Sensor data boundary estimation for anomaly detection in wireless sensor networks

Fuzzy boundaries and unpredictable anomalies displayed in the raw sensor data make the process of defining a strong ellipsoid boundary for the raw data in the ellipsoid-based anomaly detection algorithms in wireless sensor networks a difficult problem. We have shown, using synthetic and real sensor data, that the random variable that represents the difference between any two randomly selected raw data points follows an identically independently distributed Gaussian distribution. We have used this statistical property to calculate ellipsoid boundaries for the Gaussian distribution which displays a robust ellipsoid shape and then to map each point of the distribution function to its corresponding raw data point to isolate anomalies from the sensor data. We have demonstrated the performance of the proposed approach by comparing it with the standard approach using both synthetic datasets and real Intel Berkeley Research Laboratory and Grand St Bernard datasets.