A Method for Offloading Vehicle Collaborative Tasks for Green Computing

ZHANG Hongxia; LÜ Zhihao; XI Shiyu; LIU Jiamin; GUO Jiashu; ZHANG Peiying

doi:10.11999/JEIT230051

Volume 46 Issue 1

Jan. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 175-183

ZHANG Hongxia, LÜ Zhihao, XI Shiyu, LIU Jiamin, GUO Jiashu, ZHANG Peiying. A Method for Offloading Vehicle Collaborative Tasks for Green Computing[J]. Journal of Electronics & Information Technology, 2024, 46(1): 175-183. doi: 10.11999/JEIT230051

Citation:

ZHANG Hongxia, LÜ Zhihao, XI Shiyu, LIU Jiamin, GUO Jiashu, ZHANG Peiying. A Method for Offloading Vehicle Collaborative Tasks for Green Computing[J]. Journal of Electronics & Information Technology, 2024, 46(1): 175-183. doi: 10.11999/JEIT230051

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A Method for Offloading Vehicle Collaborative Tasks for Green Computing

doi: 10.11999/JEIT230051

1.
Qingdao Institute of Software, China University of Petroleum (East China), Qingdao 266000, China
2.
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266000, China

Funds: The Natural Science Foundation of Shandong Province (ZR2020MF006, ZR2022LZH015)

Received Date: 2023-02-14
Rev Recd Date: 2023-05-12

Available Online: 2023-05-22

Publish Date: 2024-01-17

Abstract

Abstract

Vehicular Edge Computing (VEC) has become a promising and prospective paradigm for computation-intensive and delay-sensitive tasks. However, edge servers are less capable of integrating renewable energy. Therefore, in order to improve the energy efficiency of edge servers, a green computing oriented vehicle collaborative task offloading framework is proposed. In this framework, vehicles equipped with Energy Harvest (EH) devices cooperate to perform tasks by sharing green energy and computing resources with each other. To effectively enhance the participation enthusiasm of vehicles, dynamic pricing is adopted to motivate vehicles, and the mobility and task priority are also considered comprehensively. In order to adapt the offloading decisions to the dynamic environment, a Twin Delayed Deep Deterministic policy gradient (TD3) based task offloading method is proposed to maximize the average task completion utility of all vehicles while reducing the use of grid power. Finally, simulation results verify the effectiveness of the proposed method, and the performance achieves 7.34% and 37.47% improvement respectively compared with Deep Deterministic Policy Gradient (DDPG) based method and Greedy Principle Execution (GPE) method.
- Vehicular Edge Computing (VEC),
- Task offloading,
- Energy Harvest(EH),
- Vehicle collaboration,
- Dynamic pricing

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References(15)

References

[1]	BAI Shengxi and LIU Chunhua. Overview of energy harvesting and emission reduction technologies in hybrid electric vehicles[J]. Renewable and Sustainable Energy Reviews, 2021, 147: 111188. doi: 10.1016/j.rser.2021.111188
[2]	LIU Lei, CHEN Chen, PEI Qingqi, et al. Vehicular edge computing and networking: A survey[J]. Mobile Networks and Applications, 2021, 26(3): 1145–1168. doi: 10.1007/s11036-020-01624-1
[3]	马惠荣, 陈旭, 周知, 等. 绿色能源驱动的移动边缘计算动态任务卸载[J]. 计算机研究与发展, 2020, 57(9): 1823–1838. doi: 10.7544/issn1000-1239.2020.20200184 MA Huirong, CHEN Xu, ZHOU Zhi, et al. Dynamic task offloading for mobile edge computing with green energy[J]. Journal of Computer Research and Development, 2020, 57(9): 1823–1838. doi: 10.7544/issn1000-1239.2020.20200184
[4]	SHI Jinming, DU Jun, WANG Jingjing, et al. Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 16067–16081. doi: 10.1109/TVT.2020.3041929
[5]	LIN Yan, ZHANG Yijin, LI Jun, et al. Popularity-aware online task offloading for heterogeneous vehicular edge computing using contextual clustering of bandits[J]. IEEE Internet of Things Journal, 2022, 9(7): 5422–5433. doi: 10.1109/JIOT.2021.3109003
[6]	FUJIMOTO S, HOOF H, and MEGER D. Addressing function approximation error in actor-critic methods[C]. The 35th International Conference on Machine Learning, Stockholm, Sweden, 2018: 1587–1596.
[7]	WANG Haipeng, LV Tiejun, LIN Zhipeng, et al. Energy-delay minimization of task migration based on game theory in MEC-assisted vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2022, 71(8): 8175–8188. doi: 10.1109/TVT.2022.3175238
[8]	LUO Quyuan, LI Changle, LUAN T H, et al. Collaborative data scheduling for vehicular edge computing via deep reinforcement learning[J]. IEEE Internet of Things Journal, 2020, 7(10): 9637–9650. doi: 10.1109/JIOT.2020.2983660
[9]	MIN Minghui, XIAO Liang, CHEN Ye, et al. Learning-based computation offloading for IoT devices with energy harvesting[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1930–1941. doi: 10.1109/TVT.2018.2890685
[10]	MA Huirong, HUANG Peng, ZHOU Zhi, et al. GreenEdge: Joint green energy scheduling and dynamic task offloading in multi-tier edge computing systems[J]. IEEE Transactions on Vehicular Technology, 2022, 71(4): 4322–4335. doi: 10.1109/TVT.2022.3147027
[11]	LEE J and KO H. Neighbor-aware distributed task offloading algorithm in energy-harvesting internet of things[J]. IEEE Internet of Things Journal, 2023, 10(10): 8744–8753. doi: 10.1109/JIOT.2022.3232710
[12]	KAZMI S M A, DANG T N, YAQOOB I, et al. A novel contract theory-based incentive mechanism for cooperative task-offloading in electrical vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 8380–8395. doi: 10.1109/TITS.2021.3078913
[13]	LIWANG Minghui, DAI Shijie, GAO Zhibin, et al. A truthful reverse-auction mechanism for computation offloading in cloud-enabled vehicular network[J]. IEEE Internet of Things Journal, 2019, 6(3): 4214–4227. doi: 10.1109/JIOT.2018.2875507
[14]	孙慧婷, 范艳芳, 马孟晓, 等. VEC中基于动态定价的车辆协同计算卸载方案[J]. 计算机科学, 2022, 49(9): 242–248. doi: 10.11896/jsjkx.210700166 SUN Huiting, FAN Yanfang, MA Mengxiao, et al. Dynamic pricing-based vehicle collaborative computation offloading scheme in VEC[J]. Computer Science, 2022, 49(9): 242–248. doi: 10.11896/jsjkx.210700166
[15]	XU Huiying, QIU Xiaoyu, ZHANG Weikun, et al. Privacy-preserving incentive mechanism for multi-leader multi-follower IoT-edge computing market: A reinforcement learning approach[J]. Journal of Systems Architecture, 2021, 114: 101932. doi: 10.1016/j.sysarc.2020.101932