Wireless Sensor Network Fault Sensor Recognition Algorithm Based on MM Diagnostic Model

• Published in: IEEE access Vol. 8; pp. 127084 - 127093
• Main Authors: Gui, Weixia, Lu, Qian, Su, Meili, Pan, Fulai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Convolution; Convolution neural network; Diagnostic systems; Fault diagnosis; Fireworks; fireworks algorithm; Games; Internet of Things; MM model; Neural networks; Numerical models; Optimization; Pricing; Product design; Quality assessment; Radio frequency; Sensors; Supply chains; Wireless networks; wireless sensor network; Wireless sensor networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3008255

Wireless Sensor Network (WSN) as one of the representatives of the Internet of Things technology has also received much attention. To accurately diagnose fault sensor nodes, a fault diagnosis method based on fireworks algorithm optimization convolutional neural network algorithm is proposed. The weights and biases of the convolutional neural networks are optimized by using the self-regulating mechanism of global and local searching ability of fireworks algorithm. So the problem of convolution neural network in extreme judgment and limited convergence speed is solved, to effectively realize the fault diagnosis of the WSN. Simulation experiments show that this algorithm has higher fault diagnosis accuracy than other classic WSN fault diagnosis algorithms.