Citation

Amodu, Oluwatosin Ahmed and Raja Mahmood, Raja Azlina and Althumali, Huda and Bukar, Umar Ali and Abdullah, Nor Fadzilah and Jarray, Chedia (2025) Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications. In: Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications. Studies in Computational Intelligence, 1220 (1). Springer Cham, pp. 1-142. ISBN 978-3-031-97010-8

Text 10.1007/978-3-031-97011-5 - Published Version Restricted to Repository staff only Download (263kB)

Abstract

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. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Item Type:	Book Section
Uncontrolled Keywords:	Age of information (AoI), Computational Intelligence, data acquisition, Deep Reinforcement Learning (DRL), drones, energy-efficiency, Internet of Things (IoT), Markov Decision Process, scheduling, trajectory, Unmanned Aerial Vehicles (UAVs), wireless sensor networks (WSN)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	06 Nov 2025 02:48
Last Modified:	07 Nov 2025 04:41
URII:	http://shdl.mmu.edu.my/id/eprint/14700

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