IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

• Published in: Applied sciences Vol. 9; no. 9; p. 1831
• Main Authors: Lai, Xiaozheng, Yang, Ting, Wang, Zetao, Chen, Peng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2019
• Subjects: Accuracy; Agricultural production; Agriculture; Air monitoring; Air pollution; Air quality; Air quality assessments; Algorithms; Autoregressive models; Big Data; Cloud computing; Computer applications; Data analysis; Data processing; edge computing; Environmental monitoring; Error analysis; high accuracy; Historical account; International conferences; Internet of Things; IoT; Kalman Filter; Kalman filters; Monitoring systems; Noise measurement; Noise prediction; Pollutants; prediction; Prediction models; Raspberry Pi; Sensors; Statistical analysis; Statistical methods; Time series; Traffic congestion; Wireless networks; New York; United States > US; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9091831

In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO2, NO2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture.