## 3 An Opportunistic Routing for Data Forwarding Based on Vehicle Mobility Association
### The Algorithm Design of OVMA
The analysis of the data delivery ratio metric is shown in Figure 8. It describes the degree to which data are delivered successfully. As shown in Figure 8, in this metric respect, OVMA achieved a better performance than DSR, MAV-AODV and OSTD. When the number of vehicles increases, the data delivery ratio of OVMA becomes more stable. This is because of the exchange of replica information (EM.HM) and because the degree of association between vehicles can ensure the rapid delivery of data. The routing overhead metric is shown in Figure 9. Routing overhead covers all route discovery and route maintenance control messages. This parameter reflects the routing overhead caused by the routing protocol. Due to the low bandwidth of the ad hoc network, routing overhead directly affects the effective transmission rate of the network. Figure 8: Data delivery ratio vs. number of vehicles for different algorithms. GPSR: Greedy Perimeter Stateless Routing; OLSR: Optimized Link State Routing; D-ODMRO: destination driven on demand multicast routing protocol. Figure 7: Data forwarding delay vs. value of \(\alpha\). According to Figure 9, when there are very few vehicles, OVMA has a higher routing overhead than the other three. On the contrary, when there are a large number of vehicles, the OVMA was slightly better than the other three because the route discovery becomes faster with a high route maintenance control. The data delivery delay metric is shown in Figure 10. The data delivery delay is primarily reflected in the data-storage time for the node. If the data passes through the intermediate nodes in the transmission from the source to the destination, the data are frequently stored and forwarded to the intermediate node so that the transmission delay is too long, which will inevitably affect the communication efficiency. Figure 10: Data delivery delay vs. number of vehicles for different algorithms. Figure 4:0 Routing overhead vs. number of vehicles for different algorithms. According to Figure 10, OVMA achieved better performance due to the OVMA passing data by determining the degree of association and data-transfer probability between vehicles. It can enable the fast transfer of data from the source into the destination without the need to transmit to too many intermediary vehicles. As is clear from Figure 11, the routing overhead of OVMA increases gradually as the number of vehicles increases. As the number of vehicles increases, the frequency of routing is greater. As a result, the path discovery is relatively slow by the relative increase in the number of nodes by passing messages. ## 5 Conclusions

This paper applied the mobility association of the vehicle to find an optimal data transmission path, achieving fast data forwarding and transmission function in a low-delay situation. This strategy was implemented and evaluated in a new opportunistic routing protocol for vehicular ad hoc networks called OVMA, which comprises two phases: an information efficient region phase and an information efficient region boundary phase. The use of the vehicle mobility association brings about high-speed and high-performance data forwarding in the case of low data delay and routing overhead. For vehicles which are at the boundary of an information efficient region, whether or not to exchange the replica information is judged by the angle between their moving directions and the source of information they are currently in. From the simulation results, we observed that the OVMA had a better performance than GPSR, OLSR, and D-ODMRO. There were improvements in the data delivery ratio, routing overhead, and data delay metrics. We conclude that the OVMA algorithm can not only maintain a high data delivery ratio, but also maintain the stable transmission of network information, without being influenced by node caches and the area of information perception. As a future work, we will consider more factors that affect network performance, such as bandwidth, network load, and so on.