Advertisement

Air Quality Monitoring System and Benchmarking

  • Xiufeng LiuEmail author
  • Per Sieverts Nielsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10440)

Abstract

Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.

Keywords

IoT-based Dashboard Cloud computing Benchmarking 

Notes

Acknowledgements

This research is supported by the CTT project funded by Local Governments for Sustainability (LoCaL), and the CITIES project funded by Danish Innovation Fund (1035-0027B).

References

  1. 1.
    Ahlers, D., Driscoll, P.A., Kraemer, F.A., Anthonisen, F.V., Krogstie, J.: A measurement-driven approach to understand urban greenhouse gas emissions in Nordic Cities. In: NIK (2016)Google Scholar
  2. 2.
    Apache Zeppelin. http://zeppelin.apache.org/. Accessed 1 June 2017
  3. 3.
    Carbon track and trace. http://www.carbontrackandtrace.com. Accessed 1 June 2017
  4. 4.
    Chen, X., Liu, X., Xu, P.: IoT-based air pollution monitoring and forecasting system. In: Proceedings of ICCCS, pp. 257–260 (2015)Google Scholar
  5. 5.
    Di, F.M., Li, N., Raj, M., Das, S.K.: A storage infrastructure for heterogeneous and multimedia data in the internet of things. In: Proceedings of GreenCom, pp. 26–33 (2012)Google Scholar
  6. 6.
    Ding, Z., Xu, J., Yang, Q.: SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data. J. Supercomput. 66(3), 1260–1284 (2013)CrossRefGoogle Scholar
  7. 7.
    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: Proceedings of Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1–6 (2016)Google Scholar
  8. 8.
    Goldschmidt, T., Jansen, A., Koziolek, H., Doppelhamer, J., Breivold, H.P.: Scalability and robustness of time-series databases for cloud-native monitoring of industrial processes. In: 7th International Conference on Cloud Computing, pp. 602–609 (2014)Google Scholar
  9. 9.
    Jadhav, D.A., Patane, S.A., Nandarge, S.S., Shimage, V.V., Vanjari, A.A.: Air pollution monitoring system using Zigbee and GPS module. Int. J. Emerg. Technol. Adv. Eng. 3(9), 533–536 (2013)Google Scholar
  10. 10.
    Kamal-Chaoui, L., Robert, A.: Competitive cities and climate change. OECD Regional Development Working Papers, 2009(2), 1 (2009)Google Scholar
  11. 11.
    KDB+. https://kx.com/. Accessed 1 June 2017
  12. 12.
    Li, T.L., Liu, Y., Tian, Y., Shen, S., Mao, W.: A storage solution for massive IoT data based on NoSQL. In: Proceedings of IEEE International Conference on Green Computing and Communications, pp. 50–57 (2012)Google Scholar
  13. 13.
    Liu, X., Golab, L., Golab, W., Ilyas, I.F., Jin, S.: Smart meter data analytics: systems, algorithms and benchmarking. ACM Trans. Database Syst. (TODS) 42(1), 1–39 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Liu, X., Golab, L., Golab, W., Ilyas, I.F.: Benchmarking smart meter data analytics. In: Proceedings of EDBT, pp. 385–396 (2015)Google Scholar
  15. 15.
    Olson, M.A., Bostic, K., Seltzer, M.I.: Berkeley DB. In: Proceedings of USENIX Annual Technical Conference, FREENIX Track, pp. 183–191 (1999)Google Scholar
  16. 16.
    OpenTSDB. http://OpenTSDB.net/. Accessed 1 June 2017
  17. 17.
    Pavani, M., Rao, P.T.: Real time pollution monitoring using Wireless Sensor Networks. In: Proceedings of IEMCON, pp. 1–6 (2016)Google Scholar
  18. 18.
    Phan, T.A.M., Nurminen, J.K., Di Francesco, M.: Cloud databases for Internet-of-things data. In: Proceedings of GreenCom, pp. 117–124 (2014)Google Scholar
  19. 19.
    Pintus, A., Carboni, D., Piras, A.: Paraimpu: a platform for a social web of things. In: Proceedings of the 21st International Conference on World Wide Web, pp. 401–404 (2012)Google Scholar
  20. 20.
    PostgreSQL. https://www.postgresql.org/. Accessed 1 June 2017
  21. 21.
    Prasad, R.V., Baig, M.Z., Mishra, R.K., Desai, U.B., Merchant, S.N.: Real time wireless air pollution monitoring system. ICTACT J. Commun. Technol. 2(2), 370–375 (2011)CrossRefGoogle Scholar
  22. 22.
    Sanaboyina, T.P.: Performance Evaluation of Time series Databases based on Energy Consumption. Master thesis, Blekinge Institute of Technology (2016)Google Scholar
  23. 23.
    Van der Veen, J.S., Van der Waaij, B., Meijer, R.J.: Sensor data storage performance: SQL or NoSQL, physical or virtual. In: Proceedings of Cloud Computing, pp. 431–438 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Technical University of DenmarkKongens LyngbyDenmark

Personalised recommendations