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Markov Decision Processes and Reinforcement Learning for Timely Uav- Iot Data Collection Applications - Couverture rigide
Synopsis
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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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2025
ISBN 10 : 3031970101
ISBN 13 : 9783031970108
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
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 réf. du vendeur 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely Uav- Iot Data Collection Applications
Edité par
Springer-Nature New York Inc, 2025
ISBN 10 : 3031970101
ISBN 13 : 9783031970108
Neuf
Couverture rigide
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Hardcover. Etat : Brand New. 125 pages. 9.25x6.10x9.49 inches. In Stock. N° de réf. du vendeur __3031970101
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
Edité par
Springer, Berlin, Springer, 2025
ISBN 10 : 3031970101
ISBN 13 : 9783031970108
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
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 réf. du vendeur 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV- IoT Data Collection Applications
Edité par
Springer Verlag GmbH, 2025
ISBN 10 : 3031970101
ISBN 13 : 9783031970108
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. N° de réf. du vendeur 2416350086
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2025
ISBN 10 : 3031970101
ISBN 13 : 9783031970108
Neuf
Couverture rigide
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
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 réf. du vendeur 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220)
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. N° de réf. du vendeur 26404363439
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
Edité par
Springer, Palgrave Macmillan Okt 2025, 2025
ISBN 10 : 3031970101
ISBN 13 : 9783031970108
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
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 réf. du vendeur 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220)
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220)
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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