Publication

Abstract : Wireless Sensor Networks (WSNs) have permeated various domains, such as industrial automation, healthcare, and environmental monitoring, due to their ability to effectively collect and communicate data in diverse environments. The reliability and durability of WSNs have become paramount, particularly in critical applications. Predictive maintenance, which anticipates faults and mitigates them preemptively, emerges as a pivotal strategy to enhance network longevity and reliability. This paper explores using machine learning (ML) approaches for predictive maintenance in WSNs. Various ML algorithms, including regression, classification, and clustering, are investigated in terms of their efficacy in predicting and diagnosing issues within the network, thereby facilitating timely interventions. The paper contributes a novel architecture integrating ML models into WSNs to monitor, analyze, and predict potential failures, ensuring optimal network functionality.