Development of IoT-Based Air Quality Monitoring System
Article Sidebar
Main Article Content
Abstract
This paper presents an Internet of Things (IoT)–based air- quality monitoring system for indoor and outdoor environments. To minimize sensor bias, the system combines real-time sensing with a dynamic air-speed control unit that adjusts fan speed according to the measured wind speed. Continuous outdoor operation is supported by an independent battery-management system (BMS). Sensor data are transmitted over Wi-Fi using the MQTT protocol to a cloud platform for storage and visualization. The system uses an LSTM neural network to forecast pollutant concentrations as time series, and Principal Component Analysis (PCA) is applied for dimensionality reduction. Using six principal components (cumulative explained variance ≈ 0.96), the model achieved a MAPE of 5.27% on the held-out test set. The system supports historical data access, trend analysis, and predictive alerts, and is applicable to indoor air monitoring and automated laboratory air-control systems.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Journal of TCI is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence, unless otherwise stated. Please read our Policies page for more information...
References
L. Morawska, P. K. Thai, X. Liu, A. Asumadu-Sakyi, and G. Ayoko, “Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?,” *Environ. Int.*, vol. 116, pp. 286–299, 2018.
P. Acosta-Vargas, R. Zalakeviciute, S. Luján-Mora, and W. Hernández, “Accessibility evaluation of mobile applications for monitoring air quality,” in *Proc. Inf. Technol. Syst. (ICITS)*, A. Rocha, C. Ferrás, and M. Paredes, Eds., Cham: Springer, 2019, *Adv. Intell. Syst. Comput.*, vol. 918, pp. 638–648, doi: 10.1007/978-3-030-11890-7_61.
K. V. Sharma and P. Jain, “Deep learning approach for prediction of air quality index in Delhi,” *Air Qual. Atmos. Health*, vol. 13, no. 10, pp. 1237–1247, 2020, doi: 10.1007/s11869-020-00872-8.
J. Jo, B. Jo, J. Kim, S. Kim, and W. Han, “Development of an IoT-based indoor air quality monitoring platform,” *J. Sens.*, 2020, Art. no. 8749764, doi: 10.1155/2020/8749764.
P. Chirdsiri et al., “The study of indoor air quality effect on the sick building syndrome of photocopier operators,” *Princess Naradhiwas Univ. J.*, vol. 9, no. 3, pp. 106–120, 2017.
M. Eriyadi, D. Notosudjono, H. Setiana, and M. A. A. A. Yakin, “Low-cost mobile air quality monitoring based on internet of things for factory area,” *Indones. J. Electr. Eng. Comput. Sci.*, vol. 32, no. 1, pp. 545–554, Oct. 2023, doi: 10.11591/ijeecs.v32.i1.pp545-554.
N. Zimmerman et al., “A machine learning calibration model using random forests for low-cost air quality sensors,” *ACS Sens.*, vol. 3, no. 12, pp. 2277–2286, Dec. 2018, doi: 10.1021/acssensors.8b00781.
L. Zhang, J. Li, X. Wang, and Y. Zhao, “A distributed air quality monitoring system using wireless sensor networks and machine learning,” *IEEE Sens. J.*, vol. 22, no. 9, pp. 8921–8930, May 2022, doi: 10.1109/JSEN.2022.3165432.
H. Yan, H. Zhang, and Y. Liu, “PM2.5 prediction based on attention mechanism and bidirectional LSTM,” *Atmos. Pollut. Res.*, vol. 14, no. 3, p. 101733, 2023, doi: 10.1016/j.apr.2023.101733.
T. Ma et al., “DeepAir: Deep learning for air quality prediction with real-time data,” in *Proc. 27th ACM SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst.*, 2019, pp. 369–372, doi: 10.1145/3356995.3364478.
X. Li et al., “Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation,” *Environ. Pollut.*, vol. 231, pp. 997–1004, 2017, doi: 10.1016/j.envpol.2017.08.082.
Y. Tsai, Y. Zeng, and Y. Chang, “Air pollution forecasting using RNN with LSTM,” in *Proc. IEEE 16th Int. Conf. Dependable, Autonomic and Secure Comput. (DASC)*, Aug. 2018, pp. 864–869,doi:10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00145.
Shenzhen Plantower Co., Ltd., *PMS5003 Series Data Manual*, ver. 2.3, written by Zhou Yong; verified by Zheng Haoxin, Jun. 1, 2016.
