# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
## 4 Performance Evaluation and Result Analysis  
The algorithm described in the previous section has been implemented using MATLAB. The proposed approach has been compared with different existing techniques such as ACO, GA and Greedy Approach. The parameters considered for comparison are delay, throughput and the normalized lifetime. The length of pipeline has been varied from 500 m to 3500 m. The initial energy of each sensor node has been considered to be 1 joule, the energy consumed in communication is directly proportional to communication distance. The parameters considered are similar to the closest work [16]. The simulation experiments have been repeated for sensor node communication range varying from 25 m to 100 m.The parameters considered have been summarized in the Table 1.  
The comparison of end-to-end delay between proposed technique and other three techniques namely ACO, GA and greedy approach has been shown in the Tables 2-4 and graphically in Figures 3-5.  
the delay is concerned. It is due to the dynamic setup of the pipeline by considering different factors, