## 6 Performance Evaluations
### Analysis of Results
The percentage of successfully transmitted packets was measured as the DRN percentage failure rate (percentage of DRN failures per month) was varied. As can be seen in all four figures the percentage of successfully transmitted packets decreases as the percentage of DRN failures increases. This is consistent with the expected and logical behavior of the system and can further be used to validate the simulation. Also, it \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Total Number of DDNs & 10 \\ Total Number of DRNs Per DDN & 25, 50, 100, 150 \\ Total Number of BSNs Per DRN & 6 \\ Periodic Sensing Interval & 10 seconds \\ Data transmission rate & 4,800 bps \\ DRN Data Packet Size & 512 bytes \\ MAX JUMP FACTOR & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Case study simulation parameters. can be clearly seen that the SRJ algorithm provides the better performance compared to the RA algorithm respectively. This is expected since the RA algorithm does not try to jump over a failed DRN, and only tries redirecting the packet once. If it encounters another failed DRN in the opposite direction then the packet is dropped. The SRJ algorithm offers a better performance since it considers both directions and dispatches the packet only in the direction with the smallest required energy. In addition to providing more alternatives for overcoming failed DRNs, the SRJ algorithm also ensures a smaller number of DRN failures due to battery depletion which increases network lifetime and improves its performance. Additionally, the results show that as the number of DRNs per DDN increases from 25, to 50, to 150, the percentage of successfully transmitted packets decreases for both algorithms. For example, for the SRJ algorithm case, with a percentage failure rate of 4 percent failures per month, the percentage of successfully transmitted packets decreases from 94.84 for DRN_PER_DDN = 25, to 90.18 for DRN_PER_DDN = 50, to 48.26 for DRN_PER_DDN = 100, to 16.82 for DRN_PER_DDN = 150. This decrease in performance as the number of DRNs per DDN increases is expected due to the linear structure of the network. Figure 11: Simulation results for the hierarchical acoustic wireless sensor network. Percentage of successfully transmitted packets. NUM_DRN_PER_DDN = 25. Figure 12: Simulation results for the hierarchical acoustic wireless sensor network. Percentage of successfully transmitted packets. NUM_DRN_PER_DDN = 50. With the increased number of DRNs that a packet has to use to reach the DDN, the probability of encountering a more than maximum number of consecutive failed DRNs, which prevents it from going further increases. When designing such a network, the number of DRNs per DDN must not be too large in order to ensure good network performance. Consequently, the choice of the number of DRN per DDN nodes is an important parameter which is affected by the communication range of the protocol used, as well as the desired quality of service (QoS) such as end-to-end delay, bandwidth, and throughput. For example, real-time, audio/video monitoring would have more stringent QoS requirements. This also depends on the application, the particular type of pipeline and the desired monitoring specifications. This is the subject of future research which we plan to investigate in the future. Another simulation experiment was performed for the integrated wired/RF wireless architecture discussed earlier. In the simulation, the same parameters presented in Table 1 were used, except for the fact that a data rate of 1 Gbps was assumed for the wired transmission (fiber optic medium), and 2 Mbps for the wireless transmission. The simulation results presented in Figure 15, clearly demonstrate the increased reliability of the wired/wireless. In this simulation as well, it can be seen  
Figure 14: Simulation results for the hierarchical acoustic wireless sensor network. Percentage of successfully transmitted packets. NUM_DRN_PER_DDN = 150. Figure 13: Simulation results for the hierarchical acoustic wireless sensor network.