Extensive Investigation of the Reliability of Wireless Sensor Networks: Utilising Machine Learning for Fault Detection

Zahraa A. Habeeb, Hanan A.R. Akkar and Jabbar K. Mohammed

References:

1. S. A. Shifani, A. A. Mary, M. I. M. Metilda, G. V. Rajkumar, M. S. Sutha, and S. Maheshwari, “A novel machine learning strategy for anomaly identification scheme in wireless sensor networks using supervised training principles,” in Proc. 5th Int. Conf. Electronics and Sustainable Communication Systems (ICESC), 2024, pp. 1754–1761, doi: 10.1109/ICESC60852.2024.10689838.
2. A. Haque, H. Soliman, and M. N. U. R. Chowdhury, “Wireless sensor networks data anomaly detection: A smart approach,” in Proc. 3rd Int. Conf. Intelligent Technologies (CONIT), 2023, doi: 10.1109/CONIT59222.2023.10205933.
3. B. Ahmad, W. Jian, Z. A. Ali, S. Tanvir, and M. S. A. Khan, “Hybrid anomaly detection by using clustering for wireless sensor network,” Wireless Personal Communications, vol. 106, no. 4, pp. 1841–1853, Jun. 2019, doi: 10.1007/s11277-018-5721-6.
4. A. Bader Saeed, A. K. Al-Samarrie, and H. A. R. Akkar, “Design and implementation of an interface unit communicated by a laser system within wireless sensor network,” Engineering and Technology Journal, vol. 34, no. 13, pp. 2507–2517, Dec. 2016, doi: 10.30684/etj.34.13a.13.
5. S. H. S. Sabah and M. S. Croock, “Software engineering-based fault tolerance method for wireless sensor network,” Iraqi Journal of Computer, Communication, Control and System Engineering, pp. 21–28, Oct. 2020, doi: 10.33103/uot.ijccce.20.4.3.
6. B. Chander and G. Kumaravelan, “Outlier detection strategies for WSNs: A survey,” Journal of King Saud University – Computer and Information Sciences, Sep. 2022, doi: 10.1016/j.jksuci.2021.02.012.
7. H. Ayadi, A. Zouinkhi, B. Boussaid, and M. N. Abdelkrim, “A machine learning method: Outlier detection in WSN,” in Proc. 16th Int. Conf. Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2015, pp. 722–727, doi: 10.1109/STA.2015.7505190.
8. M. Xie, S. Han, B. Tian, and S. Parvin, “Anomaly detection in wireless sensor networks: A survey,” Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1302–1325, Jul. 2011, doi: 10.1016/j.jnca.2011.03.004.
9. D. Hendrycks, M. Mazeika, and T. Dietterich, “Deep anomaly detection with outlier exposure,” Dec. 2018. [Online]. Available: http://arxiv.org/abs/1812.04606.
10. F. Fusco, V. U. Castrillo, H. M. R. Giannetta, M. Albano, and E. Cavallini, “Methods, standards and components for wireless communications and power transfer aimed at intra-vehicular applications of launchers,” Aerospace, Feb. 2024, doi: 10.3390/aerospace11020132.
11. Kurniabudi et al., “Network anomaly detection research: A survey,” Indonesian Journal of Electrical Engineering and Informatics, vol. 7, no. 1, pp. 36–49, Mar. 2019, doi: 10.11591/ijeei.v7i1.773.
12. B. Chander and Kumaravelan, “One class SVMs outlier detection for wireless sensor networks in harsh environments: Analysis.” [Online]. Available: www.ijrte.org.
13. R. Ul Islam, M. S. Hossain, and K. Andersson, “A novel anomaly detection algorithm for sensor data under uncertainty,” Soft Computing, vol. 22, no. 5, pp. 1623–1639, Mar. 2018, doi: 10.1007/s00500-016-2425-2.
14. M. M. Gharamaleki and S. Babaie, “A new distributed fault detection method for wireless sensor networks,” IEEE Systems Journal, vol. 14, no. 4, pp. 4883–4890, Dec. 2020, doi: 10.1109/JSYST.2020.2976827.
15. Y. Gao, F. Xiao, J. Liu, and R. Wang, “Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks,” IEEE Transactions on Industrial Informatics, vol. 15, no. 1, pp. 334–347, Jan. 2019, doi: 10.1109/TII.2018.2812771.
16. X. Miao, Y. Liu, H. Zhao, and C. Li, “Distributed online one-class support vector machine for anomaly detection over networks,” IEEE Transactions on Cybernetics, vol. 49, no. 4, pp. 1475–1488, Apr. 2019, doi: 10.1109/TCYB.2018.2804940.
