## 7 Conclusions  
Modern cities have to face with several challenges, and of the most important for the citizen-life is the management of an efficient ITS. In this paper, we aim at designing some possible scenarios where EC solutions allows to solve some of the challenges of VNs. Differently from other approaches we aim at integrating NTNs in the system so as to create a multi-level Air-Ground architecture. The paper discusses the possible exploitation of network softwarization technologies, vehicular EC and ML for creating an intelligent system able to fulfill the vehicular network requirements. The proposed solutions are discussed analyzing the main challenges and the solutions they are able to take into account. Our future works will be focused on implementing the considered scenarios for understanding their feasibility and how each one can cope with the given challenges. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**VN Scenarios** & **Key Features** & **Challenges** & **Proposed Solutions** \\ \hline \multirow{4}{*}{**Cost Efficient Federated Learning Platform for Vehicular Applications**} & * A cost-efficient FL platform is required over a resource-constrained VN. & * Proper FL-server selection * The communication and computation cost of each FL iteration needs to be analyzed. & * Proper FL-server selection * FL device selection * Training cost vs. convergence in FL & * Hierarchical FL platform over Multi-EC VN architecture * Computation offloading based FL platform over Multi-EC VN architecture \\ \hline \multirow{4}{*}{**Cost Efficient Federated Learning Platform for Vehicular Applications**} & * Computation offloading enables new latency-critical and data-intensive applications and services & * Resource limit of EC nodes * Transmission distance vs. capacity trade-off * An optimal amount of data should be offloaded to have benefits. & * Resource limit of EC nodes * Transmission distance vs. capacity trade-off * Vulnerability of EC servers towards third-party attacks * VUs mobility and a handover latency adapted. & * V2V technology for finding the proper VN-EC pair during Offloading * Multiple edge connectivity mode for VU-EC server task offloading \\ \hline \multirow{4}{*}{**ML-Based NFP Approach for Vehicular Services over Multi-Service Multi-EC Vehic Environments**} & * Network slicing technology can be adapted for providing multiple VN services with heterogeneous requirements. & * Resource allocation problem * VNF placement problem & * Preference weighted NFP for a heterogeneous vehicular service slices \\ \hline \hline \end{tabular}
\end{table} 
Table 7: VN Scenarios Characteristics. **Author Contributions:** Conceptualization, D.T. ; investigation, S.S.S. ; writing--original draft preparation, S.S.S. ; writing--review and editing, D.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: