# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 4 Simulations and Results
### Aus  
The simulation parameters [4] are given in Table 2. AUVs varies from 30 to 100, are deployed in a three dimensional network of 5 km \(\times\) 5 km \(\times\) 5 km. AUVs move independently with maximum speed of 15 knots (7.716 m/s). The transmission range of AUV is set 1000 m and twelve AUVs are deployed at strategic locations in the network while rest of AUVs are randomly deployed in the network. Each AUV transmits its sensed data to the destination after every 60 s.  
Figure 12 shows the packet delivery ratio. Hydrocast has low throughput when the network density is low. The reason is that the breakage of established recovery paths for void nodes due to  
Figure 11: Feasible Region for Throughput Maximization. the mobility of sensor nodes. In sparse network conditions, more void nodes are found that need recovery paths for the data packet transmission. As the network density increases, the number of void nodes decreases, and ultimately less number of recovery paths are established in the network and vice versa. Improved Hydrocast shows better performance due to the deployment of nodes at the strategic locations in the network. Co-Hydrocast also has better results than the Hydrocast due to the cooperation technique in Co-Hydrocast. Whenever, a data packet fails to successfully reach the destination, or the received signal SNR according to Equation (17) is lower than the threshold, MRC technique is used to combine signals for avoiding re-transmissions. Co-Improved Hydrocast outperforms all compared schemes, and has the highest packet delivery ratio. It takes the advantage of both opportunistic cooperation and strategic location mechanism to improve the PDR of the network nodes. In Figure 13, the average energy consumption of the successfully delivered packets is shown. Improved Hydrocast has low energy consumption due to the establishment of short recovery path for void nodes. The strategic deployment of sensor nodes in the Improved Hydrocast played an important role in reducing the length of recovery paths in the sparse network conditions. Co-Improved Hydrocast scheme consumes more energy than the Improved Hydrocast due to the opportunistic cooperation technique that is implemented to reduce the packet drop ratio in the network. In cooperation, the relay node also forwards the packet to the destination which results in more energy consumption. In Hydrocast, the energy consumption for each data packet delivery is higher than the other two proposed schemes. Recovery paths for void nodes in Hydrocast are lengthy in sparse network conditions, due to which more energy is consumed to deliver the data packet to the destination. \begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Network Dimension for Nodes & 1 km \(\times\) 1 km \(\times\)1 km \\ Transmission Range of Node & 250 m \\ Speed of nodes & 0.3 m/s \\ Network Dimension for AUV & 5 km \(\times\) 5 km \(\times\) 5 km \\ Transmission Range of AUV & 1000 m \\ Number of AUVs & 30 to 100 \\ Speed of AUV & 7.716 m/s \\ AUV transmit sensed data after & 60 s \\ AUV re-establish path after & 30 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. Figure 12: Successfully transmitted data packets to sink. Co-Hydrocast consumes more energy to deliver data packets because of cooperation technique and lengthy recovery paths for void nodes in sparse network condition. Both Co-Hydrocast and Co-Improved Hydrocast uses cooperation technique for reducing packet drop ratio however the deployment of sensor nodes at the strategic locations enabled void nodes to establish a short path for the data packet transmission to the destination. Figure 14 determines the average number of transmissions to deliver a data packet successfully to the destination. Hydrocast on average, deliver a data packet in more number of transmissions than the Improved Hydrocast and the Co-Improved Hydrocast because of lengthy recovery paths for void nodes in the sparse networks.