## 5 Discussion  
Our exploration of vehicular communication and edge computing over a 5G network at varying distances between the OBU and RSU sheds light on critical insights for real-world deployments. Our investigation combining both real-world experiments and simulations allows us to draw nuanced conclusions regarding the performance of the edge node in CAM processing. In our real-world experiments, the observed fluctuations in energy consumption were a direct result of the dynamic and evolving nature of vehicular communication. This phenomenon can be attributed to several factors inherent in the vehicular communication environment. Specifically, changes in communication distances between the OBU and RSU introduce variations in the strength and quality of the wireless signals. As vehicles move, signal strength may vary due to factors such as obstacles, interference, and the inherent mobility of the communicating entities. These fluctuations in signal strength directly influence energy consumption patterns as the communication system adapts to maintain a reliable connection. Moreover, environmental conditions such as reflections, multipath effects, and signal attenuation contribute to the observed variations in energy consumption. At shorter distances (30 m and 50 m) we observed stable energy consumption patterns, emphasizing the reliability of the system in closer proximity. However, more pronounced peaks and troughs were evident at greater distances (70 m and 80 m), suggesting potential challenges in maintaining stability as the communication range increases. These challenges may stem from factors such as signal attenuation, increased propagation delay, interference, and the dynamic nature of the vehicular communication environment. Our simulation results closely resembled the experimental results, validating the efficacy of our virtual model. The simulated energy consumption patterns demonstrated a controlled increase and occasional spikes, aligning with the corresponding real-world scenarios. This correspondence reaffirms that we see a general upward trend in energy consumption as the distance between the vehicle and the RSU increases, emphasizing the importance of considering transmission range in the design of efficient vehicular communication networks. Notably, our real-world experiments exhibited higher variability, especially in the scenarios with greater distances. Factors such as signal interference, environmental conditions, and the inherent unpredictability of real-world deployments contributed to fluctuations that were not entirely mirrored in the controlled simulation environment. To optimize edge computing in vehicular communication networks, future efforts should focus on developing algorithms that are resilient against real-world variations. Adaptive strategies that dynamically adjust computational loads, energy consumption, and communication protocols based on real-time conditions will be instrumental in ensuring reliable performance across diverse scenarios. Our examination of the CPU load at the edge node (RSU) in different communication scenarios revealed crucial insights into the computational dynamics of vehicular communication networks. A noticeable correlation emerged between the communication distance and CPU load, indicating the distance-dependent computational challenges faced by the edge node. As the distance between the OBU and RSU increases, there is a corresponding intensification in CPU load, highlighting the heightened computational demands associated with CAM processing over longer ranges. Initiation of CAM transmission consistently triggers a surge in CPU load, underscoring the substantial impact of real-time data processing on the edge node. The observed peaks during transmission emphasize the necessity for robust computational capabilities to handle the instantaneous influx of vehicular information.