Fuzzy Trust Evaluation and Secure K-Means Clustering with Trust-Based Outlier Detection (SKC-TOD) in Wireless Sensor Networks (WSNs)

Authors

  • Hamiden Abd El-Wahed Khalifa1 * Department of Mathematics, College of Science and Arts, Qassim University, Al-Badaya 51951 Saudi Arabia. https://orcid.org/0000-0002-8269-8822
  • Amanna Ghanbari Talouki2 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.48314/ceti.vi.76

Abstract

Secure communication and Secure Data Transmission (SDT) were considered as one of the major challenges in the Wireless Sensor Networks (WSN). Here, Fuzzy Trust Evaluation and Secure K-Means Clustering (KMC) with the Trust-based Outlier Detection (OD) Secure K-means Clustering Trust-based Outlier Detection (SKC-TOD) method was suggested in this study. The  Data Transmission (DT) and evidence in Trust Values (TV) supports the Fuzzy Trust Evaluation for improving the trust of connections among the Sensor Nodes (SN). The Fuzzy C-Means (FCM) approach is used to arrange the SN into uniformly distributed clusters. This FCM method also contributes in determining the optimal Cluster Head (CH) Cluster Head Selection (CHS). The suggested SKC-TOD method was employed for enhancing accuracy. The processing time was also reduced by this suggested method. Finally, the SDT was also ensured by this suggested method. When comparing the suggested method to other current methods, the suggested method performs better than the current methods. Here, ensuring the SDT becomes a complex task for conventional methods. Then, TOD system helps in strengthening the Data Integrity (DI) against both internal and external risks. The Malicious Nodes (MN) were effectively detected and gets isolated by this TOD system. The comparative analysis was conducted among the suggested SKC-TOD and current Fuzzy clustering protocols. Form the analysis, it is clear that the suggested method is effective in enhancing SDT than the current fuzzy clustering protocols. For ensuring SDT in WSN, this suggested SKC-TOD is an effective method.

Keywords:

Secure data transmission, Wireless sensor networks, K-means clustering, Outlier detection, Trust values, Fuzzy c-means, Trust-based outlier detection

References

  1. [1] Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE communications magazine, 40(8), 102–114. https://doi.org/10.1109/MCOM.2002.1024422

  2. [2] Perrig, A., Szewczyk, R., Wen, V., Culler, D., & Tygar, J. (2001). SPINS: Security protocols for sensor networks. Proceedings of the 7th annual international conference on mobile computing and networking (pp. 189–199). Association for copmputing machinery. https://doi.org/10.1145/381677.381696

  3. [3] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

  4. [4] Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, 10(2–3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7

  5. [5] Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. Morgan kaufmann publishers, san francisco. https://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790

  6. [6] Fawzy, A., Mokhtar, H. M. O., & Hegazy, O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian informatics journal, 14(2), 157–164. https://doi.org/10.1016/j.eij.2013.06.001

  7. [7] Andrade, A. T. C., Montez, C., Moraes, R., Pinto, A. R., Vasques, F., & Da Silva, G. L. (2016). Outlier detection using k-means clustering and lightweight methods for wireless sensor networks. IECON 2016-42nd annual conference of the IEEE industrial electronics society (pp. 4683–4688). IEEE. https://doi.org/10.1109/IECON.2016.7794093

  8. [8] Yang, L., Lu, Y., Yang, S. X., Guo, T., & Liang, Z. (2020). A secure clustering protocol with fuzzy trust evaluation and outlier detection for industrial wireless sensor networks. IEEE transactions on industrial informatics, 17(7), 4837–4847. https://doi.org/10.1109/TII.2020.3019286

  9. [9] Abid, A., Khediri, S. El, & Kachouri, A. (2021). Improved approaches for density-based outlier detection in wireless sensor networks. Computing, 103(10), 2275–2292. https://doi.org/10.1007/s00607-021-00939-5

