Abstract
This paper studies three techniques for outliers detection in the context of Wireless Sensor Networks, including a machine learning technique, a Principal Component Analysis-based methodology and an univariate statistics-based approach. The first methodology is based on a Least Squares-Support Vector Machine technique, together with a sliding window learning. A modification to this approach is also considered in order to improve its performance in non-stationary time-series. The second methodology relies on Principal Component Analysis, along with the robust orthonormal projection approximation subspace tracking with rank-1 modification, while the last approach is based on univariate statistics within an oversampling mechanism. All methods are implemented under a hierarchical multi-agent framework and compared through experiments carried out on a test-bed.
Original language | English |
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Pages (from-to) | 204-214 |
Journal | Applied Soft Computing |
Volume | 42 |
DOIs | |
Publication status | Published - May 2016 |
Keywords
- SUPPORT VECTOR MACHINE
- ANOMALY DETECTION