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A wireless sensor network node fault diagnosis model based on belief rule base with power set.

Guo-Wen Sun1, Wei He1,2, Hai-Long Zhu1

  • 1Harbin Normal University, Harbin, 150025, China.

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Wireless sensor networks (WSNs) face node failures. A new belief rule base with power set (PBRB) method effectively diagnoses faults, even with similar data features, improving reliability.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) are prone to node failures due to demanding operational conditions and extended usage.
  • Reliable data collection in WSNs necessitates robust fault diagnosis mechanisms.
  • Existing fault diagnosis methods struggle with ambiguous information where different fault types exhibit similar data features.

Purpose of the Study:

  • To propose a novel fault diagnosis method for WSNs capable of handling ambiguous information.
  • To enhance the accuracy and stability of WSN node fault diagnosis.
  • To address the challenge of distinguishing between similar fault types in WSN data.

Main Methods:

  • A belief rule base with power set (PBRB) framework was developed for fault diagnosis.
  • The power set identification framework was employed to represent fuzzy information.
  • Evidential reasoning (ER) was used for the diagnostic reasoning process.
  • Projection covariance matrix adaptive evolution strategy (P-CMA-ES) was utilized for parameter optimization.

Main Results:

  • The PBRB method demonstrated higher accuracy and superior stability compared to conventional fault diagnosis techniques.
  • The PBRB method successfully represented and distinguished between fault types with similar data features (ambiguous information).
  • Case studies confirmed the effectiveness and reliability of the proposed PBRB approach.

Conclusions:

  • The PBRB method offers an effective solution for WSN node fault diagnosis, particularly in scenarios with ambiguous information.
  • The approach achieves high accuracy and stability, outperforming existing methods.
  • The PBRB method benefits from small sample training, making it efficient for practical WSN applications.