# Investigating Routing in the VANET Network: Review and Classification of Approaches
## 2 A Review of Past Research (Related Work)
According to the new methods in the fuzzy theory, it is possible to adjust and reduce the rules and regulations so that the best results can be obtained with the least number of rules. **Fuzzification**: In this step, inputs are converted into fuzzy information. This means that the numbers and information to be processed will be converted into fuzzy sets and fuzzy numbers. The input data, for example, measured by sensors in a control system, are changed in this way and prepared for fuzzy logic-based processing. **Inference engine or intelligence**: This section determines the degree of compliance of the inputs obtained from fuzzification with the basic rules. Based on the compliance percentage, different decisions are made as the results of the fuzzy inference engine. **Defuzzification**: In the last step, the results of fuzzy inference, which are in the form of fuzzy sets, are converted into quantitative and numerical data and information. At this stage, you make the best decision according to the outputs, including different decisions with different compliance percentages. Usually, this choice will be based on the highest degree of compliance. In 2022, [PERSON] et al. [3] provided a routing algorithm based on multi-agent reinforcement learning (MARL) and fuzzy methods for VANETs. They also utilized Mamdani method to derive the fuzzy model. Metrics such as packet delivery ratio and delay have been improved. **Review articles**  
In 2019, [PERSON] et al. [11] reviewed and categorized articles in the field of routing in networks using reinforcement learning. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Refs**} & \multirow{2}{*}{**Short Description**} & \multirow{2}{*}{**Simulation**} & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Disadvantages**} & \multirow{2}{*}{**Disadvantages**} & \multirow{2}{*}{**Fuzzification**} \\ \hline  & & & Increase operational & & & Coordination of \\  & & & capacity & & vehicles is difficult & \\  & & Internet of Vehicles (IoV) & & Reduce latency & & & Complety of \\  & & transportation systems. & & Dynamic management of & & Transition Markov & \\  & & & the IoV network & & Algorithm & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)  
Figure 6: Fuzzy Logic. In 2021, [PERSON] et al. [24] reviewed studies on optimal routing in VANETs using artificial intelligence and reinforcement learning methods. In 2021, [PERSON] et al. [25] reviewed, categorized and compared studies in the field of reinforcement learning and artificial intelligence applications in flight networks. In addition, by examining the problems and ideas of various articles, they help researchers in conducting new research. In 2022, [PERSON] et al. [23] used Markov for Internet of Vehicles (IoV) systems, which finally proposed a resource interpretation scheme using Time Division Markov Multiple Access (TDMA) algorithm. In 2022, [PERSON] et al. [26] reviewed studies in the field of intelligent routing in VANETs. They also investigated the effectiveness of the algorithms used in the studies and evaluation of the routing methods proposed for the traffic network. According to this research, heuristic methods, fuzzy algorithms and reinforcement learning have the necessary efficiency to improve network performance. Also, Table 2 examines the advantages and disadvantages of the research that used the fuzzy method. In Table 3, different routing approaches in the VANET network, such as fuzzy logic and reinforcement learning methods, have been compared, and this comparison has been made according to the advantages, disadvantages and evaluation criteria.