# Cognitive Routing in Software-Defined Underwater Acoustic Networks
## 4 Performance Evaluation  
We evaluated the performance of CA-SDUN in ns-MIRACLE [39] connected with a Bellhop channel simulator [38] via the World Ocean Simulation System (WOSS) [34] interface. We distributed randomly placed sensor nodes in a target area of 700 m \(\times\) 700 m \(\times\) 700 m, each having a transmission range of 100 m. The spectrum band (10-40 KHz) was divided into \(M\) = 5, and each channel was able to be occupied by a licensed PU. The bandwidth of each channel was 6 KHz, with carrier frequencies of [13; 19; 25; 31; 37] KHz. This means that a band of frequencies is free for use by legitimate users in order to transmit data packets over a number of free subcarriers. The total number of subcarriers, \(S\), was 128, and each had carrier spacing of 46.875 Hz. As we know that channel sensing is affected by sensing time in any cognitive radio network, we therefore used fewer subcarriers to make it reasonable for underwater cognitive acoustic networks. For the same reason, we used the length of cyclic prefix \(T_{CP}\) at 12.4 ms, with symbol duration \(T_{s}\) at 21.33 ms, which is greater than the typical value of the delay spread in underwater networks (i.e., approximately 11 ms [30]). The number of PUs moving randomly in the network was two. The number of AUVs used was two, each moving at a speed of 1.5 m/s and having a transmission range of 300 m. There was one MC placed at the surface of the  
Figure 2: A flowchart representing the CA-SDUN protocol. MC: main controller. ocean. The number of sensor nodes varied from 10 to 30. Packet size \(L_{\text{s}}\) was assumed to be fixed (i.e., 64 bytes), and transmission power \(P_{\text{T}}=150\) dB re \(\mu\) Pa, which is within the range of the power value for acoustic signals of dolphins [27]. As argued in Section 2, there is no publicly known cognitive routing protocol for software-defined underwater networks that combines a cognitive capability with a routing technique by using the SDN approach. Therefore, we chose to compare CA-SDUN with two reference schemes (AA-RP [18] and DVRP [14]), each in combination with an energy detector-based spectrum sensing scheme [40] for underwater cognitive sensor networks. For simplicity, we denote these schemes as Cog-AA-RP and Cog-DVRP. AA-RP considers AUVs as mobile sinks that collect data from sensor nodes and forward the collected data to the surface. A gateway node (GN) is an agent of an AUV which communicates with the AUV when ordinary sensor nodes fail to make a connection. The GN stores information until it forwards it to the AUV. Cog-AA-RP modifies the AA-RP protocol such that each sensor node (including the GN and the AUV) first senses the spectrum and exchanges the local sensing results to find common idle channels. Finally, it implements the key idea of AA-RP to collect data to forward to the surface station. DVRP is a flood-based routing protocol for underwater sensor networks that forwards data packets (based on the flooding zone angle) from the sender nodes towards the surface of the ocean. It selects the next-hop node within the defined flooding zone. Like Cog-AA-RP, Cog-DVRP modifies the DVRP protocol such that each sensor node exchanges the spectrum sensing results within its defined zone in order to find a common idle channel. The two communicating nodes, having consensus about a common idle channel, then exchange data packets to establish a stable route between source and destination. Figure 3 shows the performance of average delay as a function of the number of sensor nodes with the number of channels as a parameter. Average delay is defined as the average time required by a packet sent from the source node to reach the destination. The average delay decreases with an increase in the number of sensor nodes. The pattern is the same for the three schemes, i.e., when increasing the number of sensor nodes and increasing the number of channels, delay decreases.