Elaris Computing Nexus
Edge-IoT Driven Smart Air Quality Monitoring Using the Proposed EdgeAQ-Net Distributed Deep Learning Model
Elaris Computing Nexus
Received On : 06 July 2026
Revised On : 16 August 2026
Accepted On : 05 September 2026
Published On : 26 September 2026
Volume 02, 2026
Pages : 203-214
Abstract
Ambient air quality has been greatly impaired by rapid urbanisation and industrialisation, which is a serious threat to the overall health of the population and the sustainability of the environment. Traditional air quality devices use centralised cloud-based systems, which can be characterised by a high level of latency, bandwidth consumption, and inability to scale to large volumes of real-time sensor data. In addition, most of the current monitoring frameworks do not have intelligent edge-level analytics that can conduct distributed learning among heterogeneous Internet of Things (IoT) devices. In order to overcome these shortcomings, this paper suggests an Edge-IoT-based smart air quality monitoring system based on the proposed EdgeAQ-Net distributed deep learning architecture. The proposed solution combines edge computing and IoT-based environmental sensors to allow real-time analysis and prediction of air quality to be performed at the network edge. The EdgeAQ-Net model is a hybrid of convolutional feature extraction and the use of bidirectional long short-term memory (BiLSTM) layers, which are effective in identifying spatial-temporal patterns of air pollutant data. Also, a lightweight distributed learning system is deployed to provide collaborative model updates among edge nodes and minimise communication costs. Benchmark urban air quality datasets with various pollutant indicators, including PM2.5, PM10, NO2, and CO, are used to validate the performance of the experimental model with respect to conventional machine learning and deep learning frameworks, including SVM, Random Forest, and normal LSTM. The accuracy of the proposed framework is 97.84, which is higher than the baseline models, and the inference latency is also 32% lower, and network bandwidth consumption is also 28% less. The findings indicate that the developed EdgeAQ-Net framework is very effective in increasing the efficiency and scalability of real-time air quality monitoring in the distributed smart city setting.
Keywords
Edge Computing, Internet of Things (IoT), Air Quality Monitoring, Distributed Deep Learning, EdgeAQ-Net.
- W. Mao, W. Wang, L. Jiao, S. Zhao, and A. Liu, “Modeling air quality prediction using a deep learning approach: Method optimization and evaluation,” Sustainable Cities and Society, vol. 65, Art. no. 102567, Feb. 2021, doi: 10.1016/j.scs.2020.102567.
- Q. Zhang, F. Fu, and R. Tian, “A deep learning and image-based model for air quality estimation,” Science of the Total Environment, vol. 724, Art. no. 138178, Jul. 2020, doi: 10.1016/j.scitotenv.2020.138178.
- N. Sarkar, R. Gupta, P. K. Keserwani, and M. C. Govil, “Air Quality Index prediction using an effective hybrid deep learning model,” Environmental Pollution, vol. 315, Art. no. 120404, Dec. 2022, doi: 10.1016/j.envpol.2022.120404.
- C. B. Pande, N. Radwan, S. Heddam, K. O. Ahmed, F. Alshehri, S. C. Pal, and M. Pramanik, “Forecasting of monthly air quality index and understanding the air pollution in the urban city, India based on machine learning models and cross-validation,” Journal of Atmospheric Chemistry, vol. 82, no. 1, Dec. 2024, doi: 10.1007/s10874-024-09466-x.
- Garcia, Y. Saez, I. Harris, X. Huang, and E. Collado, “Advancements in air quality monitoring: A systematic review of IoT-based air quality monitoring and AI technologies,” Artificial Intelligence Review, vol. 58, no. 9, Jun. 2025, doi: 10.1007/s10462-025-11277-9.
- S. Singh and G. Suthar, “Machine learning and deep learning approaches for PM2.5 prediction: A study on urban air quality in Jaipur, India,” Earth Science Informatics, vol. 18, no. 1, Dec. 2024, doi: 10.1007/s12145-024-01648-1.
- G. Chen, S. Chen, D. Li, and C. Chen, “A hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention,” Scientific Reports, vol. 15, no. 1, Jan. 2025, doi: 10.1038/s41598-025-88086-1.
- T. Zeng, X. Ma, Y. Luo, J. Yin, Y. Ji, and S. Lu, “Improving outdoor thermal environmental quality through kinetic canopy empowered by machine learning and control algorithms,” Building Simulation, vol. 18, no. 4, pp. 699–720, Mar. 2025, doi: 10.1007/s12273-025-1246-6.
- Q. Wei, H. Zhang, J. Yang, B. Niu, and Z. Xu, “PM2.5 concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China,” Environmental Pollution, vol. 371, Art. no. 125953, 2025.
- Z. He, Q. Guo, Z. Wang, and X. Li, “A hybrid wavelet-based deep learning model for accurate prediction of daily surface PM2.5 concentrations in Guangzhou City,” Toxics, vol. 13, no. 4, Art. no. 254, Mar. 2025, doi: 10.3390/toxics13040254.
- K. Gu, Y. Liu, H. Liu, B. Liu, J. Qiao, W. Lin, and W. Zhang, “Air pollution monitoring by integrating local and global information in self-adaptive multiscale transform domain,” IEEE Transactions on Multimedia, vol. 27, pp. 3716–3728, 2025, doi: 10.1109/TMM.2025.3535351.
- L. Wang et al., “Causal-inference machine learning reveals the drivers of China’s 2022 ozone rebound,” Environmental Science and Ecotechnology, vol. 24, Art. no. 100524, Mar. 2025, doi: 10.1016/j.ese.2025.100524.
- F. Ding et al., “Using multiple machine learning algorithms to optimize the water quality index model and their applicability,” Ecological Indicators, vol. 172, Art. no. 113299, Mar. 2025, doi: 10.1016/j.ecolind.2025.113299.
- Q. Aini, D. Manongga, U. Rahardja, I. Sembiring, and Y.-M. Li, “Technology readiness and behavioral intention to use air quality monitoring solutions—Measurement model,” PsycTESTS Dataset, American Psychological Association (APA), 2025, doi: 10.1037/t96415-000.
- N. Slimani, I. Slimani, N. Sbiti, and M. Amghar, “Machine learning and statistic predictive modeling for road traffic flow,” International Journal of Traffic and Transportation Management, vol. 3, no. 1, pp. 17–24, Mar. 2021, doi: 10.5383/jttm.03.01.003.
- K. S. Rautela, S. Singh, and M. K. Goyal, “Resilience to air pollution: A novel approach for detecting and predicting aerosol atmospheric rivers within Earth system boundaries,” Earth Systems and Environment, vol. 9, no. 1, pp. 93–115, Jul. 2024, doi: 10.1007/s41748-024-00421-0.
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Cite this Article
Yinghuan Shi, “Edge-IoT Driven Smart Air Quality Monitoring Using the Proposed EdgeAQ-Net Distributed Deep Learning Model”, Elaris Computing Nexus, pp. 203-214, 2026, doi: 10.65148/ECN/2026017.
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© 2026 Yinghuan Shi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.