single-jc.php

JACIII Vol.29 No.1 pp. 158-164
doi: 10.20965/jaciii.2025.p0158
(2025)

Research Paper:

Research on Node Fault Diagnosis in Wireless Sensor Networks Based on a Constrained Data Maximum Entropy BN Parameter Learning Algorithm

Huiqin Zhao, Jiang Luo, and Li Rong

Information and Communication Branch, State Grid Shanxi Electric Power Company
No.3 Xieyuan Road, Jinyuan District, Taiyuan, Shanxi 030001, China

Corresponding author

Received:
April 17, 2024
Accepted:
November 6, 2024
Published:
January 20, 2025
Keywords:
power communication, wireless sensor network, node failure, constrained maximum entropy BN parameter learning algorithm
Abstract

Currently, power communication wireless sensor networks (WSNs) exhibit the characteristics of uncertainty and complexity. Furthermore, the application environment is more complex, resulting in nodes damaging easily, power communication breakdown, and economic losses. Dynamic monitoring of power communication WSN nodes is necessary. To this end, a constraint data maximum entropy Bayesian network (BN) parameter learning algorithm is used to solve the problem of small data sample size in monitoring and improve the quality of power communication WSN node fault diagnosis. After the latest verification, it is found that the correct rate of WSN node fault diagnosis under small data set is as follows: in normal conditions, the fault rate is 96%, the sensor fault is 94%, the power supply fault is 100%, the wireless communication fault is 90%, and the processor fault is 91%. It can be seen that even under small sample data sets, better diagnostic accuracy can be brought.

Cite this article as:
H. Zhao, J. Luo, and L. Rong, “Research on Node Fault Diagnosis in Wireless Sensor Networks Based on a Constrained Data Maximum Entropy BN Parameter Learning Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.1, pp. 158-164, 2025.
Data files:
References
  1. [1] S. J. Tong, T. L. Xue, S. Z. Wang, and Y. T. Gao, “Application of wireless sensor network in modern environmental monitoring,” Industrial Control Computer, Vol.36, No.12, pp. 116-117+123, 2023 (in Chinese).
  2. [2] J. L. Wang et al., “Analysis of mutated virus propagation dynamics in wireless sensor networks,” Comput. Eng. Des., Vol.44, pp. 3266-3275, 2023.
  3. [3] C. Temur and Y. Zhang, “Link anomaly detection algorithm for wireless sensor networks based on convolutional neural networks,” J. of Jilin University (Engineering Science), Vol.54, No.8, pp. 2295-2300, 2024 (in Chinese). https://doi.org/10.13229/j.cnki.jdxbgxb.20230224
  4. [4] Y. Huo, B. Liu, W. Yue, Y. Qi, and D. Pei, “Robust distributed estimation algorithm for wireless sensor networks,” Trans. of Beijing Institute of Technology, Vol.44, No.9, pp. 980-989, 2024 (in Chinese). https://doi.org/10.15918/j.tbit1001-0645.2024.209
  5. [5] L. Guan, “Real-time monitoring system of meteorological information based on wireless sensor network,” Information Technology, Vol.2023, No.10, pp. 168-173, 2023 (in Chinese). https://doi.org/10.13274/j.cnki.hdzj.2023.10.031
  6. [6] D. Zhang and X. Wang, “Design of estimation algorithm for ship indoor crew position based on wireless sensor network,” Ship Science and Technology, Vol.45, No.19, pp. 197-200, 2023 (in Chinese).
  7. [7] T. Wang, “Design of intelligent fire detection and alarm system based on wireless sensor networks,” Wireless Internet Technology, Vol.20, No.18, pp. 28-30, 2023 (in Chinese).
  8. [8] Y. Li and H. Yu, “Research on privacy protection scheme of smart grid based on group blind signature,” Process Automation Instrumentation, Vol.43, No.6, pp. 85-89, 2022 (in Chinese). https://doi.org/10.16086/j.cnki.issn1000-0380.2021100008
  9. [9] J. Li, T. Zhang, D. Qu, and L. Pan, “Real-time pricing based on blockchain privacy protection of smart grid,” J. of University of Shanghai for Science and Technology, Vol.44, No.2, pp. 122-130, 2022 (in Chinese).
  10. [10] H. Liu, C. Xu, and G. Zhang, “The study of the joint tree algorithm on graph model,” J. of Qingdao University (Natural Science Edition), Vol.32, No.1, pp. 28-32, 2019 (in Chinese).
  11. [11] X. Zhao, J. Li, and Q. Duan, “Teaching design and practice of maximum likelihood estimation method,” College Mathematics, Vol.38, No.4, pp. 100-103, 2022 (in Chinese).
  12. [12] X. Ru, X. Gao, and Y. Wang, “Bayesian network parameter learning based on fuzzy constraints,” Systems Engineering and Electronics, Vol.45, No.2, pp. 444-452, 2023 (in Chinese).

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Feb. 07, 2025