A computing resource scheduling strategy of massive IoT devices in the mobile edge computing environment

Abstract

Abstract Aiming at the problem of scheduling computing resources for massive Internet of Things (IoT) devices, this paper proposes a scheduling strategy model based on mobile edge computing for massive IoT computing resources. First, the application scenarios are defined, the task offloading model and queue model are constructed. Then, task urgency and BS energy are considered to determine the optimization goal. Next, wolf colony algorithm is used to improve pheromone calculation so that the ant colony algorithm converges faster and is not easy to fall into local optimum when adjusting the computing resources of IoT devices, and then realizes the scheduling strategy of massive IoT devices. Finally, the experimental verification and comparative analysis of our proposed method are carried out. Experimental results show that proposed method is superior to the method based on Game Theory (GT) and the method based on Sub‐Optimal Policy (SOP). Besides, the proposed method can offload more tasks under the same conditions. The average energy consumption of proposed method is lower in 60–240 GHz frequency band. Moreover, it appears to increase significantly in 60–120 GHz frequency band, and tends to be stable in 120–240 GHz frequency band.