# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 5 Conclusions  
This work proposed a methodology based on the 2k factorial analysis to find the most relevant factors affecting routing protocol performance in ad hoc IoST networks. To this end, we developed an enhanced framework called ST-INETMANET to include ad hoc IoST networking capabilities in OMNET++ and proposed using 2k factorial analysis to study the performance of different routing strategies in this kind of network. In general, the 2k factorial analysis provided in Section 4 shows that Density is the most critical of the investigated factors for the routing task in ad hoc IoST networks. Then, the NRT and HMP combination is in second place, followed by HMP alone in third place. Next, we further discussed which factor combinations provide better routing performance for the analyzed proactive and reactive routing approaches. The routing approach selection for different ad hoc IoST networking scenarios varies depending on the connectivity conditions. The proactive approach achieves the best performance for low node density (Density\(-\)) scenarios when the values of NRT and HMP are set to low. However, in a high node density (Density) scenario, the contention caused by the control messages (reflected in the increase in the number of packet collisions within the network) bound the routing performance. In this case, the low contention native of the reactive routing approach manages to perform better. Still, more connectivity represents better performance for both approaches. The freshness of routing information is a crucial factor in ad hoc IoST networking. A short HMP leads to a better performance in the evaluated scenario for both proactive and reactive routing approaches. However, the periodicity of the NRT parameter must be configured according to the routing approach adopted. A short NRT helps to quickly identify broken links but also could increase errors because a temporary packet loss could be miss-identified as a permanently broken link. This could unnecessarily trigger the repair mechanism if a proactive routing approach is used in an ad hoc IoST network. Therefore, the NRT value must be carefully chosen for this approach to identify broken links without unnecessarily increasing control overhead. High routing information freshness mainly benefits the proactive routing approach with a gain of 14%. Nevertheless, it is essential to highlight that, for high node densities (Density+), the reactive routing approach outperforms the proactive routing approach for most freshness combinations analyzed. This can be explained by the significantly higher average packet collisions observed in the ad hoc IoST network when the proactive routing approach is  
Figure 7: Comparison between the percentage of collisions from results of low freshness and high freshness. used. This is compared to the number of packet collisions observed when implementing the reactive routing approach. From the analysis and corresponding discussion presented in this work, we can provide the following guidelines when addressing the routing tasks for ad hoc IoT networking:  
* If the expected number of nodes participating in the ad hoc IoT network is low, then a proactive routing approach might provide better performance than reactive routing. * However, if proactive routing is used, care must be taken in tuning the information freshness parameters to achieve good routing performance. * For low node density, the best performance for both approaches is achieved when all considered nodes are willing to participate in ad hoc IoT networking tasks (NBH\(-\)). * A large NRT with a combination of a short HMP should be preferred for IoT networks with low node density. The previous statement is for both routing approaches (see Figure 4). * For an ad hoc IoT network with high node density (Density+), the reactive approach provided the highest PDR and the lowest number of packet collisions.