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Volume 42 Issue 1
Jan.  2020
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Haitao ZHAO, Yinyang ZHU, Yi DING, Hongbo ZHU. Research on Content-aware Classification Offloading Algorithm Based on Mobile Edge Calculation in the Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2020, 42(1): 20-27. doi: 10.11999/JEIT190594
Citation: Haitao ZHAO, Yinyang ZHU, Yi DING, Hongbo ZHU. Research on Content-aware Classification Offloading Algorithm Based on Mobile Edge Calculation in the Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2020, 42(1): 20-27. doi: 10.11999/JEIT190594

Research on Content-aware Classification Offloading Algorithm Based on Mobile Edge Calculation in the Internet of Vehicles

doi: 10.11999/JEIT190594
Funds:  The National Natural Science Foundation of China (61771252), The Natural Science Foundation Project of Jiangsu Province (BK20171444), The University Natural Science Research Major Project of Jiangsu Province (18KJA510005), The "Six Talents High Peaks" Class B Funding Project of Jiangsu Province (DZXX-041), The Jiangsu Provincial Association for Science and Technology Talents Entrustment Project, Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_0949)
  • Received Date: 2019-08-06
  • Rev Recd Date: 2019-11-14
  • Available Online: 2019-11-28
  • Publish Date: 2020-01-21
  • With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time. Competition on limited resources will increase the delay of message processing and energy consumption for terminal equipment. For the equilibrium relationship between delay and energy loss, this paper proposes a content-aware classification offloading algorithm based on Mobile Edge Computing (MEC). Firstly, the security message is prioritized according to the analytic hierarchy process, and then the optimal task unloading model of delay and energy loss is established. The relational model is established by assigning different weight coefficients to delay and energy loss. The Lagrangian relaxation method is used to transform the non-convex problem into a convex problem, which combines the sub-gradient projection method and the greedy algorithm to obtain the feasible solution. The performance evaluation results show that the algorithm improves the message processing delay and energy loss to some extent.
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