Clustering Distributed Time Series in Sensor Networks
Clustering Distributed Time Series in Sensor Networks
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DOI：
10.1109/icdm.2008.58
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Jie Yin;M. Gaber
中科院分区：
文献类型：
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作者：
Jie Yin;M. Gaber
事件检测是传感器网络中的一项关键任务，特别是在环境监测应用中。传统的事件检测方案是基于对一次性数据点的分析，由于传感器数据本身不可靠且有噪声，可能会导致较高的虚警率。为了解决这个问题，我们提出了一种新的分布式单次增量聚类（DSIC）技术，根据传感器节点的潜在趋势对时间序列进行聚类。为了实现可扩展性和能源效率，我们的DSIC技术使用传感器网络的分层结构作为底层基础设施。该算法首先利用Haar小波变换将单个传感器节点产生的时间序列压缩成紧凑的表示形式，然后基于动态时间弯曲距离，以增量方式将近似时间序列分层分组为全局聚类模型。在真实数据和合成数据上的实验结果表明，DSIC算法对网络拓扑变化具有准确、高效和鲁棒性。
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.