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.
