Topology control based on dynamic graph embedding in Internet of vehicles

Yanfei SUN; Jiazheng YIN; Jin QI; Xiaoxuan HU; Mengting CHEN; Zhenjiang DONG

doi:10.11959/j.issn.1000-436x.2022122

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Topology control based on dynamic graph embedding in Internet of vehicles

Papers | 更新时间：2024-06-06

- Topology control based on dynamic graph embedding in Internet of vehicles
- Journal on Communications Vol. 43, Issue 6, Pages: 133-142(2022)
- 作者机构：
  
  1. 江苏省高性能计算与智能处理工程研究中心，江苏南京 210023
  2. 南京邮电大学物联网学院，江苏南京 210003
  3. 南京邮电大学通信与信息工程学院，江苏南京 210003
  4. 南京邮电大学计算机学院，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62172235);Natural Science Research Startup Founda-tion of Recruiting Talents of Nanjing University of Posts and Telecommunications(NY221136)
- DOI：10.11959/j.issn.1000-436x.2022122
  CLC： TN92
- Online First：2022-06，
  
  Published：25 June 2022
- 稿件说明：
移动端阅览
Yanfei SUN, Jiazheng YIN, Jin QI, et al. Topology control based on dynamic graph embedding in Internet of vehicles[J]. Journal on Communications, 2022, 43(6): 133-142.
DOI：

Yanfei SUN, Jiazheng YIN, Jin QI, et al. Topology control based on dynamic graph embedding in Internet of vehicles[J]. Journal on Communications, 2022, 43(6): 133-142. DOI： 10.11959/j.issn.1000-436x.2022122.

摘要

目的：随着汽车市场规模的增长，道路运载负荷压力也不断增加，但由于车联网的动态性、复杂性及较差的通信环境，以及车辆间快速变化的相对距离及互相遮挡，导致网络节点间出现频繁的断连和信号的衰落，使得网络拓扑的控制十分困难。为了构建更加稳定合理的车联网，本文利用模糊推理等方法提取驾驶员特征，提出针对车联网环境的图嵌入技术，充分利用车辆特征构建网络，实现车联网的拓扑发现及控制。

方法：利用本文提出的范围标签图嵌入（LRGE）方法发现并控制车联网拓扑。首先，建立车辆网络模型，按照路测单元合理地划分路段为多个子网络，使用驾驶员辅助系统获得车辆的相关历史信息，利用傅里叶变换、模糊推理方法，提取驾驶员的驾驶特征，并处理车辆信息得到低维特征向量。然后对新加入网络的车辆采用所提出的基于范围的冷启动方法，根据目标车辆加入区域的车辆特征，更新修正其特征向量，使新加入的车辆节点与新网络中车辆节点更易建立连接。最后基于车联网的应用环境，使用LRGE游走方式进行车辆节点间的随机游走，根据LRGE图嵌入模型对建立的随机游走进行优化，确定邻接矩阵，并根据历史序列信息进行动态更新。该方法针对实际车联网的特征，融合特征向量与驾驶员驾驶特征标签信息，进行拓扑发现及控制。使用平坦衰落复合信道模型仿真实际通信环境，并在 NGSIM 真实车辆数据集上进行实际效果的测试。

结果：从NGSIM数据集及平坦衰落复合信道的通信条件下的仿真结果可以看出，LRGE方法基本可以实现较为合理的网络拓扑控制，驾驶风格较为激进的车辆更偏向于同前向车辆集群建立连接，相似驾驶风格的车辆可以更长时间地建立稳定的连接。与随机网络、深度游走（DeepWalk）图嵌入、Node2Vec图嵌入方法及动态生长（DN）算法进行对比。测试建立的连边数目、通信断链概率和网络可达节点间的平均跳数指标，发现Node2Vec方法及LRGE方法建立的网络较为合理，断链概率较低，网络冗余较少，构建的网络拓扑更切实可行。为了体现主要目标属性网络连通性和稳健性优劣，对比所建立网络拓扑的连通概率、PageRank表示的重要性分布及割点占比，LRGE方法建立的网络连通概率高，网络重要性分布平坦，割点占比较少，在连通性和稳健性方面，具有优势。变更通信条件，对比不同通信条件下的相对关系，进一步验证了实验的结果。

