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Sensors 2017, 17(6), 1318; doi:10.3390/s17061318

An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms

1
Computer Science School, Sciences Faculty, Center of Information and Communication Technologies, Universidad Nacional de Ingeniería, Lima 25, Peru
2
Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Jesús Ureña, Álvaro Hernández Alonso and Jesús García Domínguez
Received: 12 March 2017 / Revised: 3 June 2017 / Accepted: 5 June 2017 / Published: 7 June 2017
View Full-Text   |   Download PDF [5571 KB, uploaded 7 June 2017]   |  

Abstract

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon. View Full-Text
Keywords: indoor positioning; location fingerprinting; bluetooth; BLE4.0; supervised learning algorithm; signal processing; RSSI; multipath fading; transmission power indoor positioning; location fingerprinting; bluetooth; BLE4.0; supervised learning algorithm; signal processing; RSSI; multipath fading; transmission power
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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. (CC BY 4.0).

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MDPI and ACS Style

Castillo-Cara, M.; Lovón-Melgarejo, J.; Bravo-Rocca, G.; Orozco-Barbosa, L.; García-Varea, I. An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms. Sensors 2017, 17, 1318.

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