Mobile Edge Computing Task Offloading Method for the Power Internet of Things

Since the 5G era, Internet of Things (IoT) technology has seen a substantial increase and is more widely used. This has led to a rapid development of the traditional distribution network towards a more intelligent and informative power IoT. However, due to the increasing amount of computing, the traditional Cloud computing paradigm (CCP) can no longer meet the demand. In order to reduce the burden of the cloud computing center and solve the offloading problem of smart terminals, and to make reasonable scheduling of computing resources and user requests, the article establishes a multi-objective optimization model for scheduling optimization in mobile edge computing (MEC) task offloading. Firstly, an inertial weighted particle swarm optimization (IWPSO) model is proposed based on a convex fitting technique to solve the power distribution (PD) problem. Then, the task offloading and resource allocation (TORA) problem is modelled as a mixed integer nonlinear model, and the TORA is decomposed into two sub-problems, TO and RA. The multi-objective optimization model based on improved binary particle swarm optimization (IBPSO) is named MO-IBPSO, and the problem is solved using MO-IBPSO. The experimental results show that the model proposed in the article is superior to the existing models, and it can solve the scheduling problem of edge computing task unloading well.