2024-03-29T12:28:34Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1759472023-07-18T07:25:59Zcom_10234_43662com_10234_9col_10234_43643
Repositori UJI
author
Belmonte-Fernández, Óscar
author
Montoliu Colás, Raul
author
Torres-Sospedra, Joaquín
author
Sansano-Sansano, Emilio
author
Chia-Aguilar, Daniel
2018-09-03T09:36:43Z
2018-09-03T09:36:43Z
2018-09-01
BELMONTE FERNÁNDEZ, Óscar; MONTOLIU COLÁS, Raúl; TORRES-SOSPEDRA, Joaquín; SANSANO-SANSANO, Emilio; CHIA-AGUILAR, Daniel (2018). A radiosity-based method to avoid calibration for indoor positioning systems. Expert Systems with Applications, v. 105, p. 89-101
http://hdl.handle.net/10234/175947
https://doi.org/10.1016/j.eswa.2018.03.054
Due to the widespread use of mobile devices, services based on the users current indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and indoor shopping. One of the most used techniques for indoor positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase; to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster.
eng
Indoor positioning
Radiosity
Classification algorithm
Machine learning
A radiosity-based method to avoid calibration for Indoor Positioning Systems
info:eu-repo/semantics/article
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URL
https://repositori.uji.es/xmlui/bitstream/10234/175947/1/Belmonte_2018_Radiosity.pdf
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https://repositori.uji.es/xmlui/bitstream/10234/175947/3/Belmonte_2018_Radiosity.pdf.txt
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Belmonte_2018_Radiosity.pdf.txt