# Simulation-Based Evaluation of V2X System with Variable Computational Infrastructure
## 2 Related Works
Study [31] is one of the first to address mRNAU deployment, proposing a self-organizing network approach where specific vehicles function as RSUs. Several studies focusing on fog computing primarily address optimization problems such as maximizing network coverage [32; 33], minimizing latency [34; 35], minimizing data loss, and cost-effectiveness [36; 37]. MATLAB, supplemented by tools from SUMO and OpenStreetMap, serves as the primary simulation environment. The dominant approaches for organizing the interaction of V2X system elements within the fog computing paradigm are based on mechanisms such as greedy algorithms, self-organizing networks, and integer linear programming. Furthermore, public transport (PT) vehicles like buses and taxis with pre-installed communication and computational equipment can function as mRNAs. This mechanism is described in detail in studies [35; 38]. In addition to the aforementioned research, hybrid mRNAU deployment approaches [37; 39] warrant mention. Study [39] proposes a hybrid RSU deployment structure combining three distinct approaches: stationary RSU deployment, utilizing bus-mRNAs on public transport, and utilizing dedicated mRNAs. The study investigates the optimal RSU deployment strategy to maximize spatiotemporal coverage within a limited budget and formulates an optimization problem, demonstrating its NP-hardness. The mRNAU concept is explored in studies by the authors of [36; 40], where mobile RSUs function as dynamically repositioning RSUs. Both studies aim to minimize data loss within the V2X system. Numerical analysis of systems with and without mRNAs demonstrated that utilizing mRNAs reduces the overall network load significantly. The lack of comprehensive models that address the challenges in this research area, along with scientifically sound approaches, concepts, models, methods, and algorithms for targeted activities to ensure the required level of ITS automation and balance between key characteristics during V2X technology operation, also hinder its effective implementation. This highlights the scientific significance and relevance of addressing this problem comprehensively. Several challenges arise during modeling--the solutions to which would enable the creation of a comprehensive management model--including limited computational resources of vehicles and ITS infrastructure, energy efficiency, and data transmission network characteristics. The quest for a comprehensive model that enables the distribution and prediction of system parameters and resources under various computational tasks is a primary objective for many researchers. This issue is of considerable importance for the implementation and utilization of V2X technology. Building a comprehensive model invariably involves selecting key optimization parameters. Study [41] identifies four main parameters:  
1. Network coverage: A primary goal of most routing algorithms is to ensure high and efficient network coverage to deliver essential and useful information to as many network nodes as possible. 2. Network latency: The impact of utilizing fog and edge data processing methodologies within the network is described by calculating the average network latency. The average message delivery latency in the network is calculated by summing the latencies for each unique message across all nodes where it was first received. 3. Processing ratio: The ratio of data processing at the edge, fog, and cloud layers--a metric describing the effectiveness and interrelation between these layers within the FOGO architecture, which heavily depends on the number of fog nodes. The \"edge/ fog/cloud\" processing ratio is calculated as the ratio of the total number of messages processed in a layer to the total number of messages generated during the simulation. 4. Processing efficiency: Data processing efficiency is calculated for each network architectural layer: edge, fog, and cloud.