ENERGY EFFICIENCY OPTIMIZATION IN IOT-ENABLED MANUFACTURING USING FUZZY ALGORITHMS

This review examines the integration of the Internet of Things (IoT) and fuzzy logic algorithms for improving energy efficiency in manufacturing systems. Rising energy costs, environmental concerns, and the need for sustainable industrial operations have increased interest in intelligent energy management approaches. IoT technologies enable real-time monitoring of machine performance, process conditions, and energy consumption through interconnected sensors and devices. However, the large volume of data generated in such environments is often uncertain, incomplete, or imprecise. Fuzzy logic algorithms provide a suitable solution by supporting flexible decision-making under uncertain conditions. The article discusses how the combination of IoT and fuzzy logic can optimize energy use, reduce waste, improve operational efficiency, and support predictive maintenance in manufacturing environments. It also highlights key implementation challenges, including data processing complexity, system integration, and the need for adaptive control strategies. In addition, the review outlines the major benefits of this approach, such as cost reduction, environmental sustainability, and improved production performance. Overall, the review shows that IoT-enabled manufacturing systems supported by fuzzy logic offer strong potential for intelligent and sustainable energy optimization in modern industrial settings.