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Markov decision processes and reinforcement learning for timely UAV-IoT data collection applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Markov decision processes and reinforcement learning for timely UAV-IoT data collection applicationsby Oluwatosin Ahmed Amodu ... [et al.].
other author:	Amodu, Oluwatosin Ahmed.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xiv, 142 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Markov processes.
Online resource:	https://doi.org/10.1007/978-3-031-97011-5
ISBN:	9783031970115$q(electronic bk.)

Markov decision processes and reinforcement learning for timely UAV-IoT data collection applications
Markov decision processes and reinforcement learning for timely UAV-IoT data collection applications[electronic resource] /by Oluwatosin Ahmed Amodu ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xiv, 142 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 12201860-9503 ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783031970115$q(electronic bk.)

Standard No.: 10.1007/978-3-031-97011-5doiSubjects--Topical Terms:

181910
Markov processes.

LC Class. No.: QA274.7

Dewey Class. No.: 519.233

Markov decision processes and reinforcement learning for timely UAV-IoT data collection applications
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520 $a 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.
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