Improved approaches for density-based outlier detection in wireless sensor networks

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.

Automated summary

This article compares two density-based clustering methods, DBSCAN and OPTICS, for identifying outliers and normal clusters. The results suggest that the DBSCAN scheme is more accurate and comprehensive for WSNs, while OPTICS remains a suitable solution for hierarchical study of datasets.