Topology prediction for opportunistic network based on spatiotemporal convolution
The high dynamics of opportunistic network topology leads to the challenges of topology prediction.The existing research mainly focuses on the long-term spatiotemporal dependence of networks, ignoring the short-term spatiotemporal features.A topology prediction method for opportunistic network based...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023060/ |
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| author | Jian SHU Jiawei SHI Linlan LIU Al-Kali Manar |
| author_facet | Jian SHU Jiawei SHI Linlan LIU Al-Kali Manar |
| author_sort | Jian SHU |
| collection | DOAJ |
| description | The high dynamics of opportunistic network topology leads to the challenges of topology prediction.The existing research mainly focuses on the long-term spatiotemporal dependence of networks, ignoring the short-term spatiotemporal features.A topology prediction method for opportunistic network based on dynamic time warping algorithm and spatiotemporal convolution (DTW-STC) was proposed, which integrated long-short term spatiotemporal dependence in opportunistic network.The time slot was determined based on dynamic time warping algorithm, so that the opportunistic network was sliced into snapshots which topology was presented by link state matrix.Temporal convolution was employed to extract short-term temporal features.The spatiotemporal graph, representing the short-term spatiotemporal relationship, was constructed by temporal features and network changes.The short-term spatiotemporal features were captured by graph convolution.After stacks of spatiotemporal convolution, the long-short term spatiotemporal features of network were achieved.Based on the autoencoder structure, vector space transformation was realized, so that the future network topology was predicted.The results on three real opportunistic network datasets, ITC, MIT, and Asturias-er, show that the proposed DTW-STC has better prediction performance than ones of other baseline methods. |
| format | Article |
| id | doaj-art-55ff6c4bbdff4b24b038107a9d6d445c |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-55ff6c4bbdff4b24b038107a9d6d445c2025-01-14T06:23:22ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-03-014414515659387789Topology prediction for opportunistic network based on spatiotemporal convolutionJian SHUJiawei SHILinlan LIUAl-Kali ManarThe high dynamics of opportunistic network topology leads to the challenges of topology prediction.The existing research mainly focuses on the long-term spatiotemporal dependence of networks, ignoring the short-term spatiotemporal features.A topology prediction method for opportunistic network based on dynamic time warping algorithm and spatiotemporal convolution (DTW-STC) was proposed, which integrated long-short term spatiotemporal dependence in opportunistic network.The time slot was determined based on dynamic time warping algorithm, so that the opportunistic network was sliced into snapshots which topology was presented by link state matrix.Temporal convolution was employed to extract short-term temporal features.The spatiotemporal graph, representing the short-term spatiotemporal relationship, was constructed by temporal features and network changes.The short-term spatiotemporal features were captured by graph convolution.After stacks of spatiotemporal convolution, the long-short term spatiotemporal features of network were achieved.Based on the autoencoder structure, vector space transformation was realized, so that the future network topology was predicted.The results on three real opportunistic network datasets, ITC, MIT, and Asturias-er, show that the proposed DTW-STC has better prediction performance than ones of other baseline methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023060/opportunistic networktopology predictiontemporal convolutiongraph convolutionspatiotemporal graph |
| spellingShingle | Jian SHU Jiawei SHI Linlan LIU Al-Kali Manar Topology prediction for opportunistic network based on spatiotemporal convolution Tongxin xuebao opportunistic network topology prediction temporal convolution graph convolution spatiotemporal graph |
| title | Topology prediction for opportunistic network based on spatiotemporal convolution |
| title_full | Topology prediction for opportunistic network based on spatiotemporal convolution |
| title_fullStr | Topology prediction for opportunistic network based on spatiotemporal convolution |
| title_full_unstemmed | Topology prediction for opportunistic network based on spatiotemporal convolution |
| title_short | Topology prediction for opportunistic network based on spatiotemporal convolution |
| title_sort | topology prediction for opportunistic network based on spatiotemporal convolution |
| topic | opportunistic network topology prediction temporal convolution graph convolution spatiotemporal graph |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023060/ |
| work_keys_str_mv | AT jianshu topologypredictionforopportunisticnetworkbasedonspatiotemporalconvolution AT jiaweishi topologypredictionforopportunisticnetworkbasedonspatiotemporalconvolution AT linlanliu topologypredictionforopportunisticnetworkbasedonspatiotemporalconvolution AT alkalimanar topologypredictionforopportunisticnetworkbasedonspatiotemporalconvolution |