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.  

List of contents

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.

Product details

• Authors: Nor Fadzilah Abdullah, Huda Althumali, Oluwatosin Ahmed Amodu, Umar Ali Bukar, Chedia Jarray, Raja Azlina Raja Mahmood
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 28.08.2025
• EAN: 9783031970108
• ISBN: 978-3-0-3197010-8
• No. of pages: 107
• Illustrations: XI, 107 p. 34 illus. in color.
• Series: Studies in Computational Intelligence
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; 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)