Double Deep RL-based strategy for UAV-assisted energy harvesting optimization in disaster-resilient IoT networks

Unmanned Aerial Vehicles (UAVs) are increasingly crucial for emergency-response scenarios, including tasks like wireless power transfer (WPT) and data collection in disaster zones. This paper proposes a Double Deep Reinforcement Learning (DDRL) framework for energy harvesting (EH) in such scenarios....

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Bibliographic Details

Main Authors:	Elmadina, Nahla Nur, Saeed, Rashid A, Saeid, Elsadig, Ali, Elmustafa Sayed, Nafea, Ibtehal, Ahmed, Mayada A, Mokhtar, Rania A, Khalifa, Othman Omran
Format:	Proceeding Paper
Language:	en en
Published:	IEEE 2024
Subjects:	T Technology (General) T10.5 Communication of technical information TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/114449/1/114449_Double%20Deep%20RL-based%20strategy.pdf http://irep.iium.edu.my/114449/7/114449_Double%20Deep%20RL-based%20strategy_SCOPUS.pdf http://irep.iium.edu.my/114449/ https://ieeexplore.ieee.org/document/10652500
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