RSSI-Based Localizations -

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Editeur : Taylor & Francis Ltd

Langue : Anglais

Parution : 01/05/2026

Format : Moyen, de 350g à 1kg

Nombre de pages : 200.0

ISBN : 104112421X

Résumé :

This book explores RSSI-based localization, device-free detection, and machine learning integration, providing practical insights into wireless sensing applications for navigation, security, healthcare, and intelligent IoT systems. With its emphasis on practical implementation and real-world applications, this book serves as an Invaluable resource for those looking to harness RSSI for robust, efficient, and scalable solutions. It empowers the reader to develop advanced wireless sensing solutions across various domains. By starting with measurement techniques for RSSI and localization algorithms, the authors provide a strong foundation in RSSI localization. The reader also learns Device-Free Detection (DFD) using RSSI, applied in security, healthcare, and smart homes, which enables the design of more intelligent smart environments.

An important coverage in the book is the integration of machine learning (ML) with RSSI data. The authors cover supervised, unsupervised, and deep learning techniques, focusing on enhancing accuracy, scalability, and adaptability. The reader learns how to apply ML techniques and gain further insight into the advanced applications of RSSI data. Such knowledge allows for the development of more accurate and scalable systems, creation of intelligent IoT systems. An important hospital case is included to study RSSI-based monitoring in healthcare. It features a real-world example which details the implementation, challenges, and results of the case study. The practical insights demonstrate the potential benefits and challenges of RSSI-based healthcare solutions and inspires the development of innovative solutions in healthcare and potentially other domains, integrating machine learning capabilities.

The readership for this book is graduate students in wireless sensor network and IoT courses, professionals such as developers and researchers developing smart communications in factories, hospitals, and buildings.

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Biographie:

Nattha Jindapetch obtained her PhD from the University of Tokyo in advanced science and technology. She is currently an Associate Professor with the Department of Electrical Engineering at Prince of Songkla University, Thailand. Her research interests include FPGAs, embedded systems, and sensor networks.

Thradon Wattananavin is a lecturer at the Faculty of Industrial Technology, Nakhon Si Thammarat Rajabhat University, Thailand. His research interests include wireless sensor networks, RSSI-based localization, medium access control (MAC) protocols, wireless network communications, fingerprinting, and machine learning.

Kittikhun Thongpull is Director of Next-generation Innovations in Connected and Digital Technology Research at the Prince of Songkla University, Thailand. He received his PhD from the Kaiserslautern University of Technology in Germany.

Apidet Booranawong teaches electrical engineerin at the Prince of Songkla University, Thailand. His research is in the areas of wireless sensor networks, wireless sensor and actuator networks, and routing algorithms.

Sommaire:

This book explores RSSI-based localization, device-free detection, and machine learning integration, providing practical insights into wireless sensing applications for navigation, security, healthcare, and intelligent IoT systems. With its emphasis on practical implementation and real-world applications, this book serves as an Invaluable resource for those looking to harness RSSI for robust, efficient, and scalable solutions. It empowers the reader to develop advanced wireless sensing solutions across various domains. By starting with measurement techniques for RSSI and localization algorithms, the authors provide a strong foundation in RSSI localization. The reader also learns Device-Free Detection (DFD) using RSSI, applied in security, healthcare, and smart homes, which enables the design of more intelligent smart environments.

An important topic covered in the book is the integration of machine learning (ML) with RSSI data. The authors cover supervised, unsupervised, and deep learning techniques, focusing on enhancing accuracy, scalability, and adaptability. The reader learns how to apply ML techniques and gain further insight into the advanced applications of RSSI data. Such knowledge allows for the development of more accurate and scalable systems, creation of intelligent IoT systems. An important hospital case is included to study RSSI-based monitoring in healthcare. It features a real-world example which details the implementation, challenges, and results of the case study. The practical insights demonstrate the potential benefits and challenges of RSSI-based healthcare solutions and inspires the development of innovative solutions in healthcare and potentially other domains, integrating machine learning capabilities.

The readership for this book is graduate students in wireless sensor network and IoT courses, as well as professionals such as developers and researchers developing smart communications in factories, hospitals, and buildings.