# Simulation-Based Evaluation of V2X System with Variable Computational Infrastructure
## 1 Introduction  
In recent years, vehicle-to-everything (V2X) communication technology within intelligent transportation systems (ITSs) has garnered significant attention due to its potential to address contemporary ITS requirements. V2X offers the potential to mitigate the strain on transportation network infrastructure, enhance road safety, improve driving comfort, provide an alternative emergency communication system during natural disasters, and enable a high degree of vehicle automation [1, 2, 3]. To achieve these objectives, V2X technology must ensure low latency, high reliability, energy efficiency, and optimal computational resource allocation. Furthermore, the specific characteristics of the data transmission network must be considered, including its highly dynamic nature, constant topology changes, and the exchange of large data volumes. Failure to meet these requirements may prevent critical safety applications from responding effectively in potentially hazardous situations. The development of a comprehensive V2X system management framework is also of considerable interest. For the design of transportation systems with high automation levels, vehicles must possess the capacity to process substantial real-time data related to nearby object recognition, road incidents, navigation tasks, and more. However, the computational resources of most existing vehicles are limited for executing such applications. Any disruption in system functionality and stability can jeopardize human safety and vehicular integrity,potentially leading to significant recovery and maintenance costs. Consequently, ensuring data transmission reliability and high ITS performance becomes paramount. The application of V2X in conjunction with edge computing is crucial for the automation of vehicles, autonomous transport, and the management of connectivity and system stability [4; 5; 6]. Utilizing this class of V2X systems enables low latency, high reliability, and high throughput [7]. These advantages highlight the effectiveness of such systems in supporting Level 3 (Conditional Automation) or Level 4 (Highly Automation) driving, according to the levels of driving automation classification [8; 9]. These challenges can be addressed through the implementation of clustering mechanisms during roadside infrastructure deployment, as well as through the utilization of edge and fog computing. The impact of these mechanisms on the overall V2X system functionality is discussed extensively in the literature [10; 11; 12; 13]. This paper proposes a mathematical model for a simulation system and its numerical solution for V2X applications in a traditional roadside unit (RSU) deployment configuration, utilizing a two-channel connection between onboard units (OBUs) and RSUs. The novelty of this research lies in conducting numerical experiments within a new three-tier system for organizing the interaction of V2X system elements, incorporating both stationary (edge) and dynamically repositioning (fog) objects. The primary focus of our study is on the aspects of V2X applications aimed at optimizing traffic flow in high-density conditions during accidents. Specifically, it concerns applications that provide emergency alerts to reduce traffic congestion and reroute vehicles based on external conditions. Furthermore, an evaluation of the model's operational effectiveness is conducted based on calculations of the percentage of unprocessed requests generated by OBUs during their mobility, as well as the latency of request transmission and processing within the RSU-OBU system. We compare simulation results with and without mobile roadside units (mRSUs), which we consider as elements of a dynamic fog, using targeted and random movement routes to assess their impact on overall system serviceability. Our contributions can be summarized as follows:  
1.