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Monitoring Approach of Cyber-Physical Systems by Quality Measures

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Sensor Systems and Software (S-CUBE 2016)

Abstract

Modern cities, industrial plants, cars, trucks, and vessels, among others, make extensive use of cyber-physical systems and sensors. These systems are very critical and contribute to assist decision making. Large data streams are thus produced and analyzed to extract information that allows building knowledge through a set of principles called wisdom. However, because of multiple imperfections, as well as intrinsic, contextual, and extrinsic conditions that alter data, the quality of the generated streams must be evaluated, to determine how relevant they are for decision support. This paper presents a methodology to monitor cyber-physical systems by quality estimation, which defines suitable evaluation characteristics for pertinent analysis. Quality assessment is defined for data imperfections, information dimensions, knowledge factors, and wisdom aspects. The case study of a cyber-physical network of a liquid container training platform is presented in detail, to show how the approach can be applied. Obtained measures are multidimensional, heterogeneous, and variable.

Funded and supported by École navale, Télécom Bretagne, Thales and DCNS.

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Notes

  1. 1.

    Modbus is an open OSI level 7 protocol developed by Scheinder Electric in 1979 and largely used in SCADA (Supervisory Control and Data Acquisition) systems (http://modbus.org/docs/PI_MBUS_300.pdf).

References

  1. Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Data-Centric Systems and Applications. Springer, New York (2006)

    MATH  Google Scholar 

  2. De Amicis, B.: A methodology for data quality assessment on financial data. Stud. Commun. Sci. 4, 115–136 (2004)

    Google Scholar 

  3. English, L.P.: Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits. Wiley, New York (1999)

    Google Scholar 

  4. Guo, J., Liu, F.: Automatic data quality control of observations in wireless sensor network. IEEE Geosci. Remote Sens. Lett. 12(4), 716–720 (2015)

    Article  Google Scholar 

  5. Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y.: AIMQ: a methodology for information quality assessment. Inf. Manag. 40(2), 133–146 (2002)

    Article  Google Scholar 

  6. Loshin, D.: Enterprise Knowledge Management. The Data Quality Approach. Academic Press, San Diego (2001)

    Google Scholar 

  7. Motro, A., Smets, P.: Uncertainty Management in Information Systems: From Needs to Solutions. Springer Science & Business Media, New York (1996)

    MATH  Google Scholar 

  8. Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45(4), 211–218 (2002)

    Article  Google Scholar 

  9. Puentes, J., Montagner, J., Lecornu, L., Lähteenmäki, J.: Quality analysis of sensors data for personal health records on mobile devices. In: Bali, R., Troshani, I., Goldberg, S., Wickramasinghe, N. (eds.) Pervasive Health Knowledge Management. Healthcare Delivery in the Information Age, pp. 103–133. Springer, New York (2013)

    Chapter  Google Scholar 

  10. Scannapieco, M., Virgillito, A., Marchetti, C., Mecella, M., Baldoni, R.: The DaQuinCIS architecture: a platform for exchanging and improving data quality in cooperative information systems. Inf. Syst. 29(7), 551–582 (2004)

    Article  Google Scholar 

  11. Todoran, I.-G., Lecornu, L., Khenchaf, A., Le Caillec, J.-M.: Information quality evaluation in fusion systems. In: 16th International Conference on Information Fusion (FUSION), pp. 906–913, July 2013

    Google Scholar 

  12. Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)

    Article  Google Scholar 

  13. Zeleny, M.: Human Systems Management: Integrating Knowledge, Management and Systems. World Scientific Publishing Co., Pte. Ltd., London (2005)

    Google Scholar 

  14. Zins, C.: Conceptual approaches for defining data, information, and knowledge. J. Am. Soc. Inform. Sci. Technol. 58(4), 479–493 (2007)

    Article  Google Scholar 

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Correspondence to Pedro Merino Laso .

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Merino Laso, P., Brosset, D., Puentes, J. (2017). Monitoring Approach of Cyber-Physical Systems by Quality Measures. In: Magno, M., Ferrero, F., Bilas, V. (eds) Sensor Systems and Software. S-CUBE 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 205. Springer, Cham. https://doi.org/10.1007/978-3-319-61563-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-61563-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61562-2

  • Online ISBN: 978-3-319-61563-9

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