基于复杂网络的车载自组织网络脆弱性分析

张宏; 吕悦晶

doi:10.13700/j.bh.1001-5965.2020.0292

基于复杂网络的车载自组织网络脆弱性分析

doi: 10.13700/j.bh.1001-5965.2020.0292

张宏^{1, 2, ,},
吕悦晶³

1.
内蒙古大学交通学院, 呼和浩特 010070
2.
内蒙古自治区城市交通数据科学及应用工程技术研究中心, 呼和浩特 010070
3.
武汉科技大学汽车与交通工程学院, 武汉 430081

基金项目:

内蒙古自治区自然科学基金 2019MS07021

详细信息

通讯作者:
张宏, E-mail: zh-hong@imu.edu.cn

中图分类号: U491.2;TP393.1
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- 被引次数: 0
出版历程
- 收稿日期: 2020-06-23
- 录用日期: 2020-09-04
- 网络出版日期: 2021-08-20

Vulnerability of vehicular ad hoc network based on complex network

ZHANG Hong^{1, 2
, ,},
LYU Yuejing³

1.
Transportation Institute, Inner Mongolia University, Hohhot 010070, China
2.
Inner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications, Hohhot 010070, China
3.
School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Funds:

Natural Science Foundation of Inner Mongolia Autonomous Region of China 2019MS07021

More Information

Corresponding author: ZHANG Hong, E-mail: zh-hong@imu.edu.cn

摘要

摘要:
车载自组织网络(VANET)作为智能交通的重要基础应用，其安全稳定地运行是交通系统乃至社会经济可持续发展的需要。以最大连通度、连通分支平均规模、全局网络效率等参数为脆弱性测度指标，基于复杂网络理论，应用车辆仿真软件(VanetMobiSim)建立了VANET网络拓扑模型，详细研究了在随机攻击和蓄意攻击模式下脆弱性量化指标随节点移除比例的变化关系；通过仿真实验分析了节点密度、信号辐射半径及不同攻击策略对VANET脆弱性的影响。仿真结果表明：VANET在蓄意攻击下比较脆弱，基于节点介数的蓄意攻击效能最强；节点密度、信号辐射半径越小，VANET连通性越差，网络越脆弱。所提方法和结果为VANET拓扑控制优化和网络管理决策提供了理论依据。
- 车载自组织网络(VANET) /
- 复杂网络 /
- 脆弱性 /
- 蓄意攻击 /
- 随机攻击
Abstract:
As an important fundamental application of intelligent traffic system, the safe and stable operation of Vehicular Ad Hoc Network (VANET) is indispensable for the transportation system and even the sustainable development of social economy. The largest connected degree, average size of connected components and global network efficiency were used as the vulnerability evaluation metrics for VANET. Based on complex network theory, the VANET simulation model was established through vehicle simulation software (VanetMobiSim). Then, the relationship between the quantitative index of vulnerability and the node removal ratio under random attacks and intentional attacks was analyzed in detail. Furthermore, the influence of the density of nodes, signal radiation radius and patterns of attack on VANET vulnerability was analyzed by simulation experiment. The experimental results show that the VANET has a high vulnerability under intentional attacks; the intentional attacks based on node betweenness destroyed the networks most strongly. The smaller node density and signal radiation radius are, the worse VANET connected degree is and the more vulnerable the network is. The research methodology and results provide the theoretical basis for VANET topology control optimization and network management decision-making.
- Vehicular Ad Hoc Network (VANET) /
- complex network /
- vulnerability /
- intentional attack /
- random attack

HTML全文

图 1 RA、RK、RB攻击模式下VANET最大连通度

Figure 1. The largest connected degree of VANET in RA, RK and RB attack modes

下载: 全尺寸图片幻灯片

图 2 RA、RK、RB攻击模式下VANET连通分支平均规模

Figure 2. Average size of connected components of VANET in RA, RK and RB attack modes

下载: 全尺寸图片幻灯片

图 3 RA、RK、RB攻击模式下信号辐射半径对VANET全局网络效率的影响

Figure 3. Influence of signal radiation radius on global network efficiency of VANET in RA, RK and RB attack modes

下载: 全尺寸图片幻灯片

图 4 RA、RK、RB攻击模式下节点密度对VANET最大连通度的影响

Figure 4. Influence of node density on the largest connected degree of VANET in RA, RK and RB attack modes

下载: 全尺寸图片幻灯片

图 5 RA、RK、RB攻击模式下节点密度对VANET连通分支平均规模的影响

Figure 5. Influence of node density on average size of connected components of VANET in RA, RK and RB attack modes

下载: 全尺寸图片幻灯片

图 6 RA、RK、RB攻击模式下节点密度对VANET全局网络效率的影响

Figure 6. Influence of node density on global network efficiency of VANET in RA, RK and RB attack modes

下载: 全尺寸图片幻灯片

表 1 模型仿真参数

Table 1. Simulation parameters of model

参数	数值
信号辐射半径r/m	100, 200, 300
仿真区域面积/(m×m)	2 000×2 000
节点数目N	100, 200, 300
车道数	双向四车道
仿真时间/s	300
节点速度/(m·s^-1)	5~20

下载: 导出CSV

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