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Intelligent signal classification in industrial distributed wireless sensor networks-based IIoT
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Liu, M., Yang, K., Zhao, N., Song, H., Chen, Yunfei and Gong, F. (2021) Intelligent signal classification in industrial distributed wireless sensor networks-based IIoT. IEEE Transactions on Industrial Informatics, 17 (7). pp. 4946-4956. doi:10.1109/TII.2020.3016958
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Official URL: https://doi.org/10.1109/TII.2020.3016958
Abstract
In industrial sensor networks, complex industrial environments may be encountered leading to a mix of signals of different types. Complicated interference caused by mixed signals on industrial equipments may significantly degrade the classification rate of signals, which may result in a long training time in order to extract features. In addition, with limited channel resources, it is difficult to make the global optimal decision in industrial distributed wireless sensor networks (IDWSN). To address this problem, a signal classification method using feature fusion is proposed for industrial Internet of things (IIoT) in this paper. In the proposed method, the received signals of nodes are processed by frequency reduction and sampling pretreatment, based on which intelligent representations of signals are obtained. Using federated learning, the data samples are trained with the feature fusion network. Moreover, the trained deep learning network is used on each sensor node to classify signals, the results of which will be transmitted to aggregation center. In the aggregation center, the improved evidence theory method is used to aggregate the recognition results of each sensor node to achieve the final classification. Simulation shows that the proposed method has excellent classification performances. Notably, it is not required for the proposed method to transmit signals from nodes to the aggregation center, which could effectively protect the privacy of industrial information.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||||||||||||
| Divisions: | Faculty of Science > Engineering | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Wireless sensor networks | ||||||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||||
| ISSN: | 1551-3203 | ||||||||||||||||||
| Official Date: | July 2021 | ||||||||||||||||||
| Dates: |
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| Volume: | 17 | ||||||||||||||||||
| Number: | 7 | ||||||||||||||||||
| Page Range: | pp. 4946-4956 | ||||||||||||||||||
| DOI: | 10.1109/TII.2020.3016958 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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