# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 5 Results and Analysis
### Introduction of Evaluation  
We simulated these above approaches in VANET using MATLAB with the Monte Carlo method [42]. Since we focused on the relay selection in the link level, the simulation environment just includes the 802.11p MAC layers. The major simulation parameters of VANET are given in Table 1, and are identical to those used in [20; 23; 25]. Figure 3: Flow diagram of adaptive relay-node selection (ARNS). In each simulation, Node S\({}_{0}\) was used as the original sender. The intersections of each road and RoI boundary were used as the terminal points of broadcast on this road. Since the roads in Figure 1 have different widths, for ease of expression, we classified them with the number of the lanes \(n_{\text{lane}}\) in both directions (\(n_{\text{lane}}\)= 2,4,6), and vehicle density \(\lambda\) in this paper is defined as the vehicle density on a single lane. In order to assess the performance of ARNS under a wide range of vehicle densities, we set the minimum interval between vehicles to be 4 m, and the minimum number of vehicles within communication range to be two vehicles. Thus, when the communication range was set to 200 meters, the lowest vehicle density was 0.01 vehicles/meter and the highest was 0.25 vehicles/meter. The vehicles were located randomly following the Poisson distribution with \(\lambda n_{\text{lane}}\). The maximum speed \(v_{\text{max}}\) of vehicles complies with the rule related to safe inter-vehicle distance [43; 44]. Note that the inter-vehicle distance is defined as the distance between the heads of the adjacent vehicles. Each vehicle chose a random speed following a uniform distribution in \(\left[\frac{1}{2}v_{\text{max}},v_{\text{max}}\right]\) at the beginning of the simulation, and kept the chosen speed during the simulation. Lane change and overtaking were not modeled for vehicle movement. From the simulation results shown in Figure 4, a single simulation duration, i.e., the end-end delay, is less than 6.2 ms, and the maximum movement distance of a node is 0.21 m corresponding the vehicle speed of 120 km/s. Thus, the above assumptions about the vehicle running are reasonable. The experimental environment was simulated in MATLAB, the same as [25], because the conclusion in [45] pointed out that the vehicle movement has little influence on the relay-node selection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Default Values** \\ \hline Communication Range & 200 m \\ DIFS & 58 \(\upmu\)s \\ SIFS & 32 \(\upmu\)s \\ Slot Time & 13 \(\upmu\)s \\ Transceiver’s Switching Time & 1 \(\upmu\)s \\ Beacon Interval & 100 ms \\ Bit Rate & 18 Mbps \\ RTB Packet Size & 20 Bytes \\ CTB Packet Size & 14 Bytes \\ ACK Size & 14 Bytes \\ Message Packet Size & 500 Bytes \\ Confidence Interval & 95\% \\ Simulation Times & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major communication parameters. Figure 4: End-to-end delay. End-to-end delay and packet delivery ratio (PDR) are metrics widely used to evaluate the efficiency and reliability of message broadcasting in IoV [21; 22; 23; 24; 25; 26; 27]. In addition, a metric called maximum hops was proposed to evaluate the reliability of end-to-end delay. The metrics of broadcast coverage, partition delay, and contention delay were used to measure the improvement of considering obstacles. In this section, we show the comparisons of all method schemes in terms of six metrics: end-to-end delay, partition delay, contention delay, PDR, maximum hops, and broadcast coverage. The definitions of the metrics are described below. **End-to-end delay \(T_{\text{end}}\)** is expressed as a total delay from the instant when Node S\({}_{0}\) starts broadcasting to the instant when RoI is completely covered. \(T_{\text{end}}\) is the sum of one-hop delay. In the black-burst-based methods, the partition delay \(T_{\text{part}}\) and the contention delay \(T_{\text{cont}}\) dominate the one-hop delay.