Advanced fault detection in wireless sensor networks: A metaheuristic-driven deep learning approach to enhance the quality of service

Abstract

Wireless sensor networks (WSNs) face critical challenges in fault detection that can compromise their quality of service in dynamic environments. This study introduces an integrated framework that enhances fault detection by combining advanced noise filtering, optimized feature selection, and a robust deep learning (DL) model. The framework employs a dynamic noise filtering technique with adaptive thresholding to effectively remove noise while preserving essential data integrity. Complementing this, the rank-based whale optimization algorithm refines feature selection, boosts model performance, and reduces computational demands. At its core, the hierarchical attention-based DL model utilizes temporal convolutional layers, long short-term memory units, and hierarchical attention mechanisms to capture both short-term and long-term dependencies in the data. Experimental evaluations on the WSN dataset demonstrate outstanding performance, with a precision of 0.98, a recall of 0.99, an F1-score of 0.98, and an area under the curve of 0.99 for all fault classes. Comparative analysis reveals that this framework outperforms existing approaches in terms of accuracy, sensitivity, specificity, and computational efficiency. Overall, the proposed solution improves fault detection and enhances network reliability, minimizes false alarms, and extends the operational lifespan of WSNs, offering a scalable approach for mission-critical applications in healthcare, environmental monitoring, and industrial automation.

Keywords

Dynamic Noise Filtering / Hierarchical Attention-based Deep Learning / Long Short-term Memory / Quality of Service / Rank-based Whale Optimization Algorithm / Wireless Sensor Networks

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