# Simulation-Based Evaluation of V2X System with Variable Computational Infrastructure
## 2 Related Works  
This section provides a concise overview of research within related thematic areas, highlighting the key advantages and disadvantages of approaches developed by other authors. Studies [14; 15; 16; 17] emphasize the utilization of a cluster-based approach for organizing ITS element interaction. A subset of transport system flow management elements is grouped into blocks with shared caches, thereby significantly increasing system throughput and reducing message processing delays. Simulation systems such as SUMO, MATLAB, and the network simulator NS-3 are frequently employed for organizing cluster structures. The primary objectives where the cluster-based approach proves most effective include minimizing data transmission delays [14; 15] and constructing a comprehensive resource management scheme [16; 17]. An approach based on edge computing [18; 19; 20; 21; 22; 23; 24; 25] is often employed to tackle challenges related to efficient offloading, system performance enhancement, and waiting time reduction. Vehicular Edge Computing (VEC) has emerged as a promising paradigm for service provisioning and management in V2X systems [25]. VEC facilitates low latency by bringing computational resources closer to the vehicles. However, the high dynamism of vehicular networks poses significant difficulties in designing optimal V2X service provision schemes while ensuring safety and timely service delivery. Studies [18; 19; 20] primarily focus on offloading and resource allocation to minimize the overall processing time for requests from vehicles to the infrastructure. The increasing intensity of computations and latency-sensitive onboard applications makes data processing directly on vehicles while maintaining the required computational capacity and, therefore, performance quite challenging. Nevertheless, offloading schemes where all vehicles offload their tasks to the same edge server can limit system performance. Consequently, several studies [21; 22] pro pose and implement the concept of load balancing through the introduction of multi-user, multi-service systems. Studies [23; 24] present energy-efficient secure offloading schemes using non-orthogonal multiple access based on deep reinforcement learning. The proposed solutions enable a balance between energy consumption and overall task processing delay under conditions of rapidly changing wireless channels and high vehicular mobility. Another effective method for addressing optimization issues in V2X systems involves a relatively new tool: Fog computing (FOG). The concept of fog computing was introduced in 2011 due to the need to extend cloud computing capabilities [26]. Numerous ITS devices connect to the cloud simultaneously, which can often overwhelm the system. The core idea of FOG is to migrate a portion of computational tasks closer to the \"ground.\" Fog elements collect raw data (e.g., from sensors) and perform processing using their local computers instead of transmitting it to cloud servers [27; 28; 29]. An additional advantage is that the collected data are likely required by the same devices that generated them. Therefore, local processing minimizes latency between input and response compared to remote processing. The deployment of stationary RSUs is hindered by several factors, including high costs, difficulties in adapting to existing roadside infrastructure, and coordination issues among the public sector, telecom operators, and the automotive industry. It is important to highlight several studies addressing these challenges by utilizing vehicles as mRNAs [30]. This approach is scalable and adaptable to current road traffic. mRNAU routes can be planned to traverse the most challenging road sections, and the presence of a route control system provides a significant advantage. The dynamic scaling of computational capacity at the roadside network infrastructure level allows for mRNAs to be considered as fog elements.