UAV ad hoc network link prediction based on deep graph embedding
Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021083/ |
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| _version_ | 1841539277948715008 |
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| author | Jian SHU Qining WANG Linlan LIU |
| author_facet | Jian SHU Qining WANG Linlan LIU |
| author_sort | Jian SHU |
| collection | DOAJ |
| description | Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated based on linear probability.The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training.With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment.AUC, MAP, and Error Rate were employed as evaluation indexes.The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy. |
| format | Article |
| id | doaj-art-5cbcbc818cf74858b2e92782022cf3a2 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5cbcbc818cf74858b2e92782022cf3a22025-01-14T07:22:33ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-07-014213714959744087UAV ad hoc network link prediction based on deep graph embeddingJian SHUQining WANGLinlan LIUAiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated based on linear probability.The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training.With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment.AUC, MAP, and Error Rate were employed as evaluation indexes.The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021083/UAV ad hoc networkgraph embeddinglink predictionlong short-term memory network |
| spellingShingle | Jian SHU Qining WANG Linlan LIU UAV ad hoc network link prediction based on deep graph embedding Tongxin xuebao UAV ad hoc network graph embedding link prediction long short-term memory network |
| title | UAV ad hoc network link prediction based on deep graph embedding |
| title_full | UAV ad hoc network link prediction based on deep graph embedding |
| title_fullStr | UAV ad hoc network link prediction based on deep graph embedding |
| title_full_unstemmed | UAV ad hoc network link prediction based on deep graph embedding |
| title_short | UAV ad hoc network link prediction based on deep graph embedding |
| title_sort | uav ad hoc network link prediction based on deep graph embedding |
| topic | UAV ad hoc network graph embedding link prediction long short-term memory network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021083/ |
| work_keys_str_mv | AT jianshu uavadhocnetworklinkpredictionbasedondeepgraphembedding AT qiningwang uavadhocnetworklinkpredictionbasedondeepgraphembedding AT linlanliu uavadhocnetworklinkpredictionbasedondeepgraphembedding |