Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning

✍ Scribed by Fei Hu (editor), Qi Hao (editor)

• Publisher: CRC Press
• Year: 2012
• Tongue: English
• Leaves: 675
• Edition: 1
• Category: Library

✦ Synopsis

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning.

Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts:

• Machine Learning―describes the application of machine learning and other AI principles in sensor network intelligence―covering smart sensor/transducer architecture and data representation for intelligent sensors
• Signal Processing―considers the optimization of sensor network performance based on digital signal processing techniques―including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems
• Networking―focuses on network protocol design in order to achieve an intelligent sensor networking―covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation

Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications―including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Editors
Contributors
Part I Intelligent Sensor Networks: Machine Learning Approach
1 Machine Learning Basics
2 Modeling Unreliable Data and Sensors: Using Event Log Performance and F-Measure Attribute Selection
3 Intelligent Sensor Interfaces and Data Format
4 Smart Wireless Sensor Nodes for Structural Health Monitoring
5 Knowledge Representation and Reasoning for the Design of Resilient Sensor Networks
6 Intelligent Sensor-to-Mission Assignment
7 Prediction-Based Data Collection in Wireless Sensor Networks
8 Neuro-Disorder Patient Monitoring via Gait Sensor Networks: Toward an Intelligent, Context-Oriented Signal Processing
9 Cognitive Wireless Sensor Networks
Part II Intelligent Sensor Networks: Signal Processing
10 Routing for Signal Processing
11 On-Board Image Processing in Wireless Multimedia Sensor Networks: A Parking Space Monitoring Solution for Intelligent Transportation Systems
12 Signal Processing for Sensing and Monitoring of Civil Infrastructure Systems
13 Data Cleaning in Low-Powered Wireless Sensor Networks
14 Sensor Stream Reduction
15 Compressive Sensing and Its Application in Wireless Sensor Networks
16 Compressive Sensing for Wireless Sensor Networks
17 Framework for Detecting Attacks on Sensors of Water Systems
Part III Intelligent Sensor Networks: Sensors and Sensor Networks
18 Reliable and Energy-Efficient Networking Protocol Designin Wireless Sensor Networks
19 Agent-Driven Wireless Sensors Cooperation for Limited Resources Allocation
20 Event Detection in Wireless Sensor Networks
21 Dynamic Coverage Problems in Sensor Networks
22 Self-Organizing Distributed State Estimators
23 Low-Power Solutions for Wireless Passive Sensor Network Node Processor Architecture
24 Fusion of Pre/Post-RFID Correction Techniques to Reduce Anomalies
25 Radio Frequency Identification Systems and Sensor Integration for Telemedicine
26 A New Generation of Intrusion Detection Networks
Index