Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video broadcasting- terrestrial (DVB-T) signals in three real testbed environments. The consideration of readily available, ambient radio signals frees the need for dedicated radio transmitters. Noise-reduction techniques such as feature selection and ensemble learning are proposed in conjunction with SVM. Our results examine the performance comparisons between SVM and k-NN, as well as the performance comparisons of SVM-based methods incorporating different noise-reduction schemes, with noisy RSS data. Insights into the performance of learning- based localization schemes working with real database collected from real environments are provided.


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    Titel :

    A Comparative Study of Machine-Learning Indoor Localization Using FM and DVB-T Signals in Real Testbed Environments


    Beteiligte:
    Cheng, Yen-Kai (Autor:in) / Chang, Ronald Y. (Autor:in) / Chen, Ling-Jyh (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    1028968 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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