An efficient dual layer data aggregation scheme in clustered wireless sensor networks

Abstract

Abstract In wireless sensor network monitoring system, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. In order to reduce energy consumption of sensor nodes and prolong the life of wireless sensor networks, this paper proposed a dual layer intra-cluster data fusion scheme based on ring buffer (DLIDF). To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experiment results reveal that the scheme proposed in this paper has clear advantages in three aspects: the number of remaining nodes, the residual energy, and the number of packets transmitted.