An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

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dc.contributor.authorAraújo, Gustavo Medeiros de
dc.contributor.authorPinto, Alex Sandro Roschildt [UNESP]
dc.contributor.authorKaiser, Jörg
dc.contributor.authorBecker, Leandro Buss
dc.contributor.institutionUniversidade Federal de Santa Catarina (UFSC)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionOtto-Von-Guericke-Univesitat Magdeburg
dc.date.accessioned2015-04-27T11:55:47Z
dc.date.available2015-04-27T11:55:47Z
dc.date.issued2012
dc.description.abstractConnectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.en
dc.description.affiliationUnespUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Ciência da Computação e Estatística, Instituto de Biociências Letras e Ciências Exatas de São José do Rio Preto
dc.format.extent1100-1105
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S1877050912005133
dc.identifier.citationProcedia Computer Science, v. 10, p. 1100-1105, 2012.
dc.identifier.doi10.1016/j.procs.2012.06.156
dc.identifier.fileISSN1877-0509-2012-10-1100-1105.pdf
dc.identifier.issn1877-0509
dc.identifier.lattes0555619693238543
dc.identifier.urihttp://hdl.handle.net/11449/122477
dc.language.isoeng
dc.relation.ispartofProcedia Computer Science
dc.relation.ispartofsjr0,258
dc.rights.accessRightsAcesso aberto
dc.sourceCurrículo Lattes
dc.subjectWireless sensor networksen
dc.subjectMobilityen
dc.subjectConnectivity predictionen
dc.subjectGenetic algorithmen
dc.subjectClassifier Systemsen
dc.titleAn evolutionary approach to improve connectivity prediction in mobile wireless sensor networksen
dc.typeArtigo
unesp.author.lattes0555619693238543
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt

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Artigos - Ciência da Computação e Estatística - IBILCE