# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 1 Introduction  
In recent years, with the development of intelligent devices and wireless communication technology, the communication distance of devices has gradually increased and has steadily expanded, encompassing diverse geographical terrains and different types of communication services. Numerous communication devices are interconnected, and play an important role in various fields. With the increase in the global urban population and the rapid development of transportation infrastructures, the number of vehicles has grown rapidly and become an important means of transport for people to travel. In this context, recurrent traffic congestion and accidents, traffic inefficiency, and other problems are becoming increasingly serious. Road safety is now a significant societal issue. Intelligent Traffic System (ITS) establishes a holistic intelligent integrated transportation system by integrating advanced technologies, and the application of this system is an effective strategy to solve the above problems. Among them, the IoV mesh network, as an important part of ITS, carries substantial responsibility for information exchange and is the key to the operation of the whole system [1]. In contrast to conventional mobile communication networks, the IoV mesh network utilizes vehicles with interconnected interface devices as network nodes. According to the different communication objects, the communication links in vehicular networks include Vehicle-to-Vehicle (V2V) communication and Vehicle-to-Infrastructure (V2I) communication [2]. Among them, V2V communication facilitates the process of information exchangeamong vehicles within their communication range. V2I communication realizes the information interaction between vehicles and the Road Side Unit (RSU). V2I communication enables RSUs to offer services like collision warnings, blind spot detection, dynamic route planning, and other vehicle-road cooperative services. Vehicles in IoV mesh networks have sensors and other devices that can autonomously collect information and transmit it via V2V communication links to enable data interaction between vehicles. Therefore, each node of the vehicle cluster follows the rules of autonomy; cluster members cooperate using information transmission and sharing, showing strong self-organizational capabilities, and can handle tasks collaboratively [3]. Based on the above analysis, unlike individual vehicles, IoV mesh networks are richer in resources and can handle more complex tasks and provide better services. However, at the same time, environmental interference, vehicle density and distribution, and high-speed mobility also pose challenges for scheduling, optimization, and decision-making of IoV mesh networks [4]. Addressing the bottleneck of collaborative communication modeling in IoV mesh networks is a current research focus in IoV mesh network construction. To optimize the problem of IoV mesh network construction, it is firstly necessary to comprehensively analyze the various performances of IoV mesh networks and comprehend how various network parameters affect performance. Among the factors affecting the performance of IoV mesh networks, the primary factor is task traffic [5]. Because of the high-speed mobility and uneven distribution of vehicles, the topology in IoV mesh networks rapidly changing, leading to uncertain task forwarding and transmission. With exceptionally high vehicle density, the task traffic in the network rises sharply and could potentially result in node cache congestion. Therefore, it is of great practical significance to study the task model and performance metrics system in IoV mesh networks and explore the traffic distribution. In addressing vehicular network performance, many researchers have carried out studies on system analysis modeling. For example, [PERSON] et al. [6] assessed performance under the 802.11p standard using four factors: communication distance, node transmission power, packet transmission rate, and task flow, as input variables in order to examine the packet reception rate.