دانلود کتاب Resource-aware data fusion algorithms for wireless sensor networks

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Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks
Preface
Contents
List of Figures
List of Tables
List of Abbreviations
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Chapter 1: Introduction
1.1 Wireless Sensor Network Applications
1.2 Sensor Node Evaluation Metrics
1.3 Sensor Network Architecture
1.4 Wireless Sensor Network Challenges
Bibliography
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Chapter 2: Data Fusion in WSN
2.1 Introduction
2.2 Information Fusion, Sensor Fusion, and Data Fusion
2.3 Data Fusion Classification
2.3.1 Classification Based on Relationship Among the Sources
2.3.2 Classification Based on Levels of Abstraction
2.3.3 Classification Based on Input and Output
2.4 Data Fusion: Techniques, Methods, and Algorithms
2.4.1 Inference
2.4.2 Estimation
2.5 Data Fusion: Architectures and Models
2.5.1 Data-Based Models
2.5.2 Activity-Based Models
2.5.3 Role-Based Model
Bibliography
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Chapter 3: Proposed Centralized Data Fusion Algorithms
3.1 Introduction
3.2 Sand Measuring in Pipelines
3.2.1 The Intrusive Devices
3.2.2 The Non-intrusive Devices
3.3 Proposed Remote Measuring for Sand in Pipelines
3.3.1 Sensors Used in the Proposed System
3.3.2 WSDA Framework
3.3.3 Proposed Centralized Fusion Methods
3.4 Simulation and Experimental Results
Bibliography
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Chapter 4: Kalman Filter
4.1 Wireless Sensor Network Representation
4.2 Introduction to Graph Theory
4.3 Graphs and Their Plane Figures
4.3.1 Direct Graph
4.3.2 Undirected Graph
4.3.3 Network Representations
4.3.4 Node Degree
4.3.5 Distance Matrix
4.3.6 Incidence Matrix
4.3.7 Adjacency Matrix
4.3.8 Degree Matrix
4.3.9 Laplacian Matrix
4.4 Central Kalman Filter in Wireless Sensor Network
4.5 Distributed Kalman Filter (DKF) Literature Work
4.6 Olfati-Saber´s Distributed Kalman Filter
4.7 Consensus Filters
4.7.1 Information Consensus in Networked Systems
4.7.2 Distributed Kalman Filter with Embedded Consensus Filters
Bibliography
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Chapter 5: Proposed Distributed Kalman Filter
5.1 Distributed Kalman Filter (DKF) in WSN and Related Work
5.2 Network Representations
5.3 Asymptotic Average Consensus with Polynomial Filter
5.4 Proposed Distributed Kalman Filter
5.5 Simulation Results
Bibliography
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Chapter 6: Proposed Multiplication Algorithm for DKF
6.1 Introduction
6.2 Overview of Multiplication Algorithms
6.3 Proposed Method
6.4 Simulation Result
6.5 Case Study
6.6 Counter Example Power Measurement
Bibliography
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Chapter 7: Experimental Results for the Proposed DKF
7.1 Test Bed
7.2 Experimental Results
Bibliography
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Index