In this paper, we investigate different scenarios of anomaly detection on decentralised Internet of Things (IoT) applications. Specifically, an anomaly detector is devised to detect different types of anomalies for an IoT data management system, based on the decentralised alternating direction method of multipliers (ADMM), which was proposed in our previous work. The anomaly detector only requires limited information from the IoT system, and can be operated using both a mathematical-rule-based approach and the deep learning approach proposed in the paper. Our experimental results show that detection based on mathematical approach is simple to implement, but it also comes with lower detection accuracy ($78.88\%$). In contrast, the deep-learning-enabled approach can easily achieve a higher detection accuracy ($96.28\%$) in the real world working environment.

中文翻译：

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基于 ADMM 的物联网设备最优传输频率管理系统的实时异常检测

在本文中，我们研究了去中心化物联网 (IoT) 应用程序中异常检测的不同场景。具体来说，基于我们之前工作中提出的分散交替方向乘法器 (ADMM) 方法，设计了一种异常检测器来检测物联网数据管理系统的不同类型的异常。异常检测器只需要来自物联网系统的有限信息，并且可以使用基于数学规则的方法和论文中提出的深度学习方法进行操作。我们的实验结果表明，基于数学方法的检测实现简单，但检测精度较低（$78.88\%$）。相比之下，启用深度学习的方法可以轻松实现更高的检测精度（$96.28\%$) 在现实世界的工作环境中。