Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43106
Title: Performance analysis of classification methods for indoor localization in VLC networks
Authors: Sánchez-Rodríguez, D. 
Alonso-González, I.
Sánchez-Medina, J. 
Ley-Bosch, C. 
Díaz-Vilariño, L.
UNESCO Clasification: 3307 Tecnología electrónica
Issue Date: 2017
Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Conference: ISPRS Geospatial Week 2017 
Abstract: © Authors 2017.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.
URI: http://hdl.handle.net/10553/43106
ISSN: 2194-9042
DOI: 10.5194/isprs-annals-IV-2-W4-385-2017
Source: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences [ISSN 2194-9042], v. 4, p. 385-391
Appears in Collections:Actas de congresos
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