The Time-Extended V2V2I path Prediction Method for Multi-RSU VANET Data Offloading based on the Multi-Access Edge Computing (MEC) Architecture

Chung Ming Huang, Tzu Yu Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In the Vehicle-to-Vehicle-to-Infrastructure (V2V2I) VANET data offloading scenario, the source vehicle Vs that wants to have VANET data offloading can use (i) a Vehicle to Infrastructure (V2I) link to do VANET data offloading when Vs is inside the signal coverage of a Road Side Unit (RSU), which is the traditional self-offloading scenario, i.e., Vs connects the RSU directly by itself, or (ii) a V2V2I path, which denotes a n-hop V2V2I path connecting Vs and the ahead/rear RSU, to do VANET data offloading when Vs is outside the signal coverage of the corresponding RSU. When the signal coverages of some ahead/rear RSUs are overlapped, the RSU handoff processing between the signal-overlapped RSUs need to be tackled such that the V2V2I data offloading time can be extended. This work uses the Multi-Access Edge Computing (MEC) architecture to find the n-hop V2V2I offloading path. Based on the periodically received context reports from vehicles and a time-extended prediction mechanism, the MEC server can find whether there are some candidate V2V2I offloading paths that exist in the coming time period or not. Then, the MEC server selects the best one as the V2V2I offloading path. The performance evaluation shown that the proposed method is better than the traditional self-offloading method and can enhance the data offloading performance.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages651-658
Number of pages8
ISBN (Electronic)9781665421744
DOIs
Publication statusPublished - 2021
Event19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 - Virtual, Online, Canada
Duration: 2021 Oct 252021 Oct 28

Publication series

NameProceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

Conference

Conference19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Country/TerritoryCanada
CityVirtual, Online
Period21-10-2521-10-28

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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