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Tasks Offloading for Connected Autonomous Vehicles in Edge Computing

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Abstract

Internet of vehicles (IoV) is gradually combined with connected autonomous vehicles (CAV), which accelerates the development of CAV. In order to meet the service requirements of CAV, mobile edge computing (MEC) provides IoV with a novel paradigm which provides services by fast processing vehicle tasks at the road side units distributed near target vehicles. In this way, vehicle tasks can be offloaded to edge servers deployed in road side units (RSU). A vehicle tasks offloading problem requires load balance of edge servers to be maintained with minimum total time cost. Thus, we proposed a vehicle tasks offloading method (VTO) in which the vehicle tasks offloading problem is formulated as a multi-objective optimization problem. Hence, we design a multi-objective optimization evolutionary algorithm basing on improving the strength pare to evolutionary algorithm (SPEA2) and technique for order preference by similarity to ideal solution (TOPSIS) and multiple criteria decision making (MCDM). Through theoretical analysis and experimental evaluation, the results shows that the performance of VTO is effective and efficient.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under grant No.61702277 and No. 61702442. This work is also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps, under grant No. 2017DB005 and No. 2020DB005 and the Application Basic Research Project in Yunnan Province Grant No. 2018FB105. In addition, this work is supported in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX21_1018.

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Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Qi Wu & Xiaolong Xu

  2. College of Information Science & Technology, Shihezi University, Shihezi, China

    Qingzhan Zhao

  3. Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Xinjiang, China

    Qingzhan Zhao

  4. School of big data and Intelligence Engineering, Southwest Forestry University, Kunming, China

    Fei Dai

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  1. Qi Wu
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  2. Xiaolong Xu
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  3. Qingzhan Zhao
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  4. Fei Dai
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Correspondence to Fei Dai.

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Wu, Q., Xu, X., Zhao, Q. et al. Tasks Offloading for Connected Autonomous Vehicles in Edge Computing. Mobile Netw Appl 27, 2295–2304 (2022). https://doi.org/10.1007/s11036-021-01794-6

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