轻量级信号处理和边缘人工智能用于物联网传感器网络中的实时异常检测
1Engineering Departement and IEETA, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种轻量级的框架,用于实时检测物联网 (IoT) 传感器网络中的异常. 它在边缘高效地处理时间序列数据,减少关键应用程序的延迟和能源使用.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网设备产生大量的时间序列数据,需要高效的处理.
- 实时信号分析对于预测性维护和环境传感等应用至关重要.
- 边缘计算提供解决方案,以减少物联网网络中的延迟和通信开销.
研究的目的:
- 为物联网传感器网络中基于边缘的异常检测提出一个轻量级的框架.
- 将经典信号处理与边缘机器学习相结合,以实现高效的数据分析.
- 评估不同异常检测模型的性能和资源效率.
主要方法:
- 使用富里埃和波形变换的特征提取.
- 在边缘设备上部署机器学习模型 (浅层神经网络,量子化TinyML,决策树).
- 使用合成振动,声学和环境时间序列数据集进行测试.
主要成果:
- 浅层神经网络实现了高检测性能 (F1得分≈0.94).
- 量子化TinyML模型提供了良好的性能平衡 (F1得分≈0.92) 与显著减少的内存和能源使用.
- 决策树提供了低延迟,适合高度受限制的设备.
结论:
- 拟议的框架可以直接在物联网边缘设备上进行准确和资源高效的异常检测.
- 基于边缘的处理对于大规模的物联网传感器网络是可行的,提高了运营效率.
- 该方法证明了对常见数据缺陷和变异的稳定性.
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