## 5 Performance Study
### Simulation Results and Analysis  
In order to test the routing scheme proposed in Section 4, we use NS2 simulator and also incorporate the underwater channel model developed by the authors of [43]. U-sensors and AUVs are deployed within a 2,500 m \(\times\) 2,500 m underwater region. The range of numbers of U-sensors and AUV are (20,80) and (1,4), respectively. The default value of the number of U-sensors is set to 50. The depth of water is assumed to be 2,000 m, which leads to around 0.16 s propagation delay over a distance of 250 m. To obtain more practical simulation results, the specification of a commercial acoustic modem, _UWM2200_ manufactured by LinkQuest Inc. [46], is used. For example, the transmit and receive power of the acoustic interface are set to 6 W and 1 W respectively, and the maximum data rate of the acoustic interface is set to 14 Kbps. It is assumed that each U-sensor periodically (every 30 s) sends data of 100 Bytes to the sink. GWs and the sink broadcast _PHE_ messages at every 150 and 60 s, respectively. AUVs broadcast a beacon signal at every 20 s, and it is assumed that AUVs can be periodically recharged or replaced during the underwater monitoring operation. Thus, in this work, we assume that only energy consumption of U-sensors affect the network lifetime. The average speed of AUVs set to 9.7 knots, which can be achieved by latest AUVs (e.g., AUV developed in [47] can move with a speed of 15 knots). In order to verify AURP, we compare it with a sensor network routing protocol, referred as _Grad_ hereafter, which is based on interests and gradients used in Directed Diffusion [11]. In \(Grad\), The sink periodically floods \(Interests\) to the network, which results in formation of \(Gradient\) in the opposite direction to the flow of the \(Interests\). The source nodes and relaying nodes use the \(Gradient\) to deliver data. Figure 5 shows the effect of the number of U-sensors on the network performance. As many as 50 U-sensors are randomly deployed in the area, and one AUV is used. AURP with different numbers of GWs, from 1 to 3, is compared with _Grad_. As shown in Figure 5(a), AURP and _Grad_ show a low delivery ratio when the number of U-sensors is 20. The reason of a low delivery ratio with 20 U-sensors is that there are not enough U-sensors to form a connected network. In a partitioned network, as shown in Figure 5(a), _Grad_ shows a very poor performance since a large portion of the sensing data can not be relayed to the sink. Meanwhile, AURP can achieve a relatively higher delivery ratio even in a partitioned network. When there are 40 U-sensors, AURP achieves over 0.9 delivery ratio, while _Grad_ achieves only about 0.5 delivery ratio. As the number of U-sensors grows, the delivery ratios of AURP and _Grad_ decrease, since the network becomes congested due to the high network traffic. Note that the delivery ratio of AURP is much higher than _Grad_ even in a dense network. For instance, with 80 U-sensors, AURP shows the delivery ratio in the range of (0.78, 0.88) depending on the number of GW, which is higher than twice of _Grad_ case. The results shown in Figure 5(a) also implies that deploying more GWs can improve the delivery ratio. In a network using AURP, the U-sensors consume less energy over different number of U-sensors than U-sensors in a network using _Grad_, as shown in Figure 5(b). The gap of energy consumption becomes greater as the number of U-sensors increases. Note that, in this work, the energy consumption of U-sensors is of interest, hence only U-sensors' consumed energy is collected. As shown in Figure 5(c), the packet delay in AURP is much higher than that of _Grad_, since a large portion of packets are carried by AUVs to be delivered to the sink. In other words, the packet delay mainly depends on the travel distance and the speed of AUV. Even though the delay can be reduced by optimizing the trajectory and increasing the speed of AUVs, the _store-carry-forward_ process will lead to an inevitable packet delay.