Clustering distributed time series in sensor networks

J. Yin, M. Gaber

Research output: Contribution to conferencePaperpeer-review

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Event detection is a critical task in sensor networks, especially for environmental monitoring applications. Traditional solutions to event detection are based on analyzing one-shot data points, which might incur a high false alarm rate because sensor data is inherently unreliable and noisy. To address this issue, we propose a novel Distributed Single-pass Incremental Clustering (DSIC) technique to cluster the time series obtained at sensor nodes based on their underlying trends. In order to achieve scalability and energy-efficiency, our DSIC technique uses a hierarchical structure of sensor networks as the underlying infrastructure. The algorithm first compresses the time series produced at individual sensor nodes into a compact representation using Haar wavelet transform, and then, based on dynamic time warping distances, hierarchically groups the approximate time series into a global clustering model in an incremental manner. Experimental results on both real data and synthetic data demonstrate that our DSIC algorithm is accurate, energy-efficient and robust with respect to network topology changes.
Original languageEnglish
Number of pages10
Publication statusPublished - 2008
EventProceedings of the 2008 Eighth IEEE International Conference on Data Mining - Washington, DC
Duration: 15 Dec 200819 Dec 2008


ConferenceProceedings of the 2008 Eighth IEEE International Conference on Data Mining
CityWashington, DC


  • Distributed Clustering
  • Time Series
  • Sensor Networks


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