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Benchmarking real-time vehicle data streaming models for a smart city

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorFernández-Rodríguez, Jorge Yago
dc.contributor.authorÁlvarez-García, Juan Antonio
dc.contributor.authorArias Fisteus, Jesús
dc.contributor.authorLuaces, Miguel
dc.contributor.authorCorcoba Magaña, Víctor
dc.date.accessioned2018-04-10T08:40:01Z
dc.date.available2019-12-31T23:10:03Z
dc.date.issued2017-12
dc.description.abstractThe information systems of smart cities offer project developers, institutions, industry and experts the possibility to handle massive incoming data from diverse information sources in order to produce new information services for citizens. Much of this information has to be processed as it arrives because a real-time response is often needed. Stream processing architectures solve this kind of problems, but sometimes it is not easy to benchmark the load capacity or the efficiency of a proposed architecture. This work presents a real case project in which an infrastructure was needed for gathering information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads. The challenge was to support the real-time recommendation service in a city with thousands of simultaneous drivers at the lowest possible cost. In addition, in order to estimate the ability of an infrastructure to handle load, a simulator that emulates the data produced by a given amount of simultaneous drivers was also developed. Experiments with the simulator show how recent stream processing platforms like Apache Kafka could replace custom-made streaming servers in a smart city to achieve a higher scalability and faster responses, together with cost reduction.es
dc.description.sponsorshipThis research is partially supported by the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (ERDF) through the “HERMES – SmartDriver” project (TIN2013-46801-C4-2-R), the “HERMES – Smart Citizen” project (TIN2013-46801-C4-1-R), and the “HERMES –Space&Time” project (TIN2013-46801-C4-3-R).es
dc.format.extent14es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationInformation Systems, (2017), v. 72, pp. 62-76.es
dc.identifier.doihttps://doi.org/10.1016/j.is.2017.09.002
dc.identifier.issn0306-4379
dc.identifier.publicationfirstpage62es
dc.identifier.publicationlastpage76es
dc.identifier.publicationtitleInformation systemses
dc.identifier.publicationvolume72es
dc.identifier.urihttps://hdl.handle.net/10016/26188
dc.identifier.uxxiAR/0000020722
dc.language.isoenges
dc.publisherElsevieres
dc.relation.projectIDGobierno de España. TIN2013-46801-C4-2-Res
dc.relation.projectIDGobierno de España. TIN2013-46801-C4-1-Res
dc.relation.projectIDGobierno de España. TIN2013-46801-C4-3-Res
dc.rights© 2017 Elsevier Ltd. All rights reserved.es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherSmart cityes
dc.subject.otherData streaminges
dc.subject.otherDistributed systemses
dc.subject.otherSimulatores
dc.subject.otherBig dataes
dc.titleBenchmarking real-time vehicle data streaming models for a smart cityes
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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