# Cognitive Routing in Software-Defined Underwater Acoustic Networks
## 4 Performance Evaluation
We can see from Figure 4 that increasing the number of channels increases the chance for the sensor nodes to have even more common idle subcarriers. However, for CA-SDUN, the packet delivery ratio is higher for different numbers of channels in comparison with the other two reference schemes. The reason is the selection of the relay node based on minimum transmission delay. When there is only a single channel in the network, there is a smaller number of idle subcarriers; hence, CA users face difficulty in accessing subcarriers free from a PU. Increasing the number of channels allows CA users to access the common idle sub-bands, and increases the chances for more sensor nodes to participate in the network. Hence, in this regard, the delivery ratio under CA-SDUN is the highest, compared to other scenarios, when the number of channels is \(M=5\), as shown in Figure 4c. Figure 5 shows the overhead ratio of CA-SDUN, Cog-AA-RP, and Cog-DVRP as a function of the number of sensor nodes, with the number of channels as a parameter. Overhead ratio is defined as the ratio of the number of control packets to the total number of packets in the network. The routing overhead for the three schemes increases with an increasing number of sensor nodes in the network. We observed similarity in all the schemes in terms of an increase in overhead ratio when the number of sensor nodes increased and the number of channels decreased--the more sensor nodes, the higher the message update rate. However, CA-SDUN outperforms both reference schemes. This is because of the centralized controller, which reduces the number of control messages in the network. Each querying/gateway node in CA-SDUN communicates with the LCs whenever it encounters a packet mismatch or it requires a route update. When these LCs know the route to the destination, they reply to the querying nodes with the updated route without communicating with the MC, and thereby reduce the message rate. Moreover, in any case of an unstable link due to the unavailability of any sensor node or channel, the querying node directly asks the LC for a route update without sending the packet back to the source node. However, in the reference schemes, nodes send beacon messages to all neighboring nodes for each update on the network state. The overhead ratio for both Cog-AA-RP and Cog-DVRP is higher than for CA-SDUN. In Cog-AA-RP, the AUV sends hello messages to all the first-hop neighboring nodes to choose GNs in a timely manner, and these GNs exchange messages with sensor nodes to collect data; hence, a large overhead is incurred. On the other hand, in Cog-DVRP, calculating the flooding zone further reduces the chances of successful packet delivery. This is because sensor nodes may not find a common idle channel for communications, which therefore increases the overhead ratio. Figure 5 also shows that increasing the number of channels increases the free subcarriers in the network, and thereby decreases overhead by providing a larger number of unused subcarriers to all types of sensor node for stable communications. A complete analysis of our simulation results shows that the SDN-based scheme outperforms non-SDN-based schemes, and using AUVs in the network enhances network performance. Figure 4: Performance comparison between CA-SDUN, Cog-AA-RP, and Cog-DVRP for the packet delivery ratio as a function of the number of sensor nodes with different numbers of channels, \(M\). (a) packet delivery ratio when \(M=1\); (b) packet delivery ratio when \(M=3\); and (c) packet delivery ratio 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 5: Performance comparison between CA-SDUN, Cog-AA-RP, and Cog-DVRP for overhead ratio as a function of the number of sensor nodes with different numbers of channels, \(M\).