### Contribution  
Our aim in this paper is to overcome the limitations of previous studies and complement them. We strive to cover the employed technologies, algorithms, and applications that are used in MEC-enabled VNs in a comprehensive manner. Although MEC and VNs have originated to tackle different needs, they crossed paths naturally at some point of their evolution, as demonstrated by the extensive amount of literature involving both technologies. In fact, MEC and VNs represent both a technological _driver_ and _ enabler_ with respect to each other. This paper--as well as its structure--was then motivated by the need to review how the relationship between MEC and VNs has historically evolved. In this regard, we identified the use of MEC as a provider of low-latency computing capabilities to vehicles as a first cornerstone. The availability of computing resources in proximity of vehicles (possibly following them while they are moving) paved the way to task-offloading solutions, along with the related research efforts focusing on resource allocation. As a natural consequence, these enhanced capabilities evolved towards exploiting MEC resources to directly support vehicular applications themselves: this approach allowed vehicles to be relieved from the burden of running complex applications (such as collision avoidance and platooning) locally and with limited resources. More recently, the increasing availability of resources at the vehicles made possible a third perspective, that is considering vehicles as edge computing nodes themselves, hence exploiting possibly \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Year** & **Main Contribution** \\ \hline
[7] & 2018 & Various applications of MEC in different network areas of IoT \\
[8] & 2019 & Architectures of MEC, use cases, and challenges in IoT \\
[9] & 2022 & MEC-enabled 5G use cases, security \\
[10] & 2023 & Mobility of IoT devices \\
[11] & 2022 & Use cases and challenges of MEC in VNs \\
[12] & 2022 & Fundamentals, enablers, and challenges of MEC in IoT \\
[13] & 2019 & Mobility issues of both content-caching nodes and end devices \\
[14] & 2022 & MEC for V2X architectures and applications \\
[15] & 2023 & Comparison of offloading strategies in MEC \\
[16] & 2023 & Task-offloading algorithms and optimization approaches in MEC \\
[17] & 2023 & A review of the research status of MIMO-MEC networks \\  & & Using MEC for task offloading in V2X (Section 5); \\ This work & End of 2023 & using MEC to enable V2X applications (Section 6); \\  & & using vehicles to enhance MEC (Section 7) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existent surveys on MEC. idle computing resources at the vehicles to augment MEC capabilities. To the best of our knowledge, this is the first survey paper collecting works covering this last aspect. In the first part of this paper, we provide a brief introduction about the evolution and architecture of MEC, starting from cloud and fog computing paradigms. The deployment of MEC in VNs is also introduced. Then, we discuss relevant technologies in the context of MEC and VNs such as software-defined networking and digital twins, along with related works, and we provide a classification for the existing works according to three main categories identified above. Specifically, we consider works that use _MEC as an extension of vehicular capabilities_ (e.g., for task offloading), _MEC as an enabler for vehicular applications_ (e.g., platooning), and _vehicles as an extension of MEC capabilities_. Open research challenges and future research directions are also discussed. Figure 1 presents the organization of this paper, which reflects the proposed categorization of the most recent state-of-the-art works. Figure 1: Organization of the paper.