Anomaly detection in medical WSNs using enclosing ellipse and chi-square distance

In this paper, we propose an Anomaly Detection (AD) approach for medical Wireless Sensor Networks (WSNs). This approach is able to detect abnormal changes and to cope with unreliable or maliciously injected measurements in the network, without prior knowledge of anomalous events or normal data pattern. The main objective is to reduce the false alarms triggered by abnormal measurements. In our proposed framework, each sensor applies the Exponentially Weighted Moving Average (EWMA) for one-step forecasting. To reduce the energy consumed by periodic data transmission to the Local Processing Unit (LPU), the sensor transmits only when the data point (measured, expected) falls outside the dynamically updated ellipsoidal region enclosing the normal data. The LPU exploits correlation and uses chi-square distance for spatial analysis before raising a medical alarm. We evaluate our approach on real medical data set. Experimental results through computer simulation demonstrate that our proposed approach can achieve a good detection accuracy with low false alarm rate (less than 4%).