This paper introduces an original method for sensors localization in WSNs. Based on radio-location fingerprinting and machine learning, the method consists of defining a model whose inputs and outputs are, respectively, the received signal strength indicators and the sensors locations. To define this model, several kernel-based machine-learning techniques are investigated, such as the ridge regression, support vector regression, and vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data.


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    Titel :

    Kernel-based machine learning using radio-fingerprints for localization in wsns


    Beteiligte:
    Mahfouz, Sandy (Autor:in) / Mourad-Chehade, Farah (Autor:in) / Honeine, Paul (Autor:in) / Farah, Joumana (Autor:in) / Snoussi, Hichem (Autor:in)


    Erscheinungsdatum :

    2015-04-01


    Format / Umfang :

    948260 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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