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International Journal of Fuzzy Systems

, Volume 19, Issue 5, pp 1528–1545 | Cite as

Fuzzy-Based Flat Anomaly Diagnosis and Relief Measures in Distributed Wireless Sensor Network

  • Usman Barakkath Nisha
  • Natarajan Uma Maheswari
  • Ramalingam Venkatesh
  • Rabi Yasir Abdullah
Article
  • 84 Downloads

Abstract

This paper bestows a distributed adaptive scheme for diagnosing inaccurate data (anomaly) in wireless sensor networks. Faults occurring in sensor nodes are routine owing to the sensor device itself and the harsh environment in which the sensor nodes are deployed. It is mandatory for the WSNs to discover the anomaly and take actions to avoid further seediness of the network lifetime for confirming data accuracy. In this standpoint, we propose two perspectives for diagnosing and alleviating anomalies. The first view depicts input space partitioning by subtractive clustering method with robust density measure. Later, Takagi–Sugeno fuzzy inference model is applied for selection of several parameters and its membership functions, and rule-based construction is practiced to spot anomalies in distributed clustering wireless sensor network. By exploring combined correlation analysis with second perspective, the eliminated anomalous data are replaced by imputed data. Experimental results infer accuracy and reliability with a reduced amount of energy consumption than the state-of-the-art techniques.

Keywords

Accuracy Anomaly detection Subtractive clustering Fuzzy Sensor networks Takagi–Sugeno model 

References

  1. 1.
    Akyildiz, I.F., Su, W., Sankara subramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Dargie W., Poellabauer C. In: Shen X., Pen D.Y. (eds.), Fundamentals of Wireless Sensor Networks. 3rd edn, Wiley (2010)Google Scholar
  3. 3.
    Xie, Miao, Han, Song, Tian, Biming, Parvin, Sazia: Anomaly detection in wireless sensor networks: a survey. J. Netw. Comput. Appl. 34, 1302–1325 (2011)CrossRefGoogle Scholar
  4. 4.
    Sun, Bo, Shan, Xuemei, Kui, Wu, Xiao, Yang: Anomaly detection based secure in-network aggregation for wireless sensor networks. IEEE Syst. J. 7(1), 13–25 (2013)CrossRefGoogle Scholar
  5. 5.
    Roy, S., Conti, M., Setia, S., Jajodia, S.: Secure data aggregation in wireless sensor networks. IEEE Inf. Forens. Secur. 7(3), 1040–1052 (2012)CrossRefGoogle Scholar
  6. 6.
    Mitchell, Robert, Chen, Ing-Ray: A survey of intrusion detection in wireless network applications. Comput. Commun. 42, 1–23 (2014)CrossRefGoogle Scholar
  7. 7.
    Xu, H., Huang, L., Zhang, Y., Huang, H., Jiang, S., Liu, G.: Energy efficient cooperative data aggregation for wireless sensor networks. J. Parallel Distrib. Comput 70(9), 953–961 (2010)CrossRefMATHGoogle Scholar
  8. 8.
    Forero, P., Cano, A., Giannakis, G.: Distributed clustering using wireless sensor networks. IEEE J. Sel. Top. Signal Process. 5(4), 702–724 (2011)CrossRefGoogle Scholar
  9. 9.
    Takagi, T., Sugeno, M.: Fuzzy Identification of systems and its applications to modeling and control. IEEE Trans. Syst., Man, Cybern. 15(1), 116–132 (1985)CrossRefMATHGoogle Scholar
  10. 10.
    O’Reilly, C., Gluhak, A., Imran, M.A., Rajasegarar, S.: Anomaly detection in wireless sensor networks in a non-stationary environment. IEEE Commun. Surv. Tutor. 16(3), 1–20 (2013)Google Scholar
  11. 11.
    Pottie, G.J., Kaiser, W.J.: Wireless integrated network sensors. ACM Commun. 43(5), 51–58 (2000)CrossRefGoogle Scholar
  12. 12.
    Chitra Devi, N., Palanisamy, V., Baskaran, K., Prabeela, S.: Efficient distributed clustering based anomaly detection algorithm for sensor stream in clustered wireless sensor network. Eur. J. Sci. Res. 54(4), 484–498 (2011)Google Scholar
  13. 13.
    Zhang, Yang, Meratnia, Nirvana, Havinga, P.J.M.: Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. J. Ad Hoc Netw. 11, 1062–1074 (2012)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Hamm, N.A.S., Meratina, N., Stein, A., Van de Voort, M., Havinga, P.J.M.: Statistics based outlier detection for wireless sensor networks. Int. J. Geogr. Inf. Sci. 1–20 (2011)Google Scholar
  15. 15.
    Kapitanova, K., Son, S.H., Kang, K.-D.: Using fuzzy logic for robust event detection in wireless sensor networks. J. Ad Hoc Netw. 10, 709–722 (2011)CrossRefGoogle Scholar
  16. 16.
    Liang Q, Wang L.: Event detection in wireless sensor networks using fuzzy logic system. In: International Conference on Computational Intelligence for Homeland Security and Personal Safety, IEEE, pp. 52–55 (2005)Google Scholar
  17. 17.
    Sasikala, E., Rengarajan, N.: An intelligent technique to detect jamming attack in wireless sensor networks (WSNs). Int. J. Fuzzy Syst. 7(1), 76–83 (2015)CrossRefGoogle Scholar
  18. 18.
    Shamshirband, S., Amini, A., Anur, N., Kiah, M., Teh, Y., Furnell, S.: D-FICCA: a density based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. J. Meas. Elsevier 55, 212–226 (2014)CrossRefGoogle Scholar
  19. 19.
    Kumaragea, Heshan, Khalil, Ibrahim, Tari, Zahir, Zomaya, Albert: Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modeling. J. Parallel Distrib. Comput. 73, 790–806 (2013)CrossRefGoogle Scholar
  20. 20.
    Barakkath Nisha, U., Maheswari, N.U., Venkatesh, R., Yasir Abdullah, R.: Robust estimation of incorrect data using relative correlation clustering technique in wireless sensor networks. In: IEEE International Conference on Communication and Network Technologies, Issue 1, pp. 314–318 (2014)Google Scholar
  21. 21.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82, 35–45 (1960)CrossRefGoogle Scholar
  22. 22.
