Wireless Sensor Networks Signal Processing and Communications Perspectives

by Swami, Ananthram; Zhao, Qing; Hong, Yao-Win; Tong, Lang

• ISBN13: 9780470035573
• ISBN10: 0470035579
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-11-12
• Publisher: WILEY

Summary

A wireless sensor network (WSN) uses a number of autonomous devices to cooperatively monitor physical or environmental conditions via a wireless network. Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection. Technological advancements, coupled with lowering costs, suggest that wireless sensor networks will have a significant impact on 21st century life. The design of wireless sensor networks requires consideration for several disciplines such as distributed signal processing, communications and cross-layer design. Wireless Sensor Networks: Signal Processing and Communications focuses on the theoretical aspects of wireless sensor networks and offers readers signal processing and communication perspectives on the design of large-scale networks. It explains state-of-the-art design theories and techniques to readers and places emphasis on the fundamental properties of large-scale sensor networks. Wireless Sensor Networks: Signal Processing and Communications : Approaches WSNs from a new angle - distributed signal processing, communication algorithms and novel cross-layer design paradigms. Applies ideas and illustrations from classical theory to an emerging field of WSN applications. Presents important analytical tools for use in the design of application-specific WSNs. Wireless Sensor Networks will be of use to signal processing and communications researchers and practitioners in applying classical theory to network design. It identifies research directions for senior undergraduate and graduate students and offers a rich bibliography for further reading and investigation.

Author Biography

Ananthram Swami, Army Research Laboratory, USA.

Qing Zhao, Assistant Professor, University of California, USA.

Yao-Win Hong, Assistant Professor, National Tsing hua University, Taiwan, ROC.

Lang Tong, Professor, Cornell University, USA.

