DHC: Distributed, Hierarchical Clustering in Sensor Networks

Abstract

In many sensor network applications, it is essential to get the data distribution of the attribute value over the network. Such data distribution can be got through clustering, which partitions the network into contiguous regions, each of which contains sensor nodes of a range of similar readings. This paper proposes a method named Distributed, Hierarchical Clustering (DHC) for online data analysis and mining in senior networks. Different from the acquisition and aggregation of raw sensory data, DHC clusters sensor nodes based on their current data values as well as their geographical proximity, and computes a summary for each cluster. Furthermore, these clusters, together with their summaries, are produced in a distributed, bottom-up manner. The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions. It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks. We also design and evaluate the maintenance mechanisms for DHC to make it be able to work on evolving data. Our simulation results on real world datasets as well as synthetic datasets show the effectiveness and efficiency of our approach.