Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/60130
Campo DC Valoridioma
dc.contributor.authorSalvatore de Blasio, Gabrieleen_US
dc.contributor.authorRodríguez-Rodríguez, José C.en_US
dc.contributor.authorGarcia, Carmelo R.en_US
dc.contributor.authorQuesada-Arencibia, Alexisen_US
dc.date.accessioned2020-01-14T13:17:08Z-
dc.date.available2020-01-14T13:17:08Z-
dc.date.issued2019en_US
dc.identifier.issn1424-8220en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/60130-
dc.description.abstractIndoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration parameters of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (-20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%-50%, the error increase was only around 8%-9.2%.en_US
dc.languageengen_US
dc.relation.ispartofSensorsen_US
dc.sourceSensors [ISSN 1424-8220], v. 19 (14)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherIndoor positioningen_US
dc.subject.otherBluetooth 5en_US
dc.subject.otherBluetooth low energyen_US
dc.subject.otherFingerprintingen_US
dc.subject.otherSemi-automatic data acquisitionen_US
dc.titleBeacon-related parameters of bluetooth low energy: development of a semi-automatic system to study their impact on indoor positioning systemsen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s19143087
dc.identifier.scopus85070457348
dc.identifier.isi000479160300055-
dc.contributor.authorscopusid8935044600
dc.contributor.authorscopusid8925188600
dc.contributor.authorscopusid7401486323
dc.contributor.authorscopusid13006053800
dc.identifier.issue14-
dc.relation.volume19-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid31824548
dc.contributor.daisngid3336211
dc.contributor.daisngid1412377
dc.contributor.daisngid6245793
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:de Blasio, GS
dc.contributor.wosstandardWOS:Rodriguez-Rodriguez, JC
dc.contributor.wosstandardWOS:Garcia, CR
dc.contributor.wosstandardWOS:Quesada-Arencibia, A
dc.date.coverdateJulio 2019
dc.identifier.ulpgces
dc.description.sjr0,653
dc.description.jcr3,275
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-6233-567X-
crisitem.author.orcid0000-0003-2186-3094-
crisitem.author.orcid0000-0003-1433-3730-
crisitem.author.orcid0000-0002-8313-5124-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameDe Blasio, Gabriele Salvatore-
crisitem.author.fullNameRodríguez Rodríguez, José Carlos-
crisitem.author.fullNameGarcía Rodríguez, Carmelo Rubén-
crisitem.author.fullNameQuesada Arencibia, Francisco Alexis-
Colección:Artículos
miniatura
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