Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

Abstract As a new form of computing based on the core technology cloud and built edge infrastructure, can handle computing-intensive delay-sensitive tasks. In mobile (MEC) assisted by 5G technology, offloading tasks devices to servers in network effectively reduce delay. Designing reasonable task strategy resource-constrained multi-user multi-MEC system meet users’ needs is challenge issue. industrial internet things (IIoT) environment, considering rapid increase heterogenous servers, particle swarm optimization (PSO)-based proposed offload from with energy efficiency low delay style. A multi-objective problem that considers time delay, consumption execution cost proposed. The fitness function represents total all different MEC servers. PSO compared genetic algorithm (GA) simulated annealing (SA) through simulation experiments. experimental results show server, balance realize resource allocation.