## 5 Using MEC to Augment the Computing Capabilities of Vehicles
### Task Offloading
Users have three task-offloading options, modeled as a MILP problem. Due to the problem's complexity, estimations may outperform precise solutions in a real VEC  
Figure 8: Architecture of Mobility-aware Multihop Task Offloading [105]. Circles show the one-hop and two-hop vehicle groups. system. Maximizing the success rate of task offloading is crucial to avoid wasting network and processing resources. The study utilizes deep learning to predict task-offloading success rates and service latency, enhancing the VEC system's overall performance based on historical data from EdgeCloudSim [112]. A summary of the above works is presented in Table 3. Since most of the above formulate task offloading as a mathematical programming problem, we also report a comparison of the nature and size of such problems in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Methodology** & **Key Metrics** & **Computing Method** & **Focused Infrastructure** & **Mobility Consideration** \\ \hline
[96] & IoV with imperfect CSI—using DRL &
\begin{tabular}{c} Computation delay \\ and energy consumption \\ \end{tabular} & MEC server & V2I & No \\ \hline
[97] & Network slicing & Delay &
\begin{tabular}{c} Local—MEC \\ server \\ \end{tabular} & V2V-V2I & Yes \\ \hline
[99] & Multiuser MEC assisted & \
\begin{tabular}{c} Delay and energy consumption \\ \end{tabular} &
\begin{tabular}{c} Local—MEC \\ server \\ \end{tabular} & V2V-V2I & No \\ \hline
[100] & A heuristic task migration & Delay &
\begin{tabular}{c} Local—MEC \\ server \\ \end{tabular} & V2I & Yes \\ \hline
[101] & \begin{tabular}{c} Distributed task-offloading framework—DDQN optimization \\ \end{tabular} & \
\begin{tabular}{c} Delay and resource management \\ \end{tabular} &
\begin{tabular}{c} Local—MEC \\ server \\ \end{tabular} & V2I & Yes \\ \hline
[103] & NOMA and MEC merging &
\begin{tabular}{c} Delay and resource utilization \\ \end{tabular} & Local & V2V & Yes \\ \hline
[104] & \
\begin{tabular}{c} Task offloading using parked and moving vehicles \\ \end{tabular} & Processing delay &
\begin{tabular}{c} MEC \\ server—cloud server \\ \end{tabular} & V2I-I2I & No \\ \hline
[105] & Multihop task offloading & \
\begin{tabular}{c} Service-delay prediction \\ \end{tabular} &
\begin{tabular}{c} MEC \\ server—cloud server \\ \end{tabular} & V2I & No \\ \hline
[106] & \
\begin{tabular}{c} Decentralized DRI—task offloading with game theory algorithm \\ \end{tabular} &
\begin{tabular}{c} Latency and energy consumption \\ \end{tabular} & MEC server & V2I & No \\ \hline
[107] & \begin{tabular}{c} Generic MEC system—a hybrid DQN and optimization approach for task-offloading strategy \\ \end{tabular} & \
\begin{tabular}{c} Resource consumption \\ \end{tabular} &
\begin{tabular}{c} Local—MEC \\ server \\ \end{tabular} & V2V-V2I & Yes \\ \hline
[108] &
\begin{tabular}{c} Probabilistic scheduling approach—two- dimensional Markov chain \\ \end{tabular} & Load balancing & MEC server & V2I & No \\ \hline
[109] & Computation offloading & \
\begin{tabular}{c} Latency and overhead \\ \end{tabular} &
\begin{tabular}{c} Local—MEC \\ server \\ \end{tabular} & V2I & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison among recent works on Task offloading in MEC-enabled VNs.