Energy efficient clustering using tree balancing algorithms in wireless sensor networks

Various hierarchical clustering schemes have been proposed in order to efficiently maintain the energy consumption of sensor nodes. Most of these schemes, however, are hardly applicable in practice since these schemes might produce unbalanced clusters or randomly distributed clusters without consideration for the distribution of sensor nodes. To overcome the limitations of such hierarchical clustering schemes, we propose a novel scheme called CUTA (Clustering Using Tree-balancing Algorithm) that exploits node split and merge algorithms of tree-based indexing structures to efficiently construct clusters. Our extensive performance studies show that CUTA produces highly balanced clustering in an energy efficient way and achieves up to 1.4 times higher performance than the previous clustering schemes, under various operational conditions.