Machine Learning Algorithms for Predictive Maintenance in Wireless Sensor Networks

Abstract

Predictive maintenance has emerged as a pivotal strategy in managing the operational efficiency of Wireless Sensor Networks (WSNs) across various industrial applications. This paper explores the integration of machine learning (ML) algorithms to enhance predictive maintenance capabilities within WSNs. By leveraging the vast amounts of data generated by sensor nodes, ML techniques can anticipate potential failures, optimize maintenance schedules, and reduce operational costs. We evaluate several ML algorithms, including Linear Regression, Decision Trees, Neural Networks, and Support Vector Machines, assessing their effectiveness in predicting maintenance needs. The study employs a comprehensive methodology encompassing data collection from real-world WSN deployments, feature selection, model training, and evaluation based on accuracy, precision, recall, and F1-score. Our findings indicate that Neural Networks and Support Vector Machines outperform other algorithms in terms of predictive accuracy and reliability. These results underscore the significant potential of ML-driven predictive maintenance in enhancing the longevity and performance of WSNs. The implications of this research suggest that adopting advanced ML techniques can lead to more resilient and efficient sensor networks, ultimately supporting the broader goals of Industry 4.0 and the Internet of Things (IoT).