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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 applications/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Drone aircraft. -
Online resource:	https://doi.org/10.1007/978-3-031-97011-5
ISBN:	9783031970115

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. 1220..

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

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

805030
Drone aircraft.

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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245 0 0 $a Markov decision processes and reinforcement learning for timely UAV-IoT data collection applications $h [electronic resource] / $c by Oluwatosin Ahmed Amodu ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xiv, 142 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1220
505 0 $a 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.
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.
650 0 $a Drone aircraft. $3 805030
650 0 $a Internet of things. $3 2057703
650 0 $a Reinforcement learning. $3 1006373
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 0 $a Markov processes. $3 532104
700 1 $a Amodu, Oluwatosin Ahmed. $3 3802981
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773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1220. $3 3802982
856 4 0 $u https://doi.org/10.1007/978-3-031-97011-5
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)