In the Vehicular Ad hoc NETwork (VANET), vehicles are clustered to construct many small networks (clusters) so that channel interferences and flooding messages can be limited. This work presents a novel Multi-resolution Relative Speed Detection (MRSD) model to improve the clustering algorithm in VANET without using a Global Positioning System (GPS). MRSD uses the Moving Average Convergence Divergence (MACD), the Momentum of Received Signal Strength (MRSS) and an Artificial Neural Network (ANN) to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength (RSS). With the speed information, vehicles in a cluster are grouped by their speeds and a vehicle in the largest group is elected as a cluster leader. Moreover, MRSD can detect relative speed among vehicles without GPS and thus can offer better privacy for users. Simulation results show that MRSD can classify accurately vehicles speed without GPS assistance.


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    Titel :

    GPS-less speed detection in VANET


    Beteiligte:
    Cheng, Wen-Mei (Autor:in) / Chuing, Tein-Yaw (Autor:in) / Chen, Yung-Mu (Autor:in) / Yuan, Fong-Ching (Autor:in)


    Erscheinungsdatum :

    2010


    Format / Umfang :

    7 Seiten, 8 Bilder, 5 Tabellen, 12 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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