Combined Methods Based Outlier Detection for Water Pipeline in Wireless Sensor Networks

  • Oussama GhorbelEmail author
  • Aya Ayadi
  • Rami Ayadi
  • Mohammed Aseeri
  • Mohamed Abid
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)


In the last years, Wireless Sensor Networks (WSNs) has become a very interesting field for researchers. They are widely employed to solve several problems in different domains like agriculture, monitoring, health care. Then, outlier detection method is considered as a very important step in construction of sensor network systems to ensure data grade for perfect decision making. So, this task helps to create a gainful approach to find out if data is normal regular or an outlier. Therefore, in this paper, a newest outlier’s detection and classification model has been developed to complement the existing hardware redundancy and limit checking techniques. To overcome these problems, we present a Combined Outliers Detection Method (CODM) for water pipeline to detect damaged data in WSNs. To this end, the application of kernel-based non-linear approach is introduced. The main objective of this work was to combine the advantages of Kernel Fisher Discriminant Analysis (KFDA) and Support Vector Machine (SVM) to enhance the performance of the monitoring water pipeline system. The accuracy of our Combined Outliers Detection Method for classification was analyzed and compared with variety of methods. Finally, based on the experimental results, our proposed work shows a better performance to detecting outliers in the monitoring water pipeline process.


Outlier detection method Wireless Sensor Networks (WSNs) Monitoring water pipeline Kernelized techniques 


  1. 1.
    Braun, M.L., Buhmann, J.M., Muller, K.R.: On relevant dimensions in kernel feature spaces. J. Mach. Learn. Res. 9, 1875–1908 (2008)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Naumowicz, T., Freeman, R., Heil, A., Calsyn, M., Hellmich, E., Brandle, A., Guilford, T., Schiller, J.: Autonomous monitoring of vulnerable habitats using a wireless sensor network. In: Proceedings of the Workshop on Real-World Wireless Sensor Networks, REALWSN 2008, Glasgow, Scotland (2008)Google Scholar
  3. 3.
    Akyildiz, I.F., Melodia, T., Chowdhury, R.: A survey on wireless multimedia sensor networks. J. Comput. Netw.: Int. J. Comput. Telecommun. Netw. 51(4), 921–960 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhang, Y., Meratnia, N., Havinga, P.: Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 12, 159–170 (2010)CrossRefGoogle Scholar
  5. 5.
    Li, J., Cui, P.: Improved kernel fisher discriminant analysis for fault diagnosis. Expert Syst. Appl. 36(2), 1423–1432 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fodor, I.K.: A survey of dimension reduction techniques. Lawrence Livermore National Laboratory, US Department of Energy (2002)Google Scholar
  7. 7.
    Ghorbel, O., Abid, M., Snoussi, H.: Improved KPCA for outlier detection in wireless sensor networks. In: 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 507–511 (2014)Google Scholar
  8. 8.
    Nakanishi, T.: A generative wireless sensor network framework for agricultural use. In: Makassar International Conference on Electrical Engineering and Informatics (MICEEI), pp. 205–211 (2014)Google Scholar
  9. 9.
    Mutikanga, H.E., Sharma, S.K., Vairavamoorthy, K.: Methods and tools for managing losses in water distribution systems. J. Water Resour. Plan. Manag. 139(2), 166–174 (2013)CrossRefGoogle Scholar
  10. 10.
    Weinberger, K.Q., Sha, F., Saul, L.K.: Learning a kernel matrix for nonlinear dimensionality reduction. In: Li, R., Huang, H., Xin, K., Tao, T. (eds.) Proceedings of the 21st (2015). A review of methods for burst/leakage detection and location in water distribution systems. Water Sci. Technol.: Water Supply 15(3), 429–441 (2015)Google Scholar
  11. 11.
    Demirci, S., Yigit, E., Eskidemir, I.H., Ozdemir, C.: Ground penetrating radar imaging of water leaks from buried pipes based on back-projection method. NDT and E Int. 47, 35–42 (2012)CrossRefGoogle Scholar
  12. 12.
    Zheng, L., Kleiner, Y.: State of the art review of inspection technologies for condition assessment of water pipes. Measurement 46(1), 1–15 (2013)CrossRefGoogle Scholar
  13. 13.
    Colombo, A.F., Lee, P., Karney, B.W.: A selective literature review of transient-based leak detection methods. J. Hydroenviron. Res. 2(April), 212–277 (2009)Google Scholar
  14. 14.
    Lee, S.J., et al.: Online burst detection and location of water distribution systems and its practical applications. J. Water Resour. Planning Manag. (2016)Google Scholar
  15. 15.
    Martins, J.C., Seleghim Jr., P.: Assessment of the performance of acoustic and mass balance methods for leak detection in pipelines for transporting liquids. J. Fluids Eng. 132(January), 011401 (2010)CrossRefGoogle Scholar
  16. 16.
    Covas, D., Ramos, H.: Case studies of leak detection and location in water pipe systems by inverse transient analysis. J. Water Res. Plann. Manage. 136(2), 248–257 (2010)CrossRefGoogle Scholar
  17. 17.
    Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., Culler, D.: Analysis of a large scale habitet monitoring application. In: Proceedings of the Second ACM Conference en Embedded Networked Sensors Systems (SenSys), Baltimore (2004)Google Scholar
  18. 18.
    Barrenetxea, G., Ingelrest, F., Schaefer, G., Vetterli, M., Couach, O., Parlange, M.: SensorScope: out-of-the-box environmental monitoring. In: Proceeding of the 7th International Conference on Information Processing in Sensor Networks, 22–24 April, pp. 332–343 (2008)Google Scholar
  19. 19.
    Ayadi, A., Ghorbel, O., Obeid, A.M., Abid, M.: Outlier detection approaches for wireless sensor networks: a survey. Comput. Netw. 129, 319–333 (2017)CrossRefGoogle Scholar
  20. 20.
    Ayadi, A., Ghorbel, O., Bensaleh, M.S., Abid, M.: Outlier detection based on data reduction in WSNs for water pipeline. In: 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–6. IEEE (2017)Google Scholar
  21. 21.
    Breiman, L.: Classication and Regression Trees. Routledge, New York (2017)CrossRefGoogle Scholar
  22. 22.
    Karray, F., Garcia-Ortiz, A., Jmal, M.W., Obeid, A.M., Abid, M.: EARNPIPE: a testbed for smart water pipeline monitoring using wireless sensor network. Procedia Comput. Sci. 96, 285–294 (2016)CrossRefGoogle Scholar
  23. 23.
    Srirangarajan, S., Allen, M., Preis, A., Iqbal, M., Lim, H.B., Whittle, A.: Wavelet-based burst event detection and localization in water distribution systems. J. Signal Process. Syst. 72(1), 1–16 (2013)CrossRefGoogle Scholar
  24. 24.
    Kayaalp, F., Zengin, A., Kara, R., Zavrak, S.: Leakage detection and localization on water transportation pipelines: a multi-label classification approach. Neural Comput. Appl. 28(10), 2905–2914 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Oussama Ghorbel
    • 1
    • 2
    • 4
    Email author
  • Aya Ayadi
    • 1
  • Rami Ayadi
    • 2
  • Mohammed Aseeri
    • 3
  • Mohamed Abid
    • 1
    • 4
  1. 1.National Engineers School of SfaxSfax University, CES-LabSfaxTunisia
  2. 2.Jouf UniversitySakakahKingdom of Saudi Arabia
  3. 3.King Abdulaziz City for Science and Technology (KACST)RiyadhKingdom of Saudi Arabia
  4. 4.Digital Research Center of SfaxSfaxTunisia

Personalised recommendations