## 2 Background Overview
### Related Works  
Several researchers have considered bringing computing and storage capabilities closer to the edge of vehicular networks. In [34], the authors emphasize the importance of Multi-Access Edge Computing (MEC) as a crucial paradigm in IoV for supporting heterogeneous devices and emerging 5G services. In [35], the authors discuss how to provide vehicular networks with cloud computing services and introduce a new paradigm of the virtual cloud computing architecture based on a Macro-Micro-Cloud novel approach that allows hierarchically integrating available resources from vehicles into the edge computing system, with the aim of managing data, reducing communication complexity, and improving QoS. A resource management scheme based on FL is proposed by [PERSON] et al. [36] to manage resources (text, audio, and video) in VANETs, under a fog computing platform. The fuzzy logic system determines the survival time of the resources of a local server based on a data set of request time and download time for each resource, which is recorded by a V2I communication model. In this way, the system keeps updated information about the availability of resources in real-time. The simulation results show that the proposed model manages the resources effectively and satisfies the needs of clients with resource sharing in the case of dynamic topology, intermittent connectivity, or limited storage capability of local servers. In [37], the authors deal with the problem of maintaining the QoS during service migration from one node to another by targeting resource management strategies at fog nodes. The authors introduce two schemes that prioritize selected services. The first scheme reserves fog resources based on traffic load, while the second scheme frees fog resources allocated for low-priority and reallocates them for high-priority services. Both schemes show an increase in one-hop access probability, especially for high-priority services. Figure 1: Infrastructure and content distribution in the cloud-fog-edge SDN-VANETs. [PERSON] et al. [38] give another perspective for resource allocation strategies in VANETs. The authors propose a Hybrid-Fuzzy Logic guided Genetic Algorithm (H-FLGA) approach for the SDN controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. The hybrid system is based on the service requirements of customers and good results are achieved in terms of capacity, delay, traffic load, energy optimization, cost, and so on. [PERSON] et al. [39] analyze the main features of edge computing and classify the appropriate technology for different vehicular applications by comparing the network capacities with the applications' requirements. To fully support the various applications and services, the network architectures must satisfy specific QoS requirements while exploiting the available resources. In [40], the authors introduce a fuzzy-based system to estimate QoS for different broadcasting protocols in VANETs. The broadcasting protocols are suitable for delivering infotainment and road condition information to all vehicles in the network without exception. However, they cause problems such as broadcast storms. The authors compare the results of several QoS parameters such as delay, reachability, packet delivery ratio, and overhead value. Even though these are important parameters to estimate the network performance, different VANET applications have different requirements, which means the network performance changes in regard to the application requirements. In previous works, we have considered the resource management problem in vehicular networks and have proposed different intelligent approaches to the problem. In [41], we have proposed a simple fuzzy-based system that provides vehicles with a recommendation on the storage resources they can use based on factors such as data size and time sensitivity. The storage resources consist of those of the nearest vehicle, fog server, and cloud data center.