H-cluster: A Novel Efficient Algorithm for Data Clustering in Sensor Networks

T T T—This paper focuses on the problem of data clustering in wireless sensor networks (WSNs). The data time window is a landmark window, from the time WSN starts working up to the current time. The objective is to group sensory data generated by sensor nodes deployed in a two-dimensional physical space by the similarity of sensory data in the multi-dimensional sensory data space. To perform in-network data clustering efficiently, we propose HilbertMap, a novel dimensionality reduction technique based on the Hilbert Curves, to map a multi-dimensional data space to a two-dimensional physical space. Through this mapping, the communications for clustering mostly occur between geographically nearby sensor nodes. We have conducted simulation experiments on both real-world and synthetic datasets. Our results show that HilbertMap improves the communication efficiency while maintaining a good clustering quality.