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Reseach Article
Real-Time Air Quality Monitoring based on IoT using the Long Short-Term Memory
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 96 |
| Year of Publication: 2026 |
| Authors: Yolanda T.L. Pontoan, Feibe L.V. Lalujan, Yuricha M.J. Tumiwang, Marson James Budiman |
10.5120/ijcab8b0de1015a6
|
Yolanda T.L. Pontoan, Feibe L.V. Lalujan, Yuricha M.J. Tumiwang, Marson James Budiman . Real-Time Air Quality Monitoring based on IoT using the Long Short-Term Memory. International Journal of Computer Applications. 187, 96 ( Apr 2026), 15-19. DOI=10.5120/ijcab8b0de1015a6
Abstract
Air quality is an important factor that directly impacts human health and the environment, particularly in urban areas with high levels of pollution. This study proposes a real-time air quality prediction system based on the Internet of Things (IoT) using a time-series data approach. The system is designed using an ESP32 microcontroller integrated with a DHT22 sensor for measuring temperature and humidity, as well as an MQ135 sensor for detecting pollutant gases. Environmental data are transmitted in real time to a cloud platform for storage, visualization, and analysis. The prediction method used in this study is the Long Short-Term Memory (LSTM) algorithm, which is effective in modeling temporal patterns in time-series data and generating predictions of future air quality conditions. The system architecture consists of hardware, network communication, and a cloud-based application layer that enables continuous environmental monitoring and remote data access. In addition, a monitoring dashboard is developed to display air quality information and provide early warnings in the event of a decline in air quality. The implementation results indicate that the system is capable of performing real-time air quality monitoring while also providing predictions based on historical data with relatively low cost and high efficiency. The proposed system has the potential to serve as an alternative solution for IoT-based air quality monitoring, supporting rapid decision-making and increasing public awareness of environmental conditions.
References
- S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
- A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Germany: Springer, 2012.
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
- L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
- D. Evans, “The Internet of Things: How the Next Evolution of the Internet is Changing Everything,” Cisco IBSG, White Paper, 2011.
- S. Madakam, R. Ramaswamy, and S. Tripathi, “Internet of Things (IoT): A Literature Review,” Journal of Computer and Communications, vol. 3, no. 5, pp. 164–173, 2015.
- H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks. Chichester, UK: Wiley, 2005.
- A. Kumar, I. P. Singh, and S. K. Sud, “Energy Efficient System for Wireless Sensor Networks,” International Journal of Computer Applications, vol. 33, no. 12, pp. 1–6, 2011.
- P. Saini, A. Verma, and R. K. Chauhan, “Real-Time Air Quality Monitoring System Using IoT,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6, pp. 156–159, 2019.
- Y. Zheng, F. Liu, and H. Hsieh, “U-Air: When Urban Air Quality Inference Meets Big Data,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013, pp. 1436–1444.
- X. Zhang, R. Huang, and Z. Yang, “Air Quality Prediction Using LSTM Neural Network,” in IEEE International Conference on Data Science and Advanced Analytics, 2018, pp. 1–6.
- K. G. Li, J. Li, and Q. Peng, “Air Quality Prediction Based on LSTM Neural Network,” IEEE Access, vol. 7, pp. 154–162, 2019.
- M. A. Al-Kuwari, A. Ramadan, Y. Ismael, L. Al-Sughair, A. Gastli, and M. Benammar, “Smart-Home Automation Using IoT-Based Sensing and Monitoring Platform,” IEEE Access, vol. 6, pp. 192–198, 2018.
- A. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
- J. Wang, P. Wang, and Y. Liu, “Air Quality Forecasting Based on Deep Learning Model,” Applied Sciences, vol. 10, no. 23, pp. 1–15, 2020.
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