Balanced Density-Based Clustering Technique Based on Distributed Spatial Analysis in Wireless Sensor Network

Clustering in wireless sensor networks (WSNs) is an important stage for the communication between sensor nodes. Many clustering techniques were proposed with different characteristics. The main goal of them is to facilitate a power-aware communication between a large number of deployed nodes. One of the important factors which affect the clustering process is the distribution of the nodes. In many real situations, the distribution of nodes is random. This type of distribution produces a network with different density sub-regions. A different number of nodes in each sub-region of the network means different communication load and therefore different energy consumptions. This work proposes a distributed density-based clustering technique called “spatial density-based clustering for WSNs.” It aims to achieve balanced energy consumption all over the constructed clusters. This is done with the help of a simple initial spatial analysis for the distribution of the nodes before the clustering process. This analysis divides the network to sub-regions according to their density level. Clusters formed in each sub-region will use a suitable size according to the measured density level. Simulation results show that the proposed technique achieves less power consumption and therefore longer network lifetime when compared with other clustering techniques.