Sensor Networks (eBook)

Preface 8
Contents 10
Contributors 12
Part I Theory 15
Competition and Collaboration in Wireless Sensor Networks 16
H. Vincent Poor 16
1 Introduction 16
2 Energy Games in Multiple-Access Networks 17
3 Collaborative Inference 22
4 Conclusions 27
References 27
Distributed and Recursive Parameter Estimation 29
Srinivasan Sundhar Ram, Venugopal V. Veeravalli, and Angelina Nedic 29
1 Introduction 29
2 Preliminaries 30
3 Simple Non-linear Regression 32
3.1 Algorithms 33
3.1.1 Cyclic Incremental Recursive Algorithm 33
3.1.2 Markov Incremental Recursive Algorithm 34
3.1.3 Diffusive Nonlinear Recursive Algorithm 35
3.2 Convergence of the Algorithms 36
3.3 Effect of Quantization 38
3.4 Special Case: Linear Regression 40
3.5 Special Case: Accurate Model Sets 41
4 Gaussian Linear State Space Model Sets 42
4.1 Convergence of the Algorithm 43
5 Application: Determining the Source of a Diffusion Field 44
5.1 Point Source and Constant Intensity Model Sets 44
5.1.1 Numerical Results 45
5.2 Point Source and Time-Varying Intensity Model Sets 46
5.2.1 Numerical Results 47
6 Discussion 48
References 49
Self-Organization of Sensor Networks with HeterogeneousConnectivity 51
Arun Prasath, Abhinay Venuturumilli, Aravind Ranganathan, and Ali A. Minai 51
1 Introduction 51
2 Background and Motivation 53
3 System Description 55
3.1 Whisperers and Shouters 56
4 Self-Organization Algorithms 57
4.1 Basic Self-Organization (BSO) Algorithm 57
4.2 Self-Organization Algorithm A 58
4.3 Self-Organization Algorithm B 59
4.4 Self-Organization Algorithm C 59
5 Simulation, Results and Discussion 60
5.1 Simulations 60
5.2 Results and Discussion 61
5.2.1 Performance Comparison Between Algorithms 61
5.2.2 Comparison with Non-optimized Networks 61
5.2.3 Robustness Evaluation 65
6 Conclusion 67
References 68
Cooperative Strategies in Dense Sensor Networks 72
Anna Scaglione, Y.-W. Peter Hong, and Birsen Sirkeci Mergen 72
1 The Role of Correlated Information in Sensor Systems 73
1.1 Feedback and Correlation 74
2 Sensor Data Model 75
3 A Cooperative Broadcast Mechanism for Network Feedback 77
3.1 The OR Broadcast Channel 78
4 Channel Coding via Query-and-Response Strategies 79
5 Optimized Recursive Group Testing Algorithm 80
6 Binary Tree Splitting Algorithm 82
7 Conclusions 84
References 84
Multipath Diversity and Robustness for Sensor Networks 86
Christina Fragouli, Katerina Argyraki, and Lorenzo Keller 86
1 Introduction 86
2 What is a Collection Protocol? 87
2.1 Path Cost and Channel Quality 88
3 Routing on a Tree 89
4 From Tree to Multipath Routing 91
4.1 Topology Construction 91
4.1.1 Disjoint Paths 91
4.1.2 Algorithmic Complexity of Disjoint-Path Construction 92
4.1.3 Braided Paths 94
4.2 Topology Usage 95
4.2.1 Replicate Transmissions 95
4.2.2 Independent Transmissions 96
4.2.3 Erasure Coding 96
4.2.4 Path-Selective Routing 96
4.3 Room for Improvement 97
5 What Is Network Coding 97
5.1 Network Coding in Practice 99
5.2 Randomized Network Coding 99
5.2.1 Generations and Coding Vectors 100
5.2.2 Subspace Coding 101
6 Network Coding for Sensor Networks 102
6.1 Code Design 104
6.2 Opportunistic Broadcasting with Network Coding 106
7 Conclusions 108
References 108
Part II Theory and Practice 111
Data Aggregation in Wireless Sensor Networks: A Multifaceted Perspective 112
Sergio Palazzo, Francesca Cuomo, and Laura Galluccio 112
1 Background 112
1.1 Terminology 115
1.2 Typologies of Data Aggregation 117
2 Perspectives for a Taxonomy of Data Aggregation 119
3 Layer-Centric Taxonomy 120
4 Ingredient-Centric Taxonomy 121
5 Performance-Centric Taxonomy 122
6 Information-Centric Taxonomy 123
6.1 Description of Information 125
6.2 Information Propagation 129
6.2.1 Medium Access Management 129
6.2.2 Packet Scheduling 131
