Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence #1220)

By: Oluwatosin Ahmed Amodu and Raja Azlina Mahmood and Huda Althumali and Umar Ali Bukar and Nor Fadzilah Abdullah and Chedia Jarray

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Synopsis: This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications.