Preschool education optimization based on mobile edge computing under COVID‐19

Abstract

AbstractThe COVID‐19 pandemic has brought profound changes in people's live and work. It has also accelerated the development of education from traditional model to online model, which is particularly important in preschool education. Preschoolers communicate with teachers through online video, so how to provide high quality and low latency online teaching has become a new challenge. In cloud computing, users offload computing tasks to the cloud to meet the high computing demands of their devices, but cloud‐based solutions have led to huge bandwidth usage and unpredictable latency. In order to solve this problem, mobile edge computing (MEC) deploys the server at the edge of the network to provide the service with close range and low latency. In task scheduling, edge computing (EC) devices have rational thinking, and they are unwilling to collaborate with MEC server to perform tasks due to their selfishness. Therefore, it is necessary to design an effective incentive mechanism to encourage the collaboration of EC devices. Through analysis of MEC server and EC devices, we propose a distributed task scheduling algorithm—Stackelberg game approach based on alternating direction method of multipliers, which selects the appropriate incentive mechanism to encourage the collaboration of EC devices. The experimental results demonstrate that the proposed approach can rapidly converge to a certain accuracy within 40 iterations, and in incentive mechanism comparison and quality of experience, the proposed approach also has a good performance in anti‐jitter and low latency.