6.2.3 Propagation Path Structure 132
6.3 Preservation of Information 138
6.3.1 Preservation of Integrity 139
6.3.2 Preservation from Security Threats 143
7 Conclusions 148
References 149
Robust Data Dissemination for Wireless Sensor Networks in Hostile Environments 153
Jun Lu, Yi Pan, Satoshi Yamamoto, and Tatsuya Suda 153
1 Introduction 153
2 Related Work 154
3 RObust dAta Dissemination (ROAD) 156
3.1 Assumptions 156
3.2 Scheme Description 157
3.2.1 Data Publishing 158
3.2.2 Data Retrieval 161
3.2.3 Trajectory Maintenance 161
3.3 Extensions 162
3.3.1 Double-Sided Hole Circumvention 162
3.3.2 ROAD for Generic Trajectories 163
3.3.3 Time-Based Load Balancing 163
4 Simulation Evaluation 164
4.1 Communication Overhead 164
4.2 Response Time 167
4.3 Reliability of Data Retrieval 167
4.4 Robustness Against Large Scale Sensor Failures 168
5 Conclusion 172
References 173
Markov Decision Process-Based Resource and Information Management for Sensor Networks 174
David Akselrod, Thomas Lang, Michael McDonald, and Thiagalingam Kirubarajan 174
1 Introduction 174
2 Decision-Based Resource and Information Management 179
2.1 Problem Formulation 179
2.2 Sensor Management as a Decision Mechanism 179
2.3 Policy Update and Termination Criteria 181
3 Decision-Based Multitarget Tracking 182
3.1 MDP-Based Structure for Multisensor Multitarget Tracking 182
3.2 Expected Information Gain-Based Reward Structure of MDP for Sensor Management 184
4 Multi-Level Hierarchy of MDPs for Sensor Management 187
5 Dynamic Element Matching-Based Modified ValueIteration Algorithm 189
5.1 Drawbacks of Finding the Optimal Policy of MDP 189
5.2 Dynamic Element Matching 190
5.3 Modified Value Iteration Method 191
6 Distributed Data Fusion Architecture 193
6.1 Issues in Distributed Data Fusion 193
6.2 Data Fusion Control as a Decision-Based Approach 196
6.3 Data Lookup 196
6.4 MDP-Based Multisensor Fusion for Multitarget Tracking 197
6.4.1 Set of States: S 199
6.4.2 Set of Actions: A 199
6.4.3 Transition Probabilities: P 200
6.4.4 Real-Valued Reward Function on States: R 200
7 Distributed Tracking Algorithms Implementing MDP-Based Data Fusion System 201
7.1 Associated Measurements Fusion 201
7.2 Track-to-Track Fusion 202
7.3 Tracklet Fusion 203
8 Simulation Results 204
8.1 Resource Management 204
8.2 Information Management 209
9 Communication Data Rate and Computational Load in Distributed Tracking Algorithms 213
9.0.1 Communication and Computational Load Results 215
10 Conclusions 217
References 219
Part III Practice 224
Deployment Techniques for Sensor Networks 225
Jan Beutel, Kay Römer, Matthias Ringwald, and Matthias Woehrle 225
1 Introduction 225
2 Wireless Sensor Network Deployments 226
2.1 Great Duck Island 227
2.2 A Line in the Sand 228
2.3 Oceanography 229
2.4 GlacsWeb 229
2.5 Structural Health Monitoring 229
2.6 Pipenet 230
2.7 Redwood Trees 230
2.8 LOFAR-agro 231
2.9 Volcanoes 231
2.10 Soil Ecology 232
2.11 Luster 232
2.12 SensorScope 233
3 Deployment Problems 233
3.1 Node Problems 234
3.2 Link Problems 234
3.3 Path Problems 235
3.4 Global Problems 236
3.5 Discussion 237
4 Understanding the System 237
4.1 Hardware 237
4.2 Software 239
4.3 Communication 239
4.4 Environment 240
5 Node Instrumentation 240
5.1 Software Instrumentation 240
5.2 Hardware Instrumentation 242
6 Network Instrumentation Methods 244
7 Analyzing the System 246
7.1 Monitoring and Visualization 246
7.2 Inferring Network State from Node States 247
7.3 Failure Detection 247
7.4 Root Cause Analysis 248
7.5 Node-Level Debugging 249
7.6 Replay and Checkpointing 250
8 Concluding Remarks 251
References 251
Static and Dynamic Localization Techniques for Wireless Sensor Networks 255
Jean-Michel Dricot, Gianluca Bontempi, and Philippe De Doncker 255
1 Introduction 255
