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Identifying defective nodes in wireless sensor networks

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Abstract

Wireless sensor networks (WSNs) have become ubiquitous, e.g., in logistics, smart manufacturing, smart city infrastructures or vehicular ad-hoc networks. WSNs tend to rely on ad-hoc infrastructures that are prone to a wide range of different defects, e.g., communication failures, faulty sensors or nodes that have been tampered with. Additionally, dealing with defects is challenging, as defects might occur only occasionally. In this paper, we introduce SEDEL, our approach for Sensor nEtwork DEfect Localization. SEDEL helps the WSN operator to pinpoint defective nodes in the routing topology of a WSN. In particular, we let the operator store graph representations of the routing topology, together with information if the WSN has produced errors. Based on this information, SEDEL assigns each WSN node a suspiciousness score that is correlated with the defect probability. Thus, our approach can be used with any kind of defect, and the kind does not have to be known, as long as the operator can decide if a certain processing is correct or not. We have evaluated SEDEL with a real sensor-node deployment. Our evaluation shows that the defective node is assigned a high probability in the vast majority of the experiments.

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Notes

  1. https://www2.cs.fau.de/EN/research/ParSeMiS.

  2. http://db.csail.mit.edu/labdata/labdata.html.

References

  1. Bestehorn, M., Benenson, Z., Buchmann, E., Jawurek, M., Böhm, K., Freiling, F.: Query dissemination in sensor networks—predicting reachability and energy consumption. In: AHSWN (2009)

  2. Buchmann, E., Apel, S.: Piggyback meta-data propagation in distributed hash tables. In: WEBIST05 (2005)

  3. Di Fatta, G., Leue, S., Stegantova, E.: Discriminative pattern mining in software fault detection. In: 3rd International Workshop on Software Quality (SOQUA) (2006)

  4. Eichinger, F., Böhm, K., Huber, M.: Mining edge-weighted call graphs to localise software bugs. In: ECML PKDD (2008)

  5. Eichinger, F., Krogmann, K., Klug, R., Böhm, K.: Software-defect localisation by mining dataflow-enabled call graphs. In: 10th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (2010)

  6. European Parliament and the Council of the European Union: Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Official Journal L 152, 11/06/2008, p. 01 (2008)

  7. Furukawa, T., Cheng, J., Lim, S.H., Xu, F., Shioya, R.: Defect identification by sensor network under uncertainties. In: International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010, pp. 155–158 (2010)

  8. Furukawa, T., Lim, S.H., Michopoulos, J.G.: Stochastic identification of defects under sensor uncertainties. Int. J. Numer. Methods Eng. 90, 135–151 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Hoblos, G., Staroswiecki, M., Aitouche, A.: Optimal design of fault tolerant sensor networks. In: International Conference on Control Applications, pp. 467–472 (2000)

  10. Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed diffusion for wireless sensor networking. IEEE/ACM Trans. Netw. 11(1), 2–16 (2003)

    Article  Google Scholar 

  11. Kärpijoki, V.: Security in Ad Hoc Networks. Helsinki University of Technology, Espoo (2000)

    Google Scholar 

  12. Knab, P., Pinzger, M., Bernstein, A.: Predicting defect predicting defect densities in source code files with learners. In: International Workshop on Mining Software Repositories (MSR) at ICSE, pp. 119–125 (2006)

  13. Liblit, B., Aiken, A., Zheng, A.X., Jordan, M.I.: Bug bug isolation via remote program sampling. ACM SIGPLAN Not. 38, 141–154 (2003)

    Article  Google Scholar 

  14. Liu, C., Yan, X., Yu, H., Han, J., Yu, P.S.: Mining behavior graphs for “Backtrace” of noncrashing bugs. In: 5th SIAM International Conference on Data Mining (2005)

  15. Liu, C., Yan, X., Han, J.: Mining control flow abnormality for logic error isolation. In: 6th SIAM International Conference on Data Mining (SDM) (2006)

  16. Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)

    Article  Google Scholar 

  17. Nadig, D., Iyengar, S., Jayasimha, D.: A new architecture for A new architecture for distributed sensor integration. In: Southeastcon, p. 8 (1993)

  18. Nagappan, N., Ball, T., Zeller, A.: Mining metrics to predict component failures. In: 28th International Conference on Software Engineering (ICSE), pp. 452–461. ACM Press, New York (2006)

  19. Nikjoo, M., Plataniotis, K.: Detecting the defective nodes in wireless sensor networks. In 2010 25th Biennial Symposium on Communications (QBSC), pp. 72–75 (2010)

  20. Sankarasubramaniam, Y., Akyildiz, I., McLaughlin, S.: Energy efficiency based packet size optimization in wireless sensor networks. In: First International Workshop on Sensor Network Protocols and Applications, pp. 1–8 (2003)

  21. Schröter, A., Zimmermann, T., Zeller, A.: Predicting component predicting component failures at design time. In: 5th International Symposium on Empirical Software (2006)

  22. Stern, M., Buchmann, E., Böhm, K.: A wavelet transform for efficient consolidation of sensor relations with quality guarantees. In: 35th International Conference on Very Large Data Bases (VLDB 2009) (2009)

  23. Stern, M., Böhm, K., Buchmann, E.: Processing continuous join queries in sensor networks: a filtering approach. In: Proceedings of the 29th International Conference on Data (SIGMOD’10) (2010)

  24. Tosic, T., Thomos, N., Frossard, P.: Distributed sensor failure detection in sensor networks. CoRR. arXiv:1109.5636 (2011)

  25. Yan, X., Han, J.: CloseGraph: mining closed frequent graph patterns. In: KDD (2003)

  26. Yao, Y., Gehrke, J.: The cougar approach to in-network query processing in sensor networks. SIGMOD Rec. 31(3), 9–18 (2002)

    Article  Google Scholar 

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Authors and Affiliations

  1. Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Christopher Oßner, Erik Buchmann & Klemens Böhm

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  1. Christopher Oßner
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  2. Erik Buchmann
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  3. Klemens Böhm
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Correspondence to Erik Buchmann.

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Oßner, C., Buchmann, E. & Böhm, K. Identifying defective nodes in wireless sensor networks. Distrib Parallel Databases 34, 591–610 (2016). https://doi.org/10.1007/s10619-015-7189-7

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