## 5 Performance Study
### Simulation Results and Analysis
Therefore, the proposed network architecture will be suitable for an underwater monitoring system that requires high delivery ratio, low energy consumption, and allows the packet delay of several hundred seconds. Although this delay may pose a limitation in applying the network to some military/tactical applications that requires strictly real time data transmissions, the proposed architecture can be used for most applications that can tolerate several minute delay yet require high rate and reliable data transmissions. The packet delay results in other scenarios are similar due to the fact that the packet delay mostly depends on the AUVs's speed and trajectories rather than network situation. Therefore, from now on we focus on the data delivery ratio and energy consumption as performance metrics. Figure 6 shows the effect of the network load on the performance. The \(\times\) axis represents the sensing intervals from 50 s to 10 s. As shown in Figure 6(a), AURP shows a much higher data delivery ratio than _Grad_ in all cases. The figure also shows that the performance is improved with a larger number of GWs. Moreover, the difference of delivery ratio values becomes larger when the network load is  
Figure 5: Effects of number of U-sensors on the network performance. high. As shown in Figure 6(a), when the sensing interval is 10 s, the data delivery ratio of AURP with three GWs is around 0.9, while that of _Grad_ is less than 0.2. It is because, when the network load is high, the transmissions in _Grad_ experience network congestion which results in a high level of packet collision. On the other hand, in AURP, the increase of the number transmissions is much lower than that of _Grad_ due to less number of hops between U-sensors and the sink. The total energy consumption of the U-sensors using AURP is lower than those with _Grad_. As shown in Figure 6(b), _Grad_ based U-sensors consume more energy for data transmission and reception than U-sensors using AURP. This result also comes from the fact that U-sensors using AURP transmit less packets. A higher performance of AURP can be achieved by using more than one AUV. Figure 7 shows the effect of multiple AUVs on the delivery ratio and energy consumption. As the number of AUV increases, a higher delivery ratio can be achieved. For example, AURP achieves 0.97 delivery ratio when (#AUVs, #GWs) = (3,3), while it shows 0.85 delivery ratio when (#AUVs, #GWs) = (1,3) as shown in Figure 7(a). Note that _#GWs_ represents the number of GWs for each AUV; if (#AUVs, #GWs) = (3,3), then there  
Figure 6: Effects of Network Loads on the network performance. Figure 7: Effects of Number of AUVs on the network performance. are 9 GWs in total. _Grad_ has a constant value, approximately 0.5, over the simulations since it does not use AUVs. The number of AUVs also has a positive effect on the energy consumption. As shown in Figure 7(b), the U-sensors use less energy when there are more AUVs, since U-sensors can send data to AUVs with a shorter path. The trajectories that AUVs follow also affect the network performance as shown in Figure 8. Four trajectories, \(Traj_{1},\ldots,Traj_{4}\), shown in Figure 3 are used. The X-axis represents the value of \(a\) shown in Figure 3. As shown in Figure 8(a), \(Traj_{2}\) and \(Traj_{3}\) show a better data delivery ratio. This is because the number of transmissions due to multihop relay becomes larger when an AUV follows a too small or a too large trajectory, which implies that selecting the trajectory of AUVs is also important to maximize the network performance.