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Self Configurable Air Pollution Monitoring System Using IoT and Data Mining Techniques

  • M. S. Binsy
  • Nalini SampathEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Air pollution is a perilous threat to living organisms and the whole ecosystem. The purpose of this paper is to develop a self-configurable air pollution monitoring system which can monitor and predict air pollution by applying Internet of Things (IoT) and data mining technologies. Self-configurability of the device is the ability to regulate the frequency of monitoring based on pollutant predictions. Monitoring is done using the system developed in paper [1] which collects concentration of pollutants such as Carbon monoxide, harmful gases, dust level, meteorological parameters such as temperature along with GPS location. This paper deals with using the monitored data for prediction along with humidity information. Data mining technique, Regression is used to predict the level of pollutant. These predicted values in turn decides the mode of operation of the device. The monitored data send to ThingSpeak are further analyzed using MATLAB. Map of the location is updated using red and green markers based on the level of pollution. These data along with predicted pollutant levels in ThingSpeak can be viewed by the public.

Keywords

Air pollution prediction Data mining Self-configurable Regression Internet of things 

References

  1. 1.
    Binsy, M.S., Sampath, N.: User configurable and poratble air pollution monitoring system for smart cities using IoT. In: Springer International conference on Computer Networks and Inventive Communication Technologies (2018)Google Scholar
  2. 2.
    India information available - https://en.wikipedia.org/wiki/India
  3. 3.
  4. 4.
  5. 5.
  6. 6.
    Ajith, S., Harivishnu, B., Vinesh,T.K., Sooraj, S., Prasad, G.: Automated gas pollution detection system. In: 2nd International Conference for Convergence in Technology (I2CT), pp. 483–486. IEEE (2017)Google Scholar
  7. 7.
  8. 8.
    Pollution in Indian cities available: https://www.bbc.com/news/world-asia-india-43972155
  9. 9.
    George, J.E., Aravinth, J., Veni, S.: Detection of pollution content in an Urban area using landsat 8 data. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 184–190. IEEE (2017)Google Scholar
  10. 10.
  11. 11.
  12. 12.
  13. 13.
    Thakur, Amrita: Study of ambient air quality trends and analysis of contributing factors in Bengaluru, India. Orient. J. Chem. 33(2), 1051–1056 (2017)CrossRefGoogle Scholar
  14. 14.
    Marquez-Viloria, D., Botero-Valencia, J.S., Villegas-Ceballos, J: A low cost georeferenced air-pollution measurement system used as early warning Tool’. In: 2016 XXI Symposium Signal Processing, Images and Artificial Vision (STSIVA), pp. 1–6. IEEE, August 2016Google Scholar
  15. 15.
    Desai, N.S., Alex, J.S.R.: IoT based air pollution monitoring and predictor system on Beagle bone black, Nextgen Electronic Technologies. In: 2017 International Conference on IEEE Silicon to Software (ICNETS2), pp. 367–370Google Scholar
  16. 16.
    Verma, Sonal S., Desai, Birva: Effect of meteorological conditions on air pollution of Surat city. J. Int. Environ. Appl. Sci. 3(5), 358–367 (2008)Google Scholar
  17. 17.
    Wilfet, J.J., Sathyanathan, R., Aarthy, A., Vinuprakash, K.C.: Impact of meteorological factors on PM2.5 in Chennai. Rasayan J. Chem. 10(4), 1296–1301 (2017)Google Scholar
  18. 18.
    Taneja, S., Sharma, N., Oberoi, K., Navoria, Y.: Predicting trends in air pollution in Delhi using data mining. In: 1st India International Conference on Information Processing (IICIP), pp. 1–6. IEEE (2016)Google Scholar
  19. 19.
    Vijai, P., Sivakumar, P.B.: Design of IoT systems and analytics in the context of smart city initiatives in India. Procedia Comput. Sci. 92, 583–588 (2016)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Kelleher, J.D., Namee, B.M., D’arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics. The MIT Press Cambridge Massachusetts London, England, 201 Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia

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