发表机构
Xiamen University Malaysia; Multimedia University; School of Economics and Management, Xiamen University Malaysia; School of Computing and Data Science, Xiamen University Malaysia（厦门大学马来西亚分校; 多媒体大学; 厦门大学马来西亚分校经济与管理学院; 厦门大学马来西亚分校计算与数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AIGS提出一种轻量级闭环自适应门控系统，通过冲击比和连续可塑性控制器解决在线表示学习中的稳定性-可塑性困境，在保持线性复杂度下提升边缘设备对概念漂移的适应能力。
AI 中文摘要
在物联网（WoT）和边缘计算环境中，实时数据流通常通过潜在的机制变化而演变。在严格的计算约束下进行在线表示学习，核心问题是解决稳定性-可塑性困境：在快速响应概念漂移的同时，保留有用的历史知识。现有方法采用固定的更新计划或滚动窗口。然而，它们在突然变化期间遭受参数僵化，并在流保持稳定时浪费计算资源。本文提出了自适应增量门控系统（AIGS），一种轻量级闭环状态感知自适应框架。AIGS引入了冲击比（Shock Ratio），一种内生残差反馈机制，将当前重建误差相对于近期变化进行归一化。该信号驱动连续可塑性控制器（Continuous Plasticity Controller），在学习可塑性和记忆保留之间平滑插值。通过将表示学习视为闭环控制机制，AIGS避免了灾难性遗忘，并保持了严格线性的$\mathcal{O}\left(k\cdot d\right)$每步复杂度，适用于对延迟敏感的边缘设备。在真实世界智慧城市动态流上的实验——涵盖交通网络、气象系统和工业基础设施——展示了明显的领域依赖性优势。在电力变压器温度数据集上，AIGS在逐渐退化下实现了8.31（ETTm1）和9.88（ETTm2）步的预防性早期预警提前时间。在性能测量系统交通数据集上，它在突然突变后表现出显著更快的漂移后恢复。在高噪声的天气数据集上，它提高了异常召回率，同时抵抗随机噪声过拟合。这些发现确立了AIGS作为资源受限边缘监控系统的实用即插即用适配器。
英文摘要
Real-time data streams in Web of Things (WoT) and edge computing environments often evolve through latent regime changes. For online representation learning under strict computational constraints, the central problem is resolving the stability-plasticity dilemma: keeping useful historical knowledge while rapidly reacting to concept drift. Existing methods employ fixed update schedules or rolling windows. However, they suffer from parameter ossification during sudden shifts and waste computational resources when the stream remains stable. This paper proposes the Adaptive Incremental Gating System (AIGS), a lightweight closed-loop state-aware adaptation framework. AIGS introduces the Shock Ratio, an endogenous residual feedback mechanism that normalizes current reconstruction error against recent variation. This signal drives a Continuous Plasticity Controller that smoothly interpolates between learning plasticity and memory retention. By treating representation learning as a closed-loop control mechanism, AIGS avoids catastrophic forgetting and maintains a strictly linear $\mathcal{O}\left(k\cdot d\right)$ per-step complexity suitable for latency-sensitive edge devices. Experiments on real-world smart city dynamic streams-spanning traffic networks, meteorological systems, and industrial infrastructure-demonstrate distinct domain-dependent advantages. On Electricity Transformer Temperature datasets, AIGS achieves preventative early-warning lead times of 8.31 (ETTm1) and 9.88 (ETTm2) steps under gradual degradation. On Performance Measurement System traffic datasets, it shows significantly faster post-shift recovery after abrupt mutations. On the highly noisy Weather dataset, it improves anomaly recall while resisting stochastic noise overfitting. These findings establish AIGS as a practical, plug-and-play adapter for resource-constrained edge monitoring systems.
Comments11 pages, 6 figures, 8 tables
