An Adaptive Monitoring Service Exploiting Data Correlations in Fog Computing

  • Monica VitaliEmail author
  • Xuesong Peng
  • Barbara Pernici
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)


In smart environments, a big amount of information is generated by sensors and monitoring devices. Moving data from the edge where they are generated to the cloud might introduce delays with the growth of data volume. We propose an adaptive monitoring service, able to dynamically reduce the amount of data moved in a fog environment, exploiting the dependencies among the monitored variables dynamically assessed through correlation analysis. The adaptive monitoring service enables the identification of dependent variables that can be transmitted at a highly reduced rate and the training of prediction models that allow deriving the values of dependent variables from other correlated variables. The approach is demonstrated in a smart city scenario.



This work is supported by European Commission H2020 Programme through the DITAS (Data-intensive applications Improvement by moving daTA and computation in mixed cloud/fog environmentS) Project no. 731945.


  1. 1.
    Plebani, P., et al.: Information logistics and fog computing: the DITAS approach. In: Proceedings of CAiSE Forum 2017, Essen, Germany, 12–16 June 2017, pp. 129–136 (2017)Google Scholar
  2. 2.
    Vitali, M., Pernici, B., O’Reilly, U.-M.: Learning a goal-oriented model for energy efficient adaptive applications in data centers. Inf. Sci. 319, 152–170 (2015)CrossRefGoogle Scholar
  3. 3.
    Carvalho, C.G., Gomes, D.G., Agoulmine, N., de Souza, J.N.: Improving prediction accuracy for WSN data reduction by applying multivariate spatio-temporal correlation. Sensors 11(11), 10010–10037 (2011)CrossRefGoogle Scholar
  4. 4.
    Rehman, M.H.U., Liew, C.S., Abbas, A., Jayaraman, P.P., Wah, T.Y., Khan, S.U.: Big data reduction methods: a survey. Data Sci. Eng. 1(4), 265–284 (2016)CrossRefGoogle Scholar
  5. 5.
    Rehman, M.H.U., Chang, V., Batool, A., Wah, T.Y.: Big data reduction framework for value creation in sustainable enterprises. Int J. Inf. Manage 36(6), 917–928 (2016)CrossRefGoogle Scholar
  6. 6.
    Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Stankovski, V.: Monitoring self-adaptive applications within edge computing frameworks: a state-of-the-art review. J. Syst. Softw. 136, 19–38 (2018)CrossRefGoogle Scholar
  7. 7.
    Trihinas, D., Pallis, G., Dikaiakos, M.: Low-cost adaptive monitoring techniques for the internet of things. In: IEEE Transactions on Services Computing (2018)Google Scholar
  8. 8.
    Andreolini, M., Colajanni, M., Pietri, M., Tosi, S.: Adaptive, scalable and reliable monitoring of big data on clouds. J. Parallel Distrib. Comput. 79–80, 67–79 (2015). Scholar
  9. 9.
    Yassine, A., Singh, S., Hossain, M.S., Muhammad, G.: IoT big data analytics for smart homes with fog and cloud computing. Future Gener. Comput. Syst. 91, 563–573 (2019)CrossRefGoogle Scholar
  10. 10.
    Aazam, M., Zeadally, S., Harras, K.A.: Fog computing architecture, evaluation, and future research directions. IEEE Commun. Mag. 56(5), 46–52 (2018)CrossRefGoogle Scholar
  11. 11.
    Hayashi, F.: Econometrics, vol. 1, pp. 60–69. Princeton University Press, Princeton (2000) zbMATHGoogle Scholar
  12. 12.
    Peng, X., Pernici, B.: Correlation-model-based reduction of monitoring data in data centers. In: Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2016, Rome, Italy, 23–25 April 2016, pp. 395–405 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Politecnico di MilanoMilanItaly

Personalised recommendations