Study on DTN Routing Protocol of Vehicle Ad Hoc Network Based on Machine Learning

Abstract

The vehicle-mounted self-organizing network is a part of the MANET network. It is placed between the roadside vehicle and the fixed communication equipment. It can serve as a hub for road vehicles and can enable multihorsepower wireless mechanisms to exchange data between vehicles. This article is aimed at studying the DTN routing protocol based on machine learning in the vehicle self-organizing network. When data is forwarded, the node will determine the forwarding route selection according to its own coordinate information, the coordinate information of neighboring nodes, and the coordinate information of the destination node. Usually, the purpose is for the geographic coordinates of the node to be stored in the data packet. And data packets are periodically transmitted between nodes on each network. So that when you publish your own coordinate nodes, you can update the location information of nearby nodes at any time. This paper proposes that routing technology has become one of the most important challenges in vehicle self-organization, and there are many reasons for this. These reasons include frequent changes in the network topology and fast-moving mobile nodes. The experimental results in this paper show that more than 67% of the network data is obtained through the Gawk data extraction tool to quantify GPSR performance indicators and obtain the average driving speed of the current vehicle node. When increasing, the average end-to-end transmission delay of the GPSR routing protocol increases, and the average transmission rate decreases.