基于灰色指数平滑方法的VANETs信任预测

Sanshun Zhang, Li Li, Hui Xia, Rui Zhang, Ye Li
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引用次数: 0

摘要

在车辆自组织网络(即vanet)中,车辆之间的正常通信容易受到恶意车辆的攻击。基于信任的解决方案是解决路由安全问题的一种可行方法。本文将灰色模型、指数平滑预测方法和黄金分割搜索方法相结合,提出了一种基于灰色指数平滑方法的信任预测模型。为了验证该方法的有效性,提出了一种基于灰色指数平滑信任预测模型的组播路由协议ESGM-ODMRP。在实验中,对4个路由指标(即数据包发送率、开销、平均延迟和每字节发送的字节数)的评估证明了我们的协议在识别恶意车辆和建立安全路由方面具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trust prediction based on grey exponential smoothing method in VANETs
In vehicular ad hoc networks (i.e., VANETs), normal communications between vehicles are vulnerable to attacks from malicious vehicles. Trust-based solution is a feasible method to solve the routing security problem. In this paper, a trust prediction model based on grey exponential smoothing method is proposed by combining the grey model, the exponential smoothing prediction method and the golden section search method. A multicast routing protocol based on the grey exponential smoothing trust prediction model, named ESGM-ODMRP, is presented to verify the validity of this new method. In the experiments, the evaluation of four routing metrics (i.e., packet delivery ratio, overhead, average latency and byte sent per byte delievered) prove that our protocol performs better in identifying malicious vehicles and establishing secure routes.
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