Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models

TL;DR

This paper presents a performance analysis model for Internet of Vehicles mesh networks using actual switch models, addressing limitations of previous simulation tools and providing insights into network behavior under different traffic conditions.

Contribution

The study introduces a novel network performance analysis model based on actual switches, capturing dynamic link changes and real traffic distribution in IoV mesh networks.

Findings

Packet loss rate increases with higher task traffic

Task arrival rate decreases as node caching capacity diminishes

Model effectively evaluates network performance across traffic states

Abstract

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement.