The Role of Artificial Intelligence in Enhancing Fault Tolerance Of Wireless Sensor Networks: A Systematic Review
DOI:
https://doi.org/10.29070/n3a0qx64Keywords:
Wireless Sensor Networks, Fault Tolerance, Artificial Intelligence, Machine Learning, Deep Learning, Fault Detection and Diagnosis, Edge ComputingAbstract
Wireless Sensor Networks (WSNs) have emerged as indispensable infrastructures across diverse applications, including healthcare, smart cities, industrial automation, and military surveillance. Despite their utility, WSNs remain highly vulnerable to faults at the node, network, data, and security levels. Such failures can degrade network reliability, reduce lifespan, and compromise decision-making. Traditional fault detection and diagnosis (FDD) methods—statistical analysis, threshold monitoring, and rule-based techniques—struggle to cope with the increasing complexity, heterogeneity, and scale of modern WSNs. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the fault tolerance of WSNs. Drawing upon a comprehensive synthesis of prior work, the review categorizes AI-driven approaches into machine learning, deep learning, fuzzy logic, expert systems, and bio-inspired algorithms. We highlight their contributions, limitations, and integration potential within resource-constrained environments. Finally, the review identifies future research directions, including lightweight AI models, explainable AI, federated learning, and hybrid fault-tolerant frameworks.
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