Distributed, hierarchical clustering and summarization in sensor networks

Authors:
Xiuli Ma;Shuangfeng Li;Qiong Luo;Dongqing Yang;Shiwei Tang
Affiliations:
School of Electronics Engineering and Computer Science, State Key Laboratory on Machine Perception, Peking University, Beijing, China;School of Electronics Engineering and Computer Science, State Key Laboratory on Machine Perception, Peking University, Beijing, China;Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;School of Electronics Engineering and Computer Science, State Key Laboratory on Machine Perception, Peking University, Beijing, China;School of Electronics Engineering and Computer Science, State Key Laboratory on Machine Perception, Peking University, Beijing, China
Venue:
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Year:
2007

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Abstract

We propose DHCS, a method of distributed, hierarchical clustering and summarization for online data analysis and mining in sensor networks. Different from the acquisition and aggregation of raw sensory data, our method 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. Our simulation results on real world data sets as well as synthetic data sets show the effectiveness and efficiency of our approach.