Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas

As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, a...

Full description

Autores:
Píneda Lezama, Omar Bonerge
Varela Izquierdo, Noel
amelec, viloria
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7912
Acceso en línea:
https://hdl.handle.net/11323/7912
https://repositorio.cuc.edu.co/
Palabra clave:
Coverage
VANET
WIFI
DSRC
Heterogeneous
Throughput
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_71619565541b7ed1ba208b2186075589
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7912
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
title Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
spellingShingle Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
Coverage
VANET
WIFI
DSRC
Heterogeneous
Throughput
title_short Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
title_full Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
title_fullStr Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
title_full_unstemmed Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
title_sort Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas
dc.creator.fl_str_mv Píneda Lezama, Omar Bonerge
Varela Izquierdo, Noel
amelec, viloria
dc.contributor.author.spa.fl_str_mv Píneda Lezama, Omar Bonerge
Varela Izquierdo, Noel
amelec, viloria
dc.subject.spa.fl_str_mv Coverage
VANET
WIFI
DSRC
Heterogeneous
Throughput
topic Coverage
VANET
WIFI
DSRC
Heterogeneous
Throughput
description As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, and government offices have begun to invest a ton of energy together towards the acknowledgment of the idea of huge scope vehicular interchanges. One of the primary methodologies in this kind of system is the advancement of remote advances and their assorted organizations, concentrating on the association with the Internet through WiFi systems, cell systems, or specially appointed vehicular systems. VANETs are essentially intended to give data trade through Vehicle to Vehicle (V2V) and Vehicle to foundation (V2I) interchanges, permitting ceaseless network and being exceptionally utilized for short-range correspondence, with high transmission speed through which it is proposed that clients keep up an association and distinguish occasions about clog or street conditions. This exploration presents a vehicular situation that tries to acquire a sufficient presentation while executing a heterogeneous network in a few segments of the city of Bogotá, Colombia.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-02-23T19:48:00Z
dc.date.available.none.fl_str_mv 2021-02-23T19:48:00Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7912
dc.identifier.doi.spa.fl_str_mv 10.3233/jifs-189151
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7912
https://repositorio.cuc.edu.co/
identifier_str_mv 10.3233/jifs-189151
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach Journal of Intelligent & Fuzzy Systems (IF 1.851) Pub Date : 2020-12-04 ,DOI:10.3233/jifs-189134
A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks Computational Intelligence and Neuroscience (IF 2.284) Pub Date : 2020-11-24 ,DOI:10.1155/2020/8828355
Paper recommendation based on heterogeneous network embedding Knowledge-Based Systems (IF 5.921) Pub Date : 2020-09-28 ,DOI:10.1016/j.knosys.2020.106438
MFRep: Joint user and employer alignment across heterogeneous social networks Neurocomputing (IF 4.438) Pub Date : 2020-07-22 ,DOI:10.1016/j.neucom.2020.07.013
Rich heterogeneous information preserving network representation learning Pattern Recognition (IF 7.196) Pub Date : 2020-07-22 ,DOI:10.1016/j.patcog.2020.107564
HetNERec: Heterogeneous network embedding based recommendation Knowledge-Based Systems (IF 5.921) Pub Date : 2020-07-07 ,DOI:10.1016/j.knosys.2020.106218
Bio-inspired VANET routing optimization: an overview Artificial Intelligence Review (IF 5.747) Pub Date : 2020-07-06 ,DOI:10.1007/s10462-020-09868-9
Learning heterogeneous information network embeddings via relational triplet network Neurocomputing (IF 4.438) Pub Date : 2020-06-16 ,DOI:10.1016/j.neucom.2020.06.043
Multi-source information fusion based heterogeneous network embedding Information Sciences (IF 5.91) Pub Date : 2020-05-11 ,DOI:10.1016/j.ins.2020.05.012
A heterogeneous branch and multi-level classification network for person re-identification Neurocomputing (IF 4.438) Pub Date : 2020-05-08 ,DOI:10.1016/j.neucom.2020.05.007
