# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 1 Introduction  
A vehicular communication system broadcasts information about vehicles so that traffic chaos, congestion, and accidents are avoided. However, congestion is a more significant problem. Vehicle applications demand stringent QoS and unprecedented network capacity in internet technology. Vehicle applications have limited task-offloading schemes and flexibility issues [1]. The Base Station (BS) and Road-Side Units (RSUs) cooperate in task-offloading problems while dynamically adapting to current network environments. This enhances QoS in VANETs [2]. Link connectivity changes frequently based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) distances due to high mobility in a VANETsystem [3]. The packet is forwarded through vehicles using internet services modeled in a queueing network for data dissemination [4]. Heterogeneous vehicular networks and software-defined networking (SDN) are used to reduce traffic congestion [5] and improve network performance in VANETs [6; 7]. The main objective of VANET and SDN connectivity is to separate the control plane and data plane [8]. The purpose of SDN in wireless and ad hoc domains is to centralize network services, control, and flexibility, which has gained attention over the last few decades, as shown in Figure 1.  
This survey discusses how SDN technologies can be a promising solution to the challenges in vehicular networks while providing access to emergency services and high-bandwidth applications with low latency. Mobility management is a critical component of vehicular networks, with location management protocols, processes, and handoff management schemes being the key areas of concern [9]. The survey presents comprehensive mobility management in SDN-enabled vehicular networks, including models, challenges, and solutions. Mobility management solutions can be classified according to the vehicular network model used, such as SDN-based, HetNet-based, fog-based, and hybrid solutions [10]. The survey provides insights for young researchers in the field of intelligent VANETs to understand the implications and directions for futuristic wireless networks. The survey also discusses how traffic prediction in VANETs can be improved by using a stochastic vehicular mobility model that accounts for realistic variations in inter-vehicle communication over consecutive time steps [11; 12]. A VANET is a wirelessly connected mobile vehicle network in the transportation sector. The frequent failures in VANETs make it difficult to disseminate information. High mobility and isolated nodes result in topology changes, which represent a major challenge in VANETs. The SDVN paradigm is the SDN concept incorporated into VANETs, which overcomes the previous challenges of SDVNs. It consists of a logically centralized control plane that analyzes the data in the vehicular network and makes network decisions, thus enhancing the programability and flexibility of the vehicular network [13]. Traditionally, networking has been infrastructure-based, with the control plane spread throughout many routers. SDN logically isolates the fundamental network control mechanism from switches and routers to provide network control centralization [14]. With SDN, the controller gathers information about the status of the network, allowing the controller to design safe paths that are in line with the network's requirements and provide a better level of network awareness than with conventional hardware-driven networking. In SDN, there are three planes: the infrastructure plane, the control plane, and the application plane. The main advantages of SDVNs over conventional networks are flexibility and programmability. Using a single protocol, physical devices can communicate with each other via SDN, which allows control to be centralized. SDN has allowed us to perform and provide tasks such as traffic optimization, network virtualization and automation, and cloud-based service coordination.