Optimizing Environment-aware VANET Clustering using Machine Learning: International Journal of Intelligent Transportation Systems Research

Y. Fahmy, G. Alsuhli, A. Khattab

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Clustering is important to improve the quality of service in many VANET protocols and applications, such as data dissemination, media access control, Internet of vehicles and intrusion detection systems. Since the performance of a clustering algorithm is highly influenced by the surrounding environment, an environment-aware clustering algorithm that can adapt itself to follow the changes in the environment features is required. In this paper, we propose a machine learning-based framework to include this awareness to an arbitrary clustering algorithm. This is done by optimizing the clustering algorithm’s parameters taking into consideration the road structure and the traffic features that affect the clustering performance. Our framework aims to model how the optimum values of different configuration parameters change with the considered features. Then, these models are used to allow the clustering algorithm to adjust its configurations in real-time according to the measured environment features. After applying this framework on a state-of-the-art clustering algorithm, the performance of the resulting algorithm is compared to other clustering schemes as well as the original algorithm. The obtained results prove the efficiency of the proposed approach reflected by the significant improvements in the different quality metrics and maintaining these metrics in the highest possible levels despite the changes in the considered environment features. © 2023, The Author(s), under exclusive licence to Intelligent Transportation Systems Japan.
    Original languageBritish English
    Pages (from-to)394-408
    Number of pages15
    JournalInt. J. Intelligent Transp. Syst. Res.
    Volume21
    Issue number3
    DOIs
    StatePublished - 2023

    Keywords

    • Clustering algorithm
    • Machine learning
    • Vehicular Ad hoc Network (VANET)
    • Clustering algorithms
    • Intelligent systems
    • Intrusion detection
    • Learning algorithms
    • Medium access control
    • Quality control
    • Quality of service
    • Scattering parameters
    • Ad hoc network application
    • Ad-hoc network protocols
    • Clusterings
    • Data dissemination
    • Machine-learning
    • Network Clustering
    • Performance
    • Quality-of-service
    • Vehicular ad hoc network
    • Vehicular Adhoc Networks (VANETs)
    • Vehicular ad hoc networks

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