Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41514
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dc.contributor.authorDe Blasio, Gabrielen_US
dc.contributor.authorQuesada-Arencibia, A.en_US
dc.contributor.authorGarcia, C. R.en_US
dc.contributor.authorRodriguez-Rodriguez, Jose Carlosen_US
dc.contributor.authorMoreno-Díaz, Robertoen_US
dc.date.accessioned2018-07-10T10:50:55Z-
dc.date.available2018-07-10T10:50:55Z-
dc.date.issued2018en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10553/41514-
dc.description.abstractBluetooth Low Energy technology coupled with fingerprinting provides a simple way to position users with high accuracy in indoor environments. In this paper, we study the effect of BLE protocols and channels on indoor positioning using different distance and similarity measures in a controlled environment. With the aim of reproducing a real positioning system situation, we also study the effect of the user’s orientation in the positioning phase and, consequently, provide accuracy and precision results for each orientation. In a 168 m2 testbed, 12 beacons configured to broadcast with the Eddystone and iBeacon protocols were deployed and 40 distance/similarity measures were considered. According to our results, in a specific orientation there is a group of distance metrics coupled with a protocol-channel combination that produces similar accuracy results. Therefore, choosing the right distance metric in that specific orientation is not as critical as choosing the right protocol and, especially, the right channel. There is a trend whereby the protocol-channel combination that provides the best accuracy is almost unique for each orientation. Depending on the orientation, the accuracies obtained for the abovementioned group of distances are within the range of 1.1 m – 1.5 m and the precisions are 90% within the range of 1.5 m – 2.5 m.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Accessen_US
dc.sourceIEEE Access [ISSN 2169-3536], v. 6, p. 33440-33450en_US
dc.subject220990 Tratamiento digital. Imágenesen_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherBluetooth low energyen_US
dc.subject.otherIndoor positioningen_US
dc.subject.otherFingerprintingen_US
dc.subject.otherDistance and similarity measureen_US
dc.subject.otherProtocolen_US
dc.subject.otherChannelen_US
dc.titleA protocol-channel-based indoor positioning performance study for bluetooth low energyen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2018.2837497
dc.identifier.scopus85047014411
dc.identifier.isi000439110900001-
dc.contributor.authorscopusid8935044600
dc.contributor.authorscopusid13006053800
dc.contributor.authorscopusid7401486323
dc.contributor.authorscopusid8925188600
dc.contributor.authorscopusid24543463600
dc.description.lastpage33450-
dc.description.firstpage33440-
dc.relation.volume6-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid2417004
dc.contributor.daisngid6245793
dc.contributor.daisngid1412377
dc.contributor.daisngid3336211
dc.contributor.daisngid1554939
dc.identifier.externalWOS:000439110900001-
dc.contributor.wosstandardWOS:de Blasio, G
dc.contributor.wosstandardWOS:Quesada-Arencibia, A
dc.contributor.wosstandardWOS:Garcia, CR
dc.contributor.wosstandardWOS:Rodriguez-Rodriguez, JC
dc.contributor.wosstandardWOS:Moreno-Diaz, R
dc.date.coverdateMayo 2018
dc.identifier.ulpgces
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptComputación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptComputación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptComputación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptComputación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptDepartamento de Teleformación-
crisitem.author.orcid0000-0002-6233-567X-
crisitem.author.orcid0000-0002-8313-5124-
crisitem.author.orcid0000-0003-1433-3730-
crisitem.author.orcid0000-0003-2186-3094-
crisitem.author.orcid0000-0002-5314-6033-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.fullNameDe Blasio, Gabriele Salvatore-
crisitem.author.fullNameQuesada Arencibia, Francisco Alexis-
crisitem.author.fullNameGarcía Rodríguez, Carmelo Rubén-
crisitem.author.fullNameRodríguez Rodríguez, José Carlos-
crisitem.author.fullNameMoreno Díaz, Roberto-
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