(untitled) (Internet of Things and Edge Computing Journal, 2024)
The integration of Internet of Things (IoT) technologies into smart manufacturing processes has emerged as a transformative approach to addressing energy efficiency challenges while simultaneously advancing sustainability objectives. This paper critically investigates IoT-enhanced energy management strategies in smart manufacturing, focusing on their potential to optimize energy consumption and achieve sustainability goals within industrial operations. The proliferation of IoT-enabled devices, sensors, and communication systems allows for real-time data acquisition, advanced analytics, and predictive decision-making, thereby enhancing energy efficiency across manufacturing ecosystems. These technologies facilitate granular monitoring of energy usage, enabling the identification of inefficiencies, predictive maintenance, and the adaptive optimization of energy-intensive processes. The research begins with an in-depth analysis of IoT architecture tailored to energy management in industrial settings, emphasizing the interplay between physical systems, cyber-physical integration, and communication protocols such as MQTT, CoAP, and OPC UA. By leveraging big data analytics, edge computing, and artificial intelligence (AI) algorithms, manufacturers can dynamically allocate energy resources and reduce wastage. The paper highlights advanced frameworks such as digital twins and IoT-based energy monitoring systems, which provide real-time visualization of energy consumption patterns and facilitate informed, data-driven decision-making at every stage of production. A key focus of the research is the role of IoT in predictive and prescriptive analytics. Predictive models, fueled by machine learning algorithms, forecast energy demands and preempt disruptions, while prescriptive analytics recommend optimal operational adjustments. Case studies are presented to illustrate successful implementations of IoT-enhanced energy management in industries such as automotive, electronics, and chemical manufacturing. These examples underscore the significant reduction in energy costs, emissions, and downtime achieved through IoT adoption, offering quantifiable evidence of its impact. Additionally, the paper explores the integration of renewable energy sources within IoT-enabled manufacturing systems. By synchronizing IoT data streams with renewable energy systems, industries can achieve adaptive load balancing and mitigate dependence on non-renewable sources. The transition towards low-carbon manufacturing processes, supported by IoT, aligns with global sustainability frameworks such as the United Nations Sustainable Development Goals (SDGs). However, the implementation of IoT in energy management is not devoid of challenges. This study examines critical obstacles, including data security risks, interoperability issues, and the high initial investment required for IoT infrastructure deployment. Strategies to mitigate these challenges, such as robust cybersecurity frameworks and standardization efforts, are discussed in detail. Future directions for research are outlined, including the development of more energy-efficient IoT devices, enhanced AI-driven algorithms for autonomous energy management, and blockchain-based solutions for secure energy data sharing. Furthermore, the paper underscores the need for interdisciplinary collaboration between IoT engineers, energy experts, and industrial policymakers to scale these technologies effectively.