Performance analysis of classification methods for indoor localization in VLC networks

D. Sánchez-Rodríguez, I. Alonso-González, J. Sánchez-Medina, C. Ley-Bosch, L. Díaz-Vilariño

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    5 Citations (Scopus)
    15 Downloads (Pure)


    Indoor localization has gained considerable attention over the past decade because of the emergence of numerous location-aware services. Research works have been proposed on solving this problem by using wireless networks. Nevertheless, there is still much room for improvement in the quality of the proposed classification models. In the last years, the emergence of Visible Light Communication (VLC) brings a brand new approach to high quality indoor positioning. Among its advantages, this new technology is immune to electromagnetic interference and has the advantage of having a smaller variance of received signal power compared to RF based technologies. In this paper, a performance analysis of seventeen machine leaning classifiers for indoor localization in VLC networks is carried out. The analysis is accomplished in terms of accuracy, average distance error, computational cost, training size, precision and recall measurements. Results show that most of classifiers harvest an accuracy above 90 %. The best tested classifier yielded a 99.0 % accuracy, with an average error distance of 0.3 centimetres.

    Original languageEnglish
    Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Publication statusPublished - 2017
    EventISPRS Geospatial Week 2017 - Wuhan, China
    Duration: 18 Sep 201722 Sep 2017


    ConferenceISPRS Geospatial Week 2017
    Internet address


    • Fingerprinting Techniques
    • Indoor Localization
    • Machine Learning Classifiers
    • Visible Light Communication


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