Robust Sensor Fault Detection in Wireless Sensor Networks Using a Hybrid Conditional Generative Adversarial Networks and Convolutional Autoencoder

In the rapidly growing realm of the Internet of Things (IoT), reliance on sensor-generated data has become crucial for the operation of multiple services and systems. As essential components of these systems, wireless sensor networks (WSNs) are installed in a wide range of diverse and often harsh en...

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Veröffentlicht in:	IEEE sensors journal Jg. 25; H. 8; S. 13912 - 13926
Hauptverfasser:	Khan, Rehan, Saeed, Umer, Koo, Insoo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Artificial neural networks Classification Conditional generative adversarial networks (Conditional GANs) convolutional autoencoder (CAE) Fault detection Faults Feature extraction Generative adversarial networks Gramian angular field (GAF) Internet of Things Monitoring Network security Reliability sensor faults Sensor systems Sensors Synthetic data Temperature sensors Wireless sensor networks wireless sensor networks (WSNs)
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
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