## 6 Conclusions  
In conclusion, our investigation into vehicular communication and edge computing within a 5G network while considering varying communication distances between the OBU and the RSU has illuminated essential insights. Both real-world experiments and simulations consistently portrayed energy consumption patterns showcasing stable operation at shorter distances and increased fluctuations at greater ranges. The analysis of the CPU load at the RSU underscored the correlation between communication distance and computational demands, emphasizing the need for adaptive algorithms to optimize edge node performance. The results obtained from our investigation offer valuable insights for academia and industry. Our findings highlight the importance of developing adaptive algorithms capable of dynamically allocating computational resources based on real-time requirements, especially in scenarios with varying communication distances. These algorithms can help to optimize edge node performance, ensuring efficient resource utilization and minimizing the risk of performance bottlenecks. System designers can leverage these insights to develop more robust and adaptive systems capable of handling the dynamic nature of vehicular communication environments. Moreover, they can help to shape policies aimed at promoting sustainability, efficiency, and reliability in connected transportation systems. Overall, the obtained results provide a solid foundation for advancing the state of the art in the fields of vehicular communication and edge computing. Researchers can build upon our work to explore additional aspects of communication scenarios, integrate emerging technologies, and develop novel algorithms and solutions to address the evolving challenges and opportunities in connected transportation systems. However, it is crucial to acknowledge the limitations of our study. Our valuable real-world experiments exhibited higher variability due to factors such as signal interference and environmental conditions. These uncontrollable elements influenced fluctuations that were not entirely mirrored in the controlled simulation environment. Additionally, while our focus on CAM transmission represents a specific aspect of vehicular communnication, future studies may benefit from a more comprehensive exploration of diverse communication scenarios. For future works, efforts should prioritize the development of adaptive algorithms resilient against real-world variations, with the aim of optimizing edge computing in diverse vehicular communication scenarios. To mitigate the limitations of our study, it would be possible to explore a broader spectrum of communication aspects in future investigations. This exploration would involve considering factors such as data types, network congestion, and the coexistence of various communication technologies. Additionally, investigating the integration of emerging technologies such as machine learning and blockchains in vehicular communication networks holds promise for enhancing efficiency and reliability in real-world deployments. Conceptualization, S.E.M., X.L., A.G., C.E.P. and J.M.-B. ; methodology, S.E.M., X.L., A.G., C.E.P. and J.M.-B. ; software, S.E.M., X.L. and A.G.; investigation, S.E.M. and A.G.; writing--original draft preparation, S.E.M., X.L., A.G., C.E.P. and J.M.-B. ; writing--review and editing, S.E.M., X.L., A.G., C.E.P. and J.M.-B. ; supervision, A.G., C.E.P. and J.M.-B. ; project administration, C.E.P. and J.M.-B. ; funding acquisition, C.E.P. and J.M.-B. All authors have read and agreed to the published version of the manuscript. The research leading to this work-in-progress paper has received support from the MUR (Italian Ministry of Universities and Research) under the PON initiative ex DM 1061 and under the MOST--Sustainable Mobility National Research Centre initiative within the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNNR)--MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4--D.D.