漏洞分析与风险评估

车联网中移动边缘计算的安全高效节能卸载策略

  • 宋宇波 ,
  • 金星妤 ,
  • 燕锋 ,
  • 胡爱群
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  • 1. 东南大学 网络空间安全学院, 江苏省计算机网络技术重点实验室, 南京 211189;
    2. 网络通信与安全紫金山实验室, 南京 211189;
    3. 东南大学 信息科学与工程学院, 移动通信国家重点实验室, 南京 211189

收稿日期: 2020-11-15

  网络出版日期: 2021-10-19

基金资助

国家重点研发计划项目(2020YFE0200600);江苏省网络与信息安全重点实验室(BM2003201)

Secure and energy efficient offloading of mobile edge computing in the Internet of vehicles

  • SONG Yubo ,
  • JING Xingyu ,
  • YAN Feng ,
  • HU Aiqun
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  • 1. Jiangsu Key Laboratory of Computer Networking Technology, School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China;
    2. Purple Mountain Laboratories, Nanjing 211189, China;
    3. National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing 211189, China

Received date: 2020-11-15

  Online published: 2021-10-19

摘要

该文针对车联网移动边缘计算环境下,车辆在快速移动和切换时与多个边缘服务器间的任务卸载协商所面临的安全性能和系统能耗问题,提出了一个基于边缘服务器和车载服务器协同工作的任务卸载策略安全协商机制,描述了车辆移动时的安全切换交互协议,讨论了其基于边缘服务器覆盖范围的任务分配协商算法及其约束条件。仿真结果表明:该方案可以有效保证通信时的安全性能,同时其卸载能耗及卸载时间与现有方案相比分别减少了58%和17%。

本文引用格式

宋宇波 , 金星妤 , 燕锋 , 胡爱群 . 车联网中移动边缘计算的安全高效节能卸载策略[J]. 清华大学学报(自然科学版), 2021 , 61(11) : 1246 -1253 . DOI: 10.16511/j.cnki.qhdxxb.2021.25.005

Abstract

Task offloading negotiations between vehicles and multiple edge servers in mobile edge computing environments of the internet of vehicles have security problems and excessive system energy consumption. This paper presents a security offloading mechanism based on cooperation between the edge server task offload strategy and the vehicle server. The strategy has a secure switching interaction protocol when the vehicle is moving with a task assignment negotiation algorithm and constraints based on the edge server coverage. Simulations show that the scheme effectively guarantees security during communications while reducing the offloading energy consumption by 58% and the offloading time by 17% compared with an existing scheme.

参考文献

[1] MACH P, BECVAR Z. Mobile edge computing:A survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3):1628-1656.
[2] ZHANG T. Data offloading in mobile edge computing:A coalition and pricing based approach[J]. IEEE Access, 2017, 6:2760-2767.
[3] WU S Y, XIA W W, CUI W Q, et al. An efficient offloading algorithm based on support vector machine for mobile edge computing in vehicular networks[C]//2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). Hangzhou, China:IEEE, 2018:1-6.
[4] ZHANG L, ZHAO Z, WU Q W, et al. Energy-aware dynamic resource allocation in UAV assisted mobile edge computing over social internet of vehicles[J]. IEEE Access, 2018, 6:56700-56715.
[5] XIAO L, WAN X Y, DAI C H, et al. Security in mobile edge caching with reinforcement learning[J]. IEEE Wireless Communications, 2018, 25(3):116-122.
[6] LI L J, ZHOU H M, XIONG S X, et al. Compound model of task arrivals and load-aware offloading for vehicular mobile edge computing networks[J]. IEEE Access, 2019, 7:26631-26640.
[7] GUO H Z, LIU J J, ZHANG J. Computation offloading for multi-access mobile edge computing in ultra-dense networks[J]. IEEE Communications Magazine, 2018, 56(8):14-19.
[8] GUO H Z, LIU J J. Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5):4514-4526.
[9] LIU M Y, LIU Y. Price-based distributed offloading for mobile-edge computing with computation capacity constraints[J]. IEEE Wireless Communications Letters, 2018, 7(3):420-423.
[10] ADITHTHAN A, RAMESH S, SAMII S. Cloud-assisted control of ground vehicles using adaptive computation offloading techniques[C]//Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). Dresden, Germany:IEEE, 2018, 1:589-592.
[11] WANG X J, WEI X, WANG L. A deep learning based energy-efficient computational offloading method in Internet of vehicles[J]. China Communications, 2019, 16(3):81-91.
[12] DU J B, YU F R, CHU X L, et al. Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2):1079-1092.
[13] HOU X S, LI Y, CHEN M, et al. Vehicular fog computing:A viewpoint of vehicles as the infrastructures[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6):3860-3873.
[14] FAROOQ M U, PASHA M, KHAN K U R. Cloud enabled and cluster based efficient data broadcasting in VANETs[C]//2015 International Conference on Green Computing and Internet of Things (ICGCIoT). Noida, India:IEEE, 2015.
[15] WANG X J, NING Z L, WANG L. Offloading in internet of vehicles:A fog-enabled real-time traffic management system[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10):4568-4578.
[16] YU R, ZHANG Y, GJESSING S, et al. Toward cloud-based vehicular networks with efficient resource management[J]. IEEE Network, 2013, 27(5):48-55.
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