基于层次分析法的车联网多因素信誉评价模型

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.

Multi-factor reputation evaluation model based on analytic hierarchy process in vehicle Ad-hoc networks

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.