Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks

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Author(s): Xiao Han (Harbin Engineering University, China), Huiqiang Wang (Harbin Engineering University, China), Guoliang Yang (Harbin Engineering University, China)and Chengbo Wang (Harbin Engineering University, China)
Copyright: 2024
Volume: 16
Issue: 1
Pages: 23
Source title: International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/IJDCF.349133

Keywords: Digital Crime & Forensics / Information Science Reference / IT Security and Ethics / Security & Forensics

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Abstract

In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when integrating the two offloading modeth: 1) the high mobility of vehicles can easily lead to connectivity interruptions between nodes, which in turn affects the processing of the tasks that are being offloaded; and 2) vehicles on the road are not completely trustworthy, and vehicle tasks that contain private information may suffer from result errors or privacy leakage and other problems. This paper investigates the computing offloading problem for minimizing task completion delay in vehicular networks. Specifically, we design a trust model for mobile in-vehicle networks and construct a migration decision problem to minimize the overall delay of task execution for all vehicle users. The simulation results show that the scheme proposed in this paper can effectively reduce the execution delay of the task compared to the baseline scheme.

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Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks

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