Multi task dynamic edge–end computing collaboration for urban Internet of Vehicles

As the future trend, more and more vehicles access to the Internet of Vehicles, which means that a huge number of tasks of the vehicle terminals need to be transformed and completed on the network. Edge computing makes the tasks executed on the edge nodes near the terminal, but some vehicle terminals are at a relatively idle state and these additional computing resources are not utilized, causing great waste of resources. What is more, it is hard to highly and comprehensively satisfy the high real-time requirements of some tasks. In order to execute these tasks efficiently, we propose a dynamic edge–end computing collaboration architecture for urban IoV. In this architecture, edge nodes and vehicle terminals can cooperate with each other, which means tasks can be allocated more dynamically and flexibly. We evaluate the completion of the task by considering task latency and overhead, task transmission model, task priority, as well as edge node and vehicle terminal's capacity when defining task comprehensive utility. Then weformulate the task allocation as an optimization problem and propose an improved quantum particle swarm optimization algorithm to solve the problem. Simulation results show that the proposed strategy have better task allocation utility than other strategies, which can effectively solve the multi task allocation problem.