随着互联网与物联网技术的快速发展车联网也逐渐走进我们的生活。近年来随着智能汽车的发展出现了很多智能化的车联网应用如自动驾驶、辅助驾驶、远程车控、高速追尾预警应用等。这些应用需要大量计算密集型的计算而且对时延也有特别高的要求。但车载处理器往往计算资源有限因此无法达到上述应用的需求。移动边缘计算Mobile Edge Computing MEC的出现很好地解决了终端设备计算和存储能力不足等问题。但在移

With the rapid development of Internet and Internet of Things (IoT) technology, the vehicle networking system (VNS) has gradually entered our lives. In recent years, with the development of smart cars, many intelligent VNS applications have emerged, such as automatic driving, assisted driving, remote vehicle control, and high-speed rear-end collision warning applications. These applications require a large amount of computationally intensive calculations and have particularly high requirements for latency. However, vehicle processors often have limited computing resources and cannot meet the requirements of the above applications. The emergence of Mobile Edge Computing (MEC) has effectively solved the problems of insufficient computing and storage capabilities of terminal devices. However, while MEC brings solutions, it also faces many new problems and challenges, such as how to develop a reasonable and efficient task offloading mechanism based on limited computing resources (used for task processing) and energy resources (used for task transmission).

MEC shortens the distance between the server and the user, reduces latency, and reduces the data pressure on the core network. Through task offloading technology, users can offload some or all tasks to MEC servers to reduce task completion latency and reduce their own energy consumption. Therefore, in practical applications, task offloading decisions play an important role. This article focuses on the task offloading decision in a single server-single user network system model.

Firstly, the basic concept and advantages and disadvantages of MEC are introduced, and then the application scenarios of this technology in VNS are briefly described. The article also highlights the challenges of task offloading for VNS-MEC, and proposes a single server-single user system model. A large task is decomposed into multiple sub-tasks, and the energy consumption of mobile vehicle devices is used as a constraint condition, and the latency is used as the objective function. Through analysis, a suboptimal offloading solution is proposed, and its feasibility is verified through simulation, which can effectively reduce latency and improve user experience