Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 385-391, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-385-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 385-391, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-385-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Sep 2017

14 Sep 2017

PERFORMANCE ANALYSIS OF CLASSIFICATION METHODS FOR INDOOR LOCALIZATION IN VLC NETWORKS

D. Sánchez-Rodríguez1, I. Alonso-González1, J. Sánchez-Medina2, C. Ley-Bosch1, and L. Díaz-Vilariño3,4 D. Sánchez-Rodríguez et al.
  • 1Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Campus de Tafira, CP 35017, Las Palmas de Gran Canaria, Spain
  • 2Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, CP 35017, Las Palmas de Gran Canaria, Spain
  • 3Applied Geotechnologies Group, Dept. Natural Resources and Environmental Engineering, University of Vigo, Campus Lagoas-Marcosende, CP 36310 Vigo, Spain
  • 4TU Delft – Faculty of Architecture, OTB, section GIS Technology, Delft, the Netherlands

Keywords: Indoor Localization, Visible Light Communication, Machine Learning Classifiers, Fingerprinting Techniques

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.