## 5 Performance Evaluation
### Tuning of the Multihop P2 MP Settings
#### 5.1.3 Varying the Network Scenario  
We now show what happens when network conditions are modified. More specifically, we show what happens if quasi-simultaneous broadcasts are started at nearby UEs (which may register the same event, e.g., a collision, and start dissemination independently). Moreover, we discuss how the performance of MDB is affected by UE density in the network, and the impact of selfish users.  
In order to assess the impact of multiple originators for an event, we perform simulations where two broadcasts are started simultaneously by two UEs located at a maximum distance of 20 m. Figure 20 shows that the 95 th percentile of the delay is slightly smaller than in the case where the broadcast has a single originator. This is because two UEs transmit simultaneously at the first hop and their messages can be possibly received by a few more UEs than in the single-originator case. On the other hand, merging makes little difference as far as delay is concerned, once we assume two originators. The effects of merging two broadcasts into a single Trickle instance are instead visible in terms of allocated RBs, as shown in Figure 21. As expected, having two independent broadcasts  
Figure 19: SRA vs. ARS, delivery ratio.  
doubles the number of allocated resources, whereas the RB occupation in the case of merged broadcasts is essentially the same as the case with a single originator.  
We now assess the performance of MDB in denser networks. We increase the number of UEs served by each eNB from 30 to 150, resulting in a total number of UEs of up to 750. Again, we use a target area with a 1000 m radius. Figure 22 shows the 95 th percentile of the delay. We observe that the delay decreases with the UE density, and it is slightly above 80 ms with 750 UEs. This is explained by the larger number of UEs receiving a single broadcast message, hence a larger number of potential relays. This in turn increases the probability of reaching farther UEs. Clearly, more transmissions come at the cost of a higher resource consumption, as reported in Figure 23: the number of allocated RBs per broadcast increase from 130 RBs (with 150 UEs) to about 400 RBs with 750 UEs. Interestingly, the ratio between the allocated RBs and the number of UEs decreases. In fact, more transmissions means more duplicates too, hence more UEs abstain from transmission thanks to the Trickle suppression mechanism.  
Figure 21: Avg. allocated RBs per broadcast with multiple broadcast sources.  
Figure 20: 95 th percentile of delay with multiple broadcast sources.  
As discussed in Section 4, some selfish UEs may refuse to relay messages, e.g., to save their battery. In order to assess the impact of selfish users in MDB, we simulated scenarios with an increasing percentage of selfish UEs, at different densities. Figures 24 and 25 show respectively the 95 th percentile of the delay and the number of RBs allocated per broadcast. As expected, increasing the percentage of selfish UEs results in a larger, though tolerable, delay. This is due to the reduced number of transmissions, which in turn also reduce the number of allocated RBs. In any case, the reliable delivery of the message is ensured by the Trickle suppression mechanism: in fact, (cooperative) UEs relay the messages if they perceive that their doing so is necessary to its diffusion. It is worth noting that the results obtained with 300 UEs and 50% of non-cooperative UEs (rightmost end of the dotted line in Figures 24 and 25) are similar to those obtained with 150 UEs and no selfish UEs (leftmost end of the dotted line in Figures 24 and 25).  
Figure 23: Avg. allocated RBs per broadcast, dense scenarios.  
Figure 22: 95 th percentile of delay, dense scenarios.  
the continuous line in Figures 24 and 25). All things considered, the presence of randomly placed selfish users can be accounted for as an equivalent reduction in user density as far as latency and RB utilization are concerned.