# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 2 Related Works
Simulations in a practical road scenario using NS-2 and VanetMobiSim showed that DBDR outperforms the existing protocols in terms of packet delivery ratio, throughput, and average packet delay when the network becomes congested. [PERSON] et al. [25] constructed a new software-defined networking-based IoV heterogeneous networking measurement framework and proposed a performance measurement and analysis method. The performance indexes and measurement methods for the loss, throughput, etc. were derived in detail. The switch selection mechanism and packet sampling process were used to establish optimal measurement points of advantage and quickly obtain the needed measurement information. [PERSON] et al. [26] introduced a tree-cut mapping-based average maximum flow solution method (TCMANF) and utilized average network flow (ANF) as a metric to assess the overall network quality of a city. TCMANF quickly determined the maximum flow rate within the inter-node network to obtain the average network flow rate. [PERSON] et al. [27] combined V2V and V2I communication modes to propose a framework for assessing the communication capabilities of vehicular networks operating in mixed traffic scenarios. They introduced a predictive communication strategy to enhance vehicular network capacity in mixed-traffic by pre-caching necessary content in the infrastructure based on predicted vehicle trajectories. [PERSON] et al. [28] used IEEE 802.11p and IEEE 802.11s for static and moving vehicles working under mesh topology. The performance evaluation was accomplished by simulation on NS-3. Throughput, packet delivery functions and packet sizes are parameters that have been evaluated. [PERSON] et al. [29] developed an NS-3-based vehicular network framework to analyze performance metrics such as throughput and packet loss. Several topology-based routing protocols were compared and Expected Transmission Number (ETX) metric was proposed to improve performance. [PERSON] et al. [30] designed the Domain-based In-vehicle network Architecture (DIA) and the Zone-based In-vehicle network Architecture (ZIA) for autonomous driving, and then analyzed and validated the superiority of the ZIA and its suitability as an automotive architecture using the OMNeT++ network simulator. The above research works investigated the performance of vehicular networks in terms of deriving theoretical expressions and modeling traffic on simulation tools. However, most of them only considered a single metric, and multiple metrics were mostly studied for network capacity and packet loss rate. Due to the high-speed mobility of vehicles in IoV mesh networks and the diversity of user tasks, the topological connectivity relationships are changing rapidly, and performance analysis needs to be considered from multiple metrics. In addition, the performance of IoV mesh networks is affected not only by vehicle density, which is a factor from the overall perspective of the network, but also by the capacity of the nodes, especially the caching capacity. Furthermore, common simulation tools such as OPNET and NS cannot accurately solve the dynamic generation of links, i.e., the time-varying characteristics of links. Tools such as NS-3 do not support metrics other than hop count, and there is no suitable method to efficiently calculate key performance indicators, such as node load rate [29]. Therefore, based on the above problems, this paper constructs the task-forwarding model based on actual switches to analyze the network performance in terms of multiple metrics and multiple factors.