Trusted Task Offloading Scheme Based on Deep Reinforcement Learning
To address security concerns related to the trust worthiness of edge servers in Mobile Edge Computing(MEC)as well as the challenges of slow convergence and significant fluctuations in task offloading schemes based on Deep Reinforcement Learning(DRL),this study proposes a task offloading scheme based on trust perception and the DRL algorithm.A multisource feedback trust fusion model is first constructed that utilizes the combined weighting of objective information entropy and historical offload times to assess edge server credibility.The Priority Experience Replay(PER)-SAC algorithm,based on priority experience sampling,is then used to treat the base station as an intelligent agent responsible for offloading decisions in computational tasks.Experimental results show that the proposed scheme has superior performance and convergence as compared with the TASACO,SRTO-DDPG,and I-PPO schemes.The cumulative reward,delay,and energy consumption indicators of the proposed scheme all exhibit optimality with a faster convergence speed and minimized fluctuation range.Compared with the TASACO solution under various test scenarios,the energy consumption performance of the proposed scheme is improved by at least 5.8％,with a maximum improvement of 32.2％,and the latency performance is improved by at least 8.5％,with a maximum improvement of 21.3％.
Mobile Edge Computing(MEC)task offloadingnetwork securityDeep Reinforcement Learning(DRL)trust mechanism
万方数据
维普
