Autonomous clustering scheme for wireless sensor networks using coverage estimation-based self-pruning

Abstract

Energy-efficient operations are essential to prolonging the lifetime of wireless sensor networks. Clustering sensor nodes is one approach that can reduce energy consumption by aggregating data, controlling transmission power levels, and putting redundant sensor nodes to sleep. To distribute the role of a cluster head, clustering approaches should be based on efficient cluster configuration schemes. Therefore, low overhead in the cluster configuration process is one of the key constraints for energy-efficient clustering. In this paper, we present an autonomous clustering approach using a coverage estimation-based self-pruning algorithm. Our strategy for clustering is to allow the best candidate node within its own cluster range to declare itself as a cluster head and to dominate the other nodes in the range. This same self-declaration strategy is also used in the active sensor election process. As a result, the proposed scheme can minimize clustering overheads by obviating both the requirements of collecting neighbor information beforehand and the iterative negotiating steps of electing cluster heads. The proposed scheme allows any type of sensor network application, including spatial query execution or periodic environment monitoring, to operate in an energy-efficient manner.