Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/15152
Title: A low complexity system based on multiple weighted decision trees for indoor localization
Authors: Sánchez Rodríguez, David Cruz 
Hernández Morera, Pablo Vicente 
Quinteiro González, José María 
Alonso González, Itziar Goretti 
UNESCO Clasification: 3325 Tecnología de las telecomunicaciones
Keywords: WLAN indoor localization
Weighted decision trees
Received signal strength
Orientation
Sensor fusion
Issue Date: 2015
Journal: Sensors (Switzerland) 
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.
URI: http://hdl.handle.net/10553/15152
ISSN: 1424-8220
DOI: 10.3390/s150614809
Source: Sensors (Switzerland) [ISSN 1424-8220], v. 15 (6), p. 14809-14829 (Junio 2015)
Appears in Collections:Artículos
Thumbnail
Artículo principal
Adobe PDF (1,13 MB)
Show full item record

SCOPUSTM   
Citations

28
checked on Jun 13, 2021

WEB OF SCIENCETM
Citations

25
checked on Apr 25, 2021

Page view(s)

38
checked on Jun 14, 2021

Download(s)

39
checked on Jun 14, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



This item is licensed under a Creative Commons License Creative Commons