A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network | Zendy

Wei He | Zendy; Pei-Li Qiao | Zendy; Zhi-Jie Zhou | Zendy; Guan-Yu Hu | Zendy; Zhi-Chao Feng | Zendy; Hang Wei | Zendy

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A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network

Author(s) -

Wei He,

Pei-Li Qiao,

Zhi-Jie Zhou,

Guan-Yu Hu,

Zhi-Chao Feng,

Hang Wei

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2808605

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In the technology of wireless sensor network (WSN), wireless sensor fault diagnosis based on fusion data analysis has attracted attention in the wireless sensor field. It can detect and correct the faults of sensor nodes in time to improve the accuracy of sensor data fusion. In this paper, the data characteristics of WSN are analyzed, and a method is proposed for fault diagnosis of WSN based on a belief rule base (BRB) model. First, the sensor fault diagnosis process is described based on the characteristics of a wireless sensor in WSN. Then, the characteristics of sensors are analyzed from the aspects of time, space, and attributes. Finally, a fault diagnosis model is proposed based on the hierarchical BRB model. To make the results more accurate, a covariance matrix adaptation evolution strategy algorithm is used to optimize the initial parameters of the proposed model. A case study using the Intel lab data set of sensors is designed to verify the effectiveness of the proposed model. The results show that the proposed method is effective in fault diagnosis of WSN.

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