## 5 Discussion  
In this section, we summarize our findings from the classification of SDVNs based on the modeling and implementation schemes. The modeling strategy used in this paper to break up SDVNs' architecture helps to sort out existing VANETs' challenges addressed by integrating the SDN concept in VANETs. Some of the problems and system analysis in the process of modeling for problem-solving have been covered in Section 4.4, however, the rest of this section covers the summary of the four elements on which we centered the modeling of existing SDVN architecture in order to comprehend the current VANET's challenges solved by SDVN system. The simplicity of modeling proposed in this paper aims at encouraging a research combination towards SDVN with 5G and with edge computing as an alternative solution for future VANET's applications. Based on our study, we provide SDVNs' systems analysis of existing SDVN architecture. The comparative study of existing SDVN based on the modeling and implementation scheme as shown in Table 2 provides a list of a number of VANET issues addressing the full transformation of VANETs to SDVN. To this end, the identified VANET issues handled over to SDVN systems are summarized in the following contributions: firstly, the contributions in [10; 13; 32; 39; 40] that address the issue of loss of connectivity by controlling the data plane latency, secondly, the contributions of authors in [12] that are related to routing protocols in the mobile cloud environment, thirdly, the contributions from authors in [11; 14] which address the issue of resource utilization. Finally, the authors in [1] provide a study on the amount of data transferred for multimedia applications. Lastly, the contributions in [32; 40] address the issue of communication latency and the cost of using multiple core network for autonomous vehicles. Moreover, handover control and proper allocation of radio resource were analyzed in [42] to mitigate the challenge of mobility management and transmission delay. The mobility management in VANETs increases delays in the transmission where handover procedures are not properly implemented. To this, SDVN with fog computing would allow meeting the requirements of low transmission delay by adopting a hybrid handover scheme, optimizing radio resource allocation through the Markov decision process [42]. However, inefficient control for high mobility that causes unsteady wireless channel for SDVN and latency on the distribution of commands from controllers and interworking breach through heterogeneous networks were among ongoing VANETs' challenges to contend with SDVN. In addition, network slicing and NFV [25] in SDVN introduce potential research opportunities. In fact, SDN allows operative network slicing in a dynamic topology. The NFV with the use of hypervisor has the task of adjusting OpenFlow in the way to enable heterogeneous network interworking. The system analysis of SDVN systems identifies components, architectures, protocols directly linked to the SDVN challenge addressed. Note that there are six (6) architecture systems of SDN- based vehicular network proposed in the literature: SDVN [1, 39], SDN-based routing [12], software-defined VANETs (SDVANETs) [14, 38], SDN-based cloud/mobile (fog/edge) computing [10, 11], software-defined VANET with 5G [32, 40]. A comprehensive study of SDVN architecture, its benefits and services are described in [36]. Although six systems of SDVN architectures are currently implemented and simulated, system analysis provides insights to relevant components, architectures, protocols and simulation tools to be considered before providing a solution model to VANET's challenge.