Alarm activation, pattern discovery, and anomaly detection in sensor networks

This article describes some recent sensor network research. We have looked in more detail at three data analysis issues. First, we have considered the general issue of what constitutes normal activity and what constitutes anomalous activity. Detecting anomalies requires some measure of ‘normal’ to determine if there is an anomaly. While approaches such as Bayesian networks work well on large and diverse networks, a simpler approach may be useful for smaller networks. We describe an approach for keeping an ongoing record of sensor activation events and turning on an alarm when the frequency of events exceeds a set threshold. The approach is devised for efficiency of computation and storage. We considered groups of days such as seasons, days of the week, and work days to produce several sets of average activation levels. Second, we modeled different time intervals as independent Poisson processes and used this model to compile average data to look for and display anomalies at different sensors and different times. Using a sensor data set, we studied groups of days such as seasons, days of the week, and work days to produce several sets of average activation levels. Third, we consider possible paths that could be caused by an intruder activating several sensors in a spatial and temporal neighborhood. We are developing a simple tool to calculate and display such paths and their probabilities. WIREs Comput Stat 2012, 4:565–570. doi: 10.1002/wics.1233 This article is categorized under: Algorithms and Computational Methods > Computer Graphics Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition