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Sensors 2015, 15(6), 14809-14829; doi:10.3390/s150614809

A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

1
Institute for Technological Development and Innovation in Communications, Edificio Polivalente II, 2aplanta, Parque Científico y Tecnológico, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
2
IUMA Information and Communications Systems, Edificio Polivalente I, Parque Científico y Tecnológico, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
3
Department of Telematic Engineering, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Kourosh Khoshelham and Sisi Zlatanova
Received: 30 April 2015 / Revised: 11 June 2015 / Accepted: 16 June 2015 / Published: 23 June 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [1176 KB, uploaded 23 June 2015]   |  

Abstract

Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have. View Full-Text
Keywords: WLAN indoor localization; weighted decision trees; received signal strength; orientation; sensor fusion WLAN indoor localization; weighted decision trees; received signal strength; orientation; sensor fusion
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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

Sánchez-Rodríguez, D.; Hernández-Morera, P.; Quinteiro, J.M.; Alonso-González, I. A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization. Sensors 2015, 15, 14809-14829.

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