A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization
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 e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/6/14809 |
| _version_ | 1811306217112338432 |
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| author | David Sánchez-Rodríguez Pablo Hernández-Morera José Ma. Quinteiro Itziar Alonso-González |
| author_facet | David Sánchez-Rodríguez Pablo Hernández-Morera José Ma. Quinteiro Itziar Alonso-González |
| author_sort | David Sánchez-Rodríguez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:40:30Z |
| format | Article |
| id | doaj.art-dbd38b13055241c3ab732a6132ebabc0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:40:30Z |
| publishDate | 2015-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dbd38b13055241c3ab732a6132ebabc02022-12-22T02:53:55ZengMDPI AGSensors1424-82202015-06-01156148091482910.3390/s150614809s150614809A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor LocalizationDavid Sánchez-Rodríguez0Pablo Hernández-Morera1José Ma. Quinteiro2Itziar Alonso-González3Institute 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, SpainIUMA Information and Communications Systems, Edificio Polivalente I, Parque Científico y Tecnológico, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainIUMA Information and Communications Systems, Edificio Polivalente I, Parque Científico y Tecnológico, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, SpainInstitute 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, SpainIndoor 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.http://www.mdpi.com/1424-8220/15/6/14809WLAN indoor localizationweighted decision treesreceived signal strengthorientationsensor fusion |
| spellingShingle | David Sánchez-Rodríguez Pablo Hernández-Morera José Ma. Quinteiro Itziar Alonso-González A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization Sensors WLAN indoor localization weighted decision trees received signal strength orientation sensor fusion |
| title | A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization |
| title_full | A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization |
| title_fullStr | A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization |
| title_full_unstemmed | A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization |
| title_short | A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization |
| title_sort | low complexity system based on multiple weighted decision trees for indoor localization |
| topic | WLAN indoor localization weighted decision trees received signal strength orientation sensor fusion |
| url | http://www.mdpi.com/1424-8220/15/6/14809 |
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