17. J. S. Saini and R. Kait, “Exploring machine learning strategies for intrusion detection in wireless sensor networks,” in Proc. IEEE 9th Int. Conf. Convergence in Technology (I2CT), 2024, doi: 10.1109/I2CT61223.2024.10543320.
18. J. M. Aguiar-Pérez et al., “Understanding machine learning concepts,” in Encyclopedia of Data Science and Machine Learning, IGI Global, 2022, pp. 1007–1022, doi: 10.4018/978-1-7998-9220-5.ch058.
19. A. A. Radhi, H. N. Abdullah, and H. A. R. Akkar, “Denoised Jarque-Bera features-based K-means algorithm for intelligent cooperative spectrum sensing,” Digital Signal Processing, vol. 129, Sep. 2022, doi: 10.1016/j.dsp.2022.103659.
20. A. A. Radhi, H. A. R. Akkar, and H. N. Abdullah, “Skewness and access kurtosis as denoised mixed features-based K-medoids for cooperative spectrum sensing,” Physical Communication, vol. 54, Oct. 2022, doi: 10.1016/j.phycom.2022.101831.
21. Z. Azam, M. M. Islam, and M. N. Huda, “Comparative analysis of intrusion detection systems and machine-learning-based model analysis through decision tree,” IEEE Access, vol. 11, pp. 80348–80391, 2023, doi: 10.1109/ACCESS.2023.3296444.
22. S. Zidi, T. Moulahi, and B. Alaya, “Fault detection in wireless sensor networks through SVM classifier,” IEEE Sensors Journal, vol. 18, no. 1, pp. 340–347, Jan. 2018, doi: 10.1109/JSEN.2017.2771226.
23. H. S. Emadi and S. M. Mazinani, “A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks,” Wireless Personal Communications, vol. 98, no. 2, pp. 2025–2035, Jan. 2018, doi: 10.1007/s11277-017-4961-1.
24. Y. Yuan, S. Li, X. Zhang, and J. Sun, “A comparative analysis of SVM, naïve Bayes and GBDT for data faults detection in WSNs,” in Proc. IEEE 18th Int. Conf. Software Quality, Reliability, and Security Companion (QRS-C), 2018, pp. 394–399, doi: 10.1109/QRS-C.2018.00075.
25. Z. Noshad et al., “Fault detection in wireless sensor networks through the random forest classifier,” Sensors, vol. 19, no. 7, Apr. 2019, doi: 10.3390/s19071568.
26. I. Azzouz, B. Boussaid, A. Zouinkhi, and M. N. Abdelkrim, “Multi-faults classification in WSN: A deep learning approach,” in Proc. STA 2020 – 20th Int. Conf. Sciences and Techniques of Automatic Control and Computer Engineering, 2020, pp. 343–348, doi: 10.1109/STA50679.2020.9329325.
27. S. Gnanavel et al., “Analysis of fault classifiers to detect faults and node failures in a wireless sensor network,” Electronics, vol. 11, no. 10, May 2022, doi: 10.3390/electronics11101609.
28. R. R. Swain, P. M. Khilar, and S. K. Bhoi, “Heterogeneous fault diagnosis for wireless sensor networks,” Ad Hoc Networks, vol. 69, pp. 15–37, Feb. 2018, doi: 10.1016/j.adhoc.2017.10.012.
29. A. Adamova, T. Zhukabayeva, and Y. Mardenov, “Machine learning in action: An analysis of its application for fault detection in wireless sensor networks,” in Proc. IEEE Int. Conf. Smart Information Systems and Technologies (SIST), 2023, pp. 506–511, doi: 10.1109/SIST58284.2023.10223548.
30. A. Ayadi, O. Ghorbel, A. M. Obeid, and M. Abid, “Outlier detection approaches for wireless sensor networks: A survey,” Computer Networks, vol. 129, pp. 319–333, Dec. 2017, doi: 10.1016/j.comnet.2017.10.007.
31. X. Zhou et al., “Fault diagnosis of silage harvester based on a modified random forest,” Information Processing in Agriculture, vol. 10, no. 3, pp. 301–311, Sep. 2023, doi: 10.1016/j.inpa.2022.02.005.
32. Z. Hashim, H. Mohsin, and A. Alkhayyat, “Handwritten signature identification based on hybrid features and machine learning algorithms,” Iraqi Journal of Computer, Communication, Control and System Engineering, pp. 43–56, Mar. 2024, doi: 10.33103/uot.ijccce.24.1.4.
33. A. S. Dawood, “A comparative study using deep learning models and transfer learning for detection and classification of Alzheimer’s disease,” Iraqi Journal of Computer, Communication, Control and System Engineering, pp. 57–70, Mar. 2024, doi: 10.33103/uot.ijccce.24.1.5.