  10. [10] Yang, L., Lu, Y., Yang, S. X., Zhong, Y., Guo, T., & Liang, Z. (2021). An evolutionary game-based secure clustering protocol with fuzzy trust evaluation and outlier detection for wireless sensor networks. IEEE sensors journal, 21(12), 13935–13947. https://doi.org/10.1109/JSEN.2021.3070689

  11. [11] Ghorbel, O., Ayedi, W., Snoussi, H., & Abid, M. (2015). Fast and efficient outlier detection method in wireless sensor networks. IEEE sensors journal, 15(6), 3403–3411. https://doi.org/10.1109/JSEN.2015.2388498

  12. [12] Abid, A., Masmoudi, A., Kachouri, A., & Mahfoudhi, A. (2017). Outlier detection in wireless sensor networks based on OPTICS method for events and errors identification. Wireless personal communications, 97(1), 1503–1515. https://doi.org/10.1007/s11277-017-4583-7

  13. [13] Ahmadi Livani, M., Abadi, M., Alikhany, M., & Yadollahzadeh Tabari, M. (2013). Outlier detection in wireless sensor networks using distributed principal component analysis. Journal of AI and data mining, 1(1), 1–11. https://doi.org/10.22044/jadm.2013.98

  14. [14] Sutaone, M., Mukherj, P., & Paranjape, S. (2016). Trust-based cluster head validation and outlier detection technique for mobile wireless sensor networks. 2016 international conference on wireless communications, signal processing and networking (WiSPNET) (pp. 2066–2070). IEEE. https://doi.org/10.1109/WiSPNET.2016.7566505

  15. [15] Bharti, S., Pattanaik, K. K., & Pandey, A. (2020). Contextual outlier detection for wireless sensor networks. Journal of ambient intelligence and humanized computing, 11(4), 1511–1530. https://doi.org/10.1007/s12652-019-01194-5

  16. [16] Schmidt, S., Steele, R., Dillon, T. S., & Chang, E. (2007). Fuzzy trust evaluation and credibility development in multi-agent systems. Applied soft computing, 7(2), 492–505. https://doi.org/10.1016/j.asoc.2006.11.002

  17. [17] Safaei, M., Asadi, S., Driss, M., Boulila, W., Alsaeedi, A., Chizari, H., … ., & Safaei, M. (2020). A systematic literature review on outlier detection in wireless sensor networks. Symmetry, 12(3), 1-40. https://doi.org/10.3390/sym12030328

  18. [18] Ester, M., Kriegel, H. P., Sander, J., Xu, X., ... ., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the second international conference on knowledge discovery and data mining (KDD-96) (pp. 226–231). Association for the advancement of artificial intelligence press. https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page--------------

  19. [19] Verma, D., Agrawal, S., Iwendi, C., Sharma, B., Bhatia, S., & Basheer, S. (2022). A novel framework for abnormal risk classification over fetal nuchal translucency using adaptive stochastic gradient descent algorithm. Diagnostics, 12(11), 1-20. https://doi.org/10.3390/diagnostics12112643

  20. [20] Augustine, S., & Ananth, J. P. (2020). Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks. Wireless networks, 26(7), 5113–5132. https://doi.org/10.1007/s11276-020-02352-w

  21. [21] Zhang, T., Yan, L., & Yang, Y. (2018). Trust evaluation method for clustered wireless sensor networks based on cloud model. Wireless networks, 24(3), 777–797. https://doi.org/10.1007/s11276-016-1368-y

Published

2026-08-28

How to Cite

Abd El-Wahed Khalifa1, H., & Ghanbari Talouki2, A. (2026). Fuzzy Trust Evaluation and Secure K-Means Clustering with Trust-Based Outlier Detection (SKC-TOD) in Wireless Sensor Networks (WSNs). Computational Engineering and Technology Innovations, 2(4), 98-108. https://doi.org/10.48314/ceti.vi.76

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