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浙江大学学报(工学版)  2020, Vol. 54 Issue (4): 722-731    DOI: 10.3785/j.issn.1008-973X.2020.04.011
计算机技术、信息工程     
基于层次分析法的车联网多因素信誉评价模型
陈蔚1(),刘雪娇1,*(),夏莹杰2
1. 杭州师范大学 杭州国际服务工程学院,浙江 杭州 311121
2. 浙江大学 计算机科学与技术学院,浙江 杭州 310027
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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摘要:

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    
Abstract:

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 words: vehicle Ad-hoc networks (VANETs)    reputation evaluation model    analytic hierarchy process (AHP)    malicious node    multiple factors
收稿日期: 2019-03-23 出版日期: 2020-04-05
CLC:  TP 393  
基金资助: 国家自然科学基金资助项目(61873232);国家自然科学基金青年科学基金资助项目(61702153);浙江省自然科学基金资助项目(LY19F020021)
通讯作者: 刘雪娇     E-mail: weiic23@163.com;liuxuejiao0406@163.com
作者简介: 陈蔚(1996—),女,硕士生,从事车联网安全的研究. orcid.org/0000-0002-1515-5860. E-mail: weiic23@163.com
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引用本文:

陈蔚,刘雪娇,夏莹杰. 基于层次分析法的车联网多因素信誉评价模型[J]. 浙江大学学报(工学版), 2020, 54(4): 722-731.

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.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.04.011        http://www.zjujournals.com/eng/CN/Y2020/V54/I4/722

图 1  车联网多因素信誉评价模型
图 2  一次通信的实际场景图
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
表 1  不同信息的最佳有效时间段
图 3  多因素层次结构模型
图 4  模拟街道地图
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
表 2  “1-9”比例标度法
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
表 3  随机一致性指标
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
表 4  相对“信誉”的判断矩阵
信誉 Tsec Tman Tent
Tsec 1 3 4
Tman 1/3 1 2
Tent 1/4 1/2 1
表 5  相对“历史信誉”的判断矩阵
信誉 Tsec Tman Tent
Tsec 1 2 3
Tman 1/2 1 2
Tent 1/3 1/2 1
表 6  相对“间接信誉”的判断矩阵
信誉 Tsec Tman Tent
Tsec 1 2 4
Tman 1/2 1 2
Tent 1/4 1/2 1
表 7  相对“RSU存储的信誉”的判断矩阵
信誉 Tsec Tman Tent
Tsec 1 5 7
Tman 1/5 1 3
Tent 1/7 1/3 1
表 8  相对“时间影响因素”的判断矩阵
信誉 Tsec Tman Tent
Tsec 1 1/2 3
Tman 2 1 4
Tent 1/3 1/4 1
表 9  相对“距离影响因素”的判断矩阵
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
表 10  本文方案的信誉机制与其他方案的比较
图 5  不同方案中的车辆决策准确性
图 6  不同条件下恶意节点排除率
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