A collaborative task offloading strategy for mobile edge computing in internet of vehicles

• Published in: 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Vol. 5; pp. 1379 - 1384
• Main Authors: Song, Zhiyuan, Ma, Ruijiang, Xie, Yong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12-03-2021
• Subjects: computing resources; Data communication; Edge computing; Energy consumption; Internet; Internet of vehicles; mobile edge computing; Servers; Simulation; Task analysis; task offloading
• ISSN: 2689-6621
• DOI: 10.1109/IAEAC50856.2021.9390817

With the development of Internet of vehicles, in the future, people's demand for data communication, networking and intelligent vehicle flow calculation will be greatly increased. The limited computing capacity of vehicle equipment has been unable to meet a large number of computing needs. In the case of limited computing capacity of vehicle equipment, how to solve the problem between the limited computing resources, storage resources and the needs of a large number of application resources. And reduce the energy consumption required for the calculation task, so as to achieve the purpose of green energy saving, is a subject that we need to study. In this paper, we study the mobile edge computing of the Internet of vehicles, and propose a multi task unloading strategy scheme based on energy consumption, and set up the resource sharing and mutual utilization among mobile vehicles, Road Side Unit (RSU) and Macro Base Station (MBS). Mobile vehicles, RSU and MBS can provide computing resource services for vehicle equipment nearby, so as to alleviate the computing needs of vehicle equipment. Because the computing devices and devices on the edge are very close to each other, the network transmission is more direct, so the data transmission is relatively fast, and the response speed for computing services is also very fast. Through V2V and V2I, resources are optimized among vehicles, mobile vehicles, RSU and MBS, providing fast computing services for vehicle equipment, and reducing the overall energy consumption of computing tasks. By deploying MEC server on RSU and MBS side, the computing task can be unloaded to MEC server through wireless cellular network, which can reduce the resource pressure of vehicle equipment and reduce the energy consumption of computing task. Simulation results show that the proposed scheme is effective.