The rapid development in ubiquitous computing has enabled the use of microcontrollers as edge devices. These devices are used to develop truly distributed IoT-based mechanisms where machine learning (ML) models are utilized. However, integrating ML models to edge devices requires an understanding of various software tools such as programming languages and domain-specific knowledge. Anomaly detection is one of the domains where a high level of expertise is required to achieve promising results. In this work, we present AnoML which is an end-to-end data science pipeline that allows the integration of multiple wireless communication protocols, anomaly detection algorithms, deployment to the edge, fog, and cloud platforms with minimal user interaction. We facilitate the development of IoT anomaly detection mechanisms by reducing the barriers that are formed due to the heterogeneity of an IoT environment. The proposed pipeline supports four main phases: (i) data ingestion, (ii) model training, (iii) model deployment, (iv) inference and maintaining. We evaluate the pipeline with two anomaly detection datasets while comparing the efficiency of several machine learning algorithms within different nodes. We also provide the source code (https://gitlab.com/IOTGarage/anoml-iot-analytics) of the developed tools which are the main components of the pipeline.
翻译:无处不在的计算机的迅速发展使得微型控制器能够用作边缘装置。这些装置被用来开发真正分布的基于IOT的基于IOT的机制,以便利用机器学习(ML)模型;然而,将ML模型整合到边缘装置需要了解各种软件工具,例如编程语言和特定领域知识;异常探测是需要高水平专门知识才能取得有希望结果的领域之一。在这项工作中,我们介绍了AnoML,这是一个端到端的数据科学管道,可以将多种无线通信协议、异常检测算法、部署到边缘、雾和云层平台,并使用最低限度的用户互动。我们通过减少由于IOT环境的异质性而形成的障碍,为开发IOT异常检测机制提供便利。拟议的管道支持四个主要阶段:(一) 数据摄取,(二) 模型培训,(三) 模型部署,(四) 误判和维护。我们用两种异常检测数据集对管道进行评估,同时比较不同节点内若干机器学习算法的效率。我们还提供了主要源码(http://Gmagiam) 工具。