Wireless Sensor Networks: Signal Processing and Communications Perspectives

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.
     

Spis treści:
List of Contributors.
1. Introduction.
Part I. Fundamental Properties and Limits.
2. Information–theoretic Bounds on Sensor Network Performance (Michael Gastpar).
2.1 Introduction.
2.2 Sensor Network Models.
2.2.1 The Linear Gaussian Sensor Network.
2.3 Digital Architectures. 
2.3.1 Distributed Source Coding.
2.3.2 Distributed Channel Coding.
2.3.3 End–to–end Performance of Digital Architectures....
2.4 The Price of Digital Architectures.
2.5 Bounds on General Architectures.
2.6 Concluding Remarks and Some Interesting Questions.
Bibliography.
3 In–Network Information Processing in Wireless Sensor Networks (Arvind Giridhar and P. R. Kumar).
3.1 Introduction.
3.2 Communication Complexity Model.
3.3 Computing Functions Over Wireless Networks: Spatial Reuse and Block Computation .
3.3.1 Geographical Models of Wireless Communication Networks.
3.3.2 Block Computation and Computational Throughput.
3.3.3 Symmetric Functionsand Types.
3.3.4 The Collocated Network.
3.3.5 Subclasses of Symmetric Functions: Type–sensitive and Type–threshold.
3.3.6 Results on Maximum Throughput in Collocated Networks.
3.3.7 Multi–Hop Networks: The Random Planar Network.
3.3.8 Other Acyclic Networks.
3.4 Wireless Networks with Noisy Communications: Reliable Computation in a Collocated Broadcast Network.
3.4.1 The Sum of the Parity of the Measurements.
3.4.2 Threshold Functions.
3.5 Towards an Information Theoretic Formulation.
3.6 Conclusion.
Bibliography.
4 The Sensing Capacity of Sensor Networks (Rohit Negi, Yaron Rachlin, and Pradeep Khosla).
4.1 Introduction.
4.1.1 Large–Scale Detection Applications.
4.1.2 SensorNetworkas an Encoder.
4.1.3 Information Theory Context.
4.2 Sensing Capacity of Sensor Networks.
4.2.1 Sensor Network Model with Arbitrary Connections .
4.2.2 Random Coding and Method of Types.
4.2.3 Sensing Capacity Theorem.
4.2.4 Illustration of Sensing Capacity Bound.
4.3 Extensions to other Sensor Network Models.
4.3.1 Models with Localized Sensing.
4.3.2 Target Models.
4.4 Discussion and Open Problems.
Bibliography.
5. Law of Sensor Network Lifetime and Its Applications (Yunxia Chen and Qing Zhao). 
5.1 Introduction.
5.2 Law of Network Lifetime and General Design Principle.
5.2.1 Network characteristics and lifetime definition.
5.2.2 Law of lifetime.
5.2.3 A general design principle for lifetime maximization.
5.3 Fundamental Performance Limit: A Stochastic Shortest Path Framework.
5.3.1 Problemstatement.
5.3.2 SSPformulation.
5.3.3 Fundamental performance limit on network lifetime.
5.3.4 Computing the limiting performance with polynomial complexity in network size.
5.4 Distributed Asymptotically Optimal Transmission Scheduling.
5.4.1 Dynamicprotocol for lifetimemaximization.
5.4.2 Dynamicnature of DPLM .
5.4.3 Asymptotic optimality of DPLM.
5.4.4 Distributedimplementation.
5.4.5 Simulation studies.
5.5 A Brief Overview of Network Lifetime Analysis.
5.6 Conclusion.
Bibliography.
Part II. Signal Processing for Sensor Networks.
6. Detection in Sensor Networks.
6.1 Centralized Detection.
6.2 The Classical Decentralized Detection Framework.
6.2.1 AsymptoticRegime.
6.3 Decentralized Detection in Wireless Sensor Networks.
6.3.1 Sensor Nodes.
6.3.2 Network Architectures...
6.3.3 Data Processing.
6.4 Wireless Sensor Networks.
6.4.1 Detection under Capacity Constraint.
6.4.2 Wireless Channel Considerations.
6.4.3 Correlated Observations.
6.4.4 Attenuation and Fading.
6.5 New Paradigms.
6.5.1 Constructive Interference.
6.5.2 Message Passing.
6.5.3 Cross–Layer Considerations.
6.5.4 Energy Savings via Censoringand Sleeping.
6.6 Ext ensions and Generalizations.
6.7 Discussion and Concluding Remarks.
Bibliography.
7. Distributed Estimation Under Bandwidth and Energy Constraints  (Alejandro Ribeiro, Ioannis D. Schizas, Jin–Jun Xiao, Georgios B. Giannakis and Zhi–Quan Luo). 
7.1 Distributed Quantization–Estimation. 
7.2 Maximum Likelihood Estimation.
7.2.1 Known Noise pdf with Unknown Variance.
7.3 Unknown noise pdf.
7.3.1 Lower bound on the MSE.
7.4 Estimation of Vector parameters.
7.4.1 Colored Gaussian Noise.
7.5 Maximum a Posteriori Probability Estimation.
7.5.1 Mean–Squared Error.
7.6 Dimensionality Reduction for Distributed Estimation.
7.6.1 Decoupled Distributed Estimation–Compression.
7.6.2 Coupled Distributed Estimation–Compression.
7.7 Distortion–RateAnalysis.
7.7.1 Distortion–Rate for Centralized Estimation.
7.7.2 Distortion–RateforDistributedEstimation.
7.7.3 D–R Upper Bound via Convex Optimization.
7.8 Conclusion.
7.9 Further Reading.
Bibliography.
8. Distributed Learning in Wireless Sensor Networks (Joel B. Predd, Sanjeev R. Kulkarni, and H. Vincent Poor). 
8.1 Introduction.
8.2 Classical Learning.
8.3 Distributed Learningin Wireless Sensor Networks.
8.3.1 A Genera lModel  for Distributed Learning.
8.3.2 Related Work.
8.4 Distributed Learningin WSNs with a Fusion Center.
8.4.1 A Clustered Approach.
8.4.2 Statistical Limits of Distributed Learning.
8.5 Distributed Learningin Ad–hocWSNs with In–network Processing.
8.5.1 Message–passing Algorithms for Least–Squares Regression.
8.5.2 Other Work.
8.6 Conclusion.
Bibliography.
9. Graphical Models and Fusion in Sensor Networks (M¨ujdat C¸ etin, Lei Chen, John W. Fisher III, Alexander T. Ihler, O. Patrick Kreidl, Randolph L. Moses, Martin J. Wainwright, Jason L. Will