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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications / by Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood, Huda Althumali, Umar Ali Bukar, Nor Fadzilah Abdullah, Chedia Jarray.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2025 Available online
View online- Format:
- Book
- Author/Creator:
- Amodu, Oluwatosin Ahmed.
- Series:
- Studies in Computational Intelligence, 1860-9503 ; 1220
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Artificial intelligence.
- Computational Intelligence.
- Artificial Intelligence.
- Local Subjects:
- Computational Intelligence.
- Artificial Intelligence.
- Physical Description:
- 1 online resource (194 pages)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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. .
- Contents:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-97011-X
- 9783031970115
- OCLC:
- 1572189998
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