一个以真相为导向的信任评估模型,用于车辆互联网中的共享流量消息
Jiamin Zhang1, Lisha Shuai1, Jiuling Dong1
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Entropy (Basel, Switzerland)
|November 26, 2025
概括
本研究介绍了用于汽车互联网 (IoV) 的以真相为导向的信任模型. 该模型准确地评估了共享交通信息的可信性,提高了道路安全和对联网汽车的知情决策.
科学领域:
- 计算机科学 计算机科学
- 运输工程 运输工程
- 网络安全 网络安全
背景情况:
- 汽车互联网 (IoV) 旨在改善交通和安全,但不可靠的共享消息带来风险.
- 个人利益或传感器问题可能导致 IoV 系统中的交通信息不准确.
- 评估信息可信度对于安全高效的 IoV 操作至关重要.
研究的目的:
- 开发一个以真相为导向的信任模型,用于评估 IoV 中的共享流量消息.
- 提高车辆用于决策的信息的可靠性.
- 为了减轻不准确或有偏见的信息对IoV安全的影响.
主要方法:
- 提出了一种受人类信任建立机制 (HS-TEM) 启发的信任模型.
- 通过结合自我经验 (SEB-VT) 和同行推 (PRB-VT) 来量化发送者信任 (I-VT).
- 引入了取决于样本大小的平滑因子和用于信任计算的链接分析.
主要成果:
- 综合信任价值 (I-VT) 有效地融合了个人经验和同行反.
- 事件信任 (ET) 计算通过考虑消息态度来减少个人偏见.
- 模拟结果表明准确和公平的信息可信度评估.
结论:
- 拟议的HS-TEM准确地评估了 IoV 中共享流量消息的可靠性.
- 该模型使车辆能够做出更明智的决策,提高整体安全性.
- 该方法有效地解决了 IoV 环境中的消息偏差问题.
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