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dc.contributor.authorLópez-de-Teruel Alcolea, Pedro Enrique-
dc.contributor.authorGarcía Clemente, Félix Jesús-
dc.contributor.authorCánovas Reverte, Óscar-
dc.date.accessioned2024-01-25T12:25:11Z-
dc.date.available2024-01-25T12:25:11Z-
dc.date.issued2020-06-01-
dc.identifier.citationFuture Generation Computer Systems, Volume 107, June 2020, Pages 692-704es
dc.identifier.urihttp://hdl.handle.net/10201/137776-
dc.description© 2020. Elsevier Ltd. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc /4.0/ This document is the submitted version of a published work that appeared in final form in Future Generation Computer Systems: The International Journal of eScience.es
dc.description.abstractOccupancy is relevant information about key aspects, such as energy consumption or comfort management. Energy-saving and environmental quality strategies can be carried out in response to real-time facility occupancy. Some relevant solutions to measure and monitor occupancy information leverage radio-based indoor localization systems and employ Received Signal Strength (RSS) as the main source of data for location determination. However, those approaches usually require a previous training and calibration stage that involves a time-consuming and labor intensive site survey process, and which is also readily affected by environmental dynamics. In this paper, we propose a practical passive localization system for fast deployment of occupancy services able to track unmodified and heterogeneous devices after a quick and straightforward training phase. We present an experimental validation of the system that was conducted for 9 months in a lecture building of 6000 square meters with 20 classrooms and 4000 frequent users, where the existing teaching computers themselves were used as monitors to capture 802.11 traffic. In this environment, we test different representations and metrics to process the RSSI information and perform a thorough analysis of some important design parameters, which have a direct impact on both accuracy and time granularity of the localization system.es
dc.formatapplication/pdfes
dc.format.extent34es
dc.languageenges
dc.publisherElsevieres
dc.relationThis work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under grant TIN2015-66972-C5-3-R.es
dc.relation.isreplacedbyhttps://doi.org/10.1016/j.future.2017.09.022es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPassive localizationes
dc.subjectWirelesses
dc.subjectOcupancy serviceses
dc.subjectBuilding occupancyes
dc.subjectWiFi signalses
dc.subjectPassive localizationes
dc.subjectHeterogeneous deviceses
dc.subjectMachine learninges
dc.titlePractical passive localization system based on wireless signals for fast deployment of occupancy serviceses
dc.typeinfo:eu-repo/semantics/articlees
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0167739X17320083?via%3Dihubes
dc.identifier.doihttps://doi.org/10.1016/j.future.2017.09.022-
dc.contributor.departmentIngeniería y Tecnología de Computadores-
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