# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 4 Discussion
### Analysis of Factor Values Providing the Best Performance
#### 4.2.1 Density  
Results are presented in Figure 4 for low and high density values. The results show that density plays a significant role in the network's performance. The change from low to high density increases the PDR from 9.8% and 14.6% to 84% and 80% for the reactive and proactive approaches, respectively.  
The parameters with the best performance in low connectivity scenarios are the same for both routing approaches: large NRT, a low number of retransmissions, short HMP, and 0% of unreachable nodes (NBH\(-\)). In mission-agnostic scenarios with a low density of nodes, the routing task benefits from having a maximum number of available nodes willing to participate in packet relay. At the same time, these results highlight that for low node density ad hoc IoST network deployments, large NRT with the combination of a short HMP should be preferred. The previous result indicates that the updated neighbor information is needed as quickly as possible for this case, but with more time for each neighbor as valid in the routing table (e.g., a node needs to fast-track possible changes in its neighborhood). However, the low number of participating nodes implies that it is preferable to try to send packets through older nodes rather than having no nodes in the routing table at all. Note that this applies to both routing approaches analyzed.  
A short HMP also provides the best performance for high-density scenarios. However, the best value for the NRT factor is different for each routing approach. A high NRT value leads to the best performance for the reactive protocol. In contrast, a low NRT parameter works better for the proactive routing approach.  
Figure 4: Comparison of factor density between the best scenarios of a proactive and reactive routing approach in IoST with high and low connectivity.  
Note that the reactive algorithm outperforms the proactive approach in ad hoc IoT scenarios with high node density. Conversely, the proactive algorithm performs better in low node density scenarios. This change can be explained considering that a short HMP (low value) and short NRT (low value) increase the control information for the proactive approach. Thus, the number of packet collisions in an ad hoc IoT network with high node density should be considerably higher for proactive routing than reactive routing. This is confirmed by the results presented in Figure 5, which compare the average number of packet collisions per trial obtained using the proactive and reactive approaches analyzed in this work. Note that the results presented in Figure 5 were obtained using the factors that provide the best performance for each routing strategy. These results confirm that the average number of packet collisions in an ad hoc IoT network with high node density is significantly higher for the proactive approach than the reactive approach. Thus, this explains why, for an ad hoc IoT network with high node density, a higher PDR was achieved when using the reactive routing approach (see Figure 4).  
Figure 5: Comparison of the average number of collisions in the best scenario from the factors results of a proactive and reactive routing approach in IoT with the density factor (e5). (**A**) Low node density; (**B**) high node density.