## 5 Using MEC to Augment the Computing Capabilities of Vehicles
### Resource Allocation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Type of Problem** & **Objective Function** & \begin{tabular}{c} **Number of** \\ **Variables** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Constraints** \\ \end{tabular} &
\begin{tabular}{c} **Algorithm Used** \\ \end{tabular} \\ \hline
[96] & Convex & \
\begin{tabular}{c} Minimizing the \\ total overhead \\ \end{tabular} & \(O(N)\) & \(O(N)\) &
\begin{tabular}{c} DRL and \\ Lagrangian multiplier \\ \end{tabular} \\ \hline
[97] & Integer linear & \
\begin{tabular}{c} Maximizing the accepted \\ offloading tasks \\ \end{tabular} & \(O(N\times R\times G\times S)\) & \(O(N\times R\times G)\) &
\begin{tabular}{c} Network slicing and \\ load balancing, \\ VECSIc-LB \\ \end{tabular} \\ \hline
[99] & Non-convex & \
\begin{tabular}{c} Minimizing response time \\ of the task \\ \end{tabular} & \(O(T\times X\times L)\) & \(O(T)\) &
\begin{tabular}{c} Learning-based \\ task-offloading \\ framework based on \\ the multiarm bandit \\ \end{tabular} \\ \hline
[100] & Convex & \
\begin{tabular}{c} Diminishing \\ average delay \\ \end{tabular} & \(O(T)\) & \(O(T\times X)\) &
\begin{tabular}{c} Task migration \\ cooperative offloading \\ \end{tabular} \\ \hline
[101] & Non-convex &
\begin{tabular}{c} Minimizing the total \\ system delay \\ \end{tabular} & \(O(N\times L)\) & \(O(N)\) & DRL-DDQN \\ \hline
[105] & \begin{tabular}{c} Mixed-integer \\ linear \\ \end{tabular} & \
\begin{tabular}{c} Minimizing execution \\ time and computation cost \\ \end{tabular} & \(O(N\times T)\) & \(O(N)\) &
\begin{tabular}{c} Quadratically \\ Constrained Quadratic \\ Programming (QCQP) \\ problem and \\ semidefinite relaxation \\ \end{tabular} \\ \hline
[108] & Linear & Minimizing cost & \(O(N\times T\times C)\) & \(O(N\times T\times C)\) & Markov chain \\ \hline
[109] & Non-convex & \
\begin{tabular}{c} Minimizing the \\ averageprocessing delay \\ \end{tabular} & \(O(N\times T\times R)\) & \(O(N)\) &
\begin{tabular}{c} DQN and RMSProp \\ optimizer \\ \end{tabular} \\ \hline
[110] & Integer linear & \
\begin{tabular}{c} Maximizing success \\ offloading tasks and \\ minimizing service delay \\ \end{tabular} & \(O(T)\) & \(O(T)\) &
\begin{tabular}{c} Convolutional Neural \\ Network \\ (CNN) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: An analytical comparison among the works on task offloading in MEC-assisted VNs. the configuration issue of computational resources. They utilize edge servers to provide services to all vehicles in their coverage area, and the RSU obtains rewards [117]. The RSU will receive a preprocessing request from a dynamically arriving vehicle [116], which it will then notify. The RSU will then calculate the likelihood of admission for the edge computing service. When cars enter the RSU's coverage area, they can switch between local and edge processing based on QoS, and they can adapt their task requirements. The approach uses a novel model and a threshold-based method to handle the changing dynamics of these VEC systems. Both mathematical formulation and simulations are carried out to confirm the viability of the suggested method and present its effectiveness depending on various network circumstances. Unlike the MEC-related work [118], this research examines the allocation of processor power in VEC. It focuses on server end's profit maximization, in contrast to [119] whose main goal is to maximize end users' advantages. This approach also tries to optimize the RSU's long-term operating benefit, whereas [120] only concentrated on short-term optimization. Moreover, [116] differs from [121] by taking into account how service quality affects the QoS needs of vehicles. In [121], a novel approach to adaptively assign resources for VEC is introduced. The aim is to enhance the QoE in situations characterized by scarce resources and dynamic conditions. Queued latency-sensitive tasks are assigned a queuing time limit. If they miss the deadline, they are removed and discarded.