Simultaneous Sporadic Sensor Anomaly Detection for Smart Homes

Dissemination of sensors and advances in techniques (e.g., network) has led to the opportunity for smart home. However, sensor malfunctions and difficult-to-diagnose characteristics hinder robust sensor system operation. Sensor anomaly detection systems for smart home have been proposed, but they target only a few specific types of sensor anomalies of a single sensor. In this work, we propose a sensor anomaly detection method based on Deep Neural Network (DNN), which automatically extracts critical features to detect the anomalies, even for simultaneous sporadic anomalies with complex data patterns. We leverage Hypersphere Classification (HSC) [14], the state-of-the-art DNN-based supervised outlier exposure method. We evaluate our proposed method on a public smart home sensor dataset. Our results show that the performances of the baselines drop up to 54.4% while ours drops up to 1.1%.