Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks

Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks

Xiao Han, Huiqiang Wang, Guoliang Yang, Chengbo Wang
Copyright: © 2024 |Pages: 23
DOI: 10.4018/IJDCF.349133
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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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System Modelling

In this paper, we provide mobile edge computing services to users in a time-varying vehicular network, as shown in the scenario in Figure 1. Mobile vehicles with spare computing resources and roadside infrastructure together form a service unit to process computing tasks for users. The set of vehicles with task requests is Vs=Vs1,Vs2,…,Vsn, and the set of vehicles that can be used as auxiliary computing nodes is Ve=Ve1,Ve2,...,Ven.

Figure 1.

Trusted computing offloading network model scenario

IJDCF.349133.f01

All nodes can communicate with each other within the communication range for resource sharing and trust evaluation. A node's task request can be migrated to only one node within its communication range. The computational tasks commonly carry real-time road information and authentication information for the user and are highly sensitive to latency. The main parameters used in the model proposed in this paper are shown in Table 1

Table 1.
Parameter definitions
ParametersDefine
Vs
Pool of vehicles generating tasks
Ve
Calculate the vehicle pool
r
roadside unitMsn
ωsn
TaskMsn CPU RPMs required
ksn
Size of data volume for task Msn
Dij
Communication distance for link(i,j)
rij
Transmission rate for link(i,j)
Bij
Communication bandwidth for link(i,j)
pij
Transmission power of the link(i,j)
dijt
Transmission time for task Msn
R
Maximum communication radius of vehicle nodes
Cijt
Maximum connection time for link(i,j)
Lijt
Stability of the link(i,j)
Tdt
Direct trust assessment of target nodes
Tpt
Past trust assessment of target nodes
TtGlobal trust assessment of target nodes

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