IoT based real time air and noise pollution monitoring and controlling

Research Article
Open access

IoT based real time air and noise pollution monitoring and controlling

P. Sivaprakash 1 , A. Purushothaman 2 , Sandeep Kumar M. 3* , Prabhu J. 4
  • 1 Department of Computer Science and Engineering, Vel Tech Ragarajan Dr.Sagunthala R& D Institute of Science and Technology, Avadi-600062, TN, India.    
  • 2 Department of Electronics and Communication Engineering, Hindustan Institute of Technology, Coimbatore - 641 032, TN, India.    
  • 3 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.    
  • 4 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.    
  • *corresponding author sandeepkumarm322@gmail.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Air and sound pollution is the biggest problem nowadays. So It is required to monitor air quality and noise levels to keep it control for a better future and happy living for all. In order to monitor the pollution, we propose an air pollution as well as sound pollution monitoring system that allows us to monitor and automatically control the air quality and sound pollution in particular areas through IoT. Our proposed system used air sensors to sense presence of harmful pollutants in the fresh air and simultaneously transmits this data to the microcontroller and also device keeps measuring the sound level and reports it to the cloud storage through Internet. These sensors interfaced with microcontroller which senses this data and transmits it to the internet. Our system also interfaced with GPS module to find the latitude and longitude of different places. This methodology allows authorities and people to monitor the air pollution and noise pollution in different areas and take action against it. If system detects the air quality and noise issues, it alerts authorities and also living people. Once the system exceeds the threshold levels of air and noise pollution, controlling action will automatically take by its own. People are don’t need to worry about this. Pollution automatically monitored and controlled by our system.

Keywords:

Microcontroller, IoT, GPS, Air Pollution, Sound Pollution

Sivaprakash,P.;Purushothaman,A.;M.,S.K.;J.,P. (2023). IoT based real time air and noise pollution monitoring and controlling. Applied and Computational Engineering,5,237-242.
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References

[1]. Reddy, B. S., Maurya, A. K., Sathishkumar, V. E., Narayana, P. L., Reddy, M. H., Baazeem, A., ... & Reddy, N. S. (2021). Prediction of batch sorption of barium and strontium from saline water. Environmental Research, 197, 111107.

[2]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2022). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems, 39(6), e12834.

[3]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

[4]. Environmental laws of India”, in Air Act.[online].Available:https://www.environment

[5]. VE, S., Park, J., & Cho, Y. (2020). Seoul bike trip duration prediction using data mining techniques. IET Intelligent Transport Systems, 14(11), 1465-1474.

[6]. Zhang, R., VE, S., & Jackson Samuel, R. D. (2020). Fuzzy efficient energy smart home management system for renewable energy resources. Sustainability, 12(8), 3115.

[7]. Interfacing GPS module with arduino, inInsructables.[online].Available:https://www.in strctables.com/id/how-to-interface-GPS Module- with-arduino.

[8]. VE, S., Shin, C., & Cho, Y. (2021). Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city. Building Research & Information, 49(1), 127-143.

[9]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.

[10]. VE, S., & Cho, Y. (2020). Season wise bike sharing demand analysis using random forest algorithm. Computational Intelligence..

[11]. Kogilavani, S. V., Sathishkumar, V. E., & Subramanian, M. (2022, May). AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 400-403). IEEE.

[12]. Noise Control Act”, in Wikipedia. [online].available:https://en.wikipedia.org/wiki/n oise-control-act. Accessed: April 20, 2017.

[13]. Subramanian, M., Sathishkumar, V. E., Ramya, C., Kogilavani, S. V., & Deepti, R. (2022, May). A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 410-413). IEEE.

[14]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.

[15]. Easwaramoorthy, S., Thamburasa, S., Samy, G., Bhushan, S. B., & Aravind, K. (2016, April). Digital forensic evidence collection of cloud storage data for investigation. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 1-6). IEEE.

[16]. Easwaramoorthy, S., Thamburasa, S., Aravind, K., Bhushan, S. B., & Rajadurai, H. (2016, August). Heterogeneous classifier model for e-mail spam classification using FSO feature selection method. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 1, pp. 1-6). IEEE.

[17]. Babu, J. C., Kumar, M. S., Jayagopal, P., Sathishkumar, V. E., Rajendran, S., Kumar, S., ... & Mahseena, A. M. (2022). IoT-based intelligent system for internal crack detection in building blocks. Journal of Nanomaterials, 2022.

[18]. Shanthi, N., VE, S., Upendra Babu, K., Karthikeyan, P., Rajendran, S., & Allayear, S. M. (2022). Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques. Computational Intelligence & Neuroscience.

Cite this article

Sivaprakash,P.;Purushothaman,A.;M.,S.K.;J.,P. (2023). IoT based real time air and noise pollution monitoring and controlling. Applied and Computational Engineering,5,237-242.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Reddy, B. S., Maurya, A. K., Sathishkumar, V. E., Narayana, P. L., Reddy, M. H., Baazeem, A., ... & Reddy, N. S. (2021). Prediction of batch sorption of barium and strontium from saline water. Environmental Research, 197, 111107.

[2]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2022). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems, 39(6), e12834.

[3]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

[4]. Environmental laws of India”, in Air Act.[online].Available:https://www.environment

[5]. VE, S., Park, J., & Cho, Y. (2020). Seoul bike trip duration prediction using data mining techniques. IET Intelligent Transport Systems, 14(11), 1465-1474.

[6]. Zhang, R., VE, S., & Jackson Samuel, R. D. (2020). Fuzzy efficient energy smart home management system for renewable energy resources. Sustainability, 12(8), 3115.

[7]. Interfacing GPS module with arduino, inInsructables.[online].Available:https://www.in strctables.com/id/how-to-interface-GPS Module- with-arduino.

[8]. VE, S., Shin, C., & Cho, Y. (2021). Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city. Building Research & Information, 49(1), 127-143.

[9]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.

[10]. VE, S., & Cho, Y. (2020). Season wise bike sharing demand analysis using random forest algorithm. Computational Intelligence..

[11]. Kogilavani, S. V., Sathishkumar, V. E., & Subramanian, M. (2022, May). AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 400-403). IEEE.

[12]. Noise Control Act”, in Wikipedia. [online].available:https://en.wikipedia.org/wiki/n oise-control-act. Accessed: April 20, 2017.

[13]. Subramanian, M., Sathishkumar, V. E., Ramya, C., Kogilavani, S. V., & Deepti, R. (2022, May). A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 410-413). IEEE.

[14]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.

[15]. Easwaramoorthy, S., Thamburasa, S., Samy, G., Bhushan, S. B., & Aravind, K. (2016, April). Digital forensic evidence collection of cloud storage data for investigation. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 1-6). IEEE.

[16]. Easwaramoorthy, S., Thamburasa, S., Aravind, K., Bhushan, S. B., & Rajadurai, H. (2016, August). Heterogeneous classifier model for e-mail spam classification using FSO feature selection method. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 1, pp. 1-6). IEEE.

[17]. Babu, J. C., Kumar, M. S., Jayagopal, P., Sathishkumar, V. E., Rajendran, S., Kumar, S., ... & Mahseena, A. M. (2022). IoT-based intelligent system for internal crack detection in building blocks. Journal of Nanomaterials, 2022.

[18]. Shanthi, N., VE, S., Upendra Babu, K., Karthikeyan, P., Rajendran, S., & Allayear, S. M. (2022). Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques. Computational Intelligence & Neuroscience.

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