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Journal of Sensors
Volume 2017, Article ID 1928578, 22 pages
Research Article

An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism

1Center of Information and Communication Technologies, Universidad Nacional de Ingeniería, Lima, Peru
2Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Albacete, Spain

Correspondence should be addressed to Manuel Castillo-Cara; ep.ude.inu@ollitsacm

Received 2 June 2017; Revised 20 August 2017; Accepted 17 September 2017; Published 23 October 2017

Academic Editor: Jacky C. K. Chow

Copyright © 2017 Manuel Castillo-Cara et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Bluetooth Low Energy (BLE) 4.0 beacons will play a major role in the deployment of energy-efficient indoor localization mechanisms. Since BLE4.0 is highly sensitive to fast fading impairments, numerous ongoing studies are currently exploring the use of supervised learning algorithm as an alternative approach to exploit the information provided by the indoor radio maps. Despite the large number of results reported in the literature, there are still many open issues on the performance evaluation of such approach. In this paper, we start by identifying, in a simple setup, the main system parameters to be taken into account on the design of BLE4.0 beacons-based indoor localization mechanisms. In order to shed some light on the evaluation process using supervised learning algorithm, we carry out an in-depth experimental evaluation in terms of the mean localization error, local prediction accuracy, and global prediction accuracy. Based on our results, we argue that, in order to fully assess the capabilities of supervised learning algorithms, it is necessary to include all the three metrics.