Transmission network and data distribution strategy of vehicle-to-everything communication system based on genetic algorithm

Abstract

With the wide application of Internet of Things terminals and the rapid development of intelligent transportation systems, how to effectively improve the demand of automobile users for service quality has become an urgent problem. In traditional mobile communication protocols, intermediate nodes only store and forward the received data information. By using genetic algorithms, the maximum capacity of network transmission can be achieved. Applying genetic algorithm to the Vehicle-to-everything can effectively compress the massive information generated by the Vehicle-to-everything, thus improving the service quality of the Vehicle-to-everything. This paper analyzed the data receiving rate in the Vehicle-to-everything system through genetic algorithm, and optimized the system transmission network and data distribution strategy. In the experiment, the impact of vehicle distribution range and number of clusters on network throughput was analyzed, and the effects of different vehicle node numbers on data transmission efficiency and data transmission delay were also studied. In the analysis of the impact of cluster size on throughput, it was found that the average network throughput of the communication system under genetic algorithm was 27.56 Mbit/s, which was 7.09 Mbit/s higher than traditional systems. The bandwidth utilization and data transmission effect of the Vehicle-to-everything communication system based on genetic algorithm were also significantly improved, which also played an important role in the optimization of vehicle node range distribution and node range setting.