TY - GEN
T1 - Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices
AU - Quezada-Gaibor, Darwin
AU - Klus, Lucie
AU - Torres-Sospedra, Joaquiin
AU - Lohan, Elena Simona
AU - Nurmi, Jari
AU - Huerta, Joaquin
N1 - Funding Information:
Corresponding Author: D. Quezada Gaibor ([email protected]) The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/).; J. Torres-Sospedra gratefully acknowledge funding from Ministerio de Ciencia, Innovación y Universidades (INSIGNIA, PTQ2018-009981)
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
jufoid=72315
PY - 2020/10
Y1 - 2020/10
N2 - IoT devices and wearables may rely on Wi-Fi finger-printing to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational load without degrading the positioning error. Thus, the aim of this article is to improve the positioning error and reduce the dimensionality of the radio map by using an enhanced DBSCAN. Moreover, we provide an additional analysis of combining DBSCAN + PCA analysis for further dimensionality reduction. Thereby, we implement a postprocessing method based on the correlation coefficient to join noisy samples to the formed clusters with Density-based Spatial Clustering of Applications with Noise (DBSCAN). As a result, the positioning error was reduced by 10% with respect to the plain DBSCAN, and the radio map dimensionality was reduced in both dimensions, samples and Access Points (APs).
AB - IoT devices and wearables may rely on Wi-Fi finger-printing to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational load without degrading the positioning error. Thus, the aim of this article is to improve the positioning error and reduce the dimensionality of the radio map by using an enhanced DBSCAN. Moreover, we provide an additional analysis of combining DBSCAN + PCA analysis for further dimensionality reduction. Thereby, we implement a postprocessing method based on the correlation coefficient to join noisy samples to the formed clusters with Density-based Spatial Clustering of Applications with Noise (DBSCAN). As a result, the positioning error was reduced by 10% with respect to the plain DBSCAN, and the radio map dimensionality was reduced in both dimensions, samples and Access Points (APs).
KW - Clustering
KW - DBSCAN
KW - PCA
KW - RSS
KW - Wi-Fi finger-printing
U2 - 10.1109/ICUMT51630.2020.9222411
DO - 10.1109/ICUMT51630.2020.9222411
M3 - Conference contribution
AN - SCOPUS:85094898912
T3 - International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
SP - 208
EP - 213
BT - 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020
PB - IEEE
T2 - International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
Y2 - 5 October 2020 through 7 October 2020
ER -