Energy Optimization for Computing Reuse in Unmanned Aerial Vehicle-assisted Edge Computing Systems
To address the high computational performance demands of delay-sensitive tasks in complex terrains,the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle(UAV)is proposed.Firstly,the minimization of the average total energy consumption is formulated by jointly optimizing user offloading,user transmission power,server assignment on UAV,computation frequencies of users and UAV servers,as well as UAV flight trajectory,while meeting the latency constraints.Secondly,a deep reinforcement learning approach is employed to solve the optimization problem,and a Soft Actor-Critic(SAC)based optimization algorithm is introduced.The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm's exploration capabilities and accelerates the training convergence speed.Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence.
万方数据
维普
