Research On Methods Of Group Intelligence Collaboration And Efficient Information Dissemination For IOV

In recent years,road traffic congestion has become a common problem that cannot be avoided in large and even medium-sized cities due to the accelerated urbanization and the increase in the number of people travelling by car.This is an important issue that needs to be addressed urgently in order to relieve road traffic pressure,reduce road congestion,improve road traffic efficiency,prevent and avoid traffic accidents and solve potential problems caused by traffic accidents at an early stage,and protect the safety of drivers and travel efficiency.However,most of the existing intelligent transportation systems use the vehicle group intelligence collaboration method and information dissemination routing protocols,which not only cannot cope with the explosive increase in traffic flow and large-scale congestion gathering phenomenon,but also cannot fundamentally and significantly improve the traffic efficiency of the road network.In this thesis,we research on the group intelligence cooperation and efficient information dissemination method of the Internet of vehicles can effectively reduce the probability of road traffic congestion and greatly improve the traffic efficiency of road traffic.The main work and innovations of this paper are as follows:(1)In order to reduce traffic pressure,determine the level of road traffic congestion and road conditions in advance with precision detection,and reduce the occurrence of traffic congestion,a new method of cluster wise collaboration(GIC-CF)based on cluster families and fuzzy logic is proposed.In this method,vehicle nodes form clusters based on Euclidean distance,and then use the Mayfly Optimization Algorithm(MOA)to determine the cluster head;then,each cluster member transmits real-time speed information to the cluster head for adaptive processing to obtain the average moving speed of the vehicle cluster;at the same time,based on the real-time position information of the members in the cluster family and the number of members in the cluster family,the cluster head calculates the average vehicle density of the vehicle cluster;finally,based on the Finally,based on the average movement speed,average movement density and fuzzy logic,the traffic congestion condition of the road section where the cluster is currently located is determined.The comparison experiments show that the GIC-CF method can effectively reduce the occurrence of traffic congestion in urban expressways and improve the traffic flow rate of urban expressways.(2)To overcome the shortcomings of traditional vehicular networks and the limitations of existing routing protocols and to provide an efficient traffic information dissemination routing protocol,an efficient information dissemination routing protocol(EID-LC)based on the Laying-Chicken strategy is proposed.The protocol forwards packets to the optimal route for information propagation by first selecting paths satisfying the connectivity probability(PC)and signal-to-noise ratio(SINR)constraints as candidate routes,designing a discrete optimization problem in order to select the optimal route from the candidate routes,and using the improved Laying Chicken policy to select the optimal route from the candidate routes.In addition to this,a multicore objective function based on greedy factor and traffic density is proposed for intelligent decision making at intersections.Simulation analysis shows that the proposed routing protocol performs better than existing common routing solutions in terms of routing overhead,controller overhead and packet loss rate.