A Fault Diagnosis Method for Wireless Sensor Network nodes based on Belief Rule Base with Adaptive Attribute Weights

Abstract

Due to wireless sensor networks (WSNs)’ harsh operating environment and ultralong operating hours, node failures are inevitable. Ensuring the dependable collection of data necessitates the utmost importance of diagnosing faults in nodes within the wireless sensor network. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. However, a solitary data feature assigned a high weight fails to effectively discriminate between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating expert knowledge initialization parameters with the extracted data features. To optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The outcomes of the case study indicate a discernible enhancement in the accuracy of WSN node fault diagnosis when compared to the traditional BRB method.