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

References 26

1	Guo Hongzhi, Liu Jiajia. UAV-enhanced Intelligent Offloading for Internet of Things at the Edge[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2737-2746.
2	Ren Lei, Liu Yuxin, Wang Xiaokang, et al. Cloud-edge-based Lightweight Temporal Convolutional Networks for Remaining Useful Life Prediction in IIoT[J]. IEEE Internet of Things Journal, 2021, 8(16): 12578-12587.
3	Zhuo Cheng, Luo Shaoheng, Gan Houle, et al. Noise-aware DVFS for Efficient Transitions on Battery-powered IoT Devices[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 39(7): 1498-1510.
4	Shekhar S, Chhokra A, Sun Hongyang, et al. URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-aware IoT Services[J]. Journal of Systems Architecture, 2020, 107: 101710.
5	Liu Yuxin, Zeng Zhiwen, Liu Xiao, et al. A Novel Load Balancing and Low Response Delay Framework for Edge-cloud Network Based on SDN[J]. IEEE Internet of Things Journal, 2020, 7(7): 5922-5933.
6	Calderoni L, Magnani A, Maio D. IoT Manager: An Open-source IoT Framework for Smart Cities[J]. Journal of Systems Architecture, 2019, 98: 413-423.
7	Zhou Zhenyun, Yu Houjian, Xu Chen, et al. BEGIN: Big Data Enabled Energy-efficient Vehicular Edge Computing[J]. IEEE Communications Magazine, 2018, 56(12): 82-89.
8	Chowdhury M Z, Shahjalal M, Ahmed S, et al. 6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions[J]. IEEE Open Journal of the Communications Society, 2020, 1: 957-975.
9	Kakkar R, Gupta R, Jadav N K, et al. Blockchain and Reverse Auction-based EVs Energy Trading Approach for Optimal Pricing[C]//2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence). Piscataway, NJ, USA: IEEE, 2023: 412-417.
10	Hu Jinna, Chen Chen, Cai Lin, et al. UAV-assisted Vehicular Edge Computing for the 6G Internet of Vehicles: Architecture, Intelligence, and Challenges[J]. IEEE Communications Standards Magazine, 2021, 5(2): 12-18.
11	Liu Chengxiao, Feng Wei, Chen Yunfei, et al. Cell-free Satellite-UAV Networks for 6G Wide-area Internet of Things[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(4): 1116-1131.
12	Liu Run, Liu Anfeng, Qu Zhenzhe, et al. An UAV-enabled Intelligent Connected Transportation System with 6G Communications for Internet of Vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 2045-2059.
13	Xia Jingming, Wang Peng, Li Bin, et al. Intelligent Task Offloading and Collaborative Computation in Multi-UAV-enabled Mobile Edge Computing[J]. China Communications, 2022, 19(4): 244-256.
14	Fan Bo, Jiang Li, Chen Yanyan, et al. UAV Assisted Traffic Offloading in Air Ground Integrated Networks with Mixed User Traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 12601-12611.
15	Zhou Jianshan, Tian Daxin, Sheng Zhengguo, et al. Joint Mobility, Communication and Computation Optimization for UAVs in Air-ground Cooperative Networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(3): 2493-2507.
16	Guo Hongzhi, Liu Jiajia. UAV-enhanced Intelligent Offloading for Internet of Things at the Edge[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2737-2746.
17	Zhao Liang, Yang Kaiqi, Tan Zhiyuan, et al. A Novel Cost Optimization Strategy for SDN-enabled UAV-assisted Vehicular Computation Offloading[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(6): 3664-3674.
18	He Yixin, Zhai Daosen, Zhang Ruonan, et al. A Mobile Edge Computing Framework for Task Offloading and Resource Allocation in UAV-assisted VANETs[C]//IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Piscataway, NJ, USA: IEEE, 2021: 1-6.
19	Wang Liang, Wang Kezhi, Pan Cunhua, et al. Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing[J]. IEEE Transactions on Mobile Computing, 2022, 21(10): 3536-3550.
20	Diao Xianbang, Zheng Jianchao, Wu Yuan, et al. Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-based and UAV-assisted Mobile Edge Computing[J]. IEEE Access, 2019, 7: 117448-117459.
21	Du Yao, Yang Kun, Wang Kezhi, et al. Joint Resources and Workflow Scheduling in UAV-enabled Wirelessly-powered MECfor IoT Systems[J]. IEEE Transactions on Vehicular Technology, 2019, 68(10): 10187-10200.
22	Puterman M L. Markov Decision Processes: Discrete Stochastic Dynamic Programming[M]. New York: Wiley, 2014.
23	Hwang R H, Islam M M, Tanvir M A, et al. Communication and Computation Offloading for 5G V2X: Modeling and Optimization[C]//GLOBECOM 2020-2020 IEEE Global Communications Conference. Piscataway, NJ, USA: IEEE, 2020: 1-6.
24	Zhao Fei, Shi Lei, Shi Yi, et al. An Energy-saving Strategy for 5G Base Stations in Vehicular Edge Computing[C]//Collaborative Computing: Networking, Applications and Worksharing. Cham: Springer Nature Switzerland, 2022: 309-325.
25	Zheng Kan, Meng Hanlin, Chatzimisios P, et al. An SMDP-based Resource Allocation in Vehicular Cloud Computing Systems[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7920-7928.
26	胡峰, 王文轩, 顾红. V2X异构车载网络下智能任务卸载策略研究[J]. 控制与决策, 2022, 37(11): 3003-3011.
	Hu Feng, Wang Wenxuan, Gu Hong. Intelligent Task Offloading Strategy in V2X Heterogeneous Vehicular Networks[J]. Control and Decision, 2022, 37(11): 3003-3011.

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