BeAware: Convolutional Neural Network(CNN) based User Behavior Understanding through WiFi Channel State Information Neurocomputing (IF 4.438) Pub Date : 2020-04-08 ,DOI:10.1016/j.neucom.2019.09.111
Deep heterogeneous network embedding based on Siamese Neural Networks Neurocomputing (IF 4.438) Pub Date : 2020-01-13 ,DOI:10.1016/j.neucom.2020.01.012
Cross Multi-Type Objects Clustering in Attributed Heterogeneous Information Network Knowledge-Based Systems (IF 5.921) Pub Date : 2020-01-07 ,DOI:10.1016/j.knosys.2019.105458
Proximity-aware heterogeneous information network embedding Knowledge-Based Systems (IF 5.921) Pub Date : 2020-01-07 ,DOI:10.1016/j.knosys.2019.105468
Data Reconstructing Algorithm in Unreliable Links Based on Matrix Completion for Heterogeneous Wireless Sensor Networks International Journal of Pattern Recognition and Artificial Intelligence (IF 1.375) Pub Date : 2019-12-17 ,DOI:10.1142/s021800141951
Deep learning approach on information diffusion in heterogeneous networks Knowledge-Based Systems (IF 5.921) Pub Date : 2019-10-29 ,DOI:10.1016/j.knosys.2019.105153
I-DEEC: improved DEEC for blanket coverage in heterogeneous wireless sensor networks Journal of Ambient Intelligence and Humanized Computing (IF 4.594) Pub Date : 2019-10-25 ,DOI:10.1007/s12652-019-01552-3
Collaborative linear manifold learning for link prediction in heterogeneous networks Information Sciences (IF 5.91) Pub Date : 2019-09-24 ,DOI:10.1016/j.ins.2019.09.054
Trusted forensics scheme based on digital watermark algorithm in intelligent VANET Neural Computing and Applications (IF 4.774) Pub Date : 2019-05-16 ,DOI:10.1007/s00521-019-04246-1
Resilient consensus with switching networks and heterogeneous agents Neurocomputing (IF 4.438) Pub Date : 2019-03-13 ,DOI:10.1016/j.neucom.2019.03.018
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Journal of Intelligent & Fuzzy Systems
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.x-mol.com/paper/1294387404837888000
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/18feabbb-cbe2-4cfd-8c38-e9a0354d73c0/download
https://repositorio.cuc.edu.co/bitstreams/c0f155b2-9a07-445c-8a77-40fa5bab7ba5/download
https://repositorio.cuc.edu.co/bitstreams/abe89a79-0f34-4b24-98e0-df9613c6582e/download
https://repositorio.cuc.edu.co/bitstreams/4d022c21-1bb0-4042-8a29-43c7868e6588/download
https://repositorio.cuc.edu.co/bitstreams/ba3a4167-0a91-4238-9d4c-59ee8f00631f/download
bitstream.checksum.fl_str_mv 244ea4db1526b1318e383c1f27a92403
4460e5956bc1d1639be9ae6146a50347
e30e9215131d99561d40d6b0abbe9bad
51fc1621f0c400a51e299c609884a79e
4b9a09f2d26c32f9efa434edee4531aa
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760784309485568
spelling Píneda Lezama, Omar BonergeVarela Izquierdo, Noelamelec, viloria2021-02-23T19:48:00Z2021-02-23T19:48:00Z2020https://hdl.handle.net/11323/791210.3233/jifs-189151Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, and government offices have begun to invest a ton of energy together towards the acknowledgment of the idea of huge scope vehicular interchanges. One of the primary methodologies in this kind of system is the advancement of remote advances and their assorted organizations, concentrating on the association with the Internet through WiFi systems, cell systems, or specially appointed vehicular systems. VANETs are essentially intended to give data trade through Vehicle to Vehicle (V2V) and Vehicle to foundation (V2I) interchanges, permitting ceaseless network and being exceptionally utilized for short-range correspondence, with high transmission speed through which it is proposed that clients keep up an association and distinguish occasions about clog or street conditions. This exploration presents a vehicular situation that tries to acquire a sufficient presentation while executing a heterogeneous network in a few segments of the city of Bogotá, Colombia.Píneda Lezama, Omar BonergeVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Intelligent & Fuzzy Systemshttps://www.x-mol.com/paper/1294387404837888000CoverageVANETWIFIDSRCHeterogeneousThroughputVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areasArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionOptimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach Journal of Intelligent & Fuzzy Systems (IF 1.851) Pub Date : 2020-12-04 ,DOI:10.3233/jifs-189134A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks Computational Intelligence and Neuroscience (IF 2.284) Pub Date : 2020-11-24 ,DOI:10.1155/2020/8828355Paper recommendation based on heterogeneous network embedding Knowledge-Based Systems (IF 5.921) Pub Date : 2020-09-28 ,DOI:10.1016/j.knosys.2020.106438MFRep: Joint user and employer alignment across heterogeneous social networks Neurocomputing (IF 4.438) Pub Date : 2020-07-22 ,DOI:10.1016/j.neucom.2020.07.013Rich heterogeneous information preserving network representation learning Pattern Recognition (IF 7.196) Pub Date : 2020-07-22 ,DOI:10.1016/j.patcog.2020.107564HetNERec: Heterogeneous network embedding based recommendation Knowledge-Based Systems (IF 5.921) Pub Date : 2020-07-07 ,DOI:10.1016/j.knosys.2020.106218Bio-inspired VANET routing optimization: an overview Artificial Intelligence Review (IF 5.747) Pub Date : 2020-07-06 ,DOI:10.1007/s10462-020-09868-9Learning heterogeneous information network embeddings via relational triplet network Neurocomputing (IF 4.438) Pub Date : 2020-06-16 ,DOI:10.1016/j.neucom.2020.06.043Multi-source information fusion based heterogeneous network embedding Information Sciences (IF 5.91) Pub Date : 2020-05-11 ,DOI:10.1016/j.ins.2020.05.012A heterogeneous branch and multi-level classification network for person re-identification Neurocomputing (IF 4.438) Pub Date : 2020-05-08 ,DOI:10.1016/j.neucom.2020.05.007BeAware: Convolutional Neural Network(CNN) based User Behavior Understanding through WiFi Channel State Information Neurocomputing (IF 4.438) Pub Date : 2020-04-08 ,DOI:10.1016/j.neucom.2019.09.111Deep heterogeneous network embedding based on Siamese Neural Networks Neurocomputing (IF 4.438) Pub Date : 2020-01-13 ,DOI:10.1016/j.neucom.2020.01.012Cross Multi-Type Objects Clustering in Attributed Heterogeneous Information Network Knowledge-Based Systems (IF 5.921) Pub Date : 2020-01-07 ,DOI:10.1016/j.knosys.2019.105458Proximity-aware heterogeneous information network embedding Knowledge-Based Systems (IF 5.921) Pub Date : 2020-01-07 ,DOI:10.1016/j.knosys.2019.105468Data Reconstructing Algorithm in Unreliable Links Based on Matrix Completion for Heterogeneous Wireless Sensor Networks International Journal of Pattern Recognition and Artificial Intelligence (IF 1.375) Pub Date : 2019-12-17 ,DOI:10.1142/s021800141951Deep learning approach on information diffusion in heterogeneous networks Knowledge-Based Systems (IF 5.921) Pub Date : 2019-10-29 ,DOI:10.1016/j.knosys.2019.105153I-DEEC: improved DEEC for blanket coverage in heterogeneous wireless sensor networks Journal of Ambient Intelligence and Humanized Computing (IF 4.594) Pub Date : 2019-10-25 ,DOI:10.1007/s12652-019-01552-3Collaborative linear manifold learning for link prediction in heterogeneous networks Information Sciences (IF 5.91) Pub Date : 2019-09-24 ,DOI:10.1016/j.ins.2019.09.054Trusted forensics scheme based on digital watermark algorithm in intelligent VANET Neural Computing and Applications (IF 4.774) Pub Date : 2019-05-16 ,DOI:10.1007/s00521-019-04246-1Resilient consensus with switching networks and heterogeneous agents Neurocomputing (IF 4.438) Pub Date : 2019-03-13 ,DOI:10.1016/j.neucom.2019.03.018PublicationORIGINALVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas.pdfVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas.pdfapplication/pdf98024https://repositorio.cuc.edu.co/bitstreams/18feabbb-cbe2-4cfd-8c38-e9a0354d73c0/download244ea4db1526b1318e383c1f27a92403MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/c0f155b2-9a07-445c-8a77-40fa5bab7ba5/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/abe89a79-0f34-4b24-98e0-df9613c6582e/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas.pdf.jpgVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas.pdf.jpgimage/jpeg37123https://repositorio.cuc.edu.co/bitstreams/4d022c21-1bb0-4042-8a29-43c7868e6588/download51fc1621f0c400a51e299c609884a79eMD54TEXTVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas.pdf.txtVanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas.pdf.txttext/plain1556https://repositorio.cuc.edu.co/bitstreams/ba3a4167-0a91-4238-9d4c-59ee8f00631f/download4b9a09f2d26c32f9efa434edee4531aaMD5511323/7912oai:repositorio.cuc.edu.co:11323/79122024-09-17 11:08:33.419http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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