Table of Contents

List of Contributors	p. xiii
Introduction	p. 1
Fundamental Properties and Limits	p. 7
Information-theoretic Bounds on Sensor Network Performance	p. 9
Introduction	p. 9
Sensor Network Models	p. 10
The Linear Gaussian Sensor Network	p. 12
Digital Architectures	p. 14
Distributed Source Coding	p. 14
Distributed Channel Coding	p. 24
End-to-end Performance of Digital Architectures	p. 31
The Price of Digital Architectures	p. 33
Bounds on General Architectures	p. 36
Concluding Remarks	p. 38
Bibliography	p. 39
In-Network Information Processing in Wireless Sensor Networks	p. 43
Introduction	p. 43
Communication Complexity Model	p. 46
Computing Functions over Wireless Networks: Spatial Reuse and Block Computation	p. 49
Geographical Models of Wireless Communication Networks	p. 49
Block Computation and Computational Throughput	p. 51
Symmetric Functions and Types	p. 51
The Collocated Network	p. 52
Subclasses of Symmetric Functions: Type-sensitive and Type-threshold	p. 53
Results on Maximum Throughput in Collocated Networks	p. 55
Multi-Hop Networks: The Random Planar Network	p. 57
Other Acyclic Networks	p. 59
Wireless Networks with Noisy Communications: Reliable Computation in a Collocated Broadcast Network	p. 60
The Sum of the Parity of the Measurements	p. 61
Threshold Functions	p. 62
Towards an Information Theoretic Formulation	p. 62
Conclusion	p. 65
Bibliography	p. 66
The Sensing Capacity of Sensor Networks	p. 69
Introduction	p. 69
Large-Scale Detection Applications	p. 70
Sensor Network as an Encoder	p. 71
Information Theory Context	p. 73
Sensing Capacity of Sensor Networks	p. 74
Sensor Network Model with Arbitrary Connections	p. 74
Random Coding and Method of Types	p. 76
Sensing Capacity Theorem	p. 78
Illustration of Sensing Capacity Bound	p. 83
Extensions to Other Sensor Network Models	p. 84
Models with Localized Sensing	p. 86
Target Models	p. 87
Conclusion	p. 88
Bibliography	p. 89
Law of Sensor Network Lifetime and Its Applications	p. 93
Introduction	p. 93
Law of Network Lifetime and General Design Principle	p. 94
Network Characteristics and Lifetime Definition	p. 94
Law of Lifetime	p. 95
A General Design Principle For Lifetime Maximization	p. 96
Fundamental Performance Limit: A Stochastic Shortest Path Framework	p. 96
Problem Statement	p. 97
SSP Formulation	p. 98
Fundamental Performance Limit on Network Lifetime	p. 100
Computing the Limiting Performance with Polynomial Complexity in Network Size	p. 101
Distributed Asymptotically Optimal Transmission Scheduling	p. 103
Dynamic Protocol for Lifetime Maximization	p. 104
Dynamic Nature of DPLM	p. 105
Asymptotic Optimality of DPLM	p. 106
Distributed Implementation	p. 107
Simulation Studies	p. 108
A Brief Overview of Network Lifetime Analysis	p. 113
Conclusion	p. 114
Bibliography	p. 114
Signal Processing for Sensor Networks	p. 117
Detection in Sensor Networks	p. 119
Centralized Detection	p. 120
The Classical Decentralized Detection Framework	p. 121
Asymptotic Regime	p. 124
Decentralized Detection in Wireless Sensor Networks	p. 124
Sensor Nodes	p. 125
Network Architectures	p. 126
Data Processing	p. 127
Wireless Sensor Networks	p. 127
Detection under Capacity Constraint	p. 129
Wireless Channel Considerations	p. 131
Correlated Observations	p. 134
Attenuation and Fading	p. 136
New Paradigms	p. 139
Constructive Interference	p. 139
Message Passing	p. 140
Cross-Layer Considerations	p. 141
Energy Savings via Censoring and Sleeping	p. 141
Extensions and Generalizations	p. 142
Conclusion	p. 143
Bibliography	p. 144
Distributed Estimation under Bandwidth and Energy Constraints	p. 149
Distributed Quantization-Estimation	p. 150
Maximum Likelihood Estimation	p. 150
Known Noise pdf with Unknown Variance	p. 152
Unknown Noise pdf	p. 156
Lower Bound on the MSE	p. 160
Estimation of Vector Parameters	p. 160
Colored Gaussian Noise	p. 162
Maximum a Posteriori Probability Estimation	p. 165
Mean-Squared Error	p. 166
Dimensionality Reduction for Distributed Estimation	p. 167
Decoupled Distributed Estimation-Compression	p. 168
Coupled Distributed Estimation-Compression	p. 171
Distortion-Rate Analysis	p. 172
Distortion-Rate for Centralized Estimation	p. 174
Distortion-Rate for Distributed Estimation	p. 178
D-R Upper Bound via Convex Optimization	p. 180
Conclusion	p. 181
Further Reading	p. 182
Bibliography	p. 183
Distributed Learning in Wireless Sensor Networks	p. 185
Introduction	p. 185
Classical Learning	p. 188
The Supervised Learning Model	p. 188
Kernel Methods and the Principle of Empirical Risk Minimization	p. 189
Other Learning Algorithms	p. 191
Distributed Learning in Wireless Sensor Networks	p. 192
A General Model for Distributed Learning	p. 193
Related Work	p. 196