结论：虽然车联网的高度动态性及复杂性使得网络难以合理稳定构建，但是车辆及驾驶员本身具有不同的特征，因此，可以提取并利用这些特征来辅助网络拓扑的控制。通过模糊推理等方法提取驾驶员特征，使用可以充分利用车辆特征信息的针对车联网特征的图嵌入方法，可以更加合理有效地构建网络拓扑。并且图嵌入方法计算简单，建立的网络性能优秀，能够对动态性较强的车联网做出快速地反应，及时更新网络拓扑，最终可以实现具有良好动态性、连通性以及健壮性的车联网拓扑控制。

Abstract

Objectives:With the growth of the automotive market

the road carrying pressure is increasing. However

due to the dynamics

complexity and poor communication environment of Internet of vehicles (IoV)

as well as the rapidly changing distance and occlusion between vehicles

frequent chain scission and signal fading among the nodes of the network. It is difficult to control the topology of network. In order to build a more stable and reasonable IoV

fuzzy inference and other methods was used to extract vehicle features

and a graph embedding method for the IoV environment was proposed to make full use of vehicle features to build a network

so as to realize the topology discovery and control of IoV.

Methods:The proposed label-range graph embedding (LRGE) method was used to discover and control the topology of IoV. The first thing is to establish the vehiclar network model. The road was reasonably divided into several sub networks according to the road side unit (RSU). The driver assistance system was used to obtain the relevant historical information of the vehicle. Fourier transform and fuzzy inference methods were used to extract the driving features of the drivers

and the low-dimensional feature vector was obtained by processing the vehicle information. Then

proposed range based cold boot method was applied to the vehicles newly added to the network. According to the vehicle features of the target vehicle in join area

the feature vector was updated and modified to make it easier to establish the connections with the vehicle nodes in the new network. Finally

based on the application environment of the IoV

the LRGE method was used to perform random walk among vehicle nodes

and then the established random walk was optimized according to the LRGE model to determine the adjacency matrix. This matrix was dynamically updated according to the historical sequence information. According to the characteristics of the actual IoV

this method fused the feature vector and the driver feature label to realize topology discovery and control. The flat fading composite channel model was used to simulate the actual communication environment and the actual effect was tested on the NGSIM dataset.

Results:From the simulation results under NGSIM dataset and flat fading composite channel

it can be seen that LRGE method could basically achieve more reasonable network topology control. Vehicles with more aggressive driving styles prefer to establish connections with forward vehicle clusters

and vehicles with similar driving styles are able to establish stable connections for a longer time. It is contrasted with random network

DeepWalk methods

Node2Vec methods and dynamic growth (DN) algorithm. By testing the established connection number

chain scission probability and average hops between reachable nodes

it is found that the networks established by Node2Vec method and LRGE method are more reasonable

with low chain scission probability

less network redundancy and more practical network topology. In order to reflect the difference of the main target

connectivity and robustness of the network

the connected probability

the importance distribution represented by PageRank and the proportion of cut-vertices were contrasted. The network established by LRGE method has higher connected probability. Its centrality distribution of nodes is flat

and the proportion of cut-vertices is relatively small

so it has advantages in connectivity and robustness. The experimental results were further verified by comparing the relative relations under different communication environment.

Conclusions:Although the high dynamics and complexity of IoV make it difficult to build a reasonable and stable network

vehicles and drivers have different features. Therefore

these features can be extracted and used to assist in controlling the topology of network. Driver features are extracted through fuzzy inference and other methods

and the graph embedding method for IoV can make full use of vehicle feature information. Hence

the network topology can be constructed more reasonably and effectively. Moreover

the graph embedding method is simple to calculate

and the performance of established network is better. It can make a rapid response to the dynamic IoV

update the network topology in time

and finally realize the topology control of IoV with good dynamics

connectivity and robustness.

关键词

Keywords

references

辛燕 , 冯霞 , 李婷婷 . VANET中位置相关的轻量级Sybil攻击检测方法 [J ] . 通信学报 , 2017 , 38 ( 4 ): 110 - 119 .

XIN Y , FENG X , LI T T . Position related lightweight Sybil detection approach in VANET [J ] . Journal on Communications , 2017 , 38 ( 4 ): 110 - 119 .

张海波 , 王子心 , 贺晓帆 . SDN和MEC架构下V2X卸载与资源分配 [J ] . 通信学报 , 2020 , 41 ( 1 ): 114 - 124 .

ZHANG H B , WANG Z X , HE X F . V2X offloading and resource allocation under SDN and MEC architecture [J ] . Journal on Communications , 2020 , 41 ( 1 ): 114 - 124 .

KCHAOU A , ABASSI R , GUEMARA S . Towards the performance evaluation of a clustering and trust based security mechanism for VANET [C ] // Proceedings of the 15th International Conference on Availability,Reliability and Security . New York:ACM Press , 2020 : 1 - 6 .

