Volterra Graph-Based Outlier Detection for Air Pollution Sensor Networks

Abstract

Today's air pollution sensor networks pose new challenges given their heterogeneity of low-cost sensors and high-cost instrumentation. Recently, with the advent of graph signal processing, sensor network measurements have been successfully represented by graphs depicting the relationships between sensors. However, one of the main problems of these sensor networks is their reliability, especially due to the inclusion of low-cost sensors, so the detection and identification of outliers is extremely important for maintaining the quality of the network data. In order to better identify the outliers of the sensors composing a network, we propose the Volterra graph-based outlier detection (VGOD) mechanism, which uses a graph learned from data and a Volterra-like graph signal reconstruction model to detect and localize abnormal measurements in air pollution sensor networks. The proposed unsupervised decision process is compared with other outlier detection methods, state-of-the-art graph-based methods and non-graph-based methods, showing improvements in both detection and localization of anomalous measurements, so that anomalous measurements can be corrected and malfunctioning sensors can be replaced.