# Investigating Routing in the VANET Network: Review and Classification of Approaches  
[PERSON]  
1 International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Doulio 64002, Taiwan; [EMAIL_ADDRESS]  
2 Department of Electrical and Computer Engineering, Lebanese American University, Bylbos 13-5053, Lebanon 2  
[PERSON]  
3444  
[PERSON]  
555  
[PERSON]  
64002, Taiwan 64  
algorithms have been used to improve VANET performance by predicting vehicle speed and position and identifying the most appropriate route [5]. The use of a centralized SDN controller as a learning agent for VANET routing has also been investigated, and recent methods including the use of satellites and drones to achieve better routing and convergence in VANET networks have been investigated [6]. Various researches have been conducted to evaluate and classify routing problems in this field, focusing on further analyzing the potential of reinforcement learning methods and artificial intelligence [7]. In some researches, the combination of fuzzy logic and reinforcement learning approaches has been effective to improve vehicle routing in VANET network [8]. Further analysis has been done on the effectiveness of fuzzy logic and reinforcement learning methods in network [9]. In addition, the use of multi-agent reinforcement learning techniques for traffic flow optimization has been promising [10]. Another study has investigated and classified routing and scheduling methods for emergency vehicles, including ambulance, fire trucks, and police, in VANET networks [11]. These studies provide valuable insights into the challenges and potential solutions for achieving optimal routing in VANETs. The goal of this research is to propose the most suitable method for achieving optimal routing in VANETs. The advancement of technology in the automotive industry, including the development of connected and autonomous vehicles and computing, has created a need for optimal use of resources. One way to achieve this is through automotive cloud computing, which can perform computing tasks from the edge or remote cloud. VANET is a subclass of mobile systems in which nodes are constantly moving and does not depend on a specific infrastructure. Routing protocols in VANET networks are divided into vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) routing. The research addresses challenges such as security and privacy issues, energy management, scheduling between centers at the origin and destination, network congestion control, and multiple routing and scheduling in VANET. In addition, the routing protocols in VANET networks are reviewed in Figure 1.  
Figure 1: Classification of Routing Protocols. The classification of position-based routing protocols is as follows:  
* GSR: Geographic Resource Routing
* GPCR: Greedy Peripheral Coordinator Routing
* GPSRJ+: The Geographic Perimeter Stateless Routing Junction+
* A-STAR: Anchor-based Street and Traffic Aware Routing
* GyTAR: Greedy Traffic Aware Routing
* E-GyTAR: Enhanced Greedy Traffic Aware Routing
* TFOR: Traffic Flow Oriented Routing
* DGSR: Directional Greedy Source Routing
* E-GyTAR-D: Enhanced Greedy Traffic Aware Routing Directional
* GPSR: Greedy Perimeter Stateless Routing
* DGR: Directional Greedy Routing
* PDGR: Predictive Directional Greedy Routing
* SADV: Static-Node-Assisted Adaptive Data Dissemination in Vehicular Networks
* MIBR: Mobile Infrastructure-Based VANET Routing Protocol
* MGRP: Mobile Gateway Routing Protocol  
There are many challenges such as security and privacy, energy, scheduling among centers at the origin and destination, network congestion control, and numerous routing and scheduling problems in the VANET network. This research investigates traffic congestion control, optimal routing and network load reduction. The purpose of this study is to review various references in order to propose a suitable method for routing and load reduction in VANET network. The proposed method can be analyzed in several layers.