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IEEE/ICACT20240199 Slide.03        [Big Slide]	Oral Presentation
	The literature on sensor fault detection in WSNs displays a varied set of strategies, including distributed, centralized, and hybrid approaches, with a notable emphasis on machine learning-based methods. This diversity highlights the complex nature of the problem and underscores the importance of flexible solutions. The review trend indicates a growing reliance on machine learning techniques for sensor fault detection. Methods such as SVMs, recurrent neural networks, K-Nearest Neighbors, and artificial neural networks have been explored. These approaches leverage collaborative sensor efforts, fault classification, and anomaly prediction to enhance the accuracy of fault detection in WSNs.

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