Energy Optimization in Industrial Wireless Sensor Networks Using Federated Learning and Predictive Maintenance

Abstract

In the industrial Wireless Sensor Networks (WSNs), energy optimization is crucial to appealing operational effectiveness and system life span, but issues like high energy usage, predictive maintenance, and dynamic clustering to achieve communication effectiveness still exist. In this study, a flexible architecture based on federated learning is suggested to maximize the energy consumption of industrial WSNs, which incorporates Predictive Maintenance and dynamic clustering to enhance the network representation and reliability. The federated learning model enables the sensor nodes to co-train the local models and exchange information without passing the raw data, which reduces energy usage. Sensor data quality is guaranteed by data preprocessing, e.g., the normalization step, attribution, and the elimination of outliers. The models of predictive maintenance are integrated to predict possible failures, and dynamic clustering algorithms cluster sensor nodes according to their proximity to each other and the stated requirements, and make the most out of energy usage by minimizing the transmission of data. The findings indicate that energy consumption is cut by 48 %, predictive maintenance is 95 % accurate, and interruptions are cut by 30 %. Nevertheless, the communication above is decreased by 10 %, and the reliability of the systems increases by 8 %, associated with the baseline approaches. This shows how successful the framework proposed is in terms of cutting down the energy consumption and also the ability to maintain the system performance. The federated learning combined with Predictive Maintenance and dynamic collection is a promising invention for energy efficiency and reliability of the system. The background demonstrates a good potential of being placed in industrial applications, particularly in advanced large-scale and resource-constrained systems, which will provide an effective way of managing energy practice in addition to ensuring the uninterrupted functioning of WSNs.