# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
###### Abstract  
Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad-hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad-hoc vehicle networks, vehicles of everything, and the internet of things. ACO; QoS; VANET; firefighting UGV; V2X; RS 20232331431 28 January 202323231431 28 January 202323231431 28 January 20232  
Some research has employed VADD and A-STAR to address the problem of QoS, but it remains a theory that is not appropriate for dealing with dynamic networks, such as VANETs, but rather for structured networks [6]. Other research employed SADV to redirect packets and increase the QoS in real time. However, this strategy might cause network congestion, especially during peak hours, which creates a bottleneck problem in emergency scenarios [7]. Moreover, some researchers have employed local motion characteristics, such as the GyTAR and TADS protocols, to predict the next junction, although neither the density of the chemicals nor their distribution can reflect the overall pattern of the QoS improvement. The increased vehicle density can improve the network connectivity, but it also can cause an increase in the data throughput on the network [4]. The CAR protocol for the routing packets employs the idea of blind flooding in its schemes, rendering it ineffectual in improving the QoS and potentially burdening the network [8]. According to prior research, the primary issues that VANETs have in constructing acceptable networks for services are: the path inaccuracy, the ineffectiveness of the QoS assessment in the traditional or incomplete protocols, and their inability to adapt to the changes in the network architecture. Meeting the requirements for the implementation speed and efficiency has become an essential demand in modern cultures. For a more in-depth evaluation of prior studies, along with their limitations, has been shown in Table 1. In this research, the network throughput is proposed as a factor to measure the fastest route for firefighting UGVs to reach the fire region because it employs an unstructured network of VANETs to communicate among the firefighting UGVs, that are represented by a group of wireless mobile nodes, each node representing a firefighting UGV. The ant colony optimization (ACO) theory is utilized to tackle the problem of selecting the best path and avoid accidents and collisions. To choose the optimal route, the firefighting UGVs apply the principle of opportunism, rather than the theory of deep flooding. Following the selection of the best tracking route for the firefighting UGV, the VANETs use the selected road to direct all firefighting UGVs and internally increase the QoS. The following are the primary contributions of this paper:  
1. Contribute to the presentation of a model diagram depicting how firefighting UGVs will interact with one another in the case of a fire, discuss the optimization of the QoS based on the throughput factor and select the fastest access method;
2.