2 Static Localization Techniques -- Range-free 257
2.1 Weighted Centroid 258
2.2 Bounding Box 259
2.3 Point-in-Triangle 260
3 Distance Estimation Techniques 262
3.1 Estimation of the Distance Based on the Received Power 262
3.2 Angle-of-Arrival 263
3.3 Time-of-Flight 265
4 Static Localization Techniques -- Range-based 266
4.1 Circular Lateration and Multilateration 266
4.2 Hyperbolic Lateration 269
5 Dynamic Localization and Tracking 270
5.1 Kalman Filtering Loop 272
5.2 Filtering Process when the Speed and the Position are Unknown 273
5.3 Filtering Process for an Unknown Position and an Approximated Speed 275
6 Accuracy and Precision 275
7 Advanced Localization Techniques -- Data Fusion by means of Machine Learning 277
7.1 Introduction 278
7.2 Observation of the Sensor Data -- Motion vs. Static Classification 278
7.3 A Data Fusion Approach for the Kalman Filter 281
7.4 Performance Evaluation of the Fusion Schemes 282
7.5 Fusion of Localization Estimators 283
8 Open Issues in Localization and Conclusion 284
References 286
Enhancing Underwater Acoustic Sensor Networks Using Surface Radios: Issues, Challenges and Solutions 288
Zhong Zhou, Hai Yan, Saleh Ibrahim, Jun-Hong Cui, Zhijie Shi,and Reda Ammar 288
1 Introduction 288
2 UASN-MG Architecture and Its Benefits 290
3 Research Challenges and Design Issues 292
4 Design Examples 294
4.1 Optimal Surface Node Deployment 294
4.1.1 Network Model 294
4.1.2 Problem Formulation 295
4.1.3 Simulation Results 298
4.2 Efficient Routing Protocol 299
4.2.1 Network Model 300
4.2.2 Protocol Overview 300
4.2.3 Redundant Packet Suppression 301
4.2.4 Holding Time Calculation 301
4.2.5 Protocol Summary 303
4.2.6 Simulation Results 304
4.3 Cross Layer Design 305
4.3.1 Multi-Path Routing 306
4.3.2 Source Initiated Power-Control Transmission 307
4.3.3 Destination Packet Combining 307
4.3.4 Optimal Energy Distribution 308
4.3.5 Simulation Results 309
5 Conclusions 310
References 311
Communication Through Soil in Wireless Underground Sensor Networks -- Theory and Practice 313
M. Can Vuran and Agnelo R. Silva 313
1 Introduction 313
2 Classification of Underground Communication Networks 315
3 Recent Developments 316
4 Underground Channel Model: The Theory 318
4.1 Signal Propagation Through Soil 320
4.2 Underground Channel Characteristics 323
4.2.1 Reflection from Ground Surface 324
4.2.2 Multi Path Fading and Bit Error Rate 327
4.3 Effects of Volumetric Water Content Variations in Soil 329
4.3.1 Long-Term VWC Effects 330
4.3.2 Transient VWC Effects 333
5 Underground Experiments -- The Practice 335
5.1 Antenna Orientation 337
5.2 Effects of Burial Depth 339
5.3 Effects of Inter-Node Distance 342
5.4 Temporal Characteristics 342
5.5 Effects of Soil Moisture 344
6 Open Research Issues 347
6.1 Energy Efficiency 347
6.2 Topology Design 348
6.3 Operating Frequency 348
6.4 Cross-Layer and Environment-Aware Protocol Design 349
References 350
Body Sensor Networks for Sport, Wellbeing and Health 352
Douglas McIlwraith and Guang-Zhong Yang 352
1 Introduction 352
1.1 Body Sensor Networks 354
1.1.1 Network Topology 354
1.1.2 Requirements of Body Sensor Networks 356
1.1.3 Operating Modes 357
1.2 Sensors and Modalities of Sensing 358
1.2.1 Biomechanical 359
1.2.2 Ambient and Visual Sensors 360
1.2.3 Respiratory and Circulatory Monitoring 361
1.2.4 Neural, Biological and Chemical Analysis 362
1.2.5 Implants, Ingests, Actuation and Feedback 363
1.3 Directions and Challenges 365
2 Data Modelling and Pattern Recognition 366
2.1 Signal Processing and Reconditioning 367
2.2 Sensor Placement and Feature Selection 368
2.3 Data Modelling and Inference 368
2.4 Context Awareness 370
3 Emerging Applications 371
3.1 Sport 371
3.2 Wellbeing 375
3.3 Healthcare 376
4 Conclusions 377
References 378
Index 385