Clustering in Vehicular Ad-Hoc Networks (VANETs) is essential to mitigate different challenges and meet the required quality of communications. However, most of the available clustering protocols were designed for highways, and thus become unstable in realistic urban environments with many intersections. In this paper, a Clustering Adaptation Near Intersection (CANI) approach is proposed to ensure clustering stability at intersections. This approach exploits Online Sequential Extreme Learning Machine (OS-ELM) to predict the behavior of the vehicles near an intersection and adapt the clusters accordingly. The main advantage of the developed OS-ELM prediction model is its ability to continuously learn and update in real time. After being validated, the proposed adaptation approach is included in a highway clustering scheme. The resultant clustering protocol is compared to other schemes in a realistic urban environment, and shows significant stability and efficiency performance improvement.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Enhanced Urban Clustering in VANETs Using Online Machine Learning


    Contributors:


    Publication date :

    2019-09-01


    Size :

    1782937 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Security Monitoring for VANETs Using Machine Learning

    Pape, Nathan / Mansour, Christopher | IEEE | 2024


    An intelligent clustering algorithm for VANETs

    Bali, Rasmeet Singh / Kumar, Neeraj / Rodrigues, Joel J. P. C. | IEEE | 2014


    Medium Access Concept for VANETs Based on Clustering

    Guenter, Yvonne / Wiegel, Bernhard / Grossmann, Hans Peter | IEEE | 2007


    Deterministic MAC Protocol Based on Clustering for VANETs

    Hernandez-Jayo, Unai / Mammu, Aboobeker Sidhik Koyamparambil / Sainz, Nekane | Springer Verlag | 2016


    Intersection Based Routing in Urban VANETs

    Acarman, Tankut / Yaman, Cagdas / Peksen, Yavuz et al. | IEEE | 2015