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dc.contributor.authorBelmonte, Oscar
dc.contributor.authorMontoliu Colás, Raul
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorSansano-Sansano, Emilio
dc.contributor.authorChia-Aguilar, Daniel
dc.date.accessioned2018-09-03T09:36:43Z
dc.date.available2018-09-03T09:36:43Z
dc.date.issued2018-09-01
dc.identifier.citationBELMONTE 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. 89-101ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/175947
dc.description.abstractDue 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.ca_CA
dc.format.extent48 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfExpert Systems with Applications (2018), v. 105ca_CA
dc.subjectIndoor positioningca_CA
dc.subjectRadiosityca_CA
dc.subjectClassification algorithmca_CA
dc.subjectMachine learningca_CA
dc.titleA radiosity-based method to avoid calibration for Indoor Positioning Systemsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2018.03.054
dc.relation.projectID1) Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme 620 (TIN2015-70202-P) and 2) Jaume I University “Research promotion plan 2017” programme (UJI-B2017-45).ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.sciencedirect.com/science/article/pii/S0957417418302112ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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