Wireless sensor networks : signal processing and communications perspectives / edited by Ananthram Swami [and others].

Includes bibliographical references and index.

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 -- 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 -- 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 -- 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 --

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. Extensions and Generalizations -- 6.7. Discussion and Concluding Remarks -- 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 -- 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 -- 9. Graphical Models and Fusion in Sensor Networks / Mujdat Cetin, Lei Chen, John W. Fisher III, Alexander T. Ihler, O. Patrick Kreidl, Randolph L. Moses, Martin J. Wainwright, Jason L. Williams, and Alan S. Willsky -- 9.1. Introduction -- 9.2. Graphical Models -- 9.2.1. Definitions and Properties -- 9.2.2. Sum - Product Algorithms -- 9.2.3. Max - Product Algorithms -- 9.2.4. Loopy Belief Propagation -- 9.2.5. Nonparametric Belief Propagation -- 9.3. From Sensor Network Fusion to Graphical Models -- 9.3.1. Self-Localization in Sensor Networks -- 9.3.2. Multi-Object Data Association in Sensor Networks -- 9.4. Message Censoring, Approximation,and Impacton Fusion -- 9.4.1. Message Censoring -- 9.4.2. Trading Off Accuracy for Bits in Particle-Based Messaging -- 9.5. The Effects of Message Approximation -- 9.6. Optimizing theUse of Constrained Resources in Network Fusion -- 9.6.1. Resource management forobject trackingin sensor networks -- 9.6.2. Distributed Inference with Severe Communication Constraints -- 9.7. Conclusion --

Part III. Communications, Networking and Cross-Layered Designs -- 10. Randomized Cooperative Transmission in Large-Scale Sensor Networks / Birsen Sirkeci-Mergen and Anna Scaglione -- 10.1. Introduction -- 10.2. Transmit cooperation in sensor networks -- 10.2.1. Physical Layer Model for Cooperative Radios -- 10.2.2. Cooperative schemes with centralized code assignment -- 10.3. Randomized distributed cooperative schemes -- 10.3.1. Randomized code construction and system model -- 10.4. Performance of Randomized Cooperative Codes -- 10.4.1. Characterization of the Diversity Order -- 10.4.2. Simulations and Numerical Evaluations -- 10.5. Analysis of Cooperative Large-scale Networks utilizing Randomized Cooperative Codes -- 10.5.1. Numerical Evaluations and Further Discussions -- 10.6. Conclusion -- 10.7. Appendix -- 11. Application Dependent Shortest Path Routing in Ad-Hoc Sensor Networks / Saswat Misra, Lang Tong, and Anthony Ephremides -- 11.1. Introduction -- 11.1.1. Major Classifications -- 11.2. Fundamental SPR -- 11.2.1. Broadcast Routing -- 11.2.2. Static Shortest Path Routing -- 11.2.3. Adaptive Shortest Path Routing -- 11.2.4. Other Approaches -- 11.3. SPR for MobileWireless Networks -- 11.3.1. Broadcast Methods -- 11.3.2. Shortest Path Routing -- 11.3.3. Other Approaches -- 11.4. SPRf or Ad-Hoc Sensor Networks -- 11.4.1. A Short Survey of Current Protocols -- 11.4.2. An Argument for Application Dependent Design -- 11.4.3. Application Dependent SPR:An Illustrative Example -- 11.5. Conclusion -- 11.6. A Short Review of Basic Graph Theory -- 11.6.1. Undirected Graph.s -- 11.6.2. DirectedGraphs --

12. Data-Centric and CooperativeMAC Protocols for Sensor Networks / Yao-Win Hong and Pramod K. Varshney -- 12.1. Introduction -- 12.2. Traditional Medium Access Control Protocols: Random Access and Deterministic Scheduling -- 12.2.1. Carrier Sense Multiple Access (CSMA) -- 12.2.2. Time-Division Multiple Access / Tdma -- 12.3. Energy Efficient MAC Protocols for Sensor Networks -- 12.4. Data-Centric MAC Protocols for Sensor Networks -- 12.4.1. Data Aggregation -- 12.4.2. Distributed Source Coding -- 12.4.3. Spatial Samplingof a Correlated Sensor Field -- 12.5. Cooperative MAC Protocol for Independent Sources -- 12.6. Cooperative MAC Protocol for Correlated Sensors -- 12.6.1. Data Retrieval from Correlated Sensors -- 12.6.2. Generalized Data-Centric Cooperative MAC -- 12.6.3. MAC for Distributed Detection and Estimation -- 12.7. DiscussionandFutureResearchDirections -- 13. Game Theoretic Activation and Transmission Scheduling in Unattended Ground Sensor Networks: A Correlated Equilibrium Approach / Vikram Krishnamurthy, Michael Maskery, and Minh Hanh Ngo -- 13.1. Introduction -- 13.1.1. UGSN Sensor Activation and Transmission Scheduling Methodology -- 13.1.2. Fundamental Tools and Literature -- 13.2. Unattended Ground Sensor Network: Capabilities and Objectives -- 13.2.1. Practicalities: Sensor Network Model and Architecture -- 13.2.2. Energy Efficient Sensor Activation and Transmission Control -- 13.3. Sensor Activation as the Correlated Equilibrium of a Noncooperative Game -- 13.3.1. From Nash to Correlated Equilibrium - An Overview -- 13.3.2. Adaptive Sensor Activation through Regret Tracking -- 13.3.3. Convergence Analysis of Regret-based Algorithms -- 13.4. Energy Efficient Transmission Scheduling in UGSN - A Markov Decision Process Approach -- 13.4.1. Outline of Markov Decision Processes and Supermodularity -- 13.4.2. Optimal Channel-Aware Transmission Scheduling as a Markov Decision Process -- 13.4.3. Optimality of Threshold Transmission Policies -- 13.5. Numerical Results -- 13.5.1. UGSN Sensor Activation Algorithm -- 13.5.2. Energy Throughput Tradeoff via Optimal Transmission Scheduling -- 13.6. Discussion and Extensions -- 13.7. Appendix -- 13.7.1. List of Symbols -- 13.7.2. Proof of Lemma -- 13.7.3. Proof of Theorem.

"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."--Publisher's website.

Machine converted from AACR2 source record.