Density-based algorithms are important data clustering techniques used to find arbitrary shaped clusters and outliers. Recently, outlier detectors through density-based clustering are applied to supervise data streams including wireless sensor networks (WSN’s). In this article, we compare two density-based methods, DBSCAN and OPTICS, using proposed configuration and specific classifier to identify outlier and normal clusters. For simulation, in MATLAB, we use real data of WSN’s from Intel Berkeley lab in that we introduce white Gaussian noise for different signal-to-noise ratio per data vector. We evaluate the two algorithms under different input parameters using several performance metrics as detection rate, false alarm rate. Results indicate that the DBSCAN scheme is more accurate and comprehensive compared with existing approaches for WSN’s. At the same time, OPTICS remains an interesting solution for a hierarchical study of datasets with an identification of anomalies.

基于密度的算法是重要的数据聚类技术，用于发现任意形状的聚类和离群值。近来，通过基于密度的聚类的离群值检测器被应用于监督包括无线传感器网络（WSN）在内的数据流。在本文中，我们比较了两种基于密度的方法，即DBSCAN和OPTICS，它们使用建议的配置和特定的分类器来识别异常和正常聚类。为了进行仿真，在MATLAB中，我们使用了来自Intel Berkeley实验室的WSN的真实数据，因为我们针对每个数据向量引入了不同信号信噪比的高斯白噪声。我们使用几种性能指标（如检测率，误报率）对不同输入参数下的两种算法进行评估。结果表明，与现有的WSN方法相比，DBSCAN方案更加准确和全面。