Buch, Englisch, 142 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 405 g
Buch, Englisch, 142 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 405 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-031-97010-8
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
