IoT-Enabled Particulate Matter Monitoring and Forecasting Method Based on Cluster Analysis

Abstract

In recent years, particulate matter (PM) having a diameter smaller than 2.5 mu m has become a significant issue due to its severe impact on human health. With the advent of IoT-enabling technologies, a ubiquitous IoT sensing infrastructure is now used to constantly monitor aspects of our surrounding environment, such as ambient air pollution. In this article, we introduce a PM-sensing system composed of off-the-shelf LoRa-based wireless hardware boards and low-cost PM sensors. By leveraging software platforms that are compliant with an IoT standard called oneM2M, PM data sets can be collected and accessed in a standardized manner, i.e., via oneM2M-defined representational state transfer application programmable interfaces. Also, for reliable PM monitoring, a short-term (i.e., within 2 h) PM forecasting method based on autoregressive integrated moving average and vector autoregressive moving average (VARMA) models is proposed and evaluated with a 30-day PM data set collected from 15 LoRa-based PM sensor nodes installed at a university campus. The experimental results show that the overall root-mean square error and correlation coefficient of the VARMA models integrated with hierarchical clustering are improved by 7.77% and 3.7%, respectively, compared with the single node-based forecast model.