A spatio-temporal approach to individual mobility modeling in on-device cognitive computing platforms

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dc.contributor.authorPérez-Torres, Rafael
dc.contributor.authorTorres-Huitzil, César
dc.contributor.authorGaleana-Zapién, Hiram
dc.contributor.funderConsejo Nacional de Ciencia y Tecnologíaen
dc.contributor.funderMinistério da Educaçãoen
dc.date.accessioned2019-10-23T04:58:54Z
dc.date.available2019-10-23T04:58:54Z
dc.date.issued2019-09-12
dc.description.abstractThe increased availability of GPS-enabled devices makes possible to collect location data for mining purposes and to develop mobility-based services (MBS). For most of the MBSs, determining interesting locations and frequent Points of Interest (POIs) is of paramount importance to study the semantic of places visited by an individual and the mobility patterns as a spatio-temporal phenomenon. In this paper, we propose a novel approach that uses mobility-based services for on-device and individual-centered mobility understanding. Unlike existing approaches that use crowd data for cloud-assisted POI extraction, the proposed solution autonomously detects POIs and mobility events to incrementally construct a cognitive map (spatio-temporal model) of individual mobility suitable to constrained mobile platforms. In particular, we focus on detecting POIs and enter-exits events as the key to derive statistical properties for characterizing the dynamics of an individual’s mobility. We show that the proposed spatio-temporal map effectively extracts core features from the user-POI interaction that are relevant for analytics such as mobility prediction. We also demonstrate how the obtained spatio-temporal model can be exploited to assess the relevance of daily mobility routines. This novel cognitive and on-line mobility modeling contributes toward the distributed intelligence of IoT connected devices without strongly compromising energy.en
dc.description.sponsorshipMinistério da Educação (PRODEP); CONACYT (Mexico, 237417)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid3949en
dc.identifier.citationPérez-Torres, R., Torres-Huitzil, C. and Galeana-Zapién, H. (2019) 'A Spatio-Temporal Approach to Individual Mobility Modeling in On-Device Cognitive Computing Platforms', Sensors, 19(18), 3949. (20pp.) DOI: 10.3390/s19183949en
dc.identifier.doi10.3390/s19183949en
dc.identifier.eissn1424-8220
dc.identifier.endpage20en
dc.identifier.issued18en
dc.identifier.journaltitleSensorsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/8839
dc.identifier.volume19en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP3::PEOPLE/237417/EU/Evolving Phenotypic plasticity and Plant Invasiveness: An inter-disciplinary approach/EVOPLASTINVen
dc.relation.urihttps://www.mdpi.com/1424-8220/19/18/3949/htm
dc.rights©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectHuman mobilityen
dc.subjectTrajectoryen
dc.subjectPOIen
dc.subjectCognitive computingen
dc.subjectSmartphoneen
dc.titleA spatio-temporal approach to individual mobility modeling in on-device cognitive computing platformsen
dc.typeArticle (peer-reviewed)en
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