Amodu / Mahmood / Althumali Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications

Erscheinungsjahr 2025
ISBN: 978-3-031-97011-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 142 Seiten

Reihe: Intelligent Technologies and Robotics (R0)

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.

Amodu, Oluwatosin Ahmed

Mahmood, Raja Azlina Raja

Althumali, Huda

Bukar, Umar Ali

Abdullah, Nor Fadzilah

Jarray, Chedia

Inhaltsverzeichnis

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.