An Energy-Efficient Computing Offloading Strategy Based on Improved Sparrow Search Algorithm in Mobile Edge Computing

Abstract

With the development of the Internet of Things (IoT) and mobile edge computing (MEC) technologies, increasing missions are transferred to edge servers for computing. An ensuing problem is how to offload tasks to each edge server to reduce time delay or energy expenditure. Therefore, the offloading strategy plays a vital role in the offloading calculation process. To maximize energy efficiency of the offloading system, a computational offloading strategy (ISSA) based on the sparrow search algorithm is proposed in this paper. Firstly, we modeled the actual problem as a delay model and an energy consumption model. Then a penalty function was introduced to balance the delay and energy consumption, and a fitness function was obtained. Secondly, we improved the sparrow search algorithm which will be later used to solve the model. Finally, through experimental simulation, the energy consumption data obtained by the local offloading strategy, the random offloading strategy, the offloading strategy derived from Particle Swarm Optimization (PASO) and the ISSA were discussed. The experimental results demonstrate the superiority of the modified method.