Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/15152
Título: A low complexity system based on multiple weighted decision trees for indoor localization
Autores/as: Sánchez Rodríguez, David Cruz 
Hernández Morera, Pablo Vicente 
Quinteiro González, José María 
Alonso González, Itziar Goretti 
Clasificación UNESCO: 3325 Tecnología de las telecomunicaciones
Palabras clave: WLAN indoor localization
Weighted decision trees
Received signal strength
Orientation
Sensor fusion
Fecha de publicación: 2015
Publicación seriada: Sensors (Switzerland) 
Resumen: 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.
URI: http://hdl.handle.net/10553/15152
ISSN: 1424-8220
DOI: 10.3390/s150614809
Fuente: Sensors (Switzerland) [ISSN 1424-8220], v. 15 (6), p. 14809-14829 (Junio 2015)
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