UAV-enabled Task Offloading Strategy for Vehicular Edge Computing Networks

doi:10.16182/j.issn1004731x.joss.23-0307

Abstract

Abstract:

As intelligent vehicles are equipped with more and more sensors, the explosive growth of sensor data is generated, which brings severe challenges to vehicular communication and computing. In addition, the modern road presents a three-dimensional structure, and the system architecture of traditional vehicular networks cannot guarantee full coverage and seamless computing. A task offloading strategy for UAV-assisted and 6G-enabled (Sixth Generation) vehicular edge computing networks is proposed. Furthermore, a flexible and intelligent vehicular edge computing mode is composed by vehicles and UAVs, which provide three-dimensional edge computing services for delay-sensitive and computation-intensive vehicular tasks, and ensure timely processing and fusion of massive sensor data. Finally, the optimal task offloading strategy in the network is obtained by an algorithm based on reinforcement learning.

Key words: task offloading, vehicular computing networks, UAV, 6G(sixth generation), reinforcement learning

CLC Number:

TP273

Hu Feng, Gu Haiyang, Lin Jun. UAV-enabled Task Offloading Strategy for Vehicular Edge Computing Networks[J]. Journal of System Simulation, 2023, 35(11): 2373-2384.

Figures/Tables 7

Fig. 1

Fig. 2

Table 1

Simulation parameter table

参数	含义	取值	参数	含义	取值
$M$	资源总数目	15	$K$	资源最大数目	8~15
$N U$	无人机数目	2	$T U$	无人机资源数目	5~10
$λ t$	任务到达速率	5	$μ t$	任务离开速率	5
$λ v$	车辆到达速率	10	$T 0$	本地计算时间	20
$μ v$	车辆离开速率	10	$P$	车辆发射功率	5
$E$	能量开销	20	$η$	卸载失败惩罚	20
$γ$	传输时延价格	2	$α$	收益折扣率	0.1
$ζ e$	节省能量价格	2	$ζ t$	节省时延价格	2
$ω e$	节省能量权重	0.5	$ω t$	节省时延权重	0.5

Table 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

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