为物联网设备设计自适应持续学习框架，提升故障检测精度同时节能42.8%。
Link-Aware Energy-Frugal Continual Learning for Fault Detection in IoT Networks
- 根据无线链路和能量预算动态调整模型更新策略
- 在严苛能源与带宽限制下，召回率提升最高达42.8%
- 适合资源受限的物联网故障检测场景
轻量级机器学习模型使物联网设备可在本地执行关键应用的推理。然而，由于物联网环境的非平稳性及初始训练数据有限，推理精度会下降。通过定期用新数据更新模型可缓解此问题，但会额外消耗能量，对能量受限设备不利。本文提出一种事件驱动通信框架，将持续学习（CL）融入物联网网络，实现能效优化的故障检测。该框架使物联网设备与边缘服务器协同更新轻量级模型，依据无线链路状态和可用能量预算动态调整通信与更新策略。在真实数据集上的评估显示，相比周期采样和非自适应持续学习，本方法在推理召回率上表现更优，即使在严苛的能源与带宽约束下，仍可实现最高42.8%的性能提升。
原文摘要 · Abstract (English)
The use of lightweight machine learning (ML) models in internet of things (IoT) networks enables resource constrained IoT devices to perform on-device inference for several critical applications. However, the inference accuracy deteriorates due to the non-stationarity in the IoT environment and limited initial training data. To counteract this, the deployed models can be updated occasionally with new observed data samples. However, this approach consumes additional energy, which is undesirable for energy constrained IoT devices. This letter introduces an event-driven communication framework that strategically integrates continual learning (CL) in IoT networks for energy-efficient fault detection. Our framework enables the IoT device and the edge server (ES) to collaboratively update the lightweight ML model by adapting to the wireless link conditions for communication and the available energy budget. Evaluation on real-world datasets show that the proposed approach can outperform both periodic sampling and non-adaptive CL in terms of inference recall; our proposed approach achieves up to a 42.8% improvement, even under tight energy and bandwidth constraint.
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