## 6 Evaluation Results
The UDMSim results were compared against the theoretical results obtained using Equation (1) and the parameters of Table 3 for one and two DMUs [9], a TxPower of 30 dBm, and two dBi loop antennas. The simulation results were also compared with experimental results obtained by using a underwater testbed composed of two DMUs, one SU, and one CSU, as shown in Figure 14[9]. Watertight cylinders were used, and a 2.4 GHz IEEE 802.11n network was employed for the short-range link. Two different data sizes were considered: 200 MB and 500 MB. The maximum data were limited by the DTN implementation used in the testbed (IBR-DTN) [57]. Since the Wi-Fi card driver used only supported 20 and 40 MHz channels, experimental results were only obtained for the _docked_ scenario. Each ns-3 simulation was repeated five times with different seeds and the results were averaged. The confidence intervals obtained were short. For the sake of visualization, they are not represented in Figures 12-15. Figure 14: Testbed used to evaluate the GROW solution. Figure 13: The SNR effect on antenna alignment on DMU approaching the SU for a 1000 m distance using _RealisticNav_. Figure 15 shows the equivalent throughput (\(R_{b,eq}\)) over distance between 100 m and 5000 m for 1 DMU, considering the transfer of 200 and 500 MB of data for the _docked_ scenario. We can observe that UDMSim matches the experimental and theoretical values for _PerfectNav_ (no localization errors). Due to the position errors of _RealisticNav_, as seen in Figure 12, the link quality changes accordingly and so does the short-range throughput. UDMSim is able to simulate this phenomenon where the _RealisticNav_ can exceed the theoretical model, especially for ranges below 500 m, due to the fact that high SNR results in higher short-range throughput, making the data exchange faster than expected. We can also observe that the equivalent throughput increases with the amount of data to be exchanged since the travel time has the major impact on the equivalent throughput \(R_{b,eq}\). When considering two DMUs for the _docked_ scenario, we can observe in Figure 16 that UDMSim also matches the theoretical and experimental results. The usage of two DM  
The equivalent throughput for the _1 m apart_ scenario is shown in Figure 17. With an SNR around 15 dB, the theoretical short-range throughput is 3 Mbit/s. Although this value was fixed in the theoretical model, the Minstrel auto rate mechanism available in ns-3 was kept active in UDMSim; this can justify the slightly higher results obtained for _PerfectNav_ and _RealisticNav_ for the 500 MB case. _RealisticNav_ still shows sometimes lower equivalent throughput due to the signal variations, which in some cases results in TCP timeouts and re-associations, providing the realism lacking in the simple theoretical model presented in Section 4. When deploying two DMUs for the _1 m apart_ scenario, we can observe in Figure 18 that the UDMSim results also match the theoretical values, with a 28% equivalent throughput increase for _PerfectNav_ at 100 m and 10% equivalent throughput for _RealisticNav_. Although this margin faces out along the distance, UDMSim is able to simulate the advantage of using multiple DMUs for exchanging large amounts of data. Figure 16: Equivalent throughput for two DMUs in the _docked_ scenario. Figure 17: Equivalent throughput for one DMU in the _1 m apart_ scenario. These results show that UDMSim is a powerful tool for the validation of underwater communications solutions based on data muling. Although GROW was a pioneer on a mobile SU approach, other data muling oriented solutions can be evaluated using UDMSim. Due to the specific DTN implementation used in our lab testbed, which limited the data exchange analysis to 500 MB, UDMSim was able to simulate larger amounts of data, which brought up the advantages of a data muling solution even more for underwater communications when compared with point-to-point acoustic, for instance.