TY  - BOOK
AU  - Swami,Ananthram
TI  - Wireless sensor networks: signal processing and communications perspectives 
SN  - 0470035579
AV  - TK7872.D48 W585 2007
U1  - 681.2 22
PY  - 2007///]
CY  - Chichester,  England, Hoboken, NJ
PB  - J. Wiley
KW  - Sensor networks
KW  - Wireless LANs
KW  - Signal processing
KW  - Digital techniques
N1  - 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
N2  - "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
UR  - http://www.loc.gov/catdir/enhancements/fy0828/2007021100-b.html
ER  -