Clustering Distributed Time Series in Sensor Networks

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Clustering Distributed Time Series in Sensor Networks
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 d...
Jie Yin, Mohamed Medhat Gaber
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Jie Yin, Mohamed Medhat Gaber
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