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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorAlonso Gómez, Arrate
dc.contributor.otherBalador, Ali
dc.contributor.otherCinque, Elena
dc.contributor.otherPatresi, Marco
dc.contributor.otherValentini, Francesco
dc.contributor.otherBai, Chumeng
dc.contributor.otherMohammdi, Mahboubeh
dc.date.accessioned2022-02-17T07:17:36Z
dc.date.available2022-02-17T07:17:36Z
dc.date.issued2022
dc.identifier.issn2214-2096en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=164922en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5472
dc.description.abstractVehicular communications have grown in interest over the years and are nowadays recognized as a pillar for the Intelligent Transportation Systems (ITSs) in order to ensure an efficient management of the road traffic and to achieve a reduction in the number of traffic accidents. To support the safety applications, both the ETSI ITS-G5 and IEEE 1609 standard families require each vehicle to deliver periodic awareness messages throughout the neighborhood. As the vehicles density grows, the scenario dynamics may require a high message exchange that can easily lead to a radio channel congestion issue and then to a degradation on safety critical services. ETSI has defined a Decentralized Congestion Control (DCC) mechanism to mitigate the channel congestion acting on the transmission parameters (i.e., message rate, transmit power and data-rate) with performances that vary according to the specific algorithm. In this paper, a review of the DCC standardization activities is proposed as well as an analysis of the existing methods and algorithms for the congestion mitigation. Also, some applied machine learning techniques for DCC are addressed.es
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherElsevier Ltd.en
dc.rights© 2021 The Authors. Published by Elsevier Inc.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectVehicular networksen
dc.subjectWireless communicationen
dc.subjectDecentralized congestion controlen
dc.subjectETSI ITS-G5en
dc.subjectDSRCen
dc.subjectMachine learningen
dc.titleSurvey on decentralized congestion control methods for vehicular communicationen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceVehicular Communicationsen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.vehcom.2021.100394en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/876038/EU/Intelligent Secure Trustable Things/InSecTTen
local.contributor.otherinstitutionhttps://ror.org/03nnxqz81es
local.contributor.otherinstitutionhttps://ror.org/01j9p1r26es
local.contributor.otherinstitutionhttps://ror.org/04aa89262es
local.contributor.otherinstitutionhttps://ror.org/026vcq606es
local.contributor.otherinstitutionhttps://ror.org/01jw2p796es
local.source.detailsVol. 33. N. artículo 100394, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International