基于深度图嵌入的无人机自组网链路预测

舒坚; 王启宁; 刘琳岚

doi:10.11959/j.issn.1000-436x.2021083

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基于深度图嵌入的无人机自组网链路预测

学术论文 | 更新时间：2024-06-05

- 基于深度图嵌入的无人机自组网链路预测
- UAV ad hoc network link prediction based on deep graph embedding
- 通信学报 2021年42卷第7期页码：137-149
- 作者机构：
  
  1. 南昌航空大学软件学院，江西南昌 330063
  2. 南昌航空大学信息工程学院，江西南昌 330063
- 作者简介：
  
  [ "舒坚（1964− ），男，江西南昌人，南昌航空大学教授、硕士生导师，主要研究方向为分布式系统、软件工程等" ]
  [ "王启宁（1996− ），男，河北石家庄人，南昌航空大学硕士生，主要研究方向为分布式系统" ]
  [ "刘琳岚（1968− ），女，湖南东安人，南昌航空大学教授、硕士生导师，主要研究方向为无线传感器网络、分布式系统等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62062050);国家自然科学基金资助项目(61762065);国家自然科学基金资助项目(61962037);江西省自然科学基金资助项目(20202BABL202039);江西省研究生创新专项资金资助项目(YC2019-S355)
- DOI：10.11959/j.issn.1000-436x.2021083
  中图分类号： TP393
- 网络出版日期：2021-07，
  
  纸质出版日期：2021-07-25
- 稿件说明：
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舒坚, 王启宁, 刘琳岚. 基于深度图嵌入的无人机自组网链路预测[J]. 通信学报, 2021,42(7):137-149.

Jian SHU, Qining WANG, Linlan LIU. UAV ad hoc network link prediction based on deep graph embedding[J]. Journal on communications, 2021, 42(7): 137-149.
舒坚, 王启宁, 刘琳岚. 基于深度图嵌入的无人机自组网链路预测[J]. 通信学报, 2021,42(7):137-149. DOI： 10.11959/j.issn.1000-436x.2021083.

Jian SHU, Qining WANG, Linlan LIU. UAV ad hoc network link prediction based on deep graph embedding[J]. Journal on communications, 2021, 42(7): 137-149. DOI： 10.11959/j.issn.1000-436x.2021083.

摘要

针对无人机自组网的拓扑时变、节点移动、间歇性连接等特点，提出用时序化图嵌入模型对预处理后的无人机自组网进行表征，基于线性概率计算采样间隔以提高采样效率，将网络结构特征映射为节点间关系，并采用对抗训练提取节点上下文语义特征。利用长短期记忆网络提取无人机自组网的时序特征，预测下一时刻的网络连接情况。采用AUC、MAP、Error Rate作为评价指标。Ns-3仿真实验表明，与Node2vec、DDNE、E-LSTM-D等方法相比，所提方法具有更高的预测准确率。

Abstract

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.

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Keywords

references

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