Uncertainty-Aware Sensor Data Management and Early Warning for Monitoring Industrial Infrastructures

Abstract

In several industrial applications, monitoring large-scale infrastructures in order to provide notifications for abnormal behavior is of high significance. For this purpose, the deployment of large-scale sensor networks is the current trend. However, this results in handling vast amounts of low-level, and often unreliable, data, while an efficient and real-time data manipulation is a strong demand. In this paper, the authors propose an uncertainty-aware data management system capable of monitoring interrelations between large and heterogeneous sensor data streams in real-time. To this end, an efficient similarity function is employed instead of the typical correlation coefficient to monitor dynamic phenomena for timely alerting notifications, and to guarantee the validity of detected extreme events. Experimental evaluation with a set of real data recorded by distinct sensors in an industrial water desalination plant reveals a superior performance of our proposed approach in terms of achieving significantly reduced execution times, along with increased accuracy in detecting extreme events and highly correlated pairs of sensor data streams, when compared with state-of-the-art data stream processing techniques.