Discrete Wavelet Transform and One-Class Support Vector Machines for anomaly detection in wireless sensor networks

Data readings from wireless sensor networks (WSNs) may be abnormal due to detection of unusual phenomena, limited battery power, sensor malfunction, or noise from the communication channel. It is thus, important to detect such data anomalies available in WSNs to determine a suitable course of action. This paper proposes an integrated data compression and anomaly detection algorithm in WSNs which can detect anomalies accurately by employing half of sensor data measurement, instead of using all the sensor data measurement. The contribution of this paper centers on data compression by using Discrete Wavelet Transform (DWT) then feeding to anomaly detection by using One-Class Support Vector Machine (OCSVM). We tested our algorithm with several synthetic and real world datasets. The results showed that the proposed algorithm outperformed previous techniques in terms of near 100% detection rate and with marginal increase in false positive rates in presence of short and noise faults.