# Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs
## 1 Introduction
### Previous Work  
In this subsection, we have summarized the relevant research contributions in junction selection in VANETs. Diverse road-matrices-based junction selection and forwarder vehicle selection protocols have been proposed to improve communication efficiency. The author in [1] proposed the geographic source routing (GSR) routing protocol to consider the location of the vehicle at the junction and proposed a greedy routing protocol for packet transmission. The location-based greedy routing may lead the packet away from the destination. The greedy perimeter coordinator routing (GPCR) is proposed to enable greedy routing in the urban scenario [4]. The nearest neighbor vehicle is selected as the best forwarder to share packets between junctions. Moreover, an improved traffic-aware greedy scheme similar to GyTAR [5] is proposed to consider direction metrics to select the most optimal junction for communication. The traffic flow-oriented routing protocol (TFOR) suggests selecting the junction based on the traffic direction and handover of the packet to a two-hop neighbor vehicle [6]. The directional geographic source routing (DGSR) [7] and dynamic multiple junction  
Figure 1: VANETs communication systems. Figure 2: VANETs junction selection. selection-based routing (DMJSR) scheme suggested directional packet forwarding and closest junction selection strategy to overcome communication delays [8]. Most of the literature considers single-road metrics such as the number of vehicles on the road or the distance with static predefined weight values to improve the accuracy of the system. However, these matrices' junction selection decreases the communication efficiency and often leads to inappropriate junction selection. To overcome this, there is a strong need to propose a multi-metric junction-selection technique that is not only efficient but also improves the accuracy of the system. The author has proposed the multi-hop intersection-based routing protocol for VANETs [9]. It overcomes the limitation of candidate junction selection based on traffic density. However, considering one parameter does not provide the optimal solution for packet forwarding in VANETs. It does not specify the disruptive communication link issue. Moreover, in [10] the author proposed the geographic-aware routing protocol for multi-hop packet forwarding in VANETs. It also overcomes the limitation of disruptive communication links. However, few research contributions emphasize trust-based routing protocols for reliable communication. However, the packet forwarding to an optimal junction is still a challenge [11]. The authors in [8] proposed the position-based routing protocol for VANETs to overcome the limitation of frequent change in topology. Moreover, it also overcomes disruptive communication links using DMJSR. The study shows promising improvement in network efficiency. The navigation system has improved by considering data mining in VANETs. The author in [12] provides a comprehensive analysis of different artificial intelligence (AI) techniques in VANETs to improve network connectivity. The traffic density-based routing protocol in VANETs has been proposed in [13]. The high-density traffic has been considered to forward the packet between junctions to overcome the limitation of latency. The author in [14] proposed the computational intelligence method to provide smart connectivity in IoT. The study shows promising improvements in QoS requirements in a highly connected environment. Considering the above-mentioned limitations in position-based routing, we proposed the optimal weighted multiple attribute junction selection protocol using an intelligent \"Multi-Attribute Decision-Making\" technique for the junction selection. The novel proposed protocol utilizes three metrics: distance, traffic density, and angle selection, to improve the accuracy of the system. The optimal weight estimation and assignment techniques are used for updating the metrics.