An Energy-Efficient Adaptive Overlapping Clustering Method for Dynamic Continuous Monitoring in WSNs

Abstract

Clustering is a key technique to improve energy efficiency in wireless sensor networks (WSNs). In continuous monitoring applications, the clusters should be formed dynamically according to the event development for energy-efficient data gathering. In this paper, an energy-efficient adaptive overlapping clustering (EEAOC) method is proposed in WSNs for continuous monitoring applications. In EEAOC, a 2-logical-coverage overlapping clustering topology is established such that the adjacent sensors in the event area can be grouped into the same cluster for data fusion and the cluster migration operation can be processed without changing the overlapping structure among clusters. Moreover, to further reduce energy consumption, a hybrid data reporting strategy that switches between time-driven and event-driven schemes is introduced based on the QoS requirements in continuous monitoring applications. Simulation results show that EEAOC achieves a longer network lifetime cycle.