# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 5 Simulation and Results
### End-to-End Delay  
Figure 11 shows the end-to-end delay for varying the number of vehicles and CBR connections. It can be noted from Figure 11 that as the number of nodes increases, the performance of end-to-end delay becomes better in three protocols. Compared with GPSR, MOPR, and MM-GPSR, REIT obviously has smaller end-to-end latency. For instance, the end-to-end delay of RTEIT is about 20% lower than GPSR, 40% lower than MM-GPSR, and 30% than MOPR.In the process of successful transmission, the end-to-end delay is mainly determined by the number of hops. When the vehicle nodes are sparse, the packets frequently enter recovery forwarding mode which generates more hops in the routing. That can explain why the performance of end-to-end delay at 70 vehicles is clearly better than results at 30 vehicles. For GPSR,MOPR, and MM-GPSR, due to the lack of adequate consideration about network environment, data packets are likely to enter sparse road segment where the connection between nodes is not sufficient and more likely to encounter topological holes.  
Figure 10: Packet loss rate for varying the number of vehicles and CBR connections: (**a**) 5 CBR connections; (**b**) 10 CBR connections; (**c**) 15 CBR connections; (**d**) 20 CBR connections.  
For RTEIT, the establishment of routing is determined by real-time information traffic and vehicle movement. Due to the poor connectivity, the nodes around the topology hole frequently enter the recovery mode and have lower real-time information traffic in the calculation of vehicle utility value. Nodes with more available connections have a higher priority in routing. By choosing the next hop with high connectivity, RTEIT can minimize the use of recovery strategy, resulting in lower end-to-end delay than GPSR,MOPR and MM-GPSR. In most cases, GPSR outperforms the MM-GPSR because MM-GPSR provides a scheme that uses a fixed transport factor \(\lambda\) to determine the communication area Q, thus the value of \(\lambda\) is not suitable for the scenarios used in the experiment. From Figure 11d, the value of maximum end-to-end delay for MM-GPSR is 620 ms, for GPSR is 560 ms, and for REIT is 240 ms. The values of minimum end-to-end delay for these protocols are 280, 120, 170, and 100. The gap between traditional protocols and REIT on end-to-end delay reduces with the increase in the number of nodes. This clearly demonstrates that REIT has superiority compared with MM-GPSR, MOPR, and GPSR. In Figure 11b, it can be found that MM-GPSR outperforms GPSR at 30 and 40 vehicles. This result can be interpreted as: the benefits of fixed parameters \(\lambda\) equal to 0.3. From Figure 11, it also can be found that with the increase in the number of CBR connections, the end-to-end delay increases. More CBR connections lead to more serious network load, which prevents the nodes from forwarding the data packet just received because the packets in the queue have higher priority.  
Figure 11: End-to-end delay for varying the number of vehicles and CBR connections: (**a**) 5 CBR connections; (**b**) 10 CBR connections; (**c**) 15 CBR connections; (**d**) 20 CBR connections.