# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 6 Discussion  
In the following, we include a discussion of some aspects that detail our findings in the statistical analysis of the named data ultra-dense network.  
First, take a look at individual predictors one by one. It is evident from the results that when NI was at 250, the network achieved a higher mean ISR of approximately 98%. The mean ISR decreased when NI was set higher than 250, and until NI = 500, the network achieved the maximum ISR value of 60%. If the aim was to get a higher ISR in the network, then the NI value should be equal to or less than around 250. In other words, if there were multiple users in the stadium, then a maximum of approximately 250 requests per second should be allowed from individual users towards the edge node. By doing this, the network may achieve a higher packet delivery ratio or ISR. However, if the requirement was exceeded and thus the network needed to allow more than 250 requests per second, then requests must be forwarded towards other edge nodes to ease the burden on the single edge node.  
The NN had a significant impact on the ISR. Dividing the NN into two groups NN less than 10 and NN greater than 10 had a significantly different impact on the ISR. The mean effect on the ISR was relatively constant when the number of nodes was between 25 and 49. The network attained a maximum (third quartile) of 85% ISR when the NN was set at 25-49. However, when the number of nodes was less than 10, the network achieved the maximum (third quartile) 99% ISR. For the network goal to achieve at least 80% ISR, there should be approximately 25 WAPs, which are optimal for a single edge node. However, in this case, each WAP or IoT device should not forward more than 250 interests per second. Since 250 requests per second are in fact a very high number of requests from the 25 WAPs, including the NN and NI in the network analysis can provide a better prediction with a higher interest satisfaction rate.  
RB had a very significant impact on ISR. When the RB was set at 1 Mbps, the network achieved a small degree of the ISR. However, when the RB was set at 20 Mbps, the network achieved higher ISR. When the RB was in the middle bandwidths of 5 and 10 Mbps, the range of the ISR was wide and shifted upward. Because of this phenomenon, the log(RB) better fit than the RB to predict the ISR. For the RB to be at 20 Mbps, the mean ISR and maximum ISR were raised to approximately 99%, which was optimal for the maximum ISR. In other words, the bandwidth from the WAP to the edge device should be at least 20 Mbps if the goal of the network was to achieve the highest packet delivery ratio with 250 requests per second from a maximum of 25 nodes.  
Although the two interaction plots appeared to reveal slight interactions between NN and RD and NI and RB, they turned out to be insignificant. The nonexistence of interaction effects between selected predictors is a revealing discovery since the predictors can be considered independent in the design of the ultra-dense network. Based on the model that includes NI, NN with two categories of less than 10 or greater than 10 and log(RB), the behavior of ISR can be further predicted for the predictor values that are not used in the dataset or simulations.  
All three factors, NN, NI, and RB, are essential and must be included in the design considerations of ultra-dense networks. The statistical analysis results presented in our paper had the potential to address the four essential design requirements (multimedia support, real-time and reliable connection, edge computing, and bottleneck mitigation) of ultra-dense networks by choosing an appropriate level of each predictor.