Recently, Vehicular Ad Hoc Network (VANET) is becoming an important topic of Intelligent Transportation Systems (ITS). However, in urban traffic, there are a large number of vehicles; this will result in heavy load of network, which may worsen the performance of VANET. This paper proposes clustered structure, aiming at reducing data redundant and improving network reliability. A centralized clustering algorithm based on Gur-Game is proposed, which is easy to perform while its stability is highly related to specific reward function. Then, an improved self-adaptive perception clustering method based on state machine is presented, which can work without roadside station distributed. The identity of vehicle is decided by neighboring cluster heads. This algorithm is more stable and can be suitable for various scenarios. Simulations on Paramics show the effectiveness of the above two algorithms.


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    Titel :

    State Machine Based Clustering Algorithms in VANET


    Beteiligte:
    Wang, Qi (Autor:in) / Hu, Jianming (Autor:in) / Wang, Yizhi (Autor:in) / Zhang, Yi (Autor:in)

    Kongress:

    First International Conference on Transportation Information and Safety (ICTIS) ; 2011 ; Wuhan, China


    Erschienen in:

    ICTIS 2011 ; 913-919


    Erscheinungsdatum :

    16.06.2011




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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