Wifi fingerprinting using received signal strength has been widely studied for indoor localization. Classic similarity-based methods like weighted K-nearest neighbor (WKNN) localize targets by searching for the best matching fingerprint in the dataset. Performance of these methods suffers from RSS variance and they are slow under a large size of fingerprint dataset. In this paper, we propose a WKNN localization strategy using k-means clustering radio mapping that balances localization precision and computational complexity.


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    Titel :

    WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping


    Beteiligte:
    LIU, Siyang (Autor:in) / DE LACERDA, Raul (Autor:in) / FIORINA, Jocelyn (Autor:in)


    Erscheinungsdatum :

    01.04.2021


    Format / Umfang :

    2009483 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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