Markov Decision Processes and Reinforcement Learning for Timely Uav- Iot Data Collection Applications - Couverture rigide

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

Couverture rigide

ISBN 10 : 3031970101 ISBN 13 : 9783031970108

Editeur : Springer International Publishing AG, 2025

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer International Publishing AG
Date d'�dition: 2025
Langue: anglais
ISBN 10: 3031970101
ISBN 13: 9783031970108
Reliure: Reli�
Nombre de pages: 156
Coordonn�es du fabricant: ProductSafety@springernature.com
ProductSafety@springernature.com

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Markov Decision Processes and Reinforcement Learning for Timely Uav- Iot Data Collection Applications

Amodu, Oluwatosin Ahmed/ Mahmood, Raja Azlina Raja/ Althumali, Huda/ Bukar, Umar Ali/ Abdullah, Nor Fadzilah

Edit� par Springer-Nature New York Inc, 2025

ISBN 10 : 3031970101 ISBN 13 : 9783031970108

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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Hardcover)

Oluwatosin Ahmed Amodu

Edit� par Springer International Publishing AG, Cham, 2025

ISBN 10 : 3031970101 ISBN 13 : 9783031970108

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Hardcover. Etat : new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9783031970108

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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications

Oluwatosin Ahmed Amodu

Edit� par Springer, Berlin, Springer, 2025

ISBN 10 : 3031970101 ISBN 13 : 9783031970108

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 142 pp. Englisch. N� de r�f. du vendeur 9783031970108

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Markov Decision Processes and Reinforcement Learning for Timely UAV- IoT Data Collection Applications

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

Edit� par Springer Verlag GmbH, 2025

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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Hardcover)

Oluwatosin Ahmed Amodu

Edit� par Springer International Publishing AG, Cham, 2025

ISBN 10 : 3031970101 ISBN 13 : 9783031970108

Neuf Couverture rigide

Vendeur : CitiRetail, Stevenage, Royaume-Uni

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Hardcover. Etat : new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9783031970108

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Markov Decision Processes and Reinforcement Learning for Timely UAV- IoT Data Collection Applications

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

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ISBN 10 : 3031970101 ISBN 13 : 9783031970108

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Markov Decision Processes and Reinforcement Learning for Timely UAV- IoT Data Collection Applications

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

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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications

Oluwatosin Ahmed Amodu (u. a.)

Edit� par Springer, 2025

ISBN 10 : 3031970101 ISBN 13 : 9783031970108

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Markov Decision Processes and Reinforcement Learning for Timely UAV- IoT Data Collection Applications

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

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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications

Oluwatosin Ahmed Amodu

Edit� par Springer, Palgrave Macmillan Okt 2025, 2025

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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch. N� de r�f. du vendeur 9783031970108

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