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dc.creatorSalazar González, Jose Luises
dc.creatorSoria Morillo, Luis Migueles
dc.creatorÁlvarez García, Juan Antonioes
dc.creatorEnríquez de Salamanca Ros, Fernandoes
dc.creatorJiménez Ruíz, Antonio R.es
dc.date.accessioned2021-09-08T10:53:14Z
dc.date.available2021-09-08T10:53:14Z
dc.date.issued2019
dc.identifier.citationSalazar González, J.L., Soria Morillo, L.M., Álvarez García, J.A., Enríquez de Salamanca Ros, F. y Jiménez Ruíz, A.R. (2019). Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study. IEEE Access, 7, 162664-162682.
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/125569
dc.description.abstractIn order to apply indoor localization systems in real environments it is necessary to provide an accurate location without implying a high impact on the user's normal behaviour. To achieve this goal, in this paper, a combination of battery saving techniques with a system based on WiFi ngerprinting is proposed. This is done by transferring the location calculation workload to the server, leaving user's mobile devices the only responsibility of making periodic WiFi network scans at dynamic intervals based on user activity, through an application running on background. There are not many studies analyzing energy consumption of existing localization systems, even though it is an important factor in real applications. In this paper, both energy consumption and accuracy are analyzed, having an energy consumption of only 0.8 Wh (having a 3.7 V battery) during a 24-hour cycle and an average localization error of 4.51 meters. Worth to mention that as computation is done on server side the system can be expanded to multiple buildings and oors. Finally, the dataset used in this paper has been published making possible to test new algorithms in the same environment.es
dc.description.sponsorshipMinisterio de Economía y Competitividad TIN2017-82113-C2-1-Res
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades RTI2018-095168-B-C55es
dc.formatapplication/pdfes
dc.format.extent18es
dc.language.isoenges
dc.publisherIEEE Computer Societyes
dc.relation.ispartofIEEE Access, 7, 162664-162682.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIndoor localizationes
dc.subjectWiFi ngerprintinges
dc.subjectRSSIes
dc.subjectbattery lifees
dc.subjectKNNes
dc.subjectnaive Bayeses
dc.subjectdatasetes
dc.titleEnergy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Studyes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN2017-82113-C2-1-Res
dc.relation.projectIDRTI2018-095168-B-C55es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8894078es
dc.identifier.doi10.1109/ACCESS.2019.2952221es
dc.contributor.groupUniversidad de Sevilla. TIC134: Sistemas Informáticoses
dc.journaltitleIEEE Accesses
dc.publication.volumen7es
dc.publication.initialPage162664es
dc.publication.endPage162682es
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). Españaes
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (MICINN). Españaes

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