Edge Computing Offloading Strategy Based on Particle Swarm Algorithm for Power Internet of Things

With the development of smart grid, IOT devices in the field of power control are widely used. The diversity of types, features and functions of IOT devices not only integrates detailed big data, but also challenges the real-time, compatibility and big data processing capability of system control. In this paper, the MEC edge computing model for the power IoT is investigated so as to reduce the challenges of increased latency and limited system storage space caused by the access of a large number of power using devices. The industrial IoT scenario is modeled as a multiuser multi-MEC latency optimization problem to minimize the total cost of the whole system with the constraint of energy consumption. In order to achieve the optimal MEC unloading strategy, the computational offloading strategy based on the basic particle swarm algorithm is proposed. The simulation results show that the proposed scheme of the MEC computing task offload model for the power IoT has more obvious advantages in reducing system overall cost of the network. Moreover, the proposed algorithm of PSAO is convenient and fast converging at the same time.