Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Advanced Information Networking and Applications (AINA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 926))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  4. Zhang, Y., Meratnia, N., Havinga, P.: Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 12, 159–170 (2010)

    Article  Google Scholar 

  5. Li, J., Cui, P.: Improved kernel fisher discriminant analysis for fault diagnosis. Expert Syst. Appl. 36(2), 1423–1432 (2009)

    Article  MathSciNet  Google Scholar 

  6. Fodor, I.K.: A survey of dimension reduction techniques. Lawrence Livermore National Laboratory, US Department of Energy (2002)

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Ayadi, A., Ghorbel, O., Obeid, A.M., Abid, M.: Outlier detection approaches for wireless sensor networks: a survey. Comput. Netw. 129, 319–333 (2017)

    Article  Google Scholar 

  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. Breiman, L.: Classication and Regression Trees. Routledge, New York (2017)

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Engineers School of Sfax, Sfax University, CES-Lab, Sfax, Tunisia

    Oussama Ghorbel, Aya Ayadi & Mohamed Abid

  2. Jouf University, Sakakah, Kingdom of Saudi Arabia

    Oussama Ghorbel & Rami Ayadi

  3. King Abdulaziz City for Science and Technology (KACST), Riyadh, Kingdom of Saudi Arabia

    Mohammed Aseeri

  4. Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, 3021, Sfax, Tunisia

    Oussama Ghorbel & Mohamed Abid

Authors
  1. Oussama Ghorbel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aya Ayadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rami Ayadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammed Aseeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohamed Abid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oussama Ghorbel .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Advanced Sciences, Hosei University, Koganei-Shi, Tokyo, Japan

    Makoto Takizawa

  3. Department of Computer Science, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Fatos Xhafa

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghorbel, O., Ayadi, A., Ayadi, R., Aseeri, M., Abid, M. (2020). Combined Methods Based Outlier Detection for Water Pipeline in Wireless Sensor Networks. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_19

Download citation

Publish with us

Policies and ethics

Search

Navigation