This book provides a structured, data-driven analysis of AI-enabled, non-terrestrial network-assisted IoT systems, examining the key research themes, technological components, and interconnections that characterize this emerging field. Non-terrestrial networks are rapidly becoming a core component of next-generation Internet of Things systems. By integrating unmanned aerial vehicles, high-altitude platform systems, satellites, terrestrial infrastructure, and edge intelligence, these networks extend sensing, communication, and computing capabilities to environments in which conventional networks are unavailable, unreliable, or inadequate.
Drawing on a bibliometric analysis of more than 2,000 peer-reviewed publications, together with a broad review of over 600 references, the book identifies the principal research themes, enabling technologies, and cross-domain relationships shaping intelligent NTN–IoT systems.
Organized into twelve thematic domains, the book examines smart and sustainable agriculture; aerial and satellite-based visual sensing; cloud–fog data processing; mobile-edge computation offloading; autonomous navigation; information freshness; secure localization; the trustworthy Internet of Drones; integrated NTN security; 5G/6G connectivity; embedded real-time intelligence; and green networking. Particular attention is given to machine learning, deep reinforcement learning, federated learning, resource orchestration, privacy, resilience, energy efficiency, and practical deployment constraints.
Research at the intersection of non-terrestrial networks, artificial intelligence, and the Internet of Things remains widely dispersed across disciplines and application domains. By consolidating this literature within a coherent twelve-domain framework, including both established topics and rapidly developing research areas, the book provides a comprehensive reference for graduate students, researchers, engineers, and practitioners seeking to understand current advances, unresolved challenges, and future research directions in AI-enabled non-terrestrial connectivity.

Oluwatosin Ahmed Amodu is a postdoctoral researcher at Universiti Putra Malaysia. He received his PhD in Wireless Communication and Network Engineering from Universiti Putra Malaysia in 2021. His research covers UAV-assisted wireless communications, non-terrestrial networks, Internet of Things systems, stochastic geometry, and reinforcement learning. His work examines resource allocation, energy efficiency, and information freshness in wireless networks.

Zurina Mohd Hanapi is an Associate Professor in the Faculty of Computer Science and Information Technology at Universiti Putra Malaysia. She received her PhD in Electrical, Electronic, and Systems Engineering from Universiti Kebangsaan Malaysia in 2011. Her research interests include wireless sensor networks, routing protocols, network security, cryptography, and distributed computing. Her research also addresses reinforcement learning for computation offloading in multi-access edge computing.

Raja Azlina Raja Mahmood is a lecturer in the Department of Communication Technology and Network at Universiti Putra Malaysia (UPM). She holds a bachelor’s degree in Computer Science from the University of Michigan, Ann Arbor, and a master’s degree in Software Engineering from Universiti Teknologi Malaysia, and completed her PhD at UPM. Her research covers network security, wireless sensor networks, mobile ad hoc networks, and intrusion detection. Her collaborative work also examines UAV-assisted IoT data collection, information freshness, and reinforcement learning.

Huda Althumali is an Assistant Professor in the Computer Science Department at Imam Abdulrahman bin Faisal University, Jubail, Saudi Arabia. She received her PhD in Computer Networks from Universiti Putra Malaysia in 2022. Her research focuses on random access control, massive machine-type communications, cellular IoT networks, and quality-of-service support. Her collaborative research also examines deep reinforcement learning for UAV-assisted data collection and mobile edge computing.

Faten Ameen Saif is an Assistant Professor at Gulf Colleges, Saudi Arabia. She received her PhD in Computer Science, specializing in Parallel and Distributed Computing, from Universiti Putra Malaysia in 2024. Her research focuses on task scheduling, workload allocation, and computation offloading in cloud, fog, and edge computing. She studies multi-objective optimization methods for managing energy consumption and delay in distributed computing environments.

Chedia Jarray is an Assistant Professor in Computer Science and Telecommunications at the Higher Institute of Computer Science of Medenine, Tunisia. She received her PhD in Electrical Engineering from the National School of Engineers of Gabes in 2018. Her research interests include sensor networks, machine-type and device-to-device communications, stochastic geometry, UAV communications, and terahertz communications. Her technical experience also includes machine learning, reinforcement learning, and software development.