关键词: 移动边缘计算, 计算卸载, 增强现实, 粒子群优化算法, 工业生产线

Abstract: Computation offloading is one of the means to reduce delay and save energy in Mobile Edge Computing (MEC). Through reasonable offloading decisions, industrial costs can be greatly reduced. Aiming at the problems of long delay and high energy consumption after the deployment of MEC servers in the industrial production line, a computation offloading strategy based on Particle Swarm Optimization (PSO) was proposed, namely PSAO. First, the actual problem was modeled to a delay model and an energy consumption model. Since it was targeted at delay-sensitive applications, the model was transformed into a delay minimization problem under the constraints of energy consumption, and a penalty function was used to balance delay and energy consumption. Second, according to the PSO, the computation offloading decision vector was obtained, and each computation task was reasonably allocated to the corresponding MEC server through the centralized control method. Finally, through simulation experiments, the delay data of local offloading strategy, MEC baseline offloading strategy, Artificial Fish Swarm Algorithm (AFSA) based offloading strategy and PSAO were compared and analyzed. The average total delay of PSAO was much lower than those of the other three offloading strategies, and PSAO reduces the total cost of the original system by 20%. Experimental results show that the proposed strategy can effectively reduce the delay in MEC and balance the loads of MEC servers.

Key words: Mobile Edge Computing (MEC), computation offloading, Augmented Reality (AR), Particle Swarm Optimization (PSO) algorithm, industrial production line