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Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (4): 722-731    DOI: 10.3785/j.issn.1008-973X.2020.04.011
Computer Technology, Inf ormation Engineering     
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
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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.

Key wordsvehicle Ad-hoc networks (VANETs)      reputation evaluation model      analytic hierarchy process (AHP)      malicious node      multiple factors     
Received: 23 March 2019      Published: 05 April 2020
CLC:  TP 393  
  TN 915  
Corresponding Authors: Xue-jiao LIU     E-mail:;
Cite this article:

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.

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针对车联网(VANETs)场景下存在的恶意车辆节点和虚假信息的问题,提出基于层次分析法(AHP)的车联网多因素信誉评价模型. 该模型综合考虑车辆行为、消息、环境等因素对车辆节点信誉的影响,建立车辆信誉评价模型;面向多应用场景信息(安全行驶、交通管理、商业娱乐),使用层次分析法量化各因素及不同类型信息对车辆信誉的影响程度;基于反馈机制根据信息的不同类型进行车辆节点的信誉更新,实现车联网车辆的信誉评估. 实验表明,该模型在恶意车辆节点达到25%的情况下,车辆决策正确率能够达到92%以上. 该方案能够有效防止车辆接收虚假信息,准确检测出网络中的恶意车辆节点,提高车辆接收信息的可信度.

关键词: 车联网(VANETs),  信誉评价模型,  层次分析法(AHP),  恶意节点,  多因素 
Fig.1 Multi-factor reputation evaluation model in VANETs
Fig.2 Actual 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.1 Best valid time period table for different information
Fig.3 Multi-factor hierarchical model
Fig.4 Simulated 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.3 Random 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.4 Judgment 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.5 Judgment 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.6 Judgment 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.7 Judgment 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.8 Judgment 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.9 Judgment 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
VASRep[17] 权威单元 平均值、贝叶斯法
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.10 Comparison of reputation mechanism of our scheme with other schemes
Fig.5 Vehicle decision accuracy in different scenarios
Fig.6 Exclusion rate of malicious nodes under different conditions
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