IoT-Based Smart Energy Monitoring Systems in Manufacturing: Architectures, Technologies, and Challenges

Abstract

The rapid growth of smart manufacturing and Industry 4.0 technologies has significantly increased the demand for intelligent energy management systems in manufacturing industries. IoTbased smart energy monitoring systems have emerged as an effective solution for real-time monitoring, analysis, and optimization of industrial energy consumption. This review article presents a comprehensive study of IoT-enabled energy monitoring architectures, communication technologies, cloud and edge computing frameworks, Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin integration in manufacturing environments. The paper discusses various industrial applications including CNC machining, welding, foundry operations, additive manufacturing, and process industries. In addition, the study analyzes important challenges such as interoperability, cybersecurity risks, scalability limitations, communication latency, and lack of standardization in Industrial IoT systems. The review also highlights the role of predictive analytics, real-time monitoring, and intelligent optimization techniques in improving industrial sustainability and operational efficiency. Furthermore, future research directions including AI-driven manufacturing, 5G-enabled Industrial IoT, blockchain integration, and sustainable smart factories are discussed. The findings of this review indicate that IoT-based smart energy monitoring systems have strong potential to reduce energy consumption, improve manufacturing productivity, and support sustainable industrial development in next-generation smart factories.

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