Distributed Learning in WSNs with a Fusion Center	p. 197
A Clustered Approach	p. 198
Statistical Limits of Distributed Learning	p. 198
Distributed Learning in Ad-hoc WSNs with In-network Processing	p. 201
Message-passing Algorithms for Least-Squares Regression	p. 202
Other Work	p. 208
Conclusion	p. 208
Bibliography	p. 209
Graphical Models and Fusion in Sensor Networks	p. 215
Introduction	p. 215
Graphical Models	p. 216
Definitions and Properties	p. 217
Sum-Product Algorithms	p. 218
Max-Product Algorithms	p. 219
Loopy Belief Propagation	p. 220
Nonparametric Belief Propagation	p. 220
From Sensor Network Fusion to Graphical Models	p. 222
Self-Localization in Sensor Networks	p. 222
Multi-Object Data Association in Sensor Networks	p. 224
Message Censoring, Approximation, and Impact on Fusion	p. 226
Message Censoring	p. 227
Trading Off Accuracy for Bits in Particle-Based Messaging	p. 228
The Effects of Message Approximation	p. 230
Optimizing the Use of Constrained Resources in Network Fusion	p. 233
Resource Management for Object Tracking in Sensor Networks	p. 234
Distributed Inference with Severe Communication Constraints	p. 239
Conclusion	p. 243
Bibliography	p. 246
Communications, Networking and Cross-Layered Designs	p. 251
Randomized Cooperative Transmission in Large-Scale Sensor Networks	p. 253
Introduction	p. 253
Transmit Cooperation in Sensor Networks	p. 254
Physical Layer Model for Cooperative Radios	p. 254
Cooperative Schemes with Centralized Code Assignment	p. 256
Randomized Distributed Cooperative Schemes	p. 257
Randomized Code Construction and System Model	p. 257
Performance of Randomized Cooperative Codes	p. 260
Characterization of the Diversity Order	p. 260
Simulations and Numerical Evaluations	p. 263
Analysis of Cooperative Large-scale Networks Utilizing Randomized Cooperative Codes	p. 265
Numerical Evaluations and Further Discussions	p. 268
Conclusion	p. 272
Appendix	p. 272
Bibliography	p. 274
Application Dependent Shortest Path Routing in Ad-Hoc Sensor Networks	p. 277
Introduction	p. 277
Major Classifications	p. 279
Fundamental SPR	p. 279
Broadcast Routing	p. 280
Static Shortest Path Routing	p. 281
Adaptive Shortest Path Routing	p. 289
Other Approaches	p. 289
SPR for Mobile Wireless Networks	p. 290
Broadcast Methods	p. 290
Shortest Path Routing	p. 291
Other Approaches	p. 293
SPR for Ad-Hoc Sensor Networks	p. 294
A Short Survey of Current Protocols	p. 294
An Argument for Application Dependent Design	p. 296
Application Dependent SPR: An Illustrative Example	p. 296
Conclusion	p. 305
A Short Review of Basic Graph Theory	p. 305
Undirected Graphs	p. 305
Directed Graphs	p. 306
Bibliography	p. 307
Data-Centric and Cooperative MAC Protocols for Sensor Networks	p. 311
Introduction	p. 311
Traditional Medium Access Control Protocols: Random Access and Deterministic Scheduling	p. 313
Carrier Sense Multiple Access (CSMA)	p. 313
Time-Division Multiple Access (TDMA)	p. 314
Energy-Efficient MAC Protocols for Sensor Networks	p. 315
Data-Centric MAC Protocols for Sensor Networks	p. 318
Data Aggregation	p. 318
Distributed Source Coding	p. 319
Spatial Sampling of a Correlated Sensor Field	p. 321
Cooperative MAC Protocol for Independent Sources	p. 323
Cooperative MAC Protocol for Correlated Sensors	p. 327
Data Retrieval from Correlated Sensors	p. 328
Generalized Data-Centric Cooperative MAC	p. 336
MAC for Distributed Detection and Estimation	p. 340
Conclusion	p. 343
Bibliography	p. 344
Game Theoretic Activation and Transmission Scheduling in Unattended Ground Sensor Networks: A Correlated Equilibrium Approach	p. 349
Introduction	p. 349
UGSN Sensor Activation and Transmission Scheduling Methodology	p. 350
Fundamental Tools and Literature	p. 351
Unattended Ground Sensor Network: Capabilities and Objectives	p. 353
Practicalities: Sensor Network Model and Architecture	p. 354
Energy-Efficient Sensor Activation and Transmission Control	p. 355
Sensor Activation as the Correlated Equilibrium	p. 358
From Nash to Correlated Equilibrium - An Overview	p. 358
Adaptive Sensor Activation through Regret Tracking	p. 360
Convergence Analysis of Regret-based Algorithms	p. 363
Energy-Efficient Transmission Scheduling	p. 365
Outline of Markov Decision Processes and Supermodularity	p. 366
Optimal Channel-Aware Transmission Scheduling as a Markov Decision Process	p. 367
Optimality of Threshold Transmission Policies	p. 370
Numerical Results	p. 374
UGSN Sensor Activation Algorithm	p. 374
Energy Throughput Tradeoff via Optimal Transmission Scheduling	p. 378
Conclusion	p. 381
Appendix	p. 382
List of Symbols	p. 382
Proof of Lemma 13.4.3	p. 383
Proof of Theorem 13.4.4	p. 383
Bibliography	p. 385
Index	p. 389
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