Edge-Based V2X communications with big data intelligence

Siri Guleng, Celimuge Wu, Zhi Liu, Xianfu Chen

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)


Vehicular Internet-of-Things applications require an efficient Vehicle-to-Everything (V2X) communication scheme. However, it is particularly challenging to achieve a high throughput and low latency with limited wireless resources in highly dynamic vehicular networks. In this article, we propose a scheme that enhances V2V communications through integration of vehicle edge-based forwarding and learning-based edge selection policy optimization. The proposed scheme has three main characteristics. First, the Hierarchical edge-based preemptive route creation is introduced to create hierarchical edges and conduct efficient packet forwarding as well as route aggregation. Second, Two-stage learning is introduced to select efficient edge nodes using big data driven traffic prediction and reinforcement learning-based edge node selection. Third, Context-aware edge selection is employed to improve the performance of edge-based forwarding in various contexts. We use real traffic big data and realistic vehicular network simulations to evaluate the performance of the proposed scheme and show the advantage over other baseline approaches.
Original languageEnglish
Article number8951087
Pages (from-to)8603-8613
JournalIEEE Access
Publication statusPublished - 1 Jan 2020
MoE publication typeA1 Journal article-refereed


  • Edge computing
  • traffic big data
  • V2X communications
  • vehicular ad hoc networks


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