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An Adaptive Monitoring Service Exploiting Data Correlations in Fog Computing

Published: 28 October 2019 Publication History

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.

References

[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)
[2]
Vitali M, Pernici B, and O’Reilly U-M Learning a goal-oriented model for energy efficient adaptive applications in data centers Inf. Sci. 2015 319 152-170
[3]
Carvalho CG, Gomes DG, Agoulmine N, and de Souza JN Improving prediction accuracy for WSN data reduction by applying multivariate spatio-temporal correlation Sensors 2011 11 11 10010-10037
[4]
Rehman MHU, Liew CS, Abbas A, Jayaraman PP, Wah TY, and Khan SU Big data reduction methods: a survey Data Sci. Eng. 2016 1 4 265-284
[5]
Rehman MHU, Chang V, Batool A, and Wah TY Big data reduction framework for value creation in sustainable enterprises Int J. Inf. Manage 2016 36 6 917-928
[6]
Taherizadeh S, Jones AC, Taylor I, Zhao Z, and Stankovski V Monitoring self-adaptive applications within edge computing frameworks: a state-of-the-art review J. Syst. Softw. 2018 136 19-38
[7]
Trihinas, D., Pallis, G., Dikaiakos, M.: Low-cost adaptive monitoring techniques for the internet of things. In: IEEE Transactions on Services Computing (2018)
[8]
Andreolini M, Colajanni M, Pietri M, and Tosi S Adaptive, scalable and reliable monitoring of big data on clouds J. Parallel Distrib. Comput. 2015 79–80 67-79
[9]
Yassine A, Singh S, Hossain MS, and Muhammad G IoT big data analytics for smart homes with fog and cloud computing Future Gener. Comput. Syst. 2019 91 563-573
[10]
Aazam M, Zeadally S, and Harras KA Fog computing architecture, evaluation, and future research directions IEEE Commun. Mag. 2018 56 5 46-52
[11]
Hayashi F Econometrics 2000 Princeton Princeton University Press 60-69
[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)

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cover image Guide Proceedings
Service-Oriented Computing: 17th International Conference, ICSOC 2019, Toulouse, France, October 28–31, 2019, Proceedings
Oct 2019
592 pages
ISBN:978-3-030-33701-8
DOI:10.1007/978-3-030-33702-5

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 28 October 2019

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