Monitoring Sensor Measurement Anomalies of Streaming Environmental Data Using a Local Correlation Score

Real-time quality control (QC) of streaming natural resource data is needed to support the delivery of high quality data to system users. QC processes need to enable the identification of aberrations, as well as trends that may indicate degradation or component failures. These QC processes form a framework to support the goal of verified data delivered in a timely manner. In this paper, we investigate a method of computing Local Correlation Score (LCS) to detect anomalous patterns among sensor platforms in a concurrent manner. We use the R programming language and OpenMPI. Using empirical tests, we determine the benefits of computing the LCS in parallel, and on various sizes of clusters. We also analyze its use for real time mapping of Intelligent River data. Our results show that the LCS computed concurrently is an effective means for prompt quality assurance of natural resource data.