Classification of Occupancy Sensor Anomalies in Connected Indoor Lighting Systems
Giulia Violatto, Ashish Pandharipande, Shuai Li, Luca Schenato · IEEE Internet of Things Journal · 2019
We consider the problem of classifying anomalous occupancy sensor behavior in connected indoor lighting systems. Anomalous occupancy sensor behavior may occur in the form of either a high number of false alarms (type-1 anomalies) or missed detections (type-2 anomalies). Two anomaly discovery scenarios are considered: one, in which no anomalies exist post-deployment, and two, in which both anomaly types are found together with normally functional sensors. We address the problem of classifying anomalies that may occur subsequently using a machine learning approach. Under scenario 1, we consider a one class random forest classifier to determine whether an occupancy signal is normal or not. In scenario 2, we consider a supervised random forest classifier to determine whether the detection signal of an occupancy sensor is normal, or exhibits type-1 or type-2 anomalies. We devise occupancy signal features in time and frequency domains to perform 2-class classification in scenario 1, and 3-class classification in scenario 2. The proposed method is evaluated using motion sensor data from an office building, and is shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k -means method and a random forest classifier with a single signal energy feature.