Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

Abstract

With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles. Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced; second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced; then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is increased by 0.32%, which improves the practicability of the system.