车联网下基于模糊逻辑的簇头选择与数据传输策略研究

Research on Cluster Head Selection Based on Fuzzy Logic and Data Transmission Strategy in VANETs

在车联网中,由于车辆快速移动以及网络拓扑动态变化,短时间内车辆之间形成的分簇结构不稳定,导致簇内流媒体数据广播业务面临困难.本文针对这一问题,提出一种基于复杂网络理论的车联网综合通信优化方案.该方案提出基于车辆广义距离的分簇算法及基于模糊逻辑理论的簇头选择算法,在此基础上建立了簇内数据传输优化模型.本文定义的车辆广义距离综合考虑了车辆相对位置及通信链路质量.本文提出的簇头选择算法综合考虑了车辆的速度、领导力、距离,并基于模糊输出选择出最适合的簇头节点.仿真结果表明,本文提出的解决方案与现有工作相比,提高了簇内网络吞吐量并降低了数据传输时延.

In vehicular ad hoc networks,due to the rapid movement of vehicles and the dynamic change of network topology,the clustering structure formed between vehicles is unstable,which poses a challenge to the mass media streaming data transmission between cluster heads and other vehicles.In this paper,an optimum scheme for vehicle communication is proposed based on complex network theory.We design a clustering algorithm based on vehicle’s generalized distance.Moreover,we also propose a cluster head selection algorithm based on fuzzy logic theory.Based on these methods,we propose a data transmission optimization model in cluster.Our defined generalized distance considers both the relative location of vehicles and the quality of the communication between vehicles.The cluster head selection algorithm integrates the speed,leadership,and distance factors into account.Simulation results show that the proposed algorithm is more reliable than the existing works.Our proposed scheme improves the intra-cluster network throughput,and reduces the data transmission delay.