An edge computing-based evaluation and optimisation of online higher vocational education mechanism

As a skill education and employment education, higher vocational education must comprehensively improve students' various skills, so that they can fully adapt to the social economic development and the actual needs of the post after graduation. However, the outbreak of COVID-19 disrupted students' normal life, making online teaching the main teaching mode. Online teaching mainly takes mobile devices as terminals, and the number of devices at the edge of the network and the data are growing rapidly. In this case, the centralised processing mode with cloud computing model as the core will not be able to efficiently process the data generated by edge devices. In this paper, an improved binary particle swarm optimisation algorithm is proposed to study the offloading and execution sequence of user computing tasks. Simulation results reveal that the proposed method outperforms the state-of-the-art algorithms in terms of convergence, low latency and energy consumption.