4.7 Article

Diversified Technologies in Internet of Vehicles Under Intelligent Edge Computing

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3019756

关键词

Edge computing; Task analysis; Internet; Delays; Intelligent vehicles; Protocols; Internet of Vehicles; intelligent edge computing; artificial intelligence technology; transmission delay; diversified technologies

资金

  1. National Natural Science Foundation of China (NSFC) [61902203]
  2. Key Research and Development Plan-Major Scientific and Technological Innovation Projects of Shandong Province [2019JZZY020101]

向作者/读者索取更多资源

By combining intelligent edge computing and IoT technologies, an optimized model for vehicle computational task offloading and migration is proposed, showing superiority in transmission delay, energy consumption, and task completion time compared to other schemes.
To investigate the diversified technologies in Internet of Vehicles (IoV) under intelligent edge computing, artificial intelligence, intelligent edge computing, and IoV are combined. Also, it proposes an IoV model for intelligent edge computing task offloading and migration under the SDVN (Software Defined Vehicular Networks) architecture, that is, the JDE-VCO (Joint Delay and Energy-Vehicle Computational task Offloading) optimization. And the simulation is performed. The results show that in the analysis of the impact of different offloading strategies on the IoV, it is found that the JDE-VCO algorithm is superior to other schemes in terms of transmission delay and total offloading energy consumption. In the analysis of the impact of the task unloading of the IoV, the JDE-VCO algorithm is less than RTO (Random Tasks Offloading) and UTO (Uniform Tasks Offloading) algorithm schemes in terms of the number of tasks per unit time, and the average task completion time for the same amount of uploaded data. In the analysis of the packet loss ratio and transmission delay, it can be found that the packet loss ratio and transmission delay of the JDE-VCO algorithm are less than the RTO and UTO algorithms. Moreover, the packet loss ratio of the JDE-VCO algorithm is about 0.1, and the transmission delay is stable at 0.2s, which has obvious advantages. Therefore, through research, the IoV model of task offloading and migration built by intelligent edge computing can significantly improve the load sharing rate, offloading efficiency, packet loss ratio, and transmission delay when the IoV is processing tasks and uploading data. It provides experimental basis for the improvement of the IoV system.

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