Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications: 1220 (Studies in Computational Intelligence, 1220) - Hardcover
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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From the Back Cover
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
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Hardcover)
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Hardcover. Condition: 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. Seller Inventory # 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Hardcover)
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ISBN 10: 3031970101
ISBN 13: 9783031970108
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Hardcover. Condition: 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. Seller Inventory # 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
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Buch. Condition: 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. Seller Inventory # 9783031970108
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
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Buch. Condition: Neu. Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications | Oluwatosin Ahmed Amodu (u. a.) | Buch | xiv | Englisch | 2025 | Springer | EAN 9783031970108 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Seller Inventory # 134181102
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
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Buch. Condition: 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. Seller Inventory # 9783031970108