Data Fusion in Wireless Sensor Networks: A statistical signal processing perspective
โ Scribed by Domenico Ciuonzo, Pierluigi Salvo Rossi
- Publisher
- The Institution of Engineering and Technology
- Year
- 2019
- Tongue
- English
- Leaves
- 352
- Series
- Control, Robotics and Sensors
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks.
The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols.
๐ SIMILAR VOLUMES
<b>Learn the fundamental concepts, major challenges, and effective solutions in wireless sensor networking <p> This book provides a comprehensive and systematic introduction to the fundamental concepts, major challenges, and effective solutions in wireless sensor networking (WSN). Distinguish
<b>Learn the fundamental concepts, major challenges, and effective solutions in wireless sensor networking <p> This book provides a comprehensive and systematic introduction to the fundamental concepts, major challenges, and effective solutions in wireless sensor networking (WSN). Distinguish
<p><p></p><p>This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spati