Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study

In 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 ngerprint...

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Detalles Bibliográficos
Autores: Salazar González, Jose Luis, Soria Morillo, Luis Miguel, Álvarez García, Juan Antonio, Enríquez de Salamanca Ros, Fernando, Jiménez Ruíz, Antonio R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/125569
Acceso en línea:https://hdl.handle.net/11441/125569
https://doi.org/10.1109/ACCESS.2019.2952221
Access Level:acceso abierto
Palabra clave:Indoor localization
WiFi ngerprinting
RSSI
battery life
KNN
naive Bayes
dataset
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repository_id_str
spelling Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental StudySalazar González, Jose LuisSoria Morillo, Luis MiguelÁlvarez García, Juan AntonioEnríquez de Salamanca Ros, FernandoJiménez Ruíz, Antonio R.Indoor localizationWiFi ngerprintingRSSIbattery lifeKNNnaive BayesdatasetIn 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.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Ciencia, Innovación y Universidades RTI2018-095168-B-C55IEEE Computer SocietyLenguajes y Sistemas InformáticosTIC134: Sistemas InformáticosMinisterio de Economía y Competitividad (MINECO). EspañaMinisterio de Ciencia, Innovación y Universidades (MICINN). España2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/125569https://doi.org/10.1109/ACCESS.2019.2952221reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Access, 7, 162664-162682.TIN2017-82113-C2-1-RRTI2018-095168-B-C55https://ieeexplore.ieee.org/document/8894078info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1255692026-06-17T12:51:07Z
dc.title.none.fl_str_mv Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
title Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
spellingShingle Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
Salazar González, Jose Luis
Indoor localization
WiFi ngerprinting
RSSI
battery life
KNN
naive Bayes
dataset
title_short Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
title_full Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
title_fullStr Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
title_full_unstemmed Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
title_sort Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study
dc.creator.none.fl_str_mv Salazar González, Jose Luis
Soria Morillo, Luis Miguel
Álvarez García, Juan Antonio
Enríquez de Salamanca Ros, Fernando
Jiménez Ruíz, Antonio R.
author Salazar González, Jose Luis
author_facet Salazar González, Jose Luis
Soria Morillo, Luis Miguel
Álvarez García, Juan Antonio
Enríquez de Salamanca Ros, Fernando
Jiménez Ruíz, Antonio R.
author_role author
author2 Soria Morillo, Luis Miguel
Álvarez García, Juan Antonio
Enríquez de Salamanca Ros, Fernando
Jiménez Ruíz, Antonio R.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
TIC134: Sistemas Informáticos
Ministerio de Economía y Competitividad (MINECO). España
Ministerio de Ciencia, Innovación y Universidades (MICINN). España
dc.subject.none.fl_str_mv Indoor localization
WiFi ngerprinting
RSSI
battery life
KNN
naive Bayes
dataset
topic Indoor localization
WiFi ngerprinting
RSSI
battery life
KNN
naive Bayes
dataset
description In 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/125569
https://doi.org/10.1109/ACCESS.2019.2952221
url https://hdl.handle.net/11441/125569
https://doi.org/10.1109/ACCESS.2019.2952221
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Access, 7, 162664-162682.
TIN2017-82113-C2-1-R
RTI2018-095168-B-C55
https://ieeexplore.ieee.org/document/8894078
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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