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Beyond the RSSI value in BLE-based passive indoor localization: let data speak

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dc.contributor.authorLópez-de-Teruel, Pedro Enrique
dc.contributor.authorCánovas, Óscar
dc.contributor.authorGarcía, Félix J.
dc.contributor.authorGonzález, Rubén
dc.contributor.authorCarrasco, José A.
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2024-01-24T16:55:31Z
dc.date.available2024-01-24T16:55:31Z
dc.date.issued2018-11-05
dc.description.abstractIn this paper we present the results obtained from a large experimental environment that makes use of Bluetooth Low Energy (BLE) as the core technology for a location estimation system. BLE is a common technology for this kind of geopositioning systems, but most of the existing proposals are based on the RSS (Received Signal Strength) value obtained by mobile smart devices from static emitters such as iBeacons or other similar tags. This is not our case, since we adopt a passive approach where monitors obtain advertising frames emitted by mobile BLE beacons with no computing capabilities. In our particular scenario, based on a commercial application of our system, we perform fast but exhaustive training procedures to produce an initial dataset that is then analyzed paying attention to important design parameters. A thorough analysis of the data by means of different data visualization techniques reveals valuable information about the behavior of the emitters, signal characterization, radio coverage and, mainly, possible features that can be employed lately by machine learning methods in order to provide accurate location estimations. This is useful to define a quick and continuous training life-cycle which enables the detection of inconsistent data or failures. Our analysis also suggests that a representation of the observations using alternative features provides similar or even better results than the RSS values in terms of efficiency and support for heterogeneous devices.es
dc.formatapplication/pdfes
dc.format.extent10es
dc.identifier.doihttps://doi.org/10.1145/3286978.3286986
dc.identifier.urihttp://hdl.handle.net/10201/137692
dc.languageenges
dc.publisherACM Digital Libraryes
dc.relationThis work was supported by the Spanish MINECO, as well as by European Commission FEDER funds, under grant TIN2015-66972- C5-3-R.es
dc.relation.ispartofMobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. November 2018es
dc.relation.publisherversionhttps://dl.acm.org/doi/10.1145/3286978.3286986es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectBLEes
dc.subjectIndoor localizationes
dc.subjectpassive systemses
dc.subjectrealistic datasetses
dc.titleBeyond the RSSI value in BLE-based passive indoor localization: let data speakes
dc.typeinfo:eu-repo/semantics/lecturees
dspace.entity.typePublicationes
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