# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 2 Literature Review  
In this section, we review the recent literature related to task offloading in vehicular fog computing. A summary of the literature review is presented in Table 1.  
In [23], the authors put forth the significance of fog computing by efficient task offloading for mobile devices. To deal with delay-sensitive applications, an online learning-based offloading approach is applied in two stages. In the first stage, resources are distributed between fog nodes while keeping computation costs at a minimum. In the later stage, offloading delay is minimized by optimizing task allocation and spectrum scheduling. Simulation results show the performance gain of the proposed technique compared to the upper confidence bound (UCB) technique. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **Scenario** & **Proposed Technique** & **Design Objective** \\ \hline
[23] & Vehicular fog computing & Online learning-based offloading & 1. Resources are shared between fog nodes while maintaining lower computation costs \\  & & 2. Task allocation decisions and spectrum scheduling are used to minimize offloading delay \\ \hline
[24] & Vehicular fog computing & \
\begin{tabular}{l} Convex-concave optimization \\ and price-matching task \\ offloading \\ \end{tabular} &
\begin{tabular}{l} 1. The efficient assignment of servers is achieved via the convex-concave optimization approach while \\ maximizing the anticipated utility of the operator \\ \end{tabular} \\ \hline
[25] & Vehicular fog computing & \
\begin{tabular}{l} Contract theoretical modeling \\ and stable matching algorithm \\ \end{tabular} &
\begin{tabular}{l} 2. The total delay of the network is minimized using a price matching solution \\ \end{tabular} \\ \hline
[26] & Vehicular fog computing & \
\begin{tabular}{l} Contract theory and learning- \\ based matching \\ \end{tabular} &
\begin{tabular}{l} 1. A contract theory computing resource management mechanism is proposed \\ 2. A learning-based matching task offloading method is also proposed \\ \end{tabular} \\ \hline
[27] & Multi-ehicular fog computing & \
\begin{tabular}{l} Deep reinforcement learning \\ \end{tabular} &
\begin{tabular}{l} 1. Resources are allocated while reducing the complexity of the system \\ 2. A queuing model is proposed to solve the collision problem faced by simultaneous offloading \\ \end{tabular} \\ \hline
[28] & Multi-ehicular fog computing & \
\begin{tabular}{l} Lagrange dual approach \\ \end{tabular} &
\begin{tabular}{l} 1. A low-complexity algorithm is proposed to find the optimized values of offloading ratios, which vehicles to \\ select and the consumption of the system \\ \end{tabular} \\ \hline
[29] & Vehicular fog and cloud computing & Semi-Markov decision process &
\begin{tabular}{l} 1. The task offloading problem is presented as a semi-Markov decision process \\ 2. A value iteration algorithm is proposed to maximize the total long-term reward of the system \\ \end{tabular} \\ \hline
[30] & Vehicular fog computing & Stable matching & 1. Vehicle mobility is considered while offloading \\  & & 2. The Kuhn-Munkers algorithm is used to find a stable match \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent work conducted in vehicular fog computing. The work in [24] involves the efficient engagement of servers for computing, as well as the offloading of tasks under the uncertainty and asymmetry of the transmitted information. A two-phase-based design is proposed to achieve this goal. For the efficient assignment of servers, a convex-concave optimization approach is defined while maximizing the anticipated usage of the operator under the condition of symmetric information. Additionally, to minimize the total delay of the network, a price-matching solution is also proposed. This price matching is extended to information uncertainty scenarios and a matching-based offloading framework is developed, which helps to minimize total delay.