# Investigating Routing in the VANET Network: Review and Classification of Approaches
## 2 A Review of Past Research (Related Work)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs** & **Short Description** & **Advantages** & **Disadvantages** \\ \hline
[11] & \begin{tabular}{l} Reviewing and categorizing studies \\ in the field of routing in networks \\ using reinforcement learning \\ \end{tabular} & \
\begin{tabular}{l} The evaluation criteria have \\ been reviewed. \\ \end{tabular} &
\begin{tabular}{l} The advantages and disadvantages \\ of different articles are \\ not discussed. \\ \end{tabular} \\ \hline
[23] & \begin{tabular}{l} Presenting a Markov approach for \\ Internet of Vehicles (IoV) \\ transportation systems. \\ \end{tabular} & \
\begin{tabular}{l} Dynamic management of the \\ IoV network. \\ \end{tabular} &
\begin{tabular}{l} Complexity of Tran-sition Markov \\ Algo-rithm \\ \end{tabular} \\ \hline
[26] & \begin{tabular}{l} Reviewing studies in the field of \\ intelligent routing in VANETs \\ \end{tabular} & \
\begin{tabular}{l} Different studies on machine \\ learning are categorized, which \\ provides researchers with an \\ outlook for future work. \\ \end{tabular} &
\begin{tabular}{l} Deep learning methods are not \\ reviewed and each paper are not \\ reviewed in more detail \\ \end{tabular} \\ \hline
[27] & \begin{tabular}{l} This method compares PSO with \\ two routing approaches, namely \\ AODV and DSR, and examines the \\ effectiveness of the PSO algorithm \\ in terms of performance metrics \\ such as packet delivery ratio, \\ end-to-end delay, and throughput. & \
\begin{tabular}{l} The use of PSO algorithm, which \\ has improved network \\ performance, reduced delay and \\ increased packet delivery ratio \\ \end{tabular} &
\begin{tabular}{l} The network is homogeneous and \\ may not work in real-world \\ scenarios. In addition, the PSO \\ algorithm requires more iterations \\ to reach convergence. \\ \end{tabular} \\ \hline
[28] & \begin{tabular}{l} This article explores the use of \\ reinforcement learning (RL) to \\ detect impersonation attacks in \\ device-to-device (D2D) \\ communication. It describes the \\ design and implementation of an \\ RL-based detection system and \\ presents experimental results \\ demonstrating its effectiveness. The \\ article provides insights into the \\ potential benefits of using RL for \\ improving cybersecurity in \\ D2D communication. \\ \end{tabular} & \
\begin{tabular}{l} The article provides experimental \\ results that demonstrate the \\ effectiveness of the proposed \\ RL-based system compared to \\ existing methods in terms of \\ accuracy and efficiency \\ \end{tabular} &
\begin{tabular}{l} The article does not evaluate the \\ proposed system on real-world \\ data, instead relying on simulation \\ experiments and allenges related to \\ scalability and continuous learning, \\ which the authors briefly discuss. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Review of articles that have used fuzzy logic to control VANET network. merely a scale. A negative outcome (for example \(-6\)) occurs if the agent performs poorly and a positive reward (for example \(+6\)) occurs if the agent performs well. \[\mathbb{P}[S_{t+1}|S_{t}]=\mathbb{P}[S_{t+1}|S_{1},S_{2},S_{3},\ldots,S_{t}] \tag{1}\]  
The quality of the action is directly related to the quantity of the reward and it predicts the probability of solving the problem (e.g., learning how to walk). In other words, an agent's goal is to perform an action that maximizes the accumulated reward over time in any given situation. **Markov decision process**  
Markov decision process (MDP) is a time-discrete stochastic control process, the best approach we have so far for modeling the complex environment of an AI agent. Any problem that the artificial intelligence agent intends to solve can be considered as a sequence of states: \(S_{1},S_{2},S_{3},\ldots,S_{n}\). The agent performs actions and moves from one state to another. In the following, we show you the mathematics that determines which action the agent should perform in each situation.