# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 2 Survey Method
### Exclusion Criteria  
The following were excluded from this survey:  
* Surveys that are not related to SDVNs or network performance benchmarking;
* Surveys that are not published in peer-reviewed academic journals or conference proceedings;
* Surveys that are published outside the time frame specified;
* Surveys that did not use empirical research methods. Scientific databases specialize in different subject areas and types of publications. We selected databases with comprehensive coverage based on the research domain or topic of interest. Databases can be categorized according to the fields that they cover or their data types. Choosing surveys that meet these criteria was based on a systematic search of the literature using different databases (e.g., PubMed, Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, Google Scholar, and others, depending on the field of study and the research objectives, were considered) with appropriate search terms and filters. Additionally, citation tracking and reference list searches can be used to identify additional relevant surveys. After identifying potential surveys, a screening process can be used to determine which surveys meet the inclusion and exclusion criteria. The possible overall sample data collection is shown in Figure 2.  
As part of next-generation networking technologies, such as 5G, the Internet of Things, fog/edge computing [33; 34], wireless/mobile networks, Network Function Virtualization (NFV), sensor networks, IDT, and VANET, SDN has also gained importance in recent years [35]. As compared with existing surveys, the key contributions of this survey are summarized as follows: 1. This survey covers the latest advances in architectures, essential techniques, and solutions for software-defined vehicular networks. 2. We present current implications, future scope or opportunities to explore, and simulation tools that can be discussed or applied in future research. Several dimensions are used to compare and contrast our work with existing surveys on VANETs, IDT, and the controller placement problem (CPP) in SDN. We analyze each survey from a variety of perspectives, including modeling choices, objectives, techniques, and evaluation metrics. We also discuss how SDN might be used in a variety of upcoming networking paradigms. The scope of this survey and future research directions are shown in Table 1, Figure 3 summarizes the focus of our survey, and Figure 4 shows the taxonomy of the organization of iSDVN. Figure 4: Taxonomy of the organization of this iSDVN. Figure 3: The scope of our survey and future research directions. Figure 2: The whole process of collecting samples for a database. (**a**) Review construction for inclusion and elimination of papers (after eliminate duplicates). (**b**) Percentage of papers considered from various scientific databases (before eliminate duplicates). cation systems for better network performance. Increasing traffic safety and improving the quality of service would be possible with an intelligent SDN system. In addition, the multi-hop vehicular connectivity enables the forwarding mechanisms to operate with minimal topology knowledge and dynamically respond to network topology changes. In this survey, we focus on the following objectives:  
* The first objective of our study is to investigate and improve QoS strategies in SD-VNs. As highway traffic density increases and vehicles become increasingly mobile, queueing models for vehicular traffic have to be developed in order to predict vehicles' mobility based on their traveling time, behavior, and speed on the highway and to avoid collisions using routing protocols and scheduling in iSDVNs. * Afterward, we intend to assess how data traffic forwarding improves efficient resource allocation, minimizes delay and packet loss, and optimizes flow control in vehicular networks using a stochastic approach.