An IoT Implemented Dynamic Air Pollution Monitoring System

Authors

  • Sneha Edupuganti Vellore Institute of Technology University
  • Narasinga Sai Satwik Tenneti Vellore Institute of Technology University
  • M. Mohamed Iqbal Vellore Institute of Technology University
  • Gnanajeyaraman Rajaram Saveetha University

DOI:

https://doi.org/10.4108/eetiot.v9i4.4316

Keywords:

Internet of Things, Arduino Uno, Air Pollution, Cloud Environment

Abstract

In recent years, pollution of air has become a critical concern in many urban areas, causing serious health problems and environmental damage. To address this issue, an Internet-of-Things (IoT)-based air pollution monitoring system was proposed. The mechanism was designed to measure various air quality parameters such as temperature, humidity, various gases, microbes, and light intensity in real time. The proposed sys-tem consists of sensor nodes, a gateway, WIFI module, an LCD display, and a cloud server. The sensor nodes were placed at different locations to measure air quality parameters, and the data were transmitted to the gateway via wireless communication. The gateway aggregates the data from the sensor nodes and sends them to the cloud server for further analysis and processing. The cloud server processes the data, and the system also includes a web interface that displays data on the pollution levels of the air in real time. The sys-tem can also send alerts to users when the air quality is poor, allowing them to take the necessary precautions. This system could assist decision-makers in taking appropriate measures to alleviate air pollution and safeguard the health of the community by providing real-time information about air quality. The system was evaluated in a real-world environment and the results demonstrated its effectiveness in providing accurate and reliable air quality information. The proposed system has the potential to be used in various applications, including public health and environmental monitoring, and can be integrated with other IoT devices to enhance their functionality.

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References

Gupta, H., Bhardwaj, D., Agrawal, H., Tikkiwal, V. A., & Kumar, A. (2019, February). An IoT based air pollution monitoring system for smart cities. In 2019 IEEE International Conference on Sustainable Energy Technologies and Systems (ICSETS) (pp. 173-177). IEEE.

Xiaojun, C., Xianpeng, L., & Peng, X. (2015, January). IOT-based air pollution monitoring and forecasting system. In 2015 international conference on computer and computational sciences (ICCCS) (pp. 257-260). IEEE.

Shah, H. N., Khan, Z., Merchant, A. A., Moghal, M., Shaikh, A., & Rane, P. (2018). IOT based air pollution monitor-ing system. International Journal of Scientific & Engineering Research, 9(2), 62-66.

Muthukumar, S., Mary, W. S., Jayanthi, S., Kiruthiga, R., & Mahalakshmi, M. (2018, July). IoT based air pollution monitoring and control system. In 2018 International Conference on inventive research in computing applications (ICIRCA) (pp. 1286-1288). IEEE.

Pal, P., Gupta, R., Tiwari, S., & Sharma, A. (2017). IoT based air pollution monitoring system using Arduino. Interna-tional Research Journal of Engineering and Technology (IRJET), 4(10), 1137-1140.

Parmar, G., Lakhani, S., & Chattopadhyay, M. K. (2017, October). An IoT based low cost air pollution monitoring system. In 2017 International Conference on Recent Inno-vations in Signal processing and Embedded Systems (RISE) (pp. 524-528). IEEE.

Nasution, T. H., Muchtar, M. A., & Simon, A. (2019, October). Designing an IoT-based air quality monitoring system. In IOP Conference Series: Materials Science and Engineering (Vol. 648, No. 1, p. 012037). IOP Publishing.

Sai, K. B. K., Subbareddy, S. R., & Luhach, A. K. (2019). IOT based air quality moni-toring system using MQ135 and MQ7 with machine learning analysis. Scalable Compu-ting: Practice and Experience, 20(4), 599-606.

Alam, S. S., Islam, A. J., Hasan, M. M., Rafid, M. N. M., Chakma, N., & Imtiaz, M. N. (2018, September). Design and development of a low-cost IoT based environmental pollution monitoring system. In 2018 4th international conference on electrical engi-neering and information & communication technology (iCEEiCT) (pp. 652-656). IEEE.

Truong, T. P., Nguyen, D. T., & Truong, P. V. (2021). Design and deployment of an IoT-based air quality monitoring system. International Journal of Environmental Sci-ence and Development, 12(5), 139-145.

Chowdhury, S., Islam, M. S., Raihan, M. K., & Arefin, M. S. (2019, September). De-sign and implementation of an IoT based air pollution detection and monitoring system. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) (pp. 296-300). IEEE.

Kalia, P., & Ansari, M. A. (2020). IOT based air quality and particulate matter concentra-tion monitoring system. Materials Today: Proceedings, 32, 468-475.

Ng, W. J., & Dahari, Z. (2020). Enhancement of real-time IoT-based air quality moni-toring system. International Journal of Power Electronics and Drive Systems, 11(1), 390.

Ayele, T. W., & Mehta, R. (2018, April). Air pollution monitoring and prediction using IoT. In 2018 second international conference on inventive communication and compu-tational technologies (ICICCT) (pp. 1741-1745). IEEE.

Senthilkumar, R., Venkatakrishnan, P., & Balaji, N. (2020). Intelligent based novel em-bedded system based IoT enabled air pollution monitoring system. Microprocessors and Microsystems, 77, 103172.

Tsaramirsis, G., Karamitsos, I., & Apostolopoulos, C. (2016, March). Smart parking: An IoT application for smart city. In 2016 3rd International conference on computing for sustainable global development (INDIACom) (pp. 1412-1416). IEEE.

Tsaramirsis, G., Kantaros, A., Al-Darraji, I., Piromalis, D., Apostolopoulos, C., Pav-lopoulou, A., ... & Khan, F. Q. (2022). A modern approach towards an industry 4.0 model: From driving technologies to management. Journal of Sensors, 2022.

Rimpas, D., Kaminaris, S. D., Aldarraji, I., Piromalis, D., Vokas, G., Papageorgas, P. G., & Tsaramirsis, G. (2022). Energy management and storage systems on electric vehicles: A comprehensive review. Materials Today: Proceedings, 61, 813-819.

Patil, R. M., Dinde, H. T., Powar, S. K., & Ganeshkhind, P. M. (2020). A Literature Re-view on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms. International Journal of Innovative Science and Re-search Technology, 5(8).

Rawal, R. (2019). Air Quality Monitoring System. International Journal of Compu-tational Science and Engineering, 9(1), 1-9.

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470

G. P. Rout and S. N. Mohanty, "A Hybrid Approach for Network Intrusion Detection," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India, 2015, pp. 614-617, doi: 10.1109/CSNT.2015.76.

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Published

06-11-2023

How to Cite

[1]
“An IoT Implemented Dynamic Air Pollution Monitoring System”, EAI Endorsed Trans IoT, vol. 9, no. 4, p. e4, Nov. 2023, doi: 10.4108/eetiot.v9i4.4316.