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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1000-436X |