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Markov decision processes and reinfo...
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Amodu, Oluwatosin Ahmed.
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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:
Markov processes. -
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:
532104
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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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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Intelligent Technologies and Robotics (SpringerNature-42732)
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