# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 1 Introduction
However, the stationary nodes in the network are not suitable for the high-speed mobility characteristic of IoV mesh networks. [PERSON] et al. [7] proposed the Real Vehicular Wireless Network model, which allowed for a more realistic capacity analysis in vehicular networks. The actual geometry of the urban area was represented by a Euclidean planar graph, so as to analyze the interference relations and calculate the asymptotic capacity. [PERSON] et al. [8] represented the network as a geometric structure of straight lines and points for a one-dimensional V2V network with topology changing over time and analyzed network connectivity and capacity using geometric probability. Through mathematical modeling, it was found that the network capacity increases in a specific way, which verifies the validity of the analytical results. [PERSON] et al. [9] analyzed the reachable throughput of a vehicular network with finite traffic density under a cooperative communication strategy. By obtaining a closed-form expression for the reachable throughput, they unveiled the relationship between throughput and key performance-influencing parameters like communication transmission rate and vehicle density. The above study obtained the expressions for solving the network throughput through mathematical modeling, but did not account for queuing, caching, and task-forwarding processes at nodes in actual networks. [PERSON] et al. [10] built a vehicle movement model to investigate the network coverage problem to ensure stable overall performance. They also compared the performance of network throughput, average network delay, packet loss rate, average routing hops, etc. under different protocols. [PERSON] et al. [11] conducted a performance analysis of connectivity and throughput of vehicular networks on motorways, exploring its correlation to vehicle density, number of lanes, etc. The results, backed up by simulations of realistic vehicular traffic, showed that the V2V + V2I architecture can significantly improve connectivity. [PERSON] et al. [12] investigated the performance of a common reactive routing protocol where two scenarios are considered. The first scenario was a comparison between V2V and V2I communication modes. The second one was made between two maps, Basrah city and the Manhattan grid, in V2V mode. The implementation of the routing protocol revealed a comparative results analysis using Quality of Service (QoS) parameters, such as network throughput. However, these studies were conducted using prevalent network simulation tools (e.g., OPNET and NS), which cannot accurately address the dynamic link generation problem and portray time-varying features, and they do not have a suitable methodology to efficiently calculate key performance metrics such as node load rate. Within IoV mesh networks, link construction, disruption, and recovery among nodes constantly change, and the performance of individual nodes is also closely related to the network performance. In addition, based on the actual switches, packet forwarding relies on the port routing forwarding table at the nodes and undergoes a series of complex processes. Therefore, in order to solve the above problems, this paper proposes a task-forwarding strategy based on the actual switch model, on the basis of which a network indicator system is constructed to analyze the performance of IoV mesh networks under different vehicle densities, vehicle caching capacities, etc., which lays the foundation for load-balanced routing design, task offloading, and resource allocation in IoV mesh networks. The specific research is as follows:  
(1) We construct a typical network architecture for IoV mesh networks and abstract it into a mathematical model. We create V2V and V2I channel models and vehicle movement models for dynamic link generation and interruption to obtain the network topology over time.