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<p>This study proposes a digital twin (DT)-based IoT energy management framework to improve efficiency and reliability in smart manufacturing environments. The framework builds a closed-loop architecture where IoT sensor data are continuously synchronized with a DT engine to enable real-time monitoring, optimization, and adaptive control. Validation was conducted in a simulation platform designed to reflect realistic manufacturing conditions, incorporating hardware parameters and communication constraints to ensure practical relevance. The results show significant improvements in energy consumption, latency, reliability, and battery lifetime compared with conventional transmission strategies. In addition, scalability tests with varying network sizes confirmed that the framework maintains stable and predictable performance as the number of devices increases. These findings demonstrate the potential of the proposed framework to support energy-aware and resilient smart manufacturing systems, bridging the gap between conceptual design and practical deployment.</p>

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Smart Manufacturing Innovation Through the Integration of Digital Twin and IoT Energy Management

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Youngkuk Kwon

摘要

This study proposes a digital twin (DT)-based IoT energy management framework to improve efficiency and reliability in smart manufacturing environments. The framework builds a closed-loop architecture where IoT sensor data are continuously synchronized with a DT engine to enable real-time monitoring, optimization, and adaptive control. Validation was conducted in a simulation platform designed to reflect realistic manufacturing conditions, incorporating hardware parameters and communication constraints to ensure practical relevance. The results show significant improvements in energy consumption, latency, reliability, and battery lifetime compared with conventional transmission strategies. In addition, scalability tests with varying network sizes confirmed that the framework maintains stable and predictable performance as the number of devices increases. These findings demonstrate the potential of the proposed framework to support energy-aware and resilient smart manufacturing systems, bridging the gap between conceptual design and practical deployment.