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

Castillo Cara, Manuel, Lovón Melgarejo. Jesús, Bravo Rocca, Gusseppe, Orozco Barbosa, Luis y García Varea, Ismael . (2017) An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms. Sensors 2017, 17(6), 1318

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Título An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms
Autor(es) Castillo Cara, Manuel
Lovón Melgarejo. Jesús
Bravo Rocca, Gusseppe
Orozco Barbosa, Luis
García Varea, Ismael
Materia(s) Informática
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.
Palabras clave indoor positioning
location fingerprinting
bluetooth
BLE4.0
supervised learning algorithm
signal processing
RSSI
multipath fading
transmission power
Editor(es) MDPI
Fecha 2017-06-07
Formato application/pdf
Identificador bibliuned:95-Jmcastillo-0003
http://e-spacio.uned.es/fez/view/bibliuned:95-Jmcastillo-0003
DOI - identifier https://doi.org/10.3390/s17061318
ISSN - identifier 1424-8220
Nombre de la revista Sensors
Número de Volumen 17
Número de Issue 6
Publicado en la Revista Sensors 2017, 17(6), 1318
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in "Sensors 2017, 17(6), 1318", is available online at the publisher's website: https://doi.org/10.3390/s17061318
Notas adicionales La versión registrada de este artículo, publicado por primera vez en "Sensors 2017, 17(6), 1318", está disponible en línea en el sitio web del editor: https://doi.org/10.3390/s17061318

 
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Creado: Mon, 22 Jan 2024, 22:52:58 CET