# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### A Performance Comparison of the Various Flooding Mechanisms
#### 4.3.3 Channel Access Contention  
We observed the performance of the flooding mechanisms from the viewpoint of channel access contention. We first focused on \(\overline{F}_{tx}\) defined as the average number of frames transmitted per node. Since the channel access contention can be mitigated by restricting each node from sending many duplicate frames, it can be evaluated with \(\overline{F}_{tx}\). Figure 12a shows that the \(\overline{F}_{tx}\) of BASE was mostly larger than 800 in the entire range of \(N_{node}\). However, in the case of PBF, it was very close to 1000, which means that every node relayed the same number of frames as the total number of frames generated at the source node. Recall that the node in BASE and PBF tries to relay all of the received frames without discarding some of them, thus \(\overline{F}_{tx}\) becomes equal to \(\overline{F}_{val}\) for these mechanisms. On the other hand, the other flooding mechanisms decreased \(\overline{F}_{tx}\) by selectively discarding duplicate frames in the transmission buffer. As \(N_{node}\) increased, \(\overline{F}_{tx}\) gradually decreased in CBF, DRBF, and DRBF\({}_{\text{RQ}}\), but it rather slightly increased in DBF, which means that DBF less aggressively suppresses duplicate transmissions as the density of nodes increases. By considering both \(\overline{F}_{tx}\) and \(\overline{F}_{val}\), we can say that DRBF\({}_{\text{RQ}}\) is the most effective mechanism because it decreases the channel access contention while providing high reliability of flooding; i.e., compared to BASE and PBF, DRBF\({}_{\text{RQ}}\) decreased \(\overline{F}_{tx}\) by 40% and 51% on average, respectively.  
Apart from \(\overline{F}_{tx}\), we evaluated the channel access contention with another performance index. As already confirmed in Figure 10b, the traffic dissemination time was very different especially in PBF, but it was not taken into account in measuring \(\overline{F}_{tx}\). Therefore, we introduced \(\overline{R}_{tx}\), which was calculated as the total number of frames transmitted by all of the nodes divided by the traffic dissemination time. Figure 12b shows that unlike the \(\overline{F}_{tx}\), PBF had the smallest value of \(\overline{R}_{tx}\), i.e., PBF mitigated the degree of channel access contention by spreading the frame transmissions over a long time. In terms of \(\overline{R}_{tx}\), the contention of channel access was the most severe in BASE. The \(\overline{R}_{tx}\) of DBF was comparable to DRBF\({}_{\text{RQ}}\). Compared to BASE and CBF, DRBF\({}_{\text{RQ}}\) decreased \(\overline{R}_{tx}\) by 24% and 14% on average, respectively,  
Figure 12: The performance comparison of the flooding mechanisms in terms of (**a**) the number of transmitted frames \(\overline{F}_{tx}\) and (**b**) the total number of transmitted frames per second \(\overline{R}_{tx}\).  
thus confirming that the proposed schemes are effective not only at reducing redundancy but also at mitigating the channel access contention.