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Top1. Introduction
With the rapid development of the Internet of Things (IoT), it is inevitable to provide real-time, low-latency services to realize the integration of storage and processing power. Due to massive data computing have gradually been emerged and high system cost of communication and computing, it will be difficult to compute for loading of remote task based on cloud computing (Y. Dai, et al. 2021). Furthermore, it is urgent to solve the contradiction between limited computing, long-term continuous low latency and high quality of service requirements with the rapid development of mobile devices (S. Sharma, et al. 2019). As a technology integrated with the Internet of Things, MEC can provide services and computing center for users nearby and has the possibility to supply real-time, low-latency services. A lot of data centers are relatively small even with some ability of edge computing. Compared with the traditional Cloud Architecture, it seems to be more suitable for the needs of latency, responsiveness and Privacy protection. At present, it has become a trend to combine mobile computing technology with wireless communication technology to promote the development of mobile communication (L. Yao, X. Xu, et al. 2022). MEC can not only increase user experience and utilization rate of bandwidth resources, but also provide some applications for innovation of service by sinking the computing power to the mobile edge.
In order to improve the urban traffic conditions, the Internet of Vehicle (IoV) as a new paradigm is introduced to enhance the information interaction between vehicles and people (T. Ni, et al. 2019). The Internet of Vehicles (IoV) is a kind of new mode, driven by the latest advancements in vehicular communications. In the IoV environment, the vehicle is connected to devices such as smart cameras, sensors and actuators. By the transmitters and receivers in above signal collecting system, vehicles can connect to remote infrastructure and other vehicles (X. Xue, et al. 2020). Vehicles can communicate with remote infrastructures and other vehicles users through these devices (Y. Dai, et al. 2020). The computing tasks of vehicle users can be delivered to the associated MEC servers based on a strategy to improve computation performance greatly (T. Ni, Y. Yao, H. Chang, et al. 2019). However, it is difficult to Rely on lightweight edge servers which set on the roadside for a lot of computing tasks with different granularity and quality of service (QoS) requirements. How to ensure the normal and efficient computation for these complex services will be a challenge (L. Liu, et al. 2019). Some vehicles maybe idle for task operation for computing tasks in Internet of Vehicle (K. Zhang, et al. 2020). How to improve the computing offloading efficiency is an important problem in IoV. Most existing works focus on offloading tasks to MEC servers while the computing capacity of the on-board unit (OBU) was Neglected.
MEC can be used as the core access node for the transmission and processing of edge business tasks of the Internet of Vehicles (Shen, X., et al. 2022). At the center of the cloud model, features related to average task latency and resource costs cannot be ignored due to the distance between the Internet of Vehicle devices and the data center at the edge of the vehicle equipment (Song Yubo, et al. 2021). As the number of mobile terminals increase exponentially, high latency may be a problem for some applications involving communication between ends. When all tasks were offloaded to the edge server due to the channel of communication or network were be congested, the latency may be longer and the execution may be too slowly. In such condition, some potential traffic safety would be caused if the edge server undertakes all tasks