A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks
In modern smart cities, the scale of urban backbone networks used to provide Internet communication environment are constantly increasing. When faults occur, it usually takes lots of efforts to detect and locate the faults. As a result, automatic detection and positioning of faults with use of intel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10243017/ |
| _version_ | 1797674315328520192 |
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| author | Xiaoyu Wang Zixuan Fu Xiaofei Li |
| author_facet | Xiaoyu Wang Zixuan Fu Xiaofei Li |
| author_sort | Xiaoyu Wang |
| collection | DOAJ |
| description | In modern smart cities, the scale of urban backbone networks used to provide Internet communication environment are constantly increasing. When faults occur, it usually takes lots of efforts to detect and locate the faults. As a result, automatic detection and positioning of faults with use of intelligent algorithms have been a practical demand in this area. In this paper, the complicated whole urban backbone network is viewed as a graph-level object, in which massive nodes and edges are involved. On this basis, a two-stage graph deep learning-based fault detection and positioning method for Internet communication networks. For the first stage, the graph neural network is employed to extract graph-level features from Internet communication networks. This is expected to obtain proper feature representation for core characteristics of backbone networks. For the second stage, the fault detection and positioning algorithm is formulated to output final results. At last, experiments are conducted to assess performance of the proposal. The results show that the proposed method has good performance in abnormal node detection as well as high accuracy in fault positioning. The accuracy of the two-stage graph deep learning algorithm proposed in this chapter is much higher than that of KNN algorithm, reaching 96.5% in the end, slightly lower than that of pure graph deep learning algorithm, while the accuracy of IRBFG algorithm can only reach 92%. |
| first_indexed | 2024-03-11T21:57:17Z |
| format | Article |
| id | doaj.art-0680e0c151b94e2baed88057f7d6471c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T21:57:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0680e0c151b94e2baed88057f7d6471c2023-09-25T23:00:21ZengIEEEIEEE Access2169-35362023-01-011110226110227010.1109/ACCESS.2023.331300310243017A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication NetworksXiaoyu Wang0Zixuan Fu1Xiaofei Li2https://orcid.org/0009-0000-7217-0242Brain Hospital Affiliated to Nanjing Medical University, Nanjing, ChinaBrain Hospital Affiliated to Nanjing Medical University, Nanjing, ChinaBrain Hospital Affiliated to Nanjing Medical University, Nanjing, ChinaIn modern smart cities, the scale of urban backbone networks used to provide Internet communication environment are constantly increasing. When faults occur, it usually takes lots of efforts to detect and locate the faults. As a result, automatic detection and positioning of faults with use of intelligent algorithms have been a practical demand in this area. In this paper, the complicated whole urban backbone network is viewed as a graph-level object, in which massive nodes and edges are involved. On this basis, a two-stage graph deep learning-based fault detection and positioning method for Internet communication networks. For the first stage, the graph neural network is employed to extract graph-level features from Internet communication networks. This is expected to obtain proper feature representation for core characteristics of backbone networks. For the second stage, the fault detection and positioning algorithm is formulated to output final results. At last, experiments are conducted to assess performance of the proposal. The results show that the proposed method has good performance in abnormal node detection as well as high accuracy in fault positioning. The accuracy of the two-stage graph deep learning algorithm proposed in this chapter is much higher than that of KNN algorithm, reaching 96.5% in the end, slightly lower than that of pure graph deep learning algorithm, while the accuracy of IRBFG algorithm can only reach 92%.https://ieeexplore.ieee.org/document/10243017/Graph deep learningcommunication networkfault detectionnetwork management |
| spellingShingle | Xiaoyu Wang Zixuan Fu Xiaofei Li A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks IEEE Access Graph deep learning communication network fault detection network management |
| title | A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks |
| title_full | A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks |
| title_fullStr | A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks |
| title_full_unstemmed | A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks |
| title_short | A Graph Deep Learning-Based Fault Detection and Positioning Method for Internet Communication Networks |
| title_sort | graph deep learning based fault detection and positioning method for internet communication networks |
| topic | Graph deep learning communication network fault detection network management |
| url | https://ieeexplore.ieee.org/document/10243017/ |
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