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

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.

儘管全球各國政府在智能感測器網絡研究和應用方面投入了大量資源，但從機器學習和信號處理的角度來看，很少有書籍涵蓋智能感測器網絡。填補這一空白，《智能感測器網絡：感測器網絡、信號處理和機器學習的整合》專注於通過機器學習實現感測、網絡和智能信號處理的緊密結合。

本書基於屢獲殊榮的作者的世界級研究，提供了智能感測器網絡基礎的堅實基礎，包括壓縮感測和取樣、分佈式信號處理和智能信號學習。書中還介紹了世界知名感測專家的最新研究成果，分為三個部分：

- 機器學習：描述了機器學習和其他人工智能原則在感測器網絡智能中的應用，包括智能感測器/轉換器架構和智能感測器的數據表示。
- 信號處理：考慮基於數字信號處理技術的感測器網絡性能優化，包括路由和應用特定信號處理的跨層集成，以及無線多媒體感測器網絡中的板上圖像處理，用於智能交通系統。
- 網絡：專注於網絡協議設計，以實現智能感測器網絡，包括感測器網絡的節能機會式路由協議和多智能體驅動的無線感測器協作。

本書保持對“智能”設計的關注，詳細介紹了感測器網絡中的信號處理原則。它闡述了智能感測器網絡的關鍵平台，並展示了關鍵應用，包括目標跟踪、物體識別和結構健康監測。此外，本書還包括一個驗證數據源之間時空關聯程度的範例，以增強感測器網絡中的數據清理，以及一個感測器流量減少應用，並考慮了在由水位感測器和速度感測器組成的水系統感測器網絡中使用卡爾曼濾波器進行攻擊檢測的情況。