为物联网小网关设计轻量级实时预测模型，提升资源受限环境下的机器学习效率。
Adaptive Machine Learning for Resource-Constrained Environments
- 用在线持续学习预测网关可用性，基于CPU利用率动态调整。
- 集成与在线方法在准确率上优于传统模型，且内存占用低。
- 适合边缘计算、物联网等资源受限场景的实时决策需求。
物联网领域中，连接设备激增导致数据持续生成，形成不断增长的数据流。因此，需发展灵活、低成本的机器学习方案以应对数据洪流。本文针对小型网关的计算资源动态变化问题，提出一种基于CPU利用率的在线与持续学习方法，用于预测网关可用性。该方法与主流机器学习算法及近期时序基础模型Lag-Llama在微调和零样本设置下进行对比。实验基于真实IoT网关的CPU利用率时间序列数据，评估指标包括预测误差、训练与推理时间、内存消耗。研究结果表明，在多种场景下，集成与在线学习方法在保证高精度的同时，展现出极低的资源开销，为物联网环境中的高效性能预测提供了新路径。
原文摘要 · Abstract (English)
The Internet of Things is an example domain where data is perpetually generated in ever-increasing quantities, reflecting the proliferation of connected devices and the formation of continuous data streams over time. Consequently, the demand for ad-hoc, cost-effective machine learning solutions must adapt to this evolving data influx. This study tackles the task of offloading in small gateways, exacerbated by their dynamic availability over time. An approach leveraging CPU utilization metrics using online and continual machine learning techniques is proposed to predict gateway availability. These methods are compared to popular machine learning algorithms and a recent time-series foundation model, Lag-Llama, for fine-tuned and zero-shot setups. Their performance is benchmarked on a dataset of CPU utilization measurements over time from an IoT gateway and focuses on model metrics such as prediction errors, training and inference times, and memory consumption. Our primary objective is to study new efficient ways to predict CPU performance in IoT environments. Across various scenarios, our findings highlight that ensemble and online methods offer promising results for this task in terms of accuracy while maintaining a low resource footprint.
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