    Yang, H., Jiang, B., Staroswiecki, M.: Observer-based fault-tolerant control for a class of switched nonlinear systems. IET Control Theory Appl. 5, 1523–1532 (2007)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Yang, H., Cocquempot, V., Jiang, B.: Robust fault tolerant tracking control with application to hybrid nonlinear systems. IETControl Theory Appl 3(2), 211–224 (2009)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Huang, S., Tan, K.K., Lee, T.H.: Fault diagnosis and fault-tolerant control in linear drives using the Kalman filter. IEEE Trans. Ind. Electron 59(11), 4285–4292 (2012)CrossRefGoogle Scholar
  25. 25.
    Chen, Shui-Li, Fang, Yuan, Yun-Dong, Wu: A new hybrid fuzzy clustering approach to Takagi-Sugeno fuzzy modeling. Int. J. Digital Content Technol. Appl. 6(18), 341–350 (2012)CrossRefGoogle Scholar
  26. 26.
    Afifi, W.A., Hefny, H.A.: Adaptive TAKAGI-SUGENO fuzzy model using weighted fuzzy expected value in wireless sensor network. In: International Conference on Hybrid Intelligent Systems (HIS), IEEE, pp. 221–231 (2014)Google Scholar
  27. 27.
    Chen, J.-J., FAN, X.-P., QU, Z.-H., YANG, X., LIU, S.-Q.: Subtractive clustering based clustering routing algorithm for wireless sensor networks. Inf. Control 7, 201–219 (2008)Google Scholar
  28. 28.
    Lizhe, Yu., Tiaojuan, Ren, Zhangquan, Wang, Banteng, Liu: Research on vehicle networking clustering routing algorithm based on subtractive clustering. Appl. Mech. Mater. 644–650, 2366–2369 (2014)Google Scholar
  29. 29.
    Barakkath Nisha, U., Uma Maheswari, N., Venkatesh, R., Yasir Abdullah, R.: Improving data accuracy using proactive correlated fuzzy system in wireless sensor networks. KSII Trans. Internet Inf. Syst. 9(9), 3515–3537 (2015)Google Scholar
  30. 30.
    Neamatollahi, P., Mashhad I., Taheri H., Naghibzadeh M., Yaghmaee M.: A hybrid clustering approach for prolonging lifetime in wireless sensor networks. IEEE International Symposium on Computer Networks and Distributed Systems, pp. 170–174 (2011)Google Scholar
  31. 31.
    Chiu, S.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994)CrossRefGoogle Scholar
  32. 32.
    Kim, D.W., Lee, K.Y., Lee, D., Lee, K.H.: A Kernel-based subtractive clustering method. Pattern Recognit. Lett. 26, 879–891 (2005)CrossRefGoogle Scholar
  33. 33.
    Nikhil, R.P., Chakraborty, D.: Mountain and subtractive clustering method: improvements and generalizations. Int. J. Intell. Syst. 15, 329–341 (2000)CrossRefMATHGoogle Scholar
  34. 34.
    Yager, R.R., Filev, D.P.: Approximate clustering via the mountain method. IEEE Trans. Syst., Man Cybern. 24(8), 1279–1284 (1994)CrossRefGoogle Scholar
  35. 35.
    De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The Mahalanobis distance. J. Chemo Metrics Intell. Lab. Syst. Elsevier 50(1), 1–18 (2000)CrossRefGoogle Scholar
  36. 36.
    Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing, 3rd edn. Prentice hall, Upper Saddle River (1997)Google Scholar
  37. 37.
    Sugeno, M., Kang, G.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988)MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    Zadeh, L.A.: Soft computing and fuzzy logic. ACM J. Softw. 11(6), 48–56 (1994)CrossRefGoogle Scholar
  39. 39.
    Zimmermann, H.J.: Fuzzy Set Theory and Its Applications, 3rd edn. Publisher kluwer Academic Publishers Norwell, Norwell (1996)CrossRefMATHGoogle Scholar
  40. 40.
    Vuran, M.C., Akan, B., Akyildiz, I.F.: Spatio-temporal correlation: theory and applications for wireless sensor networks. Comput. Netw. Int. J. Comput. Telecommun. Netw. 45(3), 245–259 (2004)MATHGoogle Scholar
  41. 41.
    Liu, Z., Xing, W., Zeng, B., Wang, Y., Lu, D.: Distributed spatial correlation-based clustering for approximate data collection in WSNs. In: IEEE International Conference on Advanced Information Networking and Applications, pp. 56–63 (2013)Google Scholar
  42. 42.
    Ishibuchi, H. Nakashima, T., Kuroda, T.: A hybrid fuzzy GBML algorithm for designing compact fuzzy rule-based classification systems. In: IEEE International Conference on Fuzzy Systems, pp. 248–252 (1999)Google Scholar
  43. 43.
  44. 44.

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Usman Barakkath Nisha
    • 1
  • Natarajan Uma Maheswari
    • 1
  • Ramalingam Venkatesh
    • 2
  • Rabi Yasir Abdullah
    • 3
  1. 1.Department of Computer Science and EngineeringPSNA College of Engineering and TechnologyDindigulIndia
  2. 2.Department of Information TechnologyPSNA College of Engineering and TechnologyDindigulIndia
  3. 3.Department of Computer Science and EngineeringSSCETPalaniIndia

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