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Preface | |
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Introduction to Wireless Sensor Networks | |
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Overview | |
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Enabling Technologies | |
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Hardware | |
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Wireless Networking | |
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Collaborative Signal Processing | |
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Evolution of Sensor Nodes | |
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Military Networks of Sensors | |
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Next Generation Wireless Sensor Nodes | |
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WINS from UCLA | |
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Motes from UC Berkeley | |
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Medusa from UCLA | |
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PicoRadio from UC Berkeley | |
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[mu]AMPS from MIT | |
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Why Microscopic Sensor Nodes? | |
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Applications of Interest | |
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Data Gathering Applications | |
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Habitat Study | |
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Environmental Monitoring | |
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Computation-Intensive Applications | |
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Structural Health Monitoring | |
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Heavy Industrial Monitoring | |
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Research Topics and Challenges | |
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Focus of This Book | |
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Background | |
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Data-Centric Paradigm | |
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Collaborative Information Processing and Routing | |
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Cross-Layer Optimization for Energy-Efficiency | |
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Motivation | |
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Consideration for Collaborative Information Processing and Routing | |
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A Brief Survey of Cross-Layer Optimization for Energy-Efficient Collaborative Information Processing and Routing | |
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Hardware Layer | |
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Physical Layer | |
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MAC Layer | |
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Routing Layer | |
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Application Layer | |
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Summary | |
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Energy Models | |
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Definitions and Notations | |
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Mathematics and Graphs | |
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Network Topology Graph | |
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Application Graph | |
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Performance Metrics | |
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Energy Models | |
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Voltage Scaling | |
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Rate Adaptation | |
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Tunable Compression | |
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Information Processing within a Collocated Cluster | |
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Overview | |
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Motivation | |
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Technical Overview | |
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Chapter Organization | |
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Related Work | |
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Problem Definition | |
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System Model | |
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Application Model | |
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Task Allocation | |
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Integer Linear Programming Formulation | |
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Heuristic Approach | |
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Phase 1 | |
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Phase 2 | |
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Phase 3 | |
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Simulation Results | |
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Synthetic Application Graphs | |
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Simulation Setup | |
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Small Scale Problems | |
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Large Scale Problems | |
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Impact of the Number of Voltage Levels | |
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Incorporating Rate Adaptation | |
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Application Graphs from Real World Problems | |
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LU Factorization | |
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Fast Fourier Transformation (FFT) | |
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Summary | |
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Information Transportation over a Tree Substrate | |
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Overview | |
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Motivation | |
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Technical Overview | |
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Chapter Organization | |
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Related work | |
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Models and Assumptions | |
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Data Gathering Tree | |
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Data Aggregation Paradigm | |
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Problem Definition | |
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Off-Line Algorithms for PTP | |
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A Numerical Optimization Algorithm | |
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Performance Analysis for a Special Case | |
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A Dynamic Programming-Based Approximation Algorithm | |
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A Distributed On-Line Protocol | |
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Simulation Results | |
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Simulation Setup | |
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Performance of the Off-Line Algorithms | |
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Performance Overview | |
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Impact of Radio Parameters | |
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Performance of the On-Line Protocol | |
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Performance Overview | |
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Impact of Network Parameters | |
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Adaptability to System Variations | |
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Summary | |
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Information Routing with Tunable Compression | |
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Overview | |
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Technical Overview | |
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Chapter Organization | |
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Related Work | |
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Models and Assumptions | |
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Nomenclature | |
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Network Model | |
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Flow-Based Data Gathering | |
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Discussion | |
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An Example | |
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Problem Definition | |
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Optimal Flow in a Given Tree | |
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Example Revisited | |
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Determining the Optimal Flow | |
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Analytical Study of SPT and MST | |
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Analysis for a Grid Deployment | |
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Tradeoffs Between SPT and MST | |
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Tradeoffs for Entropy Model E1 | |
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Tradeoffs for Entropy Model E2 | |
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SPT is optimal for Entropy Model E3 | |
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Summary of Grid Deployment | |
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A Randomized O(log[superscript 2] v) Approximation | |
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Simulation Results | |
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Simulation Setup | |
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Results | |
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Main Results | |
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Impact of the number of source nodes R | |
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Impact of the communication range r | |
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Summary | |
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Conclusions | |
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Concluding Remarks | |
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Future Work | |
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Adaptive Fidelity Algorithms | |
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A Broad View of Future Research | |
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Mobile Sensor Nodes | |
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Routing Diversity | |
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Sleep Scheduling | |
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Bibliography | |
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Correctness of EMR-Algo | |
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Performance Bound of SPT and MST for TDG problem with grid deployment | |
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Index | |