## 3 Experimental and Simulation Setup
### Performance Metrics  
In exploring vehicular communication within the edge computing domain, our attention is directed towards two fundamental metrics: energy consumption and CPU load. These metrics carry profound implications for the sustainability and efficiency of communication processes in dynamic vehicular environments. Energy consumption played a central role in our investigation, mirroring its significance in vehicular communication day-to-day operations. As vehicles exchange critical information with RSUs, the energy expended in these communication processes becomes a pivotal factor. The optimization of energy consumption is not merely an operational concern, it is an ecological imperative; the optimization of energy consumption is a crucial factor that influences the longevity and environmental impact of the entire vehicular communication network [45]. The CPU plays a pivotal role in handling the computational demands associated with the processing and transmitting of CAMs between OBUs and RSUs. As our investigation delves into the dynamics of vehicular communication over a 5G network at varying distances, the CPU emerges as a critical component in the edge node responsible for real-time data processing [46]. In the context of our study, the CPU operates as the core computational hub, and is responsible for computational demands associated with the processing and transmitting of CAMs between OBUs and RSUs. The significance of the CPU load metric becomes pronounced in scenarios where rapid and concurrent communication between vehicles is indispensable. It serves as a critical parameter reflecting the computational intensity and efficiency required to facilitate seamless data exchange in dynamic vehicular environments. As vehicles exchange crucial information with RSUs, the CPU of the RSU becomes the focal point for handling the intricate computational demands associated with the processing and transmission of CAMs. The CPU is engaged in tasks such as data parsing, protocol processing, and message forwarding. Moreover, the CPU load metric becomes a critical parameter in the context of rapid and concurrent communication between vehicles [47]. The CPU load metric quantifies the extent to which the CPU is utilized at any given time. In scenarios where vehicles communicate rapidly and concurrently, the CPU load provides a quantitative measure of the computational burden on the RSU's CPU. High CPU load values indicate increased demand for processing power, potentially affecting the system's responsiveness and efficiency. Therefore, the relevance of monitoring the CPU load in real time lies in its direct correlation with the system's ability to handle the dynamic and demanding nature of vehicular communication. It serves as a tangible indicator of the computational stress experienced by the RSU's CPU during periods of intense data exchange, offering insights into the system's \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Overhead & 5\% \\ Period between SRSS & 200 us \\ UE speed & 0 m/s \\ RLC buffer size & 10 MB \\ One-way delay on X2 links & 1 ms \\ One-way MME delay & 10 ms \\ UDP payload size & 1024 byte \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._performance and its capacity to sustain reliable and efficient communication in dynamic vehicular environments. The intersection of energy consumption and CPU load in vehicular communication networks establishes a quantifiable link between real-time computational demands and system performance. As vehicles exchange critical information with RSUs, the CPU's engagement in tasks such as data parsing and message forwarding is reflected in its load metric, providing a tangible measure of computational intensity. High CPU load values correspond to increased energy consumption, emphasizing the direct relationship between computational demands and energy expenditure [48]. This interplay influences system responsiveness, with elevated CPU load potentially impacting prompt data processing.