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Deep Reinforcement Learning-Based Trajectory Optimization and Resource Allocation for Secure UAV-Enabled MEC Networks | IEEE Conference Publication | IEEE Xplore

Deep Reinforcement Learning-Based Trajectory Optimization and Resource Allocation for Secure UAV-Enabled MEC Networks


Abstract:

As a prominent type of mobile edge computing (MEC) server, unmanned aerial vehicles (UAVs) can be flexibly deployed to effectively shorten transmission distance and impro...Show More

Abstract:

As a prominent type of mobile edge computing (MEC) server, unmanned aerial vehicles (UAVs) can be flexibly deployed to effectively shorten transmission distance and improve the quality of information offloading. However, due to the openness and light-of-sight characteristics of air-to-ground links, the transmitting crucial information of terminal devices (TDs) is susceptible to be eavesdropped, which poses a serious threat to the UAV-enabled MEC network security. Therefore, we design a deep reinforcement learning-based trajectory optimization and resource allocation (DRTORA) scheme to improve the security calculation performance for the secure UAV-enabled MEC network. In DRTORA, the trajectory of UAV and offloading decision, time allocation of TDs are intelligently optimized to achieve the security calculation capacity maximization by deep Q-learning (DQN) with considering the constraints of time, UAV movement, minimum calculation capacity and data stability. Simulation results demonstrate the proposed DRTORA significantly enhances the security calculation performance of the network.
Date of Conference: 20-20 May 2024
Date Added to IEEE Xplore: 13 August 2024
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Conference Location: Vancouver, BC, Canada

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