Online Detection of Change on Information Streams in Wireless Sensor Network Modeled Using Gaussian Distribution

Abstract

Wireless sensor network (WSN) is deployed to monitor certain physical quantities in a region. This monitoring problem could be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. A moving window procedure is proposed to detect the systematic error, which occurs at an unknown time. It can detect the deviation in the mean of sensor measurements keeping variance as constant. The performance measures, such as the average run length (ARL) to detection delay and false alarms are computed for various window sizes. The performance comparison is done against traditional cumulative sum (CUSUM) method. The detection of change in mean using CUSUM is done with smaller delay compared to the proposed moving window detection procedure. In order to calculate CUSUM statistics, the number of measurements to keep in sensor memory increases with time. However, in the proposed moving window detection procedure, the number of stored measurements is limited by the size of the window. Therefore, it is advantageous to use the moving window procedure for change detection in sensor nodes that have very limited memory. A high probability of detection is achieved at the cost of larger window size and higher detection delay. However, we are able to achieve the maximum probability of detection even at a window size of 11.

Exponentially Weighted Ellipsoidal Model for Anomaly Detection

“…The most common online approaches for detecting anomalies in time‐series data are based on incremental statistical testing, mainly cumulative sum (CUSUM) charts . These approaches aim to detect mean shifts either in the raw data or the residuals of a state estimation model . The general multivariate CUSUM developed by Crosier is usually considered the best performing CUSUM‐based method over the raw data.…”