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：

---

基于多维数据处理的异常检测，保护 6G 大规模 IIoT 中的重要设备


随着工业物联网 (IIoT) 架构的部署不断增加，不断生成大量多维数据。这些数据的一个重要问题是，更高的维度会增加碎片程度。此外，工业物联网节点收集的数据集经常显示异常值，这通常是由异常事件或错误引起的。这些异常值包含了大量有价值的信息，阻碍了系统的正常运行。因此，能够量化所获得的信息以保护高优先级工业物联网节点的方法至关重要。本研究旨在开发一种由第六代（6G）网络驱动的方法。该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，使用自回归外生模型来消除噪声对传感器数据的影响，并帮助检测异常。最后，该算法生成累积价值系数 (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通过 6G 实现大规模物联网 (IoT)。实验结果表明，该方法能够有效处理复杂工业环境中普遍存在的干扰噪声的影响。此外，该方法可以有效地检测异常并弥补传统方法的一些缺点。