Online anomaly detection for sensor systems: A simple and efficient approach

Wireless sensor systems aid scientific studies by instrumenting the real world and collecting measurements. Given the large volume of measurements collected by sensor systems, one problem arises—an automated approach to identifying the “interesting” parts of these datasets, or anomaly detection. A good anomaly detection methodology should be able to accurately identify many types of anomaly, be robust, require relatively few resources, and perform detection in (near) real time. Thus, in this paper, we focus on an approach to online anomaly detection in measurements collected by sensor systems, where our evaluation, using real-world datasets, shows that our approach is accurate (it detects over 90% of the anomalies with few false positives), works well over a range of parameter choices, and has a small (CPU, memory) footprint.