## 5 Results  
We tested the proposed GeoTag-based forwarding strategy using ndnSIM [26], the open-source NS-3 module that implements the NDN communication model, and a new GeoTag-based forwarding strategy module we developed for it [27]. The LEO constellation was configured with 30 orbital planes of 30 satellites, an inclination angle of 60\({}^{\circ}\) and an altitude of 400 km. Regarding the positions of the ground nodes, we chose different longitudes between latitudes 35\({}^{\circ}\) and 45\({}^{\circ}\) since this area corresponds to a highly populated region in the Northern Hemisphere, where the proposed constellation should provide good coverage. However, for the longitude, we avoided differencing between land and sea masses to keep the scenario simple. Additionally, the tracking period was established to 20 s.  
We simulated a variable number of consumers executing the _ConsumerPcon_ application. With this application (analogous to iPerf [28] in TCP/IP), we can measure the network utilization since it implements a TCP-like congestion control algorithm that allows the flows between the consumers and the producer to use all the available bandwidth. Note that, in this scenario, the rate of interest packets issued per second is not fixed, but is constantly changed by the chosen window adaptation algorithm. We also configured the consumers to start sending the interest packets at different instants to prevent the synchronization of the window adaptation algorithms. Regarding the producer, it is configured to always reply to the interest packets with data packets of a fixed size (1050 bytes). Finally, to keep things simple, we configured all the links in the simulation experiments with the same capacity. For testing the performance of our forwarding strategy, we firstly measured the percentage of bandwidth usage at the link from the producer to its satellite node. Each test comprises 10 random executions of the same scenario, applying a random position displacement to the ground nodes. Initially, we simulated a scenario where all the consumers send interests for the same data name from the producer. Figure 4 shows the average link usage at the producer (with 95% confidence intervals) for 4, 8, 16 and 32 consumers. We can see that our forwarding strategy achieves an average link usage from the producer of 90% with 4 consumers, but this percentage decreases as the number of consumers increases, falling to 70% for 32 consumers. The reason for this decrease is that, as the number of consumers increases, the probability that a NDN node receives multiple interests for the same data increases. In this case, the satellite node will only forward one interest packet, thus saving some bandwidth at the producer since all these pending interests will be fulfilled with just a single data packet. Note that, under these  
Figure 3: Minimum-hop paths between edge nodes. conditions, the links to the consumers from their corresponding satellites will get fully utilized, while there is still spare capacity at the producer. To illustrate this effect, we repeat the experiment, but this time, each consumer will request different data. As expected, the results in Figure 5 show that the average link usage for 4 consumers is now slightly higher (94%). In addition, in this case, the link usage slightly increases with the number of consumers, reaching a value of 95% for 16 and 32 consumers, which is an excellent result for our Forwarding Strategy, despite the short periods of unavailability caused by the satellite handovers. (When a handover occurs at the consumer in the time between the original interest transmission and the reception of the corresponding data, the data do not reach the new satellite serving the consumer but the old one. This is because in an NDN network, data packets follow the reverse path recorded in the PIT tables by interest packets.