Recursive density-based hierarchical clustering in gaussian distributed sensor network

Abstract

Sensor networks are data-centric networks constrained with limited battery power and processing capabilities. One of the crucial challenges in sensor network is energy hole problem. In order to deal with the challenge, there exists several mechanisms, of which clustering is considered an energy-efficient solution. In general, clustering refers to the technique of grouping nodes on the basis of similarity in spatial arrangement. An appropriately clustered network helps in processing and aggregation of sensed data before routing the information to destined location. This paper proposes a soft computing based recursive approach for implementing density-based hierarchical clustering, for Gaussian distributed sensor network. Our proposed probabilistic approach creates hierarchical clusters recursively, which not only addresses the problem of energy hole, but also reduces transmission delay, thereby maintaining data freshness.