Improved approaches for density-based outlier detection in wireless sensor networks
| Autor: | Salim El Khediri, Aymen Abid, Abdennaceur Kachouri |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
DBSCAN
Numerical Analysis Computer science Data stream mining 020206 networking & telecommunications 02 engineering and technology computer.software_genre Computer Science Applications Theoretical Computer Science Constant false alarm rate Computational Mathematics symbols.namesake Additive white Gaussian noise Computational Theory and Mathematics Outlier 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Anomaly detection Data mining Cluster analysis Wireless sensor network computer Software |
| Zdroj: | Computing. 103:2275-2292 |
| ISSN: | 1436-5057 0010-485X |
| DOI: | 10.1007/s00607-021-00939-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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