Abstract
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.
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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)
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)
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)
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)
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)
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)
Trihinas, D., Pallis, G., Dikaiakos, M.: Low-cost adaptive monitoring techniques for the internet of things. In: IEEE Transactions on Services Computing (2018)
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). https://doi.org/10.1016/j.jpdc.2014.08.007
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)
Aazam, M., Zeadally, S., Harras, K.A.: Fog computing architecture, evaluation, and future research directions. IEEE Commun. Mag. 56(5), 46–52 (2018)
Hayashi, F.: Econometrics, vol. 1, pp. 60–69. Princeton University Press, Princeton (2000)
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)
Acknowledgments
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.
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Vitali, M., Peng, X., Pernici, B. (2019). An Adaptive Monitoring Service Exploiting Data Correlations in Fog Computing. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_29
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DOI: https://doi.org/10.1007/978-3-030-33702-5_29
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