Placeholder text

Go to cart

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

By Amodu, Oluwatosin Ahmed

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

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

0 - Default Title
Description
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.
Product details
Number of Pages:
156
Release Date:
2025-10-08
Publication Date:
2025-10-08
Publisher:
Springer
Languages:
Original: English
ISBN10:
3031970101
ISBN13:
9783031970108
GPSR Manufacturer Reference:
[email protected]
Weight:
405 g
Height:
160 cm
Width:
241 cm
Thickness:
15 cm
Buy on Amazon
Currently sold out