# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
## 4 Performance Evaluation and Result Analysis
It is evident from the simulation results that the throughput increases upon increasing the communication range of sensor nodes for all the algorithms. The comparison of the throughput is shown in Tables 5-7. Whereas it has been shown graphically in Figures 6-8. The comparison of the normalized life time is shown in Tables 8-10. The Figures 9-11 represents the comparison of normalized lifetime between various algorithms graphically. Figure 4: Comparison of End-to-End delay (Range 50 m). Figure 5: Comparison of End-to-End delay (Range100 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of Pipeline**} & \multicolumn{4}{c}{**Throughput (in KBps)**} \\ \cline{2-5}  & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 7.63 & 48.99 & 49.77 & 50.34 \\
1000 & 8.24 & 50.63 & 52.76 & 52.78 \\
1500 & 8.99 & 51.56 & 52.87 & 52.96 \\
2500 & 9.61 & 51.62 & 52.94 & 53.03 \\
3500 & 9.33 & 50.39 & 51.12 & 52.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Throughput (Range 50 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of Pipeline**} & \multicolumn{4}{c}{**Throughput (in KBps)**} \\ \cline{2-5}  & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 12.44 & 79.85 & 82.13 & 82.85 \\
1000 & 13.43 & 82.53 & 86.00 & 86.13 \\
1500 & 14.65 & 84.04 & 86.18 & 86.42 \\
2500 & 14.96 & 84.54 & 86.29 & 86.74 \\
3500 & 15.21 & 85.14 & 86.73 & 87.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of Throughput (Range 100 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of Pipeline**} & \multicolumn{4}{c}{**Throughput (in KBps)**} \\ \cline{2-5}  & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 6.33 & 38.70 & 39.32 & 39.77 \\
1000 & 6.85 & 40.00 & 41.68 & 41.90 \\
1500 & 7.51 & 40.73 & 41.97 & 42.54 \\
2500 & 7.59 & 40.78 & 42.12 & 42.99 \\
3500 & 7.66 & 41.81 & 42.68 & 43.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Throughput (Range 25 m). Figure 6: Comparison of Throughput (Range 25 m). Figure 8: Comparison of Throughput (Range 100 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Length of Pipeline** & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 0.6727 & 0.6946 & 0.6949 & 0.7067 \\
1000 & 0.7000 & 0.7302 & 0.7377 & 0.7406 \\
1500 & 0.7055 & 0.7453 & 0.7604 & 0.7805 \\
2500 & 0.7248 & 0.7516 & 0.7782 & 0.7929 \\
3500 & 0.7409 & 0.7781 & 0.7825 & 0.8084 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Normalized Life Time (Range 25 m). Figure 7: Comparison of Throughput (Range 50 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Length of Pipeline (Meters)** & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 0.8834 & 0.9122 & 0.9125 & 0.9280 \\
1000 & 0.9193 & 0.9290 & 0.9296 & 0.9426 \\
1500 & 0.9265 & 0.9328 & 0.9405 & 0.9512 \\
2500 & 0.9315 & 0.9411 & 0.9513 & 0.9597 \\
3500 & 0.9359 & 0.9469 & 0.9522 & 0.9667 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of Normalized Life Time (Range 100 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Length of Pipeline (Meters)** & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 0.8105 & 0.8369 & 0.8372 & 0.8514 \\
1000 & 0.8434 & 0.8798 & 0.8767 & 0.8923 \\
1500 & 0.8500 & 0.8980 & 0.9161 & 0.9404 \\
2500 & 0.9817 & 0.9417 & 0.9617 & 0.9890 \\
3500 & 0.9167 & 0.9375 & 0.9428 & 0.9740 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of Normalized Life Time (Range 50 m). Figure 10: Comparison of Normalized Lifetime (Range 50 m). Figure 9: Comparison of Normalized Lifetime (Range 25 m). The normalized lifetime of the proposed techniques signifies that lifetime of the network in each scenario with different pipeline length is better than the other techniques under consideration. The reason behind this phenomenon is like the nature of pipelines inside a building; LOA also considers crossing paths for optimization.