Cluster-Based Correlated Data Gathering in Wireless Sensor Networks

We consider the problem of optimal cluster-based data gathering in Wireless Sensor Networks (WSNs) when nearby readings are spatially correlated. Due to the dense nature of WSNs, data samples taken from nearby locations are statistically similar. We show how this data correlation can be exploited to reduce the amount of data to be transmitted in the network and thus conserve energy. While much attention in recent years has been paid to analyzing and optimizing cluster-based WSNs from various perspectives, the problem of energy-efficient clustering of WSNs in presence of data correlation is not yet fully explored. In this paper, we model a single-cluster network and analytically characterize the optimal cluster size subject to its distance from the sink as well as the degree of correlation. Contrary to existing approaches, our findings show that heterogeneous-sized clusters, where the clusters further from the sink are larger, are more energy-efficient. We also propose a heuristic greedy clustering algorithm to find a near-optimal solution to the problem of energy-efficient clustering. Simulation results confirm the effectiveness of having heterogeneous-sized clusters in WSNs.