Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications

von Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood, Huda Althumali, Umar Ali Bukar, Nor Fadzilah Abdullah, Chedia Jarray

142 Seiten, Hardcover

Kurzbeschreibung des Verlags

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.  

• Reihe: Studies in Computational Intelligence
• ISBN: 9783031970108
• Sprache: Englisch
• Erscheinungsdatum: 08.10.2025
• Genre: Technik/Allgemeines, Lexika
• Verlag: Springer International Publishing