Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation

• Published in: Neural computing & applications Vol. 31; no. 8; pp. 4019 - 4030
• Main Authors: Zhao, Mingbo, Tian, Zhaoyang, Chow, Tommy W. S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Conditional probability; Data Mining and Knowledge Discovery; Fault diagnosis; Hazardous areas; Image Processing and Computer Vision; Insurance claims; Kernels; Machine learning; Nodes; Original Article; Pattern classification; Probability and Statistics in Computer Science; Remote monitoring; Remote sensors; Residential density; Sensors; Wireless sensor networks; Wireless sensors network; Faulty nodes detection; Graph-based method; Semi-supervised learning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3342-3

Wireless sensor network (WSN) has become one of the most important technologies because of its reliable remote monitoring ability. As sensors are often deployed at remote and/or hazardous environments, it is important to be able to perform faulty sensor nodes self-diagnosing. In this paper, we formulate WSN faulty nodes identification as a pattern classification problem. This paper uses semi-supervised method for faulty sensor nodes classification. To enhance the learning performance, we also introduce a label propagation mechanism which is based on local kernel density estimation. The basic concept of the method is to estimate the posterior probability of a scene that belongs to normal or different faulty modes. In this paper, we implemented a software platform to study WSN under different number of sensor nodes and faulty conditions. Our experimental results show the proposed semi-supervised method is highly effective. Thorough comparative analyses with other state-of-art semi-supervised learning methods were included. The obtained results confirmed that our proposed algorithm can deliver improved classification performance for WSN.