A Distributed Extreme Learning Machine based approach for self-failure diagnosis in Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are vital for applications including environmental observation, disaster prevention, and deep-sea exploration, yet intermittent node defects severely degrade data fidelity and network reliability. This paper introduces a Distributed Extreme Learning Machine (D-ELM) framework for self-fault diagnosis in UWSNs, designed to efficiently detect intermittent sensor failures through localized learning and collaborative decision exchange among neighboring nodes. Leveraging the inherent advantages of extreme learning machines, fast training speed and strong generalization, D-ELM enables each sensor node to independently perform real time diagnostics while sharing minimal diagnostic information to maintain low communication overhead in bandwidth limited acoustic channels. The distributed architecture not only reduces computational complexity but also enhances fault isolation precision and robustness against transient defects. Experimental evaluations conducted on multi-hop underwater sensing datasets with simulated environmental disturbances demonstrate that D-ELM achieves an average diagnostic accuracy of 83.21% on unseen data, outperforming state-of-the-art classifiers such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Multilayer Perceptron (MLP), and Extra-Trees in terms of precision, F1-score, and training time. The proposed scheme provides a scalable, energy-efficient, and autonomous solution for long-term underwater monitoring, significantly improving network reliability, diagnostic accuracy, and operational lifespan in dynamic underwater environments.