祁志卫 , 王笳辉 , 岳昆 , 等 . 图嵌入方法与应用:研究综述 [J ] . 电子学报 , 2020 , 48 ( 4 ): 808 - 818 .

QI Z W , WANG J H , YUE K , et al . Methods and applications of graph embedding:a survey [J ] . Acta Electronica Sinica , 2020 , 48 ( 4 ): 808 - 818 .

袁立宁 , 李欣 , 王晓冬 , 等 . 图嵌入模型综述 [J ] . 计算机科学与探索 , 2022 , 16 ( 1 ): 59 - 87 .

YUAN L N , LI X , WANG X D , et al . Graph embedding models:a survey [J ] . Journal of Frontiers of Computer Science and Technology , 2022 , 16 ( 1 ): 59 - 87 .

WANG J Z , HUANG P P , ZHAO H , et al . Billion-scale commodity embedding for E-commerce recommendation in Alibaba [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2018 : 839 - 848 .

GONG Y , JIANG Z W , FENG Y F , et al . EdgeRec:recommender system on edge in mobile Taobao [C ] // Proceedings of the 29th ACM International Conference on Information ＆ Knowledge Management . New York:ACM Press , 2020 : 2477 - 2484 .

PANDHRE S , MITTAL H , GUPTA M , et al . STwalk:learning trajectory representations in temporal graphs [C ] // Proceedings of the ACM India Joint International Conference on Data Science and Management of Data . New York:ACM Press , 2018 : 210 - 219 .

MAHDAVI S , KHOSHRAFTAR S , AN A J . Dynnode2vec:scalable dynamic network embedding [C ] // Proceedings of 2018 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2018 : 3762 - 3765 .

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 .

SHU J , WANG Q N , LIU L L . UAV ad hoc network link prediction based on deep graph embedding [J ] . Journal on Communications , 2021 , 42 ( 7 ): 137 - 149 .

PAN S , WU J , ZHU X , et al . Tri-party deep network representation [J ] . Network , 2016 , 11 ( 9 ): 12 .

WANG W , XIA F , NIE H S , et al . Vehicle trajectory clustering based on dynamic representation learning of Internet of vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 6 ): 3567 - 3576 .

HUANG S E , FENG Y H , LIU H X . A data-driven method for falsified vehicle trajectory identification by anomaly detection [J ] . Transportation Research Part C:Emerging Technologies , 2021 , 128 ( 6 ): 103196 .

FAN W B , LI P , HAN Z J , et al . Dynamic virtual network embedding of mobile cloud system based on global resources in Internet of vehicles [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 8 ): 8161 - 8174 .

李方伟 , 张海波 , 王子心 . 车联网中基于MEC的V2X协同缓存和资源分配 [J ] . 通信学报 , 2021 , 42 ( 2 ): 26 - 36 .

LI F W , ZHANG H B , WANG Z X . V2X collaborative caching and resource allocation in MEC-based IoV [J ] . Journal on Communications , 2021 , 42 ( 2 ): 26 - 36 .

WANG C , SUN Q Y , GUO Y S , et al . Improving the user acceptability of advanced driver assistance systems based on different driving styles:a case study of lane change warning systems [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 10 ): 4196 - 4208 .

GROVER A , LESKOVEC J . Node2vec:scalable feature learning for networks [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2016 : 855 - 864 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv Preprint,arXiv:1301.3781 , 2013 .

GOLDSMITH A . Wireless communications [M ] . Cambridge : Cambridge University Press , 2005 .

ANDERSEN J B , RAPPAPORT T S , YOSHIDA S . Propagation measurements and models for wireless communications channels [J ] . IEEE Communications Magazine , 1995 , 33 ( 1 ): 42 - 49 .

SKLAR B . Rayleigh fading channels in mobile digital communication systems.I.Characterization [J ] . IEEE Communications Magazine , 1997 , 35 ( 7 ): 90 - 100 .

韩涛 , 贺威 , 代俊 , 等 . 基于无标度网络的车联网连通性研究 [J ] . 通信学报 , 2021 , 42 ( 4 ): 100 - 108 .

HAN T , HE W , DAI J , et al . Connectivity analysis of IoV based on scale-free network [J ] . Journal on Communications , 2021 , 42 ( 4 ): 100 - 108 .

PEROZZI B , AL-RFOU R ,, SKIENA S . DeepWalk:online learning of social representations [C ] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2014 : 701 - 710 .

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