An outlier detection algorithm in wireless sensor network based on clustering
An outlier detection algorithm in wireless sensor network based on clustering
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DOI：
10.1109/icct.2013.6820415
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发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Kun Niu;Fang Zhao;Xiuquan Qiao
中科院分区：
文献类型：
--
作者：
Kun Niu;Fang Zhao;Xiuquan Qiao
提出了一种新的基于分簇的无线传感器网络离群点检测算法（ODC）。首先，ODC算法定义了数据采样的时隙。然后，ODC以节点为属性，对所有时隙进行合理聚类。在聚类过程之后，它得到最大聚类和最小聚类。根据到两个簇中心的距离分配时隙。通过对某个时间段内不同簇交替标记的连续时隙之间的距离进行求和，得到出现规则周期时的特征周期T的长度。最后，ODC通过检测时隙的聚类标签来发现潜在的离群点。在真实的公共无线传感器数据集上的实验结果验证了该算法的有效性和鲁棒性。
This paper presents a novel wireless sensor network outlier detection algorithm based on clustering (ODC). Firstly, the ODC algorithm defines time slot for data sampling. After that, ODC get reasonable clusters for all time slots with nodes as attributes. After the clustering process, it gets the maximum cluster and the minimum cluster. It assigns all time slots according to the distances to the two cluster centers. Summarizing the distances between sequential time slots alternatively labeled by different cluster in some time period, it gets the length of feature period T when it appears regular cycle. Finally, ODC finds latent outliers by detecting cluster labels of time slots. Experimental results on real public wireless sensor data sets are provided to illustrate the efficiency and the robustness of the proposed algorithm.