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...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Jie Yin, Mohamed Medhat Gaber
• Keywords: Data Mining, ICDM 2008, Sensor, Sensor Networks, Sensor Nodes