Anomaly Detection Based on Multidimensional Data Processing for Protecting Vital Devices in 6G-Enabled Massive IIoT

被引:38
作者
Han, Guangjie [1 ]
Tu, Juntao [1 ]
Liu, Li [1 ]
Martinez-Garcia, Miguel [2 ]
Peng, Yan [3 ]
机构
[1] Hohai Univ, Coll Internet Things Engn, Changzhou 213022, Peoples R China
[2] Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, Leics, England
[3] Shanghai Univ, Sch Artificial Intelligence, Shanghai 200000, Peoples R China
关键词
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; OUTLIER DETECTION; SENSOR DATA;
D O I
10.1109/JIOT.2021.3051935
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:5219 / 5229
页数:11
相关论文
共 34 条
[11]   Anomaly Detection via Online Oversampling Principal Component Analysis [J].
Lee, Yuh-Jye ;
Yeh, Yi-Ren ;
Wang, Yu-Chiang Frank .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2013, 25 (07) :1460-1470
[12]  
Li G, COMPUT ENG, V41, P124
[13]   An Efficient Approach for Outlier Detection with Imperfect Data Labels [J].
Liu, Bo ;
Xiao, Yanshan ;
Yu, Philip S. ;
Hao, Zhifeng ;
Cao, Longbing .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2014, 26 (07) :1602-1616
[14]   SVDD-based outlier detection on uncertain data [J].
Liu, Bo ;
Xiao, Yanshan ;
Cao, Longbing ;
Hao, Zhifeng ;
Deng, Feiqi .
KNOWLEDGE AND INFORMATION SYSTEMS, 2013, 34 (03) :597-618
[15]   Fault-Tolerant Event Region Detection on Trajectory Pattern Extraction for Industrial Wireless Sensor Networks [J].
Liu, Li ;
Han, Guangjie ;
He, Yu ;
Jiang, Jinfang .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (03) :2072-2080
[16]  
Mandhare HC, 2017, 2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), P931, DOI 10.1109/ICCONS.2017.8250601
[17]   Anomaly Detection Based on Sensor Data in Petroleum Industry Applications [J].
Marti, Luis ;
Sanchez-Pi, Nayat ;
Manuel Molina, Jose ;
Bicharra Garcia, Ana Cristina .
SENSORS, 2015, 15 (02) :2774-2797
[18]  
Martinez-Garcia Miguel, 2019, 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), P649, DOI 10.1109/ICARM.2019.8834281
[19]   Model-Based Techniques for Data Reliability in Wireless Sensor Networks [J].
Mukhopadhyay, Shoubhik ;
Schurgers, Curt ;
Panigrahi, Debashis ;
Dey, Sujit .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2009, 8 (04) :528-543
[20]   Distributed detection and fusion in a large wireless sensor network of random size [J].
Niu R. ;
Varshney P.K. .
Eurasip Journal on Wireless Communications and Networking, 2005, 2005 (04) :462-472