Machine Learning Techniques in Wireless Sensor Networks: A review
Keywords:
Federated Learning, Edge Computing, Energy Efficiency, Intrusion Detection, Internet of Things, TinyML, Deep Learning, Machine Learning, Wireless Sensor NetworksAbstract
Wireless Sensor Networks (WSNs) have become a fundamental component of Internet of Things (IoT) applications, including environmental monitoring, healthcare, industrial automation, military surveillance, and smart agriculture. However, the resource-constrained nature of WSNs poses significant challenges in terms of energy efficiency, security, routing, data aggregation, and fault detection. Machine Learning (ML) has emerged as a promising solution for addressing these challenges by enabling intelligent decision-making, adaptive learning, anomaly detection, and predictive analytics. This review paper presents a comprehensive overview of machine learning techniques applied in WSNs. It discusses the fundamentals of WSNs and ML, categorizes supervised, unsupervised, semi-supervised, reinforcement, and deep learning approaches, and highlights their applications in routing, intrusion detection, localization, clustering, energy optimization, data aggregation, and fault diagnosis. The paper also reviews recent advances in edge computing, federated learning, explainable artificial intelligence (XAI), and TinyML for resource-constrained sensor networks. Furthermore, existing challenges such as limited computational resources, communication overhead, model complexity, and privacy concerns are critically analyzed. Finally, future research directions are presented to guide researchers toward developing intelligent, energy-efficient, and secure next-generation WSNs.
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