Spatial Intelligence toward Trustworthy Vehicular IoT

Celimuge Wu, Zhi Liu, Di Zhang, Tsutomu Yoshinaga, Yusheng Ji

    Research output: Contribution to journalArticlepeer-review

    40 Citations (Scopus)


    Spatial challenges for the vehicular Internet of Things come from mobility, high density, sparse connectivity, and heterogeneity. In this article, we propose two techniques, namely decentralized moving edge and multi-tier multi-access edge clustering, to handle these challenges. The vehicle as an edge concept of the decentralized moving edge provides a more suitable solution to meet the throughput and latency performance requirements by conducting distributed communication, data caching, and computing tasks at vehicles. Multi-tier multi-access edge clustering generates different levels of clusters for more efficient integration of different types of access technologies including licensed/unlicensed long-range low-throughput communications and unlicensed short-range high-throughput communications. We employ fuzzy logic to jointly consider multiple inherently contradictory metrics and use Q-learning to achieve a self-evolving capability. Realistic computer simulations are conducted to show the advantage of the proposed protocols over alternatives.

    Original languageEnglish
    Article number8493113
    Pages (from-to)22-27
    Number of pages6
    JournalIEEE Communications Magazine
    Issue number10
    Publication statusPublished - 2018 Oct 1

    ASJC Scopus subject areas

    • Computer Science Applications
    • Computer Networks and Communications
    • Electrical and Electronic Engineering

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