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<p>The transition toward 6G-enabled Industrial Internet of Things (IIoT) requires strict Ultra-Reliable Low-Latency Communication (URLLC), creating a fundamental trade-off with energy consumption. Orchestrating resources in dense, hyper-dynamic edge environments is an NP-hard problem where traditional algorithms often exhibit brittleness and premature convergence. In this paper, we propose a novel multi-objective orchestration framework, QFLN-AMRO, designed to robustly resolve the energy-latency conflict. The architecture integrates a Fractional-Order NSGA-II evolutionary engine with a cognitive Quantum Fuzzy Logic Network (QFLN) to dynamically adjust evolutionary parameters, thereby establishing a superior deterministic Pareto front. To prevent network saturation and queue drift under extreme URLLC stress, we further incorporate a Lyapunov-guided resilience mechanism. The scientific validity and robustness of the proposed framework are rigorously evaluated using a tripartite real-world data setup comprising EUA spatial topologies, Edge-IIoT payloads, and Google Cluster Traces. Empirical results demonstrate that QFLN-AMRO significantly outperforms state-of-the-art deep reinforcement learning models (PPO, GAT) and metaheuristics (GA, PSO, GWO) in minimizing energy expenditure while guaranteeing deterministic latency thresholds. The superior performance of our approach is validated by rigorous statistical analysis, including Omnibus ANOVA (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\:p\approx\:4.66\times\:{10}^{-14}\)</EquationSource></InlineEquation>) and Tukey HSD tests, confirming its reliability for next-generation edge orchestration.</p>

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A quantum fuzzy logic-enhanced evolutionary framework for energy-latency optimization in 6G Edge-IioT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Siavash Siavashian Rashidi,
  • Ali Broumandnia,
  • Abbas Mirzaei,
  • Ramin Karimi

摘要

The transition toward 6G-enabled Industrial Internet of Things (IIoT) requires strict Ultra-Reliable Low-Latency Communication (URLLC), creating a fundamental trade-off with energy consumption. Orchestrating resources in dense, hyper-dynamic edge environments is an NP-hard problem where traditional algorithms often exhibit brittleness and premature convergence. In this paper, we propose a novel multi-objective orchestration framework, QFLN-AMRO, designed to robustly resolve the energy-latency conflict. The architecture integrates a Fractional-Order NSGA-II evolutionary engine with a cognitive Quantum Fuzzy Logic Network (QFLN) to dynamically adjust evolutionary parameters, thereby establishing a superior deterministic Pareto front. To prevent network saturation and queue drift under extreme URLLC stress, we further incorporate a Lyapunov-guided resilience mechanism. The scientific validity and robustness of the proposed framework are rigorously evaluated using a tripartite real-world data setup comprising EUA spatial topologies, Edge-IIoT payloads, and Google Cluster Traces. Empirical results demonstrate that QFLN-AMRO significantly outperforms state-of-the-art deep reinforcement learning models (PPO, GAT) and metaheuristics (GA, PSO, GWO) in minimizing energy expenditure while guaranteeing deterministic latency thresholds. The superior performance of our approach is validated by rigorous statistical analysis, including Omnibus ANOVA (\(\:p\approx\:4.66\times\:{10}^{-14}\)) and Tukey HSD tests, confirming its reliability for next-generation edge orchestration.