## 4 Simulation Results
### Performance Evaluation of MMMR  
In this section, we present a performance evaluation of the MMMR protocol using the same weight in each one of the four metrics, _i.e._, \(w_{j}=1/4\). We compare the results to a modified MMMR in which routing decisions are made considering only one single metric. The evaluation was made in both low and medium density scenarios, and the results are depicted in Figure 8.  
We can see that MMMR (first column) obtains the best results in terms of packet losses (see Figure 7(a)), followed by the option of taking the forwarding decision based only on the metric \"trajectory\", that is, \((w_{1},w_{2},w_{3},w_{4})=(0,1,0,0)\). Packet losses (Figure 7(a)), end-to-end delay  
Figure 7: The total throughput of the received packets comparing the protocols, AODV, GPSR, I-GPSR, GBSR-B and MMMR, are presented in (**a**) a low density scenario (60 nodes) and (**b**) a medium density scenario (120 nodes) (CI 95%). (Figure 8b) and the average number of hops (Figure 8c) are best when the density of the scenario is 120 nodes, because the probability of finding a good neighbor is higher than in the case of low density scenarios (60 nodes). Moreover, in the low density scenario, the probability of using the buffer to store packets is higher, so that higher delays are obtained. Given that throughput is related to packet losses, we can see that in the medium density scenario, there is a higher throughput (Figure 8d), because lower losses were produced (Figure 8a). With these results, we can conclude that our proposal based on the use of four metrics to make forwarding decisions is a good choice to decrease the packet losses in vehicular scenarios and to obtain a low delay in both low and medium density scenarios. Figure 8: The performance of multimetric routing using single metrics in a low (60 nodes) and a medium (120 nodes) density scenario compared to MMMR. The results show: (**a**) the percentage of packet losses; (**b**) the average packet delay; (**c**) the average number of hops to destination; and (**d**) the throughput of the received packets (CI 95%). In Figure 9, we present the results of the MMMR protocol compared to another modified version that considers only some combinations of metrics. Packet losses are slightly lower in the medium density scenario when forwarding decisions are taken based on the combination of the metric \"distance + density\", \((w_{1},w_{2},w_{3},w_{4})=(0.5,0,0.5,0)\). However, it is not the case in the low density scenario, where the best results are obtained with MMMR \((w_{1},w_{2},w_{3},w_{4})=(0.25,0.25,0.25,0.25)\); see Figure 9a. These results prove that the density of the scenario is a very important parameter in VANETs to obtain the final score of the candidates to make the forwarding decisions. The behavior of packet delay and the number of hops in the low density scenario are as expected, _i.e._, a low number of hops produces a high delay, as is the case for the \"distance + ABE\" combination \((w_{1},w_{2},w_{3},w_{4})=(0.5,0,0,0.5)\). The reason is that the use of the buffer, _i.e._, short paths in low density scenarios, means that packets spent a  
Figure 9: The performance of MMMR using four combination of metrics in low (60 nodes) and medium (120 nodes) density scenarios, compared to MMMR using the four metrics. The results show: (**a**) the percentage of packet losses; (**b**) the average packet delay; (**c**) the average number of hops to the destination; and (**d**) the throughput of the received packets (CI 95%). significant time in the buffer. In the case of the medium density scenario, it shows a quite stable number of hops and lower delays, because there is a higher number of neighbors, and it was not necessary to use the local buffer of the nodes often to store packets temporarily until finding a good next forwarder; see Figure 9a,b. We can conclude that the metric of the vehicles' density is decisive in both scenarios, since it has a high impact on the results.