Funded PhD project: DRL-Enabled Resource Coordination for Covertness-Aware and Energy-Efficient UAV-Aided IoT

面向强隐蔽与高能效无人机辅助物联网的深度强化学习资源协同调度方法研究

297000

Unmanned aerial vehicles (UAVs) can serve as aerial base stations and edge computing
servers to enhance wireless network coverage and accelerate data processing in the Internet
of Things (IoT). However, data transmission and task offloading face severe challenges in
privacy protection and energy efficiency. To tackle these issues, this project aims to design
deep reinforcement learning (DRL)-based intelligent decision-making frameworks for
collaborative resource management. Specifically, the project will 1) propose a DRL-driven joint
UAV trajectory and transmit power optimization strategy to address the trade-off between covert
communications and transmission efficiency; and 2) develop a multi-agent DRL (MADRL)-
based joint communication and computation resource coordination algorithm to achieve
energy-efficient multi-UAV-aided multi-access edge computing. This research enables UAV-
aided IoT to achieve significant improvements in transmission covertness and offloading energy
efficiency.