帮我翻译成英文摘要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。车辆通过搭载的雷达、传感器等感知设备对外界环境信息进行快速采集然后与其他车辆、人、基础设施进行无线通信与信息传递构成了车联网系统。通过对收集到的信息数据进行分析与处理实现智能调度与管理。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

With the rapid development of the Internet and the Internet of Things (IoT), the Internet of Vehicles (IoV) has gradually entered our lives. Vehicles collect environmental information through sensing devices such as radar and sensors, and then communicate wirelessly with other vehicles, people, and infrastructure to form an IoV system. By analyzing and processing the collected information data, intelligent scheduling and management can be achieved. In recent years, with the development of intelligent vehicles, many intelligent IoV applications have emerged, such as autonomous driving, assisted driving, remote vehicle control, and high-speed rear-end collision warning applications. These applications require a large amount of computation and have special requirements for latency. However, vehicle processors often have limited computing resources, so they cannot meet the requirements of these applications. The emergence of Mobile Edge Computing (MEC) has effectively solved the problem of insufficient computing and storage capacity 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 (for task processing) and wireless resources (for task transmission).

MEC moves remote cloud services to the edge of the network, shortening the distance between the server and the user, reducing latency, and relieving 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 user energy consumption. Therefore, in practical applications, task offloading decisions play an important role. This paper studies the task offloading decision of a single-server-single-user network system model.

Firstly, the development status of mobile cloud computing and the major problems it faces are introduced to lead to MEC, and its basic concepts and advantages are briefly described. Then, the application scenarios of this technology in IoV are described, and the problem of task offloading for IoV-based MEC is proposed. To solve this problem, we consider a single-server-single-user system model and decompose the user's task into multiple sub-tasks. With the energy consumption of the mobile vehicle device as a constraint and the user's latency as the objective function, a suboptimal offloading scheme is proposed through analysis. Its feasibility is verified by simulation, which can effectively reduce latency and improve user experience