficu lties with the R ADAR system, Ji, et al developed a more sophis-
ticated ind oor localization system called ARIADNE [
90]. ARI ADNE advances the radio
10