Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

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dc.contributor.authorMasegosa, Antonio D.
dc.contributor.authorOsaba, Eneko
dc.contributor.authorAngarita-Zapata, Juan S.
dc.contributor.authorLaña, Ibai
dc.contributor.authorSer, Javier Del
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:57:45Z
dc.date.available2024-07-24T11:57:45Z
dc.date.issued2019-07-13
dc.descriptionPublisher Copyright: © 2019 Association for Computing Machinery.
dc.description.abstractConnected vehicles are revolutionizing the way in which transport and mobility are conceived. The main technology behind is the so-called Vehicular Ad-Hoc Networks (VANETs), which are communication networks that connect vehicles and facilitate various services. Usually, these services require a centralized architecture where the main server collects and disseminates information from/to vehicles. In this paper, we focus on improving the downlink information dissemination in VANETs with this centralized architecture. With this aim, we model the problem as a Vertex Covering optimization problem and we propose four new nature-inspired methods to solve it: Bat Algorithm (BA), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Cuckoo Search (CS). The new methods are tested over data from a real scenario. Results show that these metaheuristics, especially BA, FA and PSO, can be considered as powerful solvers for this optimization problem.en
dc.description.sponsorshipE. Osaba, I. Laña and J. Del Ser would like to thank the Basque Government for its support through the EMAITEK program. E. Osaba, I. Laña and J. Del Ser acknowledge the financial support from the project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking, ref. 737422). E. Osaba, I. Laña and J. Del Ser would like to thank the Basque Government for its support through the EMAITEK program. E. Osaba, I. Laña and J. Del Ser acknowledge the financial support from the project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking, ref. 737422). J. S. Angarita-Zapata thanks the funding received from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 665959. A. D. Masegosa thanks the LOGISTAR project, which received funding from European Union's Horizon 2020 research and innovation programme under grant agreement No. 769142. A. D. Masegosa thanks the LOGISTAR project, which received funding from European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769142. J. S. Angarita-Zapata thanks the funding received from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 665959.
dc.description.statusPeer reviewed
dc.format.extent9
dc.identifier.citationMasegosa , A D , Osaba , E , Angarita-Zapata , J S , Laña , I & Ser , J D 2019 , Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks . in GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion . GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion , Association for Computing Machinery, Inc , pp. 1312-1320 , 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 , Prague , Czech Republic , 13/07/19 . https://doi.org/10.1145/3319619.3326847
dc.identifier.citationconference
dc.identifier.doi10.1145/3319619.3326847
dc.identifier.isbn9781450367486
dc.identifier.urihttps://hdl.handle.net/11556/2766
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85070597344&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherAssociation for Computing Machinery, Inc
dc.relation.ispartofGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
dc.relation.ispartofseriesGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
dc.relation.projectIDHorizon 2020 Framework Programme, H2020
dc.relation.projectIDH2020 Marie Skłodowska-Curie Actions, MSCA, 665959-769142
dc.relation.projectIDEusko Jaurlaritza, 737422
dc.relation.projectIDHorizon 2020
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsInteligent Transportation Systems
dc.subject.keywordsNature-inspired Metaheuristics
dc.subject.keywordsVANETs
dc.subject.keywordsVehicular Communications
dc.subject.keywordsVertex Covering
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsSoftware
dc.titleNature-inspired metaheuristics for optimizing information dissemination in vehicular networksen
dc.typeconference output
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