# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 4 Integrating into VANET System Model for iSDVN
### Intelligent Digital Twin with SDVN  
The taxonomy of conventional and recent advances in IDTSDVN applications refers to the taxonomy of these applications based on their characteristics and features. In terms of taxonomy, conventional IDTSDVN applications are typically focused in traditional areas such as healthcare, manufacturing, and transportation. Recent advances in IDTSDVN have expanded to areas such as smart cities, fog/edge computing [128], and IoT [129]. These applications can be further classified based on factors such as their goals, functions, and technologies used. In terms of organization, a survey on IDTSDVN applications typically begins with an introduction that provides an overview of the topic and its importance. The survey then presents background information on IDTSDVN, including its definition, architecture, and components. The main body of the survey is then organized into sections or chapters that cover specific IDTSDVN applications and their characteristics, features, and implementation. The survey may also discuss the challenges and future directions of IDTSDVN applications. Finally, the survey concludes with a summary of the main findings and a discussion of the implications of the research for the field of IDTSDVN. As illustrated in Figure 7, IDT-SDVN consists of two key components: the real-world SDVN and its intelligent digital virtual counterpart [62]. The physical SDVN is a network that operates in the real world. Whenever a controller is configured as a mobile edge computing (MEC) server, routing requests are computed and vehicles are scheduled [70]. As for the controller, it represents and validates the immediately learned functional model as a virtual network (or networks). The revised routing schemes are examined in virtual networks, and the best-performing scheme is chosen to be installed in the physical network [72]. Figure 7: Architecture for intelligent digital twin SDVN [62;70;72;73]. In the case of the Policy-Based IDT-SDVN (shown in Figure 8), the controller periodically communicates the routing policy to each vehicle. Except for the computation of flow tables, the routing policy refers to all sorts of rule-related information. Whenever routing metrics are updated, routing protocols are switched, or other intelligence rules are implemented, and these rules can be locally agreed upon. In addition, routing policies can be adjusted regularly, based on findings for virtual networks from existing works [73]. In the Routing-Based IDT-SDVN, the routing table is mainly determined in the controller. Intelligent algorithms also promise to compute routes with the least amount of overhead on the network [62]. It is possible to construct the flow table predictably using temporal routing and the Markov model, for instance. Additionally, IDT can use other algorithms, such as reinforcement learning, to improve routing schemes based on vehicle availability in dynamic networks where routing methods are based on the vehicle's location information [65]. 1. _Edge Layer_: In the context of intelligent digital twin networks, the edge layer refers to the layer of computing devices and sensors that are located close to the physical systems being monitored and controlled. These edge devices are capable of collecting and processing data from sensors. They can also run local analytics and decision-making algorithms and communicate with other edge devices or higher-level cloud-based systems [130]. One of the key advantages of using edge computing in intelligent digital twin networks is the ability to process data closer to their source, reducing latency and enabling real-time decision making. This is particularly important in applications such as industrial automation, visualizing high-quality 3D contents [131], or autonomous vehicles, where even small delays in processing and decision making can have significant consequences. Additionally, edge computing can help to reduce data communications with cloud-based systems, which can help to reduce network congestion and lower costs.