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Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)

 

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dc.creator Gutiérrez Reina, Daniel es
dc.creator Sharma, Vishal es
dc.creator You, Ilsun es
dc.creator Toral, S. L. es
dc.date.accessioned 2018-07-23T11:19:12Z
dc.date.available 2018-07-23T11:19:12Z
dc.date.issued 2018
dc.identifier.citation Gutiérrez Reina, D., Sharma, V., You, I. y Toral, S.L. (2008). Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs). Sensors, 18 (2320), 1-18.
dc.identifier.issn 1424-8220 es
dc.identifier.uri https://hdl.handle.net/11441/77519
dc.description.abstract This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios. es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher MDPI es
dc.relation.ispartof Sensors, 18 (2320), 1-18.
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject VANETs es
dc.subject Genetic programming; es
dc.subject Broadcasting communications es
dc.title Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs) es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Ingeniería Electrónica es
dc.relation.publisherversion http://www.mdpi.com/1424-8220/18/7/2320 es
dc.identifier.doi 10.3390/s18072320 es
idus.format.extent 18 p. es
dc.journaltitle Sensors es
dc.publication.volumen 18 es
dc.publication.issue 2320 es
dc.publication.initialPage 1 es
dc.publication.endPage 18 es
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