QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

• Autor(a) principal: Ferreira, Leonardo N.
• Data de Publicação: 2012
• Outros Autores: Pinto, A. R. [UNESP], Zhao, Liang
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IJCNN.2012.6252477; http://hdl.handle.net/11449/73507
• topic: Cluster head; Cluster-head nodes; Clustering techniques; Community detection; Complex networks; Hybrid clustering algorithm; K-means; K-means clustering techniques; Physical phenomena; Radio coverage; Small clusters; Clustering algorithms; Neural networks; Population dynamics; Sensor nodes

Wireless 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. © 2012 IEEE.