Distributed Spatial Correlation-based Clustering for Approximate Data Collection in WSNs

Grouping sensor nodes with similar readings into the same cluster and scheduling them to alternately report their sensing readings is an effective and efficient method to perform approximate data collection, which exploits the tradeoff between data quality and energy consumption. However, to partition all sensor nodes into exclusive clusters while incurring as less communication overhead as possible in a distributed manner is a challenging task. In this paper, by exploiting inherent spatial and data correlation in wireless sensor network, we have proposed the distributed spatial correlation-based clustering algorithm to complete this tough mission. With nodes information exchange in certain region and the novel ranking strategy, our clustering algorithm can terminate in a small number of iterations. Extensive simulations show that the proposed algorithm outperforms three other noteworthy clustering algorithms, namely EEDC, ASAP and DClocal, on some key metrics, such as number of clusters, energy consumption for clustering, average dissimilarity of clusters and residual energy level of cluster heads.