Research On The Roadside Terminal Cooperation Algorithm Of Vehicle Network For I2V Information Sharing

In recent years, as an important part of intelligent transportation system, Vehicular Networks (VANET) has been widely concerned. VANET can provide people with a variety of applications, such as traffic safety, transport efficiency and entertainment. VANET has become a new hot spot in the field of intelligent transportation. Increasing researchers focus on VANET in industry and academia as VANET has been a hotspot of ITS. The information transfer mechanism in VANET is important to supporting various kinds of applications that is related to traffic safety and entertainment. In V2R communications, due to the limited range of the RSU and high speed of traveling vehicles, a vehicle can keep a short period at a RSU and download limited types of data packets. It leads to poor payoff in the entire backbone network. In view of the above problems, Walid and others put forward a type of cooperative Vehicle-to-Roadside unit communication, using a coalition formation game scheme to divide the backbone network, forming the concept of coalition in VANET. On one hand, the proposed scheme can improve the diversity of information circulating in the network and exploit the data exchange capabilities of the underlying V2V networks with the CV2R communication in the same coalition; On the other hand, the proposed scheme can decrease the CV2R communication costs with the non-cooperative V2R communication among different coalitions.This article analyzes the feature of V2V and V2R communication, and establishes the model of NCV2R communication and CV2R communication. Based on genetic algorithm, this paper propose GKA for coalition partition. K-means Operation(KMO) is used to replace the complex crossover operation, and the Euclidean distance is used to calculate the probability of mutation. Genetic algorithm can improve the efficiency of getting the global optimal solution, the clustering algorithm can improve the convergence speed. As a whole, GKA overcomes the shortcoming of poor local search ability, immaturity convergence and the random walk, maximizing network data types, and leading to the highest average payoff in backbone network.