Anomaly Detection in Edge Nodes using Sparsity Profile

Abstract

Edge devices with attentive sensors enable various intelligent services by exploring streams of sensor data. However, anomalies, which are inevitable due to faults or failures in the sensor and network, can result in incorrect or unwanted operational decisions. While promptly ensuring the accuracy of IoT data is critical, lack of labels for live sensor data and limited storage resources necessitates efficient and reliable detection of anomalies at edge nodes. Motivated by the existence of unique sparsity profiles that express original signals as a combination of a few coefficients between normal and abnormal sensing periods, we propose a novel anomaly detection approach, called ADSP (Anomaly Detection with Sparsity Profile). The key idea is to apply a transformation on the raw data, identify top-K dominant components that represent normal data behaviors, and detect data anomalies based on the disparity from K values approximating the periods of normal data in an unsupervised manner. Our evaluation using a set of synthetic datasets demonstrates that ADSP can achieve 92%–100% of detection accuracy. To validate our anomaly detection approach on real-world cases, we label potential anomalies using a range of error boundary conditions using sensors exhibiting a straight line in Q-Q plot and strong Pearson correlation and conduct a controlled comparison of the detection accuracy. Our experimental evaluation using real-world datasets demonstrates that ADSP can detect 83%– 92% of anomalies using only 1.7% of the original data, which is comparable to the accuracy achieved by using the entire datasets.