云-连续体系统中物理神经网络的可信感知输出管理
Trust-Aware Output Management for Physical Neural Network in Cloud-Continuum Systems
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中文总结 AI 辅助
针对云连续体系统中物理神经网络输出受噪声和可靠性信息缺失影响的问题，提出可信感知输出管理框架，通过边缘信任评分和雾层融合，将不安全接受率从61.5%降至5.0%，显著提升输出安全性。
中文摘要 AI 辅助
物理神经网络（PNN）为云连续体计算带来了新的机遇，但其输出可能受到噪声、漂移、延迟和不完整可靠性信息的影响。现有的底层资源管理方法主要关注发现、调用和监控，而返回输出的可靠性往往未被解决。本文提出了一种针对异构PNN的可信感知输出管理框架。每个输出都带有质量和上下文信息，一个轻量级的边缘级信任评分决定其应被接受、拒绝还是转发至雾层。在雾层，兼容的输出会被检查不一致性，并使用基于可靠性和不确定性的融合方法进行组合。同时跟踪历史信任以检测持续退化并支持重新校准请求。该框架使用受控和随机的PNN输出模型进行评估。在20个随机种子中，所提出的全信任策略将不安全接受率从原始输出处理的约61.5%降低至约5.0%，同时被接受输出的平均绝对误差（MAE）从0.504降至0.242。风险覆盖分析表明，这一改进不能仅由较低的本地接受率解释。所提出的雾融合方法在所评估的方法中也实现了最低的聚合平均误差，相对于强不确定性感知基线具有小而一致的改进。原型为每条证据记录增加了约2微秒的边缘处理时间。这些结果表明，调用后的可靠性管理可以提高边缘-雾-云系统中PNN输出的安全使用。
英文摘要
Physical Neural Networks (PNNs) introduce new opportunities for cloud continuum computing, but their outputs may be affected by noise, drift, delay, and incomplete reliability information. Existing substrate-management approaches mainly focus on discovery, invocation, and monitoring, while the reliability of the returned output is often left unaddressed. This paper proposes a trust-aware output management framework for heterogeneous PNNs. Each output is represented with quality and context information, and a lightweight edge-level trust score decides whether it should be accepted, rejected, or forwarded to the fog. At the fog layer, compatible outputs are checked for disagreement and combined using reliability- and uncertainty-aware fusion. Historical trust is also tracked to detect sustained degradation and support recalibration requests. The framework is evaluated using controlled and randomized PNN output models. Across 20 random seeds, the proposed full-trust policy reduces unsafe acceptance from approximately 61.5\% for raw output handling to about 5.0\%, while accepted-output MAE decreases from 0.504 to 0.242. Risk coverage analysis shows that this improvement is not explained only by lower local acceptance. The proposed fog fusion method also achieves the lowest aggregate mean error among the evaluated methods, with a small but consistent advantage over strong uncertainty-aware baselines. The prototype adds about $2~μ\text{s}$ of edge processing per evidence record. These results show that post-invocation reliability management can improve the safe use of PNN outputs across edge--fog--cloud systems.
