基于模糊C均值聚类和模糊逻辑的车辆信息传输组网研究

Research on Vehicle Information Transmission Networking Based on Fuzzy C-Means Clustering and Fuzzy Logic

针对在车联网中车辆间共享道路信息和车辆状态信息而出现的广播风暴、信息冗余等问题,结合模糊逻辑和模糊聚类,提出一种改善VANET广播的组合方法。方法是基于全局分簇的方案,利用模糊C均值聚类对车辆进行分簇,并在此基础上建立簇间传输,再利用模糊逻辑算法考虑车辆密度、速度、距离三个影响因素去寻找稳定的簇头。仿真结果表明与传统的方案相比,本组合方法能够减少不必要的网络传输,提高了分组传输效率,使车辆间的信息传输更加安全可靠。

Aiming at the problems of broadcast storm and information redundancy caused by sharing road information and vehicle status information among vehicles in the Internet of Vehicles, a combined method to improve VANET broadcasting is proposed by combining fuzzy logic and fuzzy clustering. The method is based on the global clustering scheme, using fuzzy C-means clustering to cluster vehicles, and establishing inter-cluster transmission, and then using fuzzy logic algorithm to consider the three influencing factors of vehicle density, speed and distance to find a stable cluster head. The simulation results show that compared with the traditional scheme, the combined method can reduce unnecessary network transmission, improve the efficiency of packet transmission, and make the information transmission between vehicles more secure and reliable.