Dynamic Trust Scoring of Railway Sensor Information

  • Marcin LenartEmail author
  • Andrzej Bielecki
  • Marie-Jeanne Lesot
  • Teodora Petrisor
  • Adrien Revault d’Allonnes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


A sensor can encounter many situations where its readings can be untrustworthy and the ability to recognise this is an important and challenging task. It opens the possibility to assess sensors for forensic or maintenance purposes, compare them or fuse their information. We present a proposition to score a piece of information produced by a sensor as an aggregation of three dimensions called reliability, likelihood and credibility into a trust value that take into account a temporal component. The approach is validated on data from the railway domain.


Information scoring Sensor Trust Reliability Likelihood Credibility 



This work was supported in part by Thales Polska.


  1. 1.
    Batini, C., Scannapieco, M.: Data and Information Quality. DSA. Springer, Cham (2016). Scholar
  2. 2.
    Besombes, J., d’Allonnes, A.R.: An extension of STANAG2022 for information scoring. In: International Conference on Information Fusion, FUSION 2008, pp. 1–7 (2008)Google Scholar
  3. 3.
    Blasch, E.P.: Derivation of a reliability metric for fused data decision making. In: IEEE National Aerospace and Electronics Conference, pp. 273–280 (2008)Google Scholar
  4. 4.
    Demolombe, R.: Reasoning about trust: a formal logical framework. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 291–303. Springer, Heidelberg (2004). Scholar
  5. 5.
    Destercke, S., Buche, P., Charnomordic, B.: Evaluating data reliability: an evidential answer with application to a web-enabled data warehouse. IEEE Trans. Knowl. Data Eng. 25(1), 92–105 (2013)CrossRefGoogle Scholar
  6. 6.
    Detyniecki, M.: Fundamentals on aggregation operators. Technical report, University of California Berkeley. Ph.D. thesis (2001)Google Scholar
  7. 7.
    Florea, M.C., Bossé, É.: Dempster-Shafer theory: combination of information using contextual knowledge. In: International Conference on Information Fusion, FUSION 2009, pp. 522–528. IEEE (2009)Google Scholar
  8. 8.
    Florea, M.C., Jousselme, A.L., Bossé, É.: Dynamic estimation of evidence discounting rates based on information credibility. RAIRO-Oper. Res. 44(4), 285–306 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Guo, H., Shi, W., Deng, Y.: Evaluating sensor reliability in classification problems based on evidence theory. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(5), 970–981 (2006)CrossRefGoogle Scholar
  10. 10.
    Lesot, M.J., Delavallade, T., Pichon, F., Akdag, H., Bouchon-Meunier, B., Capet, P.: Proposition of a semi-automatic possibilistic information scoring process. In: Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011) and LFA-2011, pp. 949–956. Atlantis Press (2011)Google Scholar
  11. 11.
    Lesot, M.-J., Revault d’Allonnes, A.: Information quality and uncertainty. In: Kreinovich, V. (ed.) Uncertainty Modeling. SCI, vol. 683, pp. 135–146. Springer, Cham (2017). Scholar
  12. 12.
    Mercier, D., Quost, B., Denœux, T.: Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inf. Fusion 9(2), 246–258 (2008)CrossRefGoogle Scholar
  13. 13.
    Pichon, F., Dubois, D., Denoeux, T.: Relevance and truthfulness in information correction and fusion. Int. J. Approx. Reason. 53(2), 159–175 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Pon, R.K., Cárdenas, A.F.: Data quality inference. In: Proceedings of the 2nd International Workshop on Information Quality in Information Systems, pp. 105–111. ACM (2005)Google Scholar
  15. 15.
    d’Allonnes, A.R., Lesot, M.-J.: Formalising information scoring in a multivalued logic framework. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014. CCIS, vol. 442, pp. 314–324. Springer, Cham (2014). Scholar
  16. 16.
    Rogova, G., Hadzagic, M., St-Hilaire, M.O., Florea, M.C., Valin, P.: Context-based information quality for sequential decision making. In: 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 16–21 (2013)Google Scholar
  17. 17.
    Sidi, F., Panahy, P.H.S., Affendey, L.S., Jabar, M.A., Ibrahim, H., Mustapha, A.: Data quality: a survey of data quality dimensions. In: Proceedings of International Conference on Information Retrieval Knowledge Management, pp. 300–304 (2012)Google Scholar
  18. 18.
    Young, S., Palmer, J.: Pedigree and confidence: issues in data credibility and reliability. In: International Conference on Information Fusion, FUSION 2007, pp. 1–8 (2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marcin Lenart
    • 1
    • 2
    • 3
    Email author
  • Andrzej Bielecki
    • 3
  • Marie-Jeanne Lesot
    • 2
  • Teodora Petrisor
    • 1
  • Adrien Revault d’Allonnes
    • 2
    • 4
  1. 1.Campus PolytechniqueThalesPalaiseauFrance
  2. 2.Laboratoire d’Informatique de Paris 6, LIP6, CNRSSorbonne UniversitéParisFrance
  3. 3.Chair of Applied Computer Science, Faculty of EAIIBAGH University of Science and TechnologyCracowPoland
  4. 4.LIASD EA 4383Université Paris 8Saint-DenisFrance

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