株式会社極東書店トップ ＞ 商品一覧 ＞ Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications.
商品詳細
Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications.
・ISBN 978-3-031-97010-8 hard EUR 149.99
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| 著者・編者 | Amodu, Oluwatosin Ahmed / Mahmood, Raja Azlina Raja / Althumali, Huda / Bukar, Umar Ali / Abdullah, Nor Fadzilah / Jarray, Chedia, |
|---|---|
| シリーズ | (Studies in Computational Intelligence) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2025 |
| ページ数 | 142 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A04-53550＞ |
解説
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.