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The advancement in Industrial Wireless Sensor Networks (IWSNs) demands intelligent energy management strategies to enhance network longevity and reliability. This research proposes an adaptive federated learning (FL) framework to optimize energy consumption in IWSNs, incorporating predictive maintenance and dynamic clustering. Utilizing lightweight machine learning models integrated with FL, the framework predicts network conditions and proactively adjusts sensor operations. Dynamic clustering facilitates efficient data aggregation and reduces transmission energy. The proposed solution significantly minimizes unnecessary energy usage by predicting sensor node failures and scheduling optimal sleep states, thus extending network lifespan and reliability. Experimental evaluation demonstrates that this approach reduces energy consumption by up to 25% compared to traditional energy management schemes, validating its effectiveness in industrial environments.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: K Chandrasekhar, chandra507shiva@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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