Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 7, Pages 5219-5229Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3051935
Keywords
Industrial Internet of Things; Anomaly detection; 6G mobile communication; Principal component analysis; Machine learning algorithms; Spatiotemporal phenomena; Data models; Artificial intelligence; Industrial Internet of Things (IIoT); multidimensional data processing (MDP); sixth-generation (6G) networks
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Funding
- National Key Research and Development Program [2017YFE0125300]
- Jiangsu Key Research and Development Program [BE2019648]
- Project of Shenzhen Science and Technology Innovation Committee [JCYJ20190809145407809]
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This study aims to address the fragmentation and outliers issues in Industrial Internet of Things by developing a method driven by sixth-generation networking. Utilizing multidimensional data relationship diagram and autoregressive exogenous model to quantify information for protecting high priority nodes, identifying high-value sensing devices, and enabling massive Internet of Things with characteristic patterns hidden in the data.
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.
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