Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/82027
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Bellavista-Parent, Vladimir | por |
dc.contributor.author | Torres-Sospedra, Joaquín | por |
dc.contributor.author | Perez-Navarro, Antoni | por |
dc.date.accessioned | 2023-01-19T16:10:40Z | - |
dc.date.available | 2023-01-19T16:10:40Z | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | Bellavista-Parent, V.; Torres-Sospedra, J.; Pérez-Navarro, A. Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review. Sensors 2022, 22, 4622. https://doi.org/10.3390/s22124622 | por |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://hdl.handle.net/1822/82027 | - |
dc.description.abstract | Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines. | por |
dc.description.sponsorship | A.P.-N. wants to acknowledge the support of GeoLibero CYTED network. J.T.-S. gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under Marie Sklodowska Curie grant agreement No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt [Accessed on 14 June 2022]). | por |
dc.language.iso | eng | por |
dc.publisher | MDPI | por |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/101023072/EU | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Indoor | por |
dc.subject | Positioning | por |
dc.subject | Wi-Fi | por |
dc.subject | Bluetooth | por |
dc.subject | Wi-Fi radio map | por |
dc.subject | Machine learning | por |
dc.title | Comprehensive analysis of applied machine learning in indoor positioning based on Wi-Fi: an extended systematic review | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/22/12/4622 | por |
oaire.citationIssue | 12 | por |
oaire.citationVolume | 22 | por |
dc.date.updated | 2023-01-19T15:56:48Z | - |
dc.identifier.doi | 10.3390/s22124622 | por |
dc.identifier.pmid | 35746404 | - |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 12500 | - |
sdum.journal | Sensors | por |
oaire.version | VoR | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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sensors-22-04622.pdf | 641,37 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons