Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence Book 1220)
by Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood, Huda Althumali, Umar Ali Bukar, Nor Fadzilah Abdullah, Chedia Jarray
Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications offers a deep dive into computational techniques for optimizing unmanned aerial vehicle operations in IoT settings. Authored by experts in the field, it covers theoretical models and practical strategies to address real-time data challenges, making it essential for advancing intelligent systems in dynamic environments.
About This Book
This volume in the Studies in Computational Intelligence series delves into advanced methodologies for UAV-IoT data collection. It focuses on the integration of Markov decision processes to model decision-making under uncertainty in real-time scenarios.
Reinforcement learning algorithms are examined as key tools for enabling efficient and timely data gathering from IoT devices using unmanned aerial vehicles. The authors present theoretical foundations alongside practical considerations for implementation.
The book addresses challenges in dynamic environments, offering insights into optimization strategies that enhance the reliability and speed of data collection processes. It serves as a valuable resource for researchers and practitioners in the field.
Through detailed analysis, the text highlights how these computational intelligence approaches can be applied to improve UAV performance in IoT networks, ensuring robust and adaptive systems.
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