基于三角模糊数的车联网簇头节点选择方法

A Cluster Head Selection Method for Internet of Vehicles Based on Triangular Fuzzy

针对现有车联网簇头节点选择方法不足的问题,提出了一种基于三角模糊数的车联网簇头节点选择方法,依赖邻近车辆相对速度和相对距离完成簇头的选择。构建了一种典型车联网应用场景,提出了基于三角模糊数和学习机制的车辆加速度预测方法,基于定义的加权稳定因子,设计了车联网簇头节点选择机制。基于i TETRIS构建了车联网仿真平台进行测试分析,结果表明,与CMCP方法和APROVE方法相比,提出的簇头节点选择方法有效性较高。

The existing Internet of Vehicles(IoVs)cluster head doesn't have sufficient selection methods,to solve this problem,this paper proposed a new cluster head selection method for IoVs based on triangular fuzzy,and made cluster head selection depending on both the relative velocity and the relative distance of neighboring vehicles on the road.Firstly,a typical application scenario of IoVs was constructed.Secondly,a vehicle acceleration forecasting method was proposed based on triangular fuzzy and learning mechanism.Then,a cluster head selection mechanism was designed based on the defined weighted stability factor.Finally,the simulation platform of IoVs was constructed based on iTETRIS.The results show that the proposed method is more effective than the CMCP and APROVE