TRADING: Traffic Aware Data Offloading for Big Data Enabled Intelligent Transportation System

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dc.contributor.authorDarwish, Tasneem S. J.es_ES
dc.contributor.authorBakar, Kamalrulnizam Abues_ES
dc.contributor.authorKaiwartya, Omprakashes_ES
dc.contributor.authorLloret, Jaimees_ES
dc.contributor.funderUniversity of Technology Malaysiaes_ES
dc.date.accessioned2022-11-07T16:34:08Z
dc.date.available2022-11-07T16:34:08Z
dc.date.issued2020-07es_ES
dc.description.abstract[EN] Todays' Intelligent Transportation System (ITS) applications majorly depend on either limited neighbouring traffic data or crowd sourced stale traffic data. Enabling big traffic data analytics in ITS environments is a step closer towards utilizing significant traffic patterns and trends for making more precise and intelligent decisions particularly in connected autonomous vehicular environments. Towards this end, this paper presents a Traffic Aware Data Offloading (TRADING) approach for big traffic data centric ITS applications in connected autonomous vehicular environments. Specifically, TRADING balances offloading data traffic among gateways focusing on vehicular traffic and network status in the vicinity of gateways. In addition, TRADING mitigates the effect of gateway advertisement overhead to liberate the transmission channels for traffic big data transmission. The performance of TRADING is comparatively evaluated in a realistic simulation environment by considering gateway access overhead, load distribution among gateways, data offloading delay, and data offloading success ratio. The comparative performance evaluation results show some significant developments towards enabling big traffic data centric ITS.en_EN
dc.description.accrualMethodSes_ES
dc.description.bibliographicCitationDarwish, TSJ.; Bakar, KA.; Kaiwartya, O.; Lloret, J. (2020). TRADING: Traffic Aware Data Offloading for Big Data Enabled Intelligent Transportation System. IEEE Transactions on Vehicular Technology. 69(7):6869-6879. https://doi.org/10.1109/TVT.2020.2991372es_ES
dc.description.issue7es_ES
dc.description.sponsorshipThis work was financially supported by the Post-doctoral Fellowship Scheme of Universiti Teknologi Malaysia for the project: "Vehicular fog computing resource allocation scheme for real-time big data processing in intelligent transportation systems." The review of this article was coordinated by Prof. J. W. Choi.es_ES
dc.description.upvformatpfin6879es_ES
dc.description.upvformatpinicio6869es_ES
dc.description.volume69es_ES
dc.identifier.doi10.1109/TVT.2020.2991372es_ES
dc.identifier.issn0018-9545es_ES
dc.identifier.urihttps://riunet.upv.es/handle/10251/189335
dc.languageIngléses_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relation.ispartofIEEE Transactions on Vehicular Technologyes_ES
dc.relation.pasarelaS\473156es_ES
dc.relation.publisherversionhttps://doi.org/10.1109/TVT.2020.2991372es_ES
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dc.rightsReserva de todos los derechoses_ES
dc.rights.accessRightsAbiertoes_ES
dc.subjectLogic gateses_ES
dc.subjectBig Dataes_ES
dc.subjectQuality of servicees_ES
dc.subjectDelayses_ES
dc.subjectReal-time systemses_ES
dc.subjectSafetyes_ES
dc.subjectRoadses_ES
dc.subjectGatewayes_ES
dc.subjectIntelligent transportation systemses_ES
dc.subjectVANETes_ES
dc.subjectVehicle-to-internetes_ES
dc.titleTRADING: Traffic Aware Data Offloading for Big Data Enabled Intelligent Transportation Systemes_ES
dc.typeArtículoes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dspace.entity.typePublication
upv.uuidef501526-0501-489f-a0cb-c282fedfd021es_ES

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