Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220, Band 1220) - Hardcover

Amodu, Oluwatosin Ahmed; Mahmood, Raja Azlina Raja; Althumali, Huda; Bukar, Umar Ali; Abdullah, Nor Fadzilah; Jarray, Chedia

 
9783031970108: Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220, Band 1220)
Hardcover
ISBN 10: 3031970101 ISBN 13: 9783031970108
Verlag: Springer, 2025

Inhaltsangabe

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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Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2025
Sprache
Englisch
ISBN 10 
3031970101
ISBN 13 
9783031970108
Einband
Gebundene Ausgabe
Anzahl der Seiten
156
Kontakt zum Hersteller
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