# Cognitive Routing in Software-Defined Underwater Acoustic Networks
## 4 Performance Evaluation
This is because the connectivity in the network increases with an increase in sensor nodes, and in the latter case, the large number of channels in the network increases the chances for the sensor nodes to have even more common idle subcarriers. With fewer sensor nodes, the delay is high for all the schemes because a packet usually has to wait longer than normal to find the next hop node. Moreover, finding a common idle channel in cognitive communications scenarios is another reason for packet delay. As our goal is to maintain network stability by providing the best route between source and destination, we therefore applied the SDN approach where controllers know the route to the destination by keeping an updated network topology. In CA-SDUN, considering that the AUVs on fixed trajectories serve as LCs further reduces the delay. When these LCs know the route to the destination, they reply to the gateway/querying node with the updated route without communicating with the MC, and thereby reduce network delay. On the other hand, AUVs in Cog-AA-RP select nodes based on the distance and neighbor information of the first-hop node after finding a common idle channel between two communicating nodes. Hence, CA-SDUN outperforms Cog-AA-RP because it allows each querying node to directly make a connection with the controller to get a stable route. Also, both CA-SDUN and Cog-AA-RP outperform Cog-DVRP. This is because Cog-DVRP restricts the neighboring set for the querying node. The querying node is bound to select a relay node within the flooding zone. As this is a cognitive routing scheme, the elementary step of selecting a common idle channel between two communicating nodes further degrades the performance of this reference scheme. As a result, finding a relay node within the flooding zone decreases network performance by reducing the number of sensor nodes. Figure 3: Performance comparison between CA-SDUN, Cog-AA-RP, and Cog-DVRP for average delay as a function of the number of sensor nodes with different numbers of channels, \(M\). (**a**) average delay when \(M=1\); (**b**) average delay when \(M=3\); and (**c**) average delay when \(M=5\). CA-SDUN: cognitive acoustic software-defined underwater network; Cog-AA-RP: cognitive AUV-aided routing method integrated path planning; Cog-DVRP: cognitive diagonal and vertical routing protocol; AUV: autonomous underwater vehicle. Figure 4 shows the performance of packet delivery ratio as a function of the number of sensor nodes with the number of channels as a parameter. Packet delivery ratio is defined as the ratio of the number of packets delivered to the destination to the number of packets generated by the source node. The delivery ratio increases with an increase in the number of sensor nodes. The SDN approach in CA-SDUN improves network performance in terms of delivery ratio because of the logically centralized controller that dictates the behavior of the network. In Cog-DVRP, a querying node has to select the next node for every hop until it reaches the surface of the ocean. By doing so, it may come across several fragile links due to greater delay, resulting in link failures and a low packet delivery ratio. However, Cog-AA-RP outperforms Cog-DVRP because the AUV is responsible for forwarding data from sensor nodes to the surface of the ocean. Nevertheless, the selection of a GN by the AUV with the restriction of a common idle channel lowers the delivery ratio in comparison with CA-SDUN. In our scheme, the MC keeps the global view of the network, which means the MC manages all the information about idle channels and relay nodes. Therefore, by calculating the path duration, the MC provides the best stable route between source and destination to each querying node. In the cognitive underwater environment, in addition to underwater environmental challenges, another factor that affects the packet delivery ratio is the selection of a common idle channel.