Wireless Sensor Networks : Signal Processing and Communications Perspectives.

By: Swami, AnanthramContributor(s): Zhao, Qing | Hong, Yao-Win | Tong, LangMaterial type: TextTextPublisher: New York : John Wiley & Sons, Incorporated, 2007Copyright date: ©2008Edition: 1st edDescription: 1 online resource (412 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780470061817Subject(s): Sensor networks | Signal processing -- Digital techniques | Wireless LANsGenre/Form: Electronic books.Additional physical formats: Print version:: Wireless Sensor Networks : Signal Processing and Communications PerspectivesDDC classification: 681.2 LOC classification: TK7872.D48W585 2007Online resources: Click to View
Contents:
Intro -- Wireless Sensor Networks -- Contents -- List of Contributors -- 1 Introduction -- Part I Fundamental Properties and Limits -- 2 Information-theoretic Bounds on Sensor Network Performance -- 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 -- Bibliography -- 3 In-Network Information Processing in Wireless Sensor Networks -- 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 Functions and 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 -- 4.1 Introduction -- 4.1.1 Large-Scale Detection Applications -- 4.1.2 Sensor Network as 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 Conclusion -- Bibliography -- 5 Law of Sensor Network Lifetime and Its Applications -- 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 Problem Statement -- 5.3.2 SSP Formulation -- 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 Dynamic Protocol for Lifetime Maximization -- 5.4.2 Dynamic Nature of DPLM -- 5.4.3 Asymptotic Optimality of DPLM -- 5.4.4 Distributed Implementation -- 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 Asymptotic Regime -- 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 Censoring and Sleeping -- 6.6 Extensions and Generalizations -- 6.7 Conclusion -- Bibliography.
7 Distributed Estimation under Bandwidth and Energy Constraints -- 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-Rate Analysis -- 7.7.1 Distortion-Rate for Centralized Estimation -- 7.7.2 Distortion-Rate for Distributed Estimation -- 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 -- 8.1 Introduction -- 8.2 Classical Learning -- 8.2.1 The Supervised Learning Model -- 8.2.2 Kernel Methods and the Principle of Empirical Risk Minimization -- 8.2.3 Other Learning Algorithms -- 8.3 Distributed Learning in Wireless Sensor Networks -- 8.3.1 A General Model for Distributed Learning -- 8.3.2 Related Work -- 8.4 Distributed Learning in WSNs with a Fusion Center -- 8.4.1 A Clustered Approach -- 8.4.2 Statistical Limits of Distributed Learning -- 8.5 Distributed Learning in Ad-hoc WSNs 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 -- 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 Impact on 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 the Use of Constrained Resources in Network Fusion -- 9.6.1 Resource Management for Object Tracking in Sensor Networks -- 9.6.2 Distributed Inference with Severe Communication Constraints -- 9.7 Conclusion -- Bibliography -- Part III Communications, Networking and Cross-Layered Designs -- 10 Randomized Cooperative Transmission in Large-Scale Sensor Networks -- 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 -- Bibliography -- 11 Application Dependent Shortest Path Routing in Ad-Hoc Sensor Networks -- 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 Mobile Wireless Networks -- 11.3.1 Broadcast Methods -- 11.3.2 Shortest Path Routing -- 11.3.3 Other Approaches -- 11.4 SPR for 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 Graphs -- 11.6.2 Directed Graphs -- Bibliography -- 12 Data-Centric and Cooperative MAC Protocols for Sensor Networks -- 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 Sampling of 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 Conclusion -- Bibliography -- 13 Game Theoretic Activation and Transmission Scheduling in Unattended Ground Sensor Networks: A Correlated Equilibrium Approach -- 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 -- 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 -- 13.4.1 Outline of Markov Decision Processes and Supermodularity.
13.4.2 Optimal Channel-Aware Transmission Scheduling as a Markov Decision Process.
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.
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Intro -- Wireless Sensor Networks -- Contents -- List of Contributors -- 1 Introduction -- Part I Fundamental Properties and Limits -- 2 Information-theoretic Bounds on Sensor Network Performance -- 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 -- Bibliography -- 3 In-Network Information Processing in Wireless Sensor Networks -- 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 Functions and 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 -- 4.1 Introduction -- 4.1.1 Large-Scale Detection Applications -- 4.1.2 Sensor Network as 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 Conclusion -- Bibliography -- 5 Law of Sensor Network Lifetime and Its Applications -- 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 Problem Statement -- 5.3.2 SSP Formulation -- 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 Dynamic Protocol for Lifetime Maximization -- 5.4.2 Dynamic Nature of DPLM -- 5.4.3 Asymptotic Optimality of DPLM -- 5.4.4 Distributed Implementation -- 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 Asymptotic Regime -- 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 Censoring and Sleeping -- 6.6 Extensions and Generalizations -- 6.7 Conclusion -- Bibliography.

7 Distributed Estimation under Bandwidth and Energy Constraints -- 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-Rate Analysis -- 7.7.1 Distortion-Rate for Centralized Estimation -- 7.7.2 Distortion-Rate for Distributed Estimation -- 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 -- 8.1 Introduction -- 8.2 Classical Learning -- 8.2.1 The Supervised Learning Model -- 8.2.2 Kernel Methods and the Principle of Empirical Risk Minimization -- 8.2.3 Other Learning Algorithms -- 8.3 Distributed Learning in Wireless Sensor Networks -- 8.3.1 A General Model for Distributed Learning -- 8.3.2 Related Work -- 8.4 Distributed Learning in WSNs with a Fusion Center -- 8.4.1 A Clustered Approach -- 8.4.2 Statistical Limits of Distributed Learning -- 8.5 Distributed Learning in Ad-hoc WSNs 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 -- 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 Impact on 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 the Use of Constrained Resources in Network Fusion -- 9.6.1 Resource Management for Object Tracking in Sensor Networks -- 9.6.2 Distributed Inference with Severe Communication Constraints -- 9.7 Conclusion -- Bibliography -- Part III Communications, Networking and Cross-Layered Designs -- 10 Randomized Cooperative Transmission in Large-Scale Sensor Networks -- 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 -- Bibliography -- 11 Application Dependent Shortest Path Routing in Ad-Hoc Sensor Networks -- 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 Mobile Wireless Networks -- 11.3.1 Broadcast Methods -- 11.3.2 Shortest Path Routing -- 11.3.3 Other Approaches -- 11.4 SPR for 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 Graphs -- 11.6.2 Directed Graphs -- Bibliography -- 12 Data-Centric and Cooperative MAC Protocols for Sensor Networks -- 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 Sampling of 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 Conclusion -- Bibliography -- 13 Game Theoretic Activation and Transmission Scheduling in Unattended Ground Sensor Networks: A Correlated Equilibrium Approach -- 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 -- 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 -- 13.4.1 Outline of Markov Decision Processes and Supermodularity.

13.4.2 Optimal Channel-Aware Transmission Scheduling as a Markov Decision Process.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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