Towards a Distributed Clustering Scheme Based on Spatial Correlation in WSNs

In the development of various large-scale sensor systems, a particularly challenging problem is how to dynamically organize the sensor nodes into clusters and route the sensing information to a remote base station. By leveraging the spatial correlation, there have been some noteworthy clustering schemes proposed recently, such as EEDC and ASAP. However, they are based on an impractical assumption of single-hop network architecture, and the cluster construction communication cost is relatively high. With such motivation, we introduce a novel distributed clustering scheme to group the sensor nodes that have the highest similarity in observations into the same cluster and also construct a dynamic backbone of efficient data collection in wireless sensor networks. Accordingly, with a given spatial accuracy requirement, only part of the sensor nodes in each cluster should be required to work for sampling and data transmitting in order to save energy. Comprehensive computer simulations show that the proposed scheme significantly reduces the overall number of communications in the cluster construction phase, whilst maintaining the small variance between the readings of sensor nodes in the same clusters.