This book presents a comprehensive framework for integrating Artificial Intelligence (AI) into Internet of Things (IoT) networks. It redefines communication paradigms by merging traditional data transmission with intelligent decision-making, adaptive feedback mechanisms, and semantic understanding. Beginning with foundational communication principles, the book progresses to advanced topics such as AI-enabled feedback coding, semantic representation, multi-agent learning for multiple access, and intelligent network control using deep reinforcement learning, graph neural networks, and large language models.

A central theme of the book is how AI can unlock new dimensions of energy efficiency, scalability, and reliability across diverse IoT environments, from industrial automation and healthcare to smart cities and edge learning. Through practical case studies and in-depth technical discussions, it demonstrates how intelligent protocols enhance communication quality, minimize energy consumption, and coordinate large-scale distributed devices. The book concludes with a forward-looking examination of societal and ethical implications, addressing key challenges in managing intelligent IoT systems at scale.

This book is intended for researchers, graduate students, and professionals in wireless communications, AI for networking, and IoT systems. System engineers and practitioners engaged in network optimization, industrial IoT, and smart city development will also find it a valuable resource.

Introduces a unified theoretical and practical framework for AI-native IoT communication systems Covers emerging topics such as semantic communication and large language models for network orchestration Presents novel AI-driven protocols for feedback coding and multiple access in resource-constrained networks

Autorentext

Dr. Yulin Shao is an Assistant Professor with the Department of Electrical and Electronic Engineering, University of Hong Kong (HKU). He received the B.S. and M.S. degrees in Communications and Information Engineering (Hons.) from Xidian University, China, in 2013 and 2016, respectively, and the Ph.D. degree in Information Engineering from the Chinese University of Hong Kong (CUHK) in 2020. He was a Research Assistant with the Institute of Network Coding, a Visiting Scholar with the Research Laboratory of Electronics at Massachusetts Institute of Technology (MIT), a Research Associate with the Department of Electrical and Electronic Engineering at Imperial College London (ICL), a Lecturer in Information Processing with the University of Exeter, and an Assistant Professor at the University of Macau. He was a Guest Lecturer at 5G Academy Italy and IEEE Information Theory Society Bangalore Chapter.

Dr. Shao's research interests include coding and modulation, machine learning for communication systems, and stochastic control. He serves as a Series Editor of IEEE Communications Magazine in the area of Artificial Intelligence and Data Science for Communications, an Editor of IEEE Transactions on Communications in the area of Machine Learning and Communications, and an Editor of IEEE Communications Letters. He received the Best Paper Awards at IEEE International Conference on Communications (ICC) 2023, and IEEE Wireless Communications and Networking Conference (WCNC) 2024.

Inhalt

Chapter 1. Introduction.- Chapter 2. AI-Enhanced Feedback Communication for IoT.- Chapter 3. Semantic Communication for IoT.- Chapter 4. AI-Driven Multiple-Access for IoT.- Chapter 5. AI-Driven Network Management and Optimization for IoT.- Chapter 6. Future Trends and Challenges.