QK-Means: A Clustering Technique Based on Community Detection and K-Means for Deployment of Cluster Head Nodes

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorFerreira, Leonardo N.-
Autor(es): dc.creatorPinto, A. R. [UNESP]-
Autor(es): dc.creatorZhao, Liang-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2022-02-22T00:08:15Z-
Data de disponibilização: dc.date.available2022-02-22T00:08:15Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2012-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196020-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196020-
Descrição: dc.descriptionWireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniv Sao Paulo, Inst Math & Comp Sci, Av Trabalhador Sao Carlense 400,Caixa Postal 668, BR-13560970 Sao Paulo, Brazil-
Descrição: dc.descriptionUNESP, DCCE, IBILCE, Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUNESP, DCCE, IBILCE, Sao Carlos, SP, Brazil-
Formato: dc.format7-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2012 International Joint Conference On Neural Networks (ijcnn)-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleQK-Means: A Clustering Technique Based on Community Detection and K-Means for Deployment of Cluster Head Nodes-
Aparece nas coleções:Repositório Institucional - Unesp

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