# Investigating Routing in the VANET Network: Review and Classification of Approaches
## 2 A Review of Past Research (Related Work)
They used the Q-learning pattern to achieve optimal routing in the VANETs. In addition, MAC layer has been used to develop Q coordination with VANETs network. Finally, this method had the necessary efficiency in terms of packet delivery ratio, delay reduction, and selection of suitable routes for VANETs network. In 2016, [PERSON] et al. [13] presented a method that stores the data in VANETs by transferring data to vehicles before moving, and then uses a fuzzy algorithm to investigate reward recognition for vehicle speed and Bandwidth efficiency in order for selecting the carrier nod. In addition, reinforcement learning and clustering have been used to investigate future reward and data collection to improve the efficiency of VANETs. In 2020, [PERSON] et al. [14] proposed a routing mechanism using reinforcement learning and clustering methods for VANETs to improve network performance and reduce the number of routes. In 2021, [PERSON] et al. [15] proposed a V2X routing mechanism for intersections in VANETs using artificial intelligence and reinforcement learning methods. This mechanism was intended for real-time networks in which Q -learning was used to calculate routing table. this routing mechanism could control network overhead, packet delivery ratio, network density and delay. In 2021, [PERSON] et al. [16] presented an algorithm for routing prediction in the traffic network using reinforcement learning methods. In 2021, [PERSON] et al. [17] provided a reinforcement learning algorithm to control and reduce VANETs network congestion. This reinforcement learning algorithm has been investigated in a multi-agent way. In this method, an agent t receives a reward in real time. The reward depends on the previous interval and the interval after sending reward. If the sending fails, a penalty will be charged. This method has led to reducing the delay and controlling the congestion of vehicles in the traffic network. In 2021, [PERSON] et al. [18] have investigated vehicle-to-vehicle (V2V) routing in VANETs. By combining fuzzy logic and reinforcement learning approaches, they have improved network performance, increased packet delivery ratio, and reduced delay. Also, dynamic management of VANET network and adaptability to network changes are also investigated. In 2022, [PERSON] et al. [19] presented a new gateway selection mechanism to find the best mobile gateway for vehicles that require Internet access. In this method, there is a system with two cloud servers, the first collects the necessary information about CVs and MGs, and the second uses the and MGs, collects the necessary information about CVs and MGs, and the second uses the data to train the agent. In 2022, [PERSON] et al. [202] combined game theory and reinforcement learning methods to improve routing in VANETs. The game theory was used to classify vehicles. and select a leader for the group. A reinforcement learning method (K-means) is also applied to expand the clusters. This proposed method causes better and more stable routing. In 2022, [PERSON] et al [21] dealt with traffic light control and traffic network congestion reduction using reinforcement learning techniques. The traffic network control problem is formulated using the Markov algorithm in this study. Problem-solving process and optimal actions are performed using reinforcement learning. Faster convergence, packet delivery, and optimal routing indicated the excellent performance of the algorithm. \begin{table}
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**Fuzzy Logic**  
The optimal routing is obtained using a fuzzy constraint and Q-learning method in the VANET network. Fuzzy logic has four main parts which are introduced below. Then, Figure 6 shows the diagram of how to connect these parts. **Basic rules**: This section contains all the rules and conditions specified as \"if...then\" by an expert to be able to control the \"decision-making system\".