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Urban Pollution Environmental Monitoring System Using IoT Devices and Data Visualization: A Case Study

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

This work presents a new approach to the Internet of Things (IoT) between sensor nodes and data analysis with visualization platform with the purpose to acquire urban pollution data. The main objective is to determine the degree of contamination in Ibarra city in real time. To do this, for one hand, thirteen IoT devices have been implemented. For another hand, a Prototype Selection and Data Balance algorithms comparison in relation to the classifier k-Nearest Neighbourhood is made. With this, the system has an adequate training set to achieve the highest classification performance. As a final result, the system presents a visualization platform that estimates the pollution condition with more than 90% accuracy.

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References

  1. Saha, A.K., et al.: A raspberry Pi controlled cloud based air and sound pollution monitoring system with temperature and humidity sensing. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018, vol. 2018, January 2018

    Google Scholar 

  2. Wang, D., Duan, E., Guo, Y., Sun, B., Bai, T.: Numerical simulation of the effect of over-fire air on NOx formation in furnace. In: 2013 International Conference on Materials for Renewable Energy and Environment, pp. 780–783. IEEE, August 2013. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6893790

  3. Guariso, G., Volta, M. (eds.): Air Quality Integrated Assessment. SAST. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-33349-6

    Book  Google Scholar 

  4. Sujatha, K., Bhavani, N.P.G., Ponmagal, R.S.: Impact of NOx emissions on climate and monitoring using smart sensor technology. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 0853–0856. IEEE, April 2017. http://ieeexplore.ieee.org/document/8286488/

  5. Bashir Shaban, K., Kadri, A., Rezk, E.: Urban air pollution monitoring system with forecasting models. IEEE Sens. J. 16(8), 2598–2606 (2016). http://ieeexplore.ieee.org/document/7370876/

    Article  Google Scholar 

  6. Maraj, A., Berzati, S., Efendiu, I., Shala, A., Dermaku, J., Melekoglu, E.: Sensing platform development for air quality measurements and analysis. In: 2017 South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1–5. IEEE, September 2017. http://ieeexplore.ieee.org/document/8088233/

  7. Lin, Y.-L., Kyung, C.-M., Yasuura, H., Liu, Y. (eds.): Smart Sensors and Systems. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14711-6

    Book  Google Scholar 

  8. Fioccola, G.B., Sommese, R., Tufano, I., Canonico, R., Ventre, G.: Polluino: an efficient cloud-based management of IoT devices for air quality monitoring. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1–6. IEEE, September 2016. http://ieeexplore.ieee.org/document/7740617/

  9. Wang, W., De, S., Zhou, Y., Huang, X., Moessner, K.: Distributed sensor data computing in smart city applications. In: 2017 IEEE 18th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–5. IEEE, June 2017. http://ieeexplore.ieee.org/document/7974338/

  10. Kafli, N., Isa, K.: Internet of Things (IoT) for measuring and monitoring sensors data of water surface platform. In: 2017 IEEE 7th International Conference on Underwater System Technology: Theory and Applications (USYS), pp. 1–6. IEEE, December 2017. http://ieeexplore.ieee.org/document/8309441/

  11. Kumar, S., Jasuja, A.: Air quality monitoring system based on IoT using Raspberry Pi. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 1341–1346. IEEE, May 2017. http://ieeexplore.ieee.org/document/8230005/

  12. Rosero-Montalvo, P.D., et al.: Intelligence in embedded systems: overview and applications. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) FTC 2018. AISC, vol. 880, pp. 874–883. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02686-8_65

    Chapter  Google Scholar 

  13. Chiu, S.-W., Hao, H.-C., Yang, C.-M., Yao, D.-J., Tang, K.-T.: Handheld gas sensing system. In: Lin, Y.-L., Kyung, C.-M., Yasuura, H., Liu, Y. (eds.) Smart Sensors and Systems, pp. 155–190. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14711-6_8

    Chapter  Google Scholar 

  14. Bae, H.: Basic principle and practical implementation of near-infrared spectroscopy (NIRS). In: Lin, Y.-L., Kyung, C.-M., Yasuura, H., Liu, Y. (eds.) Smart Sensors and Systems, pp. 281–302. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14711-6_12

    Chapter  Google Scholar 

  15. Peng, L., Danni, F., Shengqian, J., Mingjie, W.: A movable indoor air quality monitoring system. In: 2017 2nd International Conference on Cybernetics, Robotics and Control (CRC), pp. 126–129. IEEE, July 2017. http://ieeexplore.ieee.org/document/8328320/

  16. Air Quality Expert Group: air quality and climate change: a UK perspective. http://webarchive.nationalarchives.gov.uk/20130403220722/archive.defra.gov.uk/environment/quality/air/airquality/publications/airqual-climatechange/documents/fullreport.pdf

  17. Rosero-Montalvo, P.D., et al.: Air quality monitoring intelligent system using machine learning techniques. In: 2018 International Conference on Information Systems and Computer Science (INCISCOS), pp. 75–80. IEEE, November 2018. https://ieeexplore.ieee.org/document/8564511/

  18. Rosero-Montalvo, P., et al.: Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: empirical study. In: 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), pp. 1–5. IEEE, October 2017. http://ieeexplore.ieee.org/document/8247530/

  19. Rosero-Montalvo, P., et al.: Neighborhood criterion analysis for prototype selection applied in WSN data. In: 2017 International Conference on Information Systems and Computer Science (INCISCOS), pp. 128–132. IEEE, November 2017. http://ieeexplore.ieee.org/document/8328096/

  20. Rosero-Montalvo, P.D., Peluffo-Ordóñez, D.H., López Batista, V.F., Serrano, J., Rosero, E.A.: Intelligent system for identification of wheelchair user’s posture using machine learning techniques. IEEE Sens. J. 19(5), 1936–1942 (2019)

    Article  Google Scholar 

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Acknowledgment

This work is supported by the Smart Data Analysis Systems - SDAS group. http://sdas-group.com/.

Author information

Authors and Affiliations

  1. Universidad de Salamanca, Salamanca, Spain

    Paul D. Rosero-Montalvo & Vivian F. López-Batista

  2. Universidad Técnica del Norte, Ibarra, Ecuador

    Paul D. Rosero-Montalvo, Diego H. Peluffo-Ordóñez & Leandro L. Lorente-Leyva

  3. Instituto Tecnológico Superior 17 de Julio, Urcuquí, Ecuador

    Paul D. Rosero-Montalvo & Diego H. Peluffo-Ordóñez

  4. Yachay Tech University, Urcuquí, Ecuador

    X. P. Blanco-Valencia

  5. SDAS Research Group, Urcuquí, Ecuador

    Paul D. Rosero-Montalvo, Vivian F. López-Batista, Diego H. Peluffo-Ordóñez, Leandro L. Lorente-Leyva & X. P. Blanco-Valencia

Authors
  1. Paul D. Rosero-Montalvo
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  2. Vivian F. López-Batista
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  3. Diego H. Peluffo-Ordóñez
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  4. Leandro L. Lorente-Leyva
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  5. X. P. Blanco-Valencia
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Corresponding author

Correspondence to Paul D. Rosero-Montalvo .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Rosero-Montalvo, P.D., López-Batista, V.F., Peluffo-Ordóñez, D.H., Lorente-Leyva, L.L., Blanco-Valencia, X.P. (2019). Urban Pollution Environmental Monitoring System Using IoT Devices and Data Visualization: A Case Study. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_58

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29858-6

  • Online ISBN: 978-3-030-29859-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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