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<p>Achieving reliable real-time responsiveness under highly dynamic 6G conditions remains a challenging problem, despite recent progress in edge intelligence and cloud-assisted inference. This challenge is particularly critical in latency-sensitive applications, where even small communication or processing delays can significantly degrade system performance. The issue becomes more pronounced in sensorimotor feedback systems, where delays directly affect control accuracy, system stability, and user experience. Therefore, there is a need for adaptive and learning-driven mechanisms that can maintain ultra-low latency and robust performance under fluctuating network conditions. In this paper, we propose a Violin Real-Time Responsive Task Distributed Collaborative Model (VRRT–DCM), which coordinates inference across sensing, edge, and cloud layers to ensure timely response while supporting long-term analytical processing. Unlike conventional static partitioning approaches, the proposed framework integrates three key components: (i) a lightweight LSTM–CNN hybrid model designed for resource-constrained edge environments, (ii) a divergence-aware dynamic task partitioning strategy that balances latency, energy consumption, and model consistency, and (iii) an asynchronous edge–cloud synchronization mechanism that enables continuous learning without disrupting real-time operations. Furthermore, we formulate a joint optimization problem that unifies inference latency, energy expenditure, and model drift into a single utility-driven decision framework. The proposed approach is evaluated through hardware-in-the-loop experiments and trace-driven simulations under realistic 6G network conditions. Experimental results demonstrate that VRRT–DCM achieves sub-30 ms latency, reduces communication overhead by more than 40% and improves inference accuracy by 4–6% compared to static and cloud-centric baselines. These findings indicate that the proposed framework provides an effective and scalable solution for real-time collaborative intelligence in highly dynamic wireless environments.</p>

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VRRT–DCM: Low–Latency Edge–Cloud Sensor Analytics for Real–Time Violin Performance under 6G–Inspired Architectures

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Yan Han

摘要

Achieving reliable real-time responsiveness under highly dynamic 6G conditions remains a challenging problem, despite recent progress in edge intelligence and cloud-assisted inference. This challenge is particularly critical in latency-sensitive applications, where even small communication or processing delays can significantly degrade system performance. The issue becomes more pronounced in sensorimotor feedback systems, where delays directly affect control accuracy, system stability, and user experience. Therefore, there is a need for adaptive and learning-driven mechanisms that can maintain ultra-low latency and robust performance under fluctuating network conditions. In this paper, we propose a Violin Real-Time Responsive Task Distributed Collaborative Model (VRRT–DCM), which coordinates inference across sensing, edge, and cloud layers to ensure timely response while supporting long-term analytical processing. Unlike conventional static partitioning approaches, the proposed framework integrates three key components: (i) a lightweight LSTM–CNN hybrid model designed for resource-constrained edge environments, (ii) a divergence-aware dynamic task partitioning strategy that balances latency, energy consumption, and model consistency, and (iii) an asynchronous edge–cloud synchronization mechanism that enables continuous learning without disrupting real-time operations. Furthermore, we formulate a joint optimization problem that unifies inference latency, energy expenditure, and model drift into a single utility-driven decision framework. The proposed approach is evaluated through hardware-in-the-loop experiments and trace-driven simulations under realistic 6G network conditions. Experimental results demonstrate that VRRT–DCM achieves sub-30 ms latency, reduces communication overhead by more than 40% and improves inference accuracy by 4–6% compared to static and cloud-centric baselines. These findings indicate that the proposed framework provides an effective and scalable solution for real-time collaborative intelligence in highly dynamic wireless environments.