Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood, Huda Althumali

Markov Decision Processes and Reinforcement Learning for Timely UAV- IoT Data Collection Applications

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Computational Intelligence 1220
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-97010-8
• Seitenzahl: 107
• Erscheinungstermin: August 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9783031970108
• Artikelnr.: 74304505

Inhaltsangabe

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.