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

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.  

Inhaltsverzeichnis

Introduction to AoI in UAV-assisted Sensor and IoT Systems.- AoI aware UAV IoT Modeling using MDPs.- Reinforcement Learning and DRL for AoI aware UAV IoT.- Challenges and Future Considerations.

Produktdetails

• Autoren: Nor Fadzilah Abdullah, Huda Althumali, Oluwatosin Ahmed Amodu, Umar Ali Bukar, Chedia Jarray, Raja Azlina Raja Mahmood
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 28.08.2025
• EAN: 9783031970108
• ISBN: 978-3-0-3197010-8
• Seiten: 142
• Illustration: XIV, 142 p. 35 illus., 34 illus. in color.
• Serie: Studies in Computational Intelligence
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Technik > Allgemeines, Lexika; Artificial Intelligence, Drones, Trajectory, Scheduling, data acquisition, Internet of Things (IoT), Computational Intelligence, energy-efficiency, Wireless Sensor Networks (WSN), markov decision process, Unmanned Aerial Vehicles (UAVs), Deep reinforcement learning (DRL), Age of information (AoI)