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

  • Paul D. Rosero-MontalvoEmail author
  • Vivian F. López-Batista
  • Diego H. Peluffo-Ordóñez
  • Leandro L. Lorente-Leyva
  • X. P. Blanco-Valencia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11734)


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.


Intelligent system Environmental science computing Environmental monitoring Data analysis 



This work is supported by the Smart Data Analysis Systems - SDAS group.


  1. 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 2018Google Scholar
  2. 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.
  3. 3.
    Guariso, G., Volta, M. (eds.): Air Quality Integrated Assessment. SAST. Springer, Cham (2017). Scholar
  4. 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.
  5. 5.
    Bashir Shaban, K., Kadri, A., Rezk, E.: Urban air pollution monitoring system with forecasting models. IEEE Sens. J. 16(8), 2598–2606 (2016). Scholar
  6. 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.
  7. 7.
    Lin, Y.-L., Kyung, C.-M., Yasuura, H., Liu, Y. (eds.): Smart Sensors and Systems. Springer, Cham (2015). Scholar
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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). Scholar
  13. 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). Scholar
  14. 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). Scholar
  15. 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.
  16. 16.
  17. 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.
  18. 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.
  19. 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.
  20. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul D. Rosero-Montalvo
    • 1
    • 2
    • 3
    • 5
    Email author
  • Vivian F. López-Batista
    • 1
    • 5
  • Diego H. Peluffo-Ordóñez
    • 2
    • 3
    • 5
  • Leandro L. Lorente-Leyva
    • 2
    • 5
  • X. P. Blanco-Valencia
    • 4
    • 5
  1. 1.Universidad de SalamancaSalamancaSpain
  2. 2.Universidad Técnica del NorteIbarraEcuador
  3. 3.Instituto Tecnológico Superior 17 de JulioUrcuquíEcuador
  4. 4.Yachay Tech UniversityUrcuquíEcuador
  5. 5.SDAS Research GroupUrcuquíEcuador

Personalised recommendations