### Motivation  
Vehicular Networks (VNs) are a crucial component of the IoT ecosystem. These networks are designed to facilitate communication and data exchange among vehicles, as well as between vehicles and roadside infrastructure or remote servers. VNs play a pivotal role in enhancing road safety, traffic management, and overall transportation efficiency. The study of VNs has advanced to new heights thanks to the integration of computers, transportation, communication, and vehicular technology [5]. According to available data, the primary challenge that VNs are encountering is the integration of data processing, communication networks, resource allocation, and applications, which has severely hampered the growth of a fully automated Intelligent Transportation System (ITS) [6]. In traditional cloud-based vehicular architectures, vehicles connect to the cloud via Roadside Units (RSUs) and related network devices such as switches and routers. Servers running in the cloud are responsible for computing tasks and making decisions for the VNs. However, such a paradigm does not scale well, as it makes servers overloaded and response times unpredictable and hence unsuitable for applications that require real-time processing, such as autonomous driving. Fog computing, on the other hand, moves computation power and storage resources closer to the end user to reduce the time required to complete a task and prevent overloading the links connecting the devices to the cloud. Tasks with bandwidth, latency, and reliability requirements can be completed with fog computing. However, ultra-high-reliability and ultra-low-latency factors in high-density VNs cannot be addressed with the support of fog computing, as resources at fog nodes may be limited and they may need to interact with the cloud to process data. Mobile or Multicaccess Edge Computing (MEC) can satisfy the communication requirements of delay-sensitive and computation-intensive tasks and provide more storage resources and processing resources due to its close proximity to the end user. In a MEC architecture, tasks can be performed at distributed computing nodes that are near the source of data at every point of the network. A detailed review of the recent literature on MEC development is therefore required to provide insight into the state of the art of this field and aid in the discovery of future research pathways in the context of VNs. There have already been several surveys on MEC in the last few years, as illustrated in Table 1. Work [7] investigated the architecture of MEC with a focus on its various applications in different network areas. It mainly presented a brief definition of the applications and architecture of MEC and did not investigate works in the context of VNs. In ref. [8], authors illustrated a study on different architectures of MEC and discussed use cases and challenges in the MEC area. This work does not investigate the most recent works on MEC, i.e., those that have appeared since 2020. In survey [9], researchers discussed MEC-enabled 5G use cases. They studied the architecture of MEC and potential security issues in different networks. In ref. [10], authors studied mobility-aware MEC. They concentrated on the mobility aspect of IoT devices, architecture, services, and applications of things in distinct IoT networks. Work [11] presented a study on MEC and VNs. Their work only covered usage scenarios, a range of services, and technologies, without delving into the latest developments. In contrast, our paper delves into recent advancements concerning the augmentation of computational resources, commonly employed applications, and the integration of vehicles to enhance MEC capabilities. We also strived to provide readers with insights into the most up-to-date algorithms and methodologies that are currently being employed in this context. In another study [12],authors worked on fundamentals, services, enablers, and challenges in MEC. They studied key technologies, applications, and services in MEC-enabled IoT.