## 5 Performance Evaluation  
For performance evaluation, we compared Re-Intar and Intar with three existing schemes: WDFAD-DBR, Hydrocast and H2-DAB. The existing schemes avoid void hole in both sparse and dense network deployments. We compared the existing work by increasing the node density to show the effectiveness of the proposed work. The comparison was made on: packet delivery ratio, energy tax, end-to-end delay and accumulative propagation distance. In simulations, we randomly deployed 100-500 sensor nodes in a three-dimensional region of 10 km \(\times\) 10 km \(\times\) 10 km. Nine sinks were uniformly placed at the surface of the water. We assumed that the sinks remain stationary after deployment and sensor nodes change their position with water current. It is important to note that, each sensor node moved in a horizontal two-dimensional direction, i.e., X-Y plane (random walk 2D mobility model). The movement speed of a node was 1-3 m/s. In vertical direction, the movement of node was usually small, thus negligible [34; 35]. The simulation parameters are listed in Table 3.  
To evaluate the performance of Intar and Re-Intar, we used the metrics enlisted in Table 4.  
Figure 4 shows that PDR of WDFAD-DBR, Intar and Re-Intar increases with increase in the number of nodes. When the network is sparse, the probability of void hole is high and fewer packets are successfully delivered to the destination. In dense network, more packets are successfully delivered to the destination. Figure 4 also shows that the performance of Re-Intar protocol is better than WDFAD-DBR and Intar in terms of PDR in both sparse and dense networks. In WDFAD-DBR, PFN considers the depth of current hop and the depth of next expected hop while forwarding packet from the source node. Two-hop forwarding metric does not eliminate the chances of void hole occurrence, which decreases its PDR. Intar protocol shows better PDR than WDFAD-DBR because it avoids void hole(s) due to consideration of end-to-end path. In addition to the consideration of end-to-end path, Re-Intar also uses one backward transmission resulting in further improvement in terms of PDR. Moreover, WDFAD-DBR has no mechanism to reduce interference in high node densities. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Maximum transmission power & 50 W \\ Power threshold for receiving packets and idle state & 158 mW \\ Maximum transmission range & 2 km \\ Center frequency & 12 kHz \\ Bandwidth & 4 kHz \\ Data rate & 32 kbps \\ Acoustic propagation speed & 1.5 km/s \\ Deployment region & 10 km \(\times\) 10 km \(\times\) 10 km \\ Number of sinks & 9 \\ Number of sensor nodes & 100 to 500 \\ Movement model & Random walk 2D mobility model \\ Header size & 11 bytes \\ Payload & 72 bytes \\ ACK or neighbor request & 50 bits \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter setting. Intar and Re-Intar reduce channel interference by selecting PFN with the fewest neighbors as the next forwarder. Thus, the performance of Intar and Re-Intar is better than WDFAD-DBR when node density is increased. Relatively, Re-Intar is approximately 16.96% more efficient than WDFAD-DBR and 7.26% more efficient than Intar in PDR. Hyrocast has lower PDR than all the proposed and existing (H2-DAB) schemes. The reason is the backward transmissions which increase the path length and result in higher energy dissipation. The more energy dissipation means lower lifetime along with the smaller PDR which is evident from the Figure 4. The H2-DAB performs better initially, but, as the node density increases, the broadcast of hop id message to find unique identifiers results in interference. When node number is 100, the PDR is 70%, while it ends up with almost the same PDR as WDFAD-DBR.