Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks
Mobile Industrial Internet of Things (IIoT) applications have achieved the explosive growth in recent years. The mobile IIoT has flourished and become the backbone of the industry, laying a solid foundation for the interconnection of all things. The variety of application scenarios has brought serio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-04-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823000548 |
| _version_ | 1797829536202620928 |
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| author | Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu |
| author_facet | Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu |
| author_sort | Lingwei Xu |
| collection | DOAJ |
| description | Mobile Industrial Internet of Things (IIoT) applications have achieved the explosive growth in recent years. The mobile IIoT has flourished and become the backbone of the industry, laying a solid foundation for the interconnection of all things. The variety of application scenarios has brought serious challenges to mobile IIoT networks, which face complex and changeable communication environments. Ensuring data secure transmission is critical for mobile IIoT networks. This paper investigates the data secure transmission performance prediction of mobile IIoT networks. To cut down computational complexity, we propose a data secure transmission scheme employing Transmit Antenna Selection (TAS). The novel secrecy performance expressions are first derived. Then, to realize real-time secrecy analysis, we design an improved Convolutional Neural Network (CNN) model, and propose an intelligent data secure transmission performance prediction algorithm. For mobile signals, the important features may be removed by the pooling layers. This will lead to negative effects on the secrecy performance prediction. A novel nine-layer improved CNN model is designed. Out of the input and output layers, it removes the pooling layer and contains six convolution layers. Elman, Back-Propagation (BP) and LeNet methods are employed to compare with the proposed algorithm. Through simulation analysis, good prediction accuracy is achieved by the CNN algorithm. The prediction accuracy obtains a 59% increase. |
| first_indexed | 2024-04-09T13:21:49Z |
| format | Article |
| id | doaj.art-2e09efd0244b48e595703bcfe5bf0d53 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-04-09T13:21:49Z |
| publishDate | 2023-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-2e09efd0244b48e595703bcfe5bf0d532023-05-11T04:24:21ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-04-0192400410Data secure transmission intelligent prediction algorithm for mobile industrial IoT networksLingwei Xu0Hao Yin1Hong Jia2Wenzhong Lin3Xinpeng Zhou4Yong Fu5Xu Yu6Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China; Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen, 361005, China; Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, 350121, ChinaFaculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China; Corresponding author.Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen, 361005, ChinaFujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, 350121, ChinaDepartment of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, ChinaDepartment of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, ChinaMobile Industrial Internet of Things (IIoT) applications have achieved the explosive growth in recent years. The mobile IIoT has flourished and become the backbone of the industry, laying a solid foundation for the interconnection of all things. The variety of application scenarios has brought serious challenges to mobile IIoT networks, which face complex and changeable communication environments. Ensuring data secure transmission is critical for mobile IIoT networks. This paper investigates the data secure transmission performance prediction of mobile IIoT networks. To cut down computational complexity, we propose a data secure transmission scheme employing Transmit Antenna Selection (TAS). The novel secrecy performance expressions are first derived. Then, to realize real-time secrecy analysis, we design an improved Convolutional Neural Network (CNN) model, and propose an intelligent data secure transmission performance prediction algorithm. For mobile signals, the important features may be removed by the pooling layers. This will lead to negative effects on the secrecy performance prediction. A novel nine-layer improved CNN model is designed. Out of the input and output layers, it removes the pooling layer and contains six convolution layers. Elman, Back-Propagation (BP) and LeNet methods are employed to compare with the proposed algorithm. Through simulation analysis, good prediction accuracy is achieved by the CNN algorithm. The prediction accuracy obtains a 59% increase.http://www.sciencedirect.com/science/article/pii/S2352864823000548Mobile IIoT networksData secure transmissionPerformance analysisIntelligent predictionImproved CNN |
| spellingShingle | Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks Digital Communications and Networks Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN |
| title | Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks |
| title_full | Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks |
| title_fullStr | Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks |
| title_full_unstemmed | Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks |
| title_short | Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks |
| title_sort | data secure transmission intelligent prediction algorithm for mobile industrial iot networks |
| topic | Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823000548 |
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