Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications
Springer International Publishing (Verlag)
9783031970108 (ISBN)
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.
Introduction to AoI in UAV-assisted Sensor and IoT Systems.- AoI aware UAV IoT Modeling using MDPs.- Reinforcement Learning and DRL for AoI aware UAV IoT.- Challenges and Future Considerations.
| Erscheinungsdatum | 20.07.2025 |
|---|---|
| Reihe/Serie | Studies in Computational Intelligence |
| Zusatzinfo | XIV, 142 p. 35 illus., 34 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik | |
| Schlagworte | age of information (AoI) • Computational Intelligence • Data acquisition • Deep reinforcement learning (DRL) • drones • energy-efficiency • Internet of Things (IoT) • Markov decision process • Scheduling • Trajectory • Unmanned Aerial Vehicles (UAVs) • Wireless Sensor Networks (WSN) |
| ISBN-13 | 9783031970108 / 9783031970108 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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