Performance analysis of classification methods for indoor localization in VLC networks

Conference Paper (2017)
Author(s)

D. Sánchez-Rodríguez (University of Las Palmas de Gran Canaria)

I. Alonso-González (University of Las Palmas de Gran Canaria)

J. Sánchez-Medina (University of Las Palmas de Gran Canaria)

C. Ley-Bosch (University of Las Palmas de Gran Canaria)

L. Díaz-Vilarino (TU Delft - OLD Department of GIS Technology, University of Vigo)

Research Group
OLD Department of GIS Technology
Copyright
© 2017 D. Sánchez-Rodríguez, I. Alonso-González, J. Sánchez-Medina, C. Ley-Bosch, L. Díaz-Vilarino
DOI related publication
https://doi.org/10.5194/isprs-annals-IV-2-W4-385-2017
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 D. Sánchez-Rodríguez, I. Alonso-González, J. Sánchez-Medina, C. Ley-Bosch, L. Díaz-Vilarino
Research Group
OLD Department of GIS Technology
Volume number
IV-2/W4
Pages (from-to)
385-391
Reuse Rights

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Abstract

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.