Digital Twin Enabled 6G MEC Networks: Computational Energy Efficiency for Consumer IIoT in Industry 5.0

초록

The integration of digital twins (DTs) with mobile edge computing (MEC) offers transformative potential for computational energy efficiency in Industry 5.0-driven 6G networks. However, existing frameworks often lack proactive resource allocation using DTs, relying instead on reactive adjustments. This paper proposes a novel DT-MEC framework that uniquely employs virtual DT replicas of MEC servers to predict computational states (e.g., server workloads, latency trends) in real time, enabling dynamic optimization of task offloading for large-scale Industrial IoT (IIoT) networks. Unlike prior DT-MEC integrations that focus on static resource allocation, our approach leverages predictive DT modeling to preemptively adjust resources, addressing a critical gap in the literature. To maximize energy efficiency, we formulate a mixed-integer non-linear problem (MINLP) that jointly optimizes local computation, transmission power, DT-edge association, and task execution time. The problem is transformed into a tractable form using piecewise linearization to reduce computational complexity and solved via an adaptive iterative algorithm that alternates between resource allocation and DT state updates. This method ensures a balance between solution accuracy and computational overhead, a key innovation compared to conventional optimization techniques. Simulations demonstrate that our framework achieves 40% higher energy efficiency, 50% lower outage probability, and 95% task completion rates compared to state-of-the-art benchmarks. Specific improvements include a 6.63% gain over binary offloading and 150.08% over edge-only schemes, validating its practicality for Industry 5.0 deployments. These results highlight our contributions: (1) a proactive DT-driven resource allocation mechanism, (2) a linearization technique for non-convex MINLP problems, and (3) an adaptive iterative solver that outperforms existing approaches in dynamic IIoT environments.

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