ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems

As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an anomaly detection framework (ADF) for large-scale, real-world environmental sensing systems. The framework is composed of four modules: 1) time-sliced anomaly detection (TSAD), which detects spatial, temporal, and spatio-temporal anomalies in the real-time sensor measurement data stream; 2) real-time emission detection, which detects potential regional emission sources; 3) device ranking, which provides a ranking for each sensing device; and 4) malfunction detection, which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.