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
Produktnummer:
180ba1e60b8ca74013a09996554fc6233b
| Autor: | Abdullah, Nor Fadzilah Althumali, Huda Amodu, Oluwatosin Ahmed Bukar, Umar Ali Jarray, Chedia Mahmood, Raja Azlina Raja |
|---|---|
| Themengebiete: | Age of information (AoI) Computational Intelligence Deep Reinforcement Learning (DRL) Internet of Things (IoT) Markov Decision Process data acquisition drones energy-efficiency scheduling trajectory |
| Veröffentlichungsdatum: | 03.09.2025 |
| EAN: | 9783031970108 |
| Sprache: | Englisch |
| Seitenzahl: | 113 |
| Produktart: | Gebunden |
| Verlag: | Springer International Publishing |
Produktinformationen "Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection 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. 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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