Multi-factor reputation evaluation model based on analytic hierarchy process in vehicle Ad-hoc networks
Wei CHEN1(),Xue-jiao LIU1,*(),Ying-jie XIA2
1. Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou 311121, China 2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
A multi-factor reputation evaluation model based on analytic hierarchy process (AHP) was proposed aiming at the problem of malicious vehicle nodes and false information detection in VANETs. The influence of vehicle behavior, message, environment and other factors on the reputation of vehicle nodes was considered, and a vehicle reputation evaluation model was established. The model was designed for multi-application scenarios (safe driving, traffic management, business entertainment), and AHP was used to quantify the impact of various factors and different types of information on vehicle reputation. The model was based on the feedback mechanism to update the reputation of the vehicle nodes according to different types of information, and the evaluation of the reputation of the vehicles in VANETs was realized. The experimental results show that the model can achieve a correct rate of more than 92% when the malicious vehicle node reaches 25%. The scheme can effectively prevent vehicles from receiving false information, accurately detect malicious vehicles in the network, and improve the reliability of the information received by vehicles.
Wei CHEN,Xue-jiao LIU,Ying-jie XIA. Multi-factor reputation evaluation model based on analytic hierarchy process in vehicle Ad-hoc networks. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 722-731.
Fig.1Multi-factor reputation evaluation model in VANETs
Fig.2Actual scene of one communication
信息种类
最佳有效时间（0-tTN）
安全行驶类
0~1.0 min
交通管理类
0~1.0 h
商业娱乐类
0~1.0 d
Tab.1Best valid time period table for different information
Fig.3Multi-factor hierarchical model
Fig.4Simulated street map
标度
含义
1
表示2个元素相比，具有同样的重要性
3
表示2个因素相比，前者比后者稍重要
5
表示2个因素相比，前者比后者明显重要
7
表示2个因素相比，前者比后者强烈重要
9
表示2个因素相比，前者比后者极端重要
2，4，6，8
表示上述相邻判断的中间值
Tab.2“1-9” scale method
n
RI
n
RI
1
0
5
1.12
2
0
6
1.24
3
0.58
7
1.32
4
0.90
8
1.41
Tab.3Random consistency indicator
信誉
THS
TOS
TRS
Ttime
Tloc
THS
1
5
1/3
3
3
TOS
1/5
1
1/7
1/3
1/3
TRS
3
7
1
5
5
Ttime
1/3
3
1/5
1
1
Tloc
1/3
3
1/5
1
1
Tab.4Judgment matrix relative to ‘reputation value’
信誉
Tsec
Tman
Tent
Tsec
1
3
4
Tman
1/3
1
2
Tent
1/4
1/2
1
Tab.5Judgment matrix relative to ‘historical reputation value’
信誉
Tsec
Tman
Tent
Tsec
1
2
3
Tman
1/2
1
2
Tent
1/3
1/2
1
Tab.6Judgment matrix relative to ‘indirect reputation value’
信誉
Tsec
Tman
Tent
Tsec
1
2
4
Tman
1/2
1
2
Tent
1/4
1/2
1
Tab.7Judgment matrix relative to ‘RSU stored reputation value’
信誉
Tsec
Tman
Tent
Tsec
1
5
7
Tman
1/5
1
3
Tent
1/7
1/3
1
Tab.8Judgment matrix relative to ‘time influencing factors’
信誉
Tsec
Tman
Tent
Tsec
1
1/2
3
Tman
2
1
4
Tent
1/3
1/4
1
Tab.9Judgment matrix relative to ‘distance influencing factors’
方案
信誉评价来源
信誉计算方法
信誉更新依据
文献[12]方案
其他车辆、RSU
极大似然估计
消息反馈
VARS[13]
主体车辆、其他车辆
加权
消息反馈
CORE[15]
主体车辆、其他车辆
加权
无
VASRep[17]
权威单元
平均值、贝叶斯法
无
本文方案
主体车辆、其他车辆、RSU、消息属性
加权、层次分析法
消息反馈
Tab.10Comparison of reputation mechanism of our scheme with other schemes
Fig.5Vehicle decision accuracy in different scenarios
Fig.6Exclusion rate of malicious nodes under different conditions
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