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dc.contributor.authorPérez, Juan Luis
dc.contributor.authorGutiérrez Torre, Alberto
dc.contributor.authorBerral García, Josep Lluís
dc.contributor.authorCarrera Pérez, David
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2019-01-21T11:38:11Z
dc.date.available2019-01-21T11:38:11Z
dc.date.issued2018-10-01
dc.identifier.citationPérez, J., Gutierrez, A., Berral, J., Carrera, D. A resilient and distributed near real-time traffic forecasting application for Fog computing environments. "Future generation computer systems", 1 Octubre 2018, vol. 87, p. 198-212.
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/2117/127251
dc.description.abstractIn this paper we propose an architecture for a city-wide traffic modeling and prediction service based on the Fog Computing paradigm. The work assumes an scenario in which a number of distributed antennas receive data generated by vehicles across the city. In the Fog nodes data is collected, processed in local and intermediate nodes, and finally forwarded to a central Cloud location for further analysis. We propose a combination of a data distribution algorithm, resilient to back-haul connectivity issues, and a traffic modeling approach based on deep learning techniques to provide distributed traffic forecasting capabilities. In our experiments, we leverage real traffic logs from one week of Floating Car Data (FCD) generated in the city of Barcelona by a road-assistance service fleet comprising thousands of vehicles. FCD was processed across several simulated conditions, ranging from scenarios in which no connectivity failures occurred in the Fog nodes, to situations with long and frequent connectivity outage periods. For each scenario, the resilience and accuracy of both the data distribution algorithm, and the learning methods were analyzed. Results show that the data distribution process running in the Fog nodes is resilient to back-haul connectivity issues and is able to deliver data to the Cloud location even in presence of severe connectivity problems. Additionally, the proposed traffic modeling and forecasting method exhibits better behavior when run distributed in the Fog instead of centralized in the Cloud, especially when connectivity issues occur that force data to be delivered out of order to the Cloud.
dc.format.extent15 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshFog Computing
dc.subject.otherFog computing
dc.subject.otherResource management
dc.subject.otherInternet of Things
dc.subject.otherIoT
dc.subject.otherBig data
dc.subject.otherAnalytics
dc.subject.otherCloud computing
dc.titleA resilient and distributed near real-time traffic forecasting application for Fog computing environments
dc.typeArticle
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1016/j.future.2018.05.013
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X1732678X
dc.rights.accessOpen Access
local.identifier.drac22965671
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/639595/EU/Holistic Integration of Emerging Supercomputing Technologies/Hi-EST
local.citation.authorPérez, J.; Gutierrez, A.; Berral, J.; Carrera, D.
local.citation.publicationNameFuture generation computer systems
local.citation.volume87
local.citation.startingPage198
local.citation.endingPage212


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