# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 2 Comprehensive Survey
The author created an approach to select the best antenna array for unicasting information, allowing vehicles outside of the message's propagating range to do other things. Furthermore, when information is not obtained after a certain amount of time, each vehicle executes route discovery to nodes that hold information. As a result, pathways are rearranged as needed to accommodate additional vehicles. This method is only suitable for VANETs with less mobility. It is not suitable for networks with huge mobility models [9]. The Intersection-based Geographical Routing Protocol (IGRP) is a category of AODV protocol for VANETs that surpasses existing routing algorithms in metropolitan contexts. The Internet Gateway Routing Protocol (IGRP) is centered on attention for road intersections, through which a signal should traverse to access the Http server. The decision is constructed in such a way that the internet connection between crossing points is guaranteed with a strong likelihood, while meeting quality-of-service (QoS) criteria on acceptable latency, network capacity, and confidence interval. However, it is moderate in terms of efficiency when it is applied to networks with huge mobility [10]. Researchers suggested a parking-area-assisted spider-web routing protocol (PASRP) for data delivery in urban VANETs. Using remote sensing and GPS techniques with a digital map, PASRP creates a spider-web propagation design based on the parking lot. The transmission path from the source unit to the destination device is determined by sending two control packets, request-spider and confirm-spider, and the route with the shortest latency is chosen as the transmission link. The essential information is then transferred to the route using a multi-mode greedy method, with a dynamic multi-priority concept prioritizing it. This method only concentrates on packet delivery ratio and throughput, others are not taken care of [11]. Under this research, the authors present RSU-assisted Q-learning-based Traffic-Aware Routing, an innovative routing algorithm for metropolitan VANETs (QTAR). QTAR uses the Q-learning algorithm to study road network traffic statistics, combining the benefits of spatial transportation with fixed road spatial information. A routing method in QTAR is made up of many dynamically determined high-availability connecting road sections that allow payloads to effectively arrive at their destination. To decrease transmission delay and the impact of high-speed traffic flows on path vulnerability, distributed V2V Q-learning combined with Q-greedy geographical forwarding is used for routing packets inside a road segment, while distributed R2R Q-learning (Q-learning occurs between RSU units) is used for packet forwarding at every transitional link. However, this method only concentrates on throughput and delay. Overhead is not calculated [12]. In a Public Transportation System (PTS), GeOpps-N is presented as a novel hybrid routing protocol for communications between buses and procedure control centers. Every thirty seconds, the bus location must be updated. The system can be modeled as a Vehicular Advertising Network that incorporates vehicles and Road-Side Units (RSU). Since the network has a low population density and is frequently congested, data must be relayed. When contrasted to other methods, such as geographic-based routing or storm routing, topology-based routing methods have been found to be more appropriate for low-quantity environments. Rather than seeking the endpoint within the source group, these methods search for the right candidate to deliver the idea to its endpoint. This method is inefficient when it is applied to networks with huge mobility [13]. This research offers a unique routing protocol based on the fuzzy systems that can aid in the coordination and analysis of metrics that are in conflict. To choose the best next-hop for routing packets, the suggested technique considers many parameters, such as position of the vehicle, orientation, network quality, and possible bandwidth.