# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 3 Scenario Description and Problem Statement  
In real-world IoV, the selection of relay-nodes needs to consider the high mobility of vehicles, the diversity of road structures, and the existence of obstacles in RoI to achieve higher coverage with lower delay. EPBP and its derived methods can well solve the problem of real-time caused by the high mobility of vehicles, but it fails to completely solve the problem of broadcasting in RoI with various road structures and obstacles. For example, Figure 1 shows an area where various road structures and obstacles exist, and it is assumed to be an RoI of the message generated by Node S\({}_{0}\) at Point H. The road structure on the west of the road section \(\widehat{\Pi}\) (\(\widehat{\Pi}\) indicates the road section connecting Point H and Point I is a curved road with a junction J\({}_{1}\) and is surrounded randomly by green woods. Additionally, the road structures on the east of \(\widehat{\Pi}\) are the straight roads with junctions and there are buildings around these junctions. Woods and buildings are obstacles that can prevent the dissemination of messages. The message is expected to cover the RoI, so the ends of the road at the RoI boundary are the termination positions of broadcast. A process of message broadcasting is illustrated in Figure 1. Node S\({}_{0}\) is an original sender, and a message broadcasted by S\({}_{0}\) is expected to cover the region shown in the map, i.e., the RoI of the message. Obviously, the road structures in the communication ranges of Node S\({}_{1}\), S\({}_{2}\), and S\({}_{4}\) are the straight road, the junction, and the curved road, respectively, so the corresponding relay-node selection methods, i.e., the method in [24] for straight road scenarios, that in [26] for junction scenarios, and that for curved road scenarios in [25], are adopted according to the road structure. However, one problem needs to be solved, and that is how to distinguish road structures. Moreover, the road section in the communication range of one sender maybe consists of two or more road structures, not one typical road structure discussed in the existing works. This scenario is given as an example in Figure 1 as the road section in the communication range of Node S\({}_{3}\). The range covers a junction and three curved road sections, neither the typical junction with several straight branches nor the typical curve only including the curved road section. Thus, in order to realize the node-selection in real-world scenarios, the first problem should be resolved as follows. Problem 1: how to classify the road structure? Figure 1: Relay-node selection in the Region of Interest (RoI). The broadcasted message is expected to cover the whole RoI at the cost of as little time as possible. Thus, in one hop, the node at the farthest position from the sender in the direction of message broadcasting is the most favorite relay-node. The farthest position is defined as the optimal position [24]. In the real world, the obstacles will affect the location of the optimal position. The line-of-sight condition in straight road scenarios is good because no obstacle affects the communication range of the sender, thus existing relay-node selection methods [21; 22; 23; 24] use the point farthest from the sender as the optimal position on the straight road scenarios. In junction scenarios, obstacles such as buildings generally exist near junctions, and the existing relay-node selection methods [21; 22; 26] applicable for junction scenarios select a node close to the center of the junction as the relay-node of the first hop, and achieve the maximum coverage of all branches with the second hop to complete message broadcasting. In curve scenarios, the general relay-node selection methods [13; 14] consider that obstacles are generally around road corners, so the corner of the curved road is marked as the optimal position to eliminate the impact of obstacles on the message broadcasting.