## 1 Introduction  
VANET is a subclass of the Mobile Ad-hoc Networks (MANETs) [1]. It is based on the collection of vehicles instead of mobile nodes to communicate with each other and roadside infrastructure units [2]. It is a multi-hop ad-hoc network that is connected by using wireless technology belonging to the 802.11 families [3]. The environment of VANETs is crucial as it can be used in various real-time vehiclesmovement such as buses, trucks, and cars. [4]. The wireless networks may be categorized into two designs, i.e., infrastructure- and non-infrastructure-based networks [5]. In an infrastructure-based network, wireless nodes are communicating by using access points (which can be relay nodes or base stations (BSs)), which are connected to a fixed network structure, such as switches or routers [2]. However, a non-infrastructure-based network does not require any access point or other BSs to communicate [6]. With the rapid increase in the number of cars recently, the VANET technology applied to vehicle traffic systems became a significant research hotspot of the Intelligent Transportation System (ITS) [7]. VANET communication can be categorized into two types, i.e., Vehicle-to-Vehicle (V2V) and Vehicle-to-Roadside (V2R) communication [8,9]. Despite having many potentials of VANET, it suffers from two main challenges: firstly, vehicle mobility, which changes the ad-hoc network topology and severely influences the routing performance and affects VANET's main functionality of exchanging data efficiently [10,11]; secondly, packet drop rising due to traffic congestion at the intermediate vehicle, which happens when a new packet arrives, while the queue is full [12]. Thus, these crucial issues of traffic congestion should be taken into consideration in the VANET routing protocols' design phase [13].  
The LAR protocol has utilized a GPS technique to find out the node's location information [14]. The GPS resolves its localization problem based on the de facto standard solution [15]. Nevertheless, the GPS-based localization is inaccurate because of the issue of vertical positioning and the prevalent horizontal movement, which makes LAR protocol limited for lower speed networks only [14]. However, the performance of the LAR can be enhanced by applying various optimization techniques such as Particle Swarm Optimization (PSO) [16] and Genetic Algorithm (GA) [17]. In this paper, we estimated a GPS node location that led to highly accurate vehicle localization. The proposed heuristic RALAR protocol has been developed to reject bad GPS location data and accept good ones based on a moving rectangular zone according to the node's mobility model. Subsequently, the proposed RALAR protocol was optimized by using the GA to select the most suitable timeout variable. The results were compared with KALAR [18] and conventional LAR protocol [19] for various performance metrics, such as PDR, average E2E delay, routing overhead, and average energy consumption.  
The rest of the paper is organized as follows: Section 2 explains the recent research that was done for various protocols in VANETs. Section 3 focuses on the proposed RALAR protocol and applied GA optimization approach. Section 4 describes the simulation environment, data generation and node's mobility model, and the displays the performance metrics that were used to evaluate the results. Section 5 presents the results and discussion. Section 6 shows the conclusion of this work. Finally, Section 7 discusses limitations and future work.