Integrating 5G-enhanced edge computing into energy-intensive industrial systems has significant sustainability, scalability, and resilience benefits. The approach is particularly effective for managing Digital Twin (DT) systems in real-time for a dynamic, varying industrial environment. The proposed approach leverages 5G’s latency, high reliability, and vast connectivity, combined with intelligent edge computing, to enable adaptive and predictive control independent of centralized data systems.
Furthermore, the use of federated learning models at the edge allows decentralized analytics with enhanced data security. To further reduce carbon emissions, the system uses AI algorithms that optimize production and energy consumption in real-time. Additionally, the architecture incorporates enhanced cybersecurity safeguards, such as blockchain-based authentication, ensuring secure and immutable communication between digital twins and physical assets. In the analysis, the obtained results demonstrate the improved energy efficiency, system resilience, and decision-making accuracy, even under conditions of peak demand, supply chain disruptions, and regulatory changes. Overall, the approach brings the vision of an intelligent, sustainable, and autonomous industrial ecosystem closer to reality.
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