# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 2 Related Work
In addition to the work reported in [24; 41], the authors further explored a proof of concept in [43], demonstrating the behavior inside the local memory when realizing data transmission between two CubeSats. This proof of concept considered using a proactive routing approach designed after OLSR and Better Approach to Mobile Ad hoc Networking (BATMAN) routing protocols. These experiments further confirmed the feasibility of using CubeSats and ad hoc routing protocols for IoST networking. The previous review of relevant state-of-the-art addressing ad hoc IoST networking showed that it is feasible to use ad hoc routing strategies to enable IoST networks, even when using routing protocols initially developed for MANETs. Nevertheless, these protocols were not specifically designed to cope with the particular characteristics of ad hoc IoST networking. For example, as spacecraft may participate in networking tasks only during idle periods of their primary mission, the node density in an ad hoc IoST network might show high variability. Similarly, because of the different speeds, orbits, and trajectories followed by different spacecraft, the routing strategy in ad hoc IoST networks must cope with highly dynamic changes in the topology. Therefore, to fulfill the potential offered by ad hoc IoST networking, there is the need to develop new routing protocols specifically designed for this kind of network. Nevertheless, before developing these protocols, the existing conditions of ad hoc IoST network deployments must be investigated to identify critical factors that affect network performance. This is not a trivial task; in this work, we propose a methodology based on 2k factorial analysis to identify such critical factors. The 2k factorial analysis approach has been used in different fields [54; 55] to discriminate between the importance of several parameters involved in one process. In the routing research area, a few works have used this methodology [56]. For example, 2k factorial analysis was previously used in [57; 58] as a statistical tool to identify the most representative factors affecting message dissemination for traffic safety applications. Here we propose using 2k factorial analysis to find the critical factors involved in the routing task for ad hoc IoST networking, intending to provide a baseline upon which new routing protocols can be designed and validated. For the analysis, we considered the two main approaches for routing in MANETs: proactive and reactive routing. These approaches differ in the mechanism used to search and maintain network routes. Proactive protocols continuously broadcast control packets to search and maintain routes [59]. The de-facto accepted standard for this approach is the \"Optimized Link State Routing Protocol\" or \"OLSR\" [52]. On the other hand, reactive routing primarily executes route-search mechanics when information packets need to be disseminated [30]. For this approach, the de facto standard is \"Ad hoc On-Demand Distance Vector\" (AODV). In addition, there are hybrid protocols that combine the two approaches. The suitability of each approach is closely related to the targeted network topology and its dynamics. Proactive routing protocols are commonly used when network nodes are static or have low mobility [49], as communication links last longer. Conversely, reactive protocols are commonly used in highly dynamic topologies with frequent link disruptions [60]. However, tuning proactive protocols can improve their performance for dynamic topologies, as was shown in [50]. Thus, selecting the routing approach for ad hoc IoST networking is not a trivial task, as critical parameters for protocol performance must be identified first. To the best of our knowledge, no previous research has formally identified the factors that significantly affect the performance of routing protocols in ad hoc IoST networks. Thus, in the remainder of this paper, we introduce our proposed methodology to identify such factors.