As a result of the increasing deployment of Industrial-Internet-of-Things (IIoT) architectures, large volumes of multidimensional data are continuously generated. An important issue with these data is that higher dimensionality increases the degree of fragmentation. Furthermore, data sets collected by IIoT nodes often display outliers, which are usually caused by anomalous events or errors. These outliers contain considerable valuable information, which prevent the normal operation of the system. Thus, methodologies are able to quantify the obtained information to protect the high priority IIoT nodes, are crucial. This study aims at developing such a method driven by sixth-generation (6G) networks. The proposed algorithm uses a multidimensional data relationship diagram to characterize the spatiotemporal correlations among heterogeneous data. Then, an autoregressive exogenous model is used to eliminate the effects of noise on sensor data, and to help in detecting anomalies. Finally, the algorithm produces a Cumulative Coefficient of Value (CCoV), to identify high-value sensing devices and enable massive Internet of Things (IoT) with 6G—using the characteristic patterns hidden within the data. The experimental results demonstrate that the proposed method can effectively handle the effects of the ubiquitous interference noise in complex industrial environments. Moreover, the method yields effective anomaly detection and compensates for some of the shortcomings in traditional methods.

中文翻译：

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基于多维数据处理的异常检测可保护启用6G的大规模IIoT中的重要设备

由于物联网工业（IIoT）架构的部署不断增加，因此不断生成大量多维数据。这些数据的一个重要问题是更高的维数会增加碎片程度。此外，由IIoT节点收集的数据集通常显示异常值，这些异常值通常是由异常事件或错误引起的。这些异常值包含大量有价值的信息，这些信息会妨碍系统的正常运行。因此，能够量化获得的信息以保护高优先级IIoT节点的方法至关重要。这项研究旨在开发一种由第六代（6G）网络驱动的方法。所提出的算法使用多维数据关系图来表征异构数据之间的时空相关性。然后，自回归外生模型用于消除噪声对传感器数据的影响，并有助于检测异常。最终，该算法产生一个累积价值系数（CCoV），以识别高价值传感设备，并利用隐藏在数据中的特征模式，实现具有6G的大规模物联网（IoT）。实验结果表明，该方法可以有效地应对复杂工业环境中普遍存在的干扰噪声的影响。而且，该方法产生了有效的异常检测并弥补了传统方法中的一些缺点。利用隐藏在数据中的特征模式，识别高价值的传感设备，并通过6G实现大规模的物联网（IoT）。实验结果表明，该方法可以有效地应对复杂工业环境中普遍存在的干扰噪声的影响。而且，该方法产生了有效的异常检测并弥补了传统方法中的一些缺点。利用隐藏在数据中的特征模式，识别高价值的传感设备，并通过6G实现大规模的物联网（IoT）。实验结果表明，该方法可以有效地应对复杂工业环境中普遍存在的干扰噪声的影响。而且，该方法产生了有效的异常检测并弥补了传统方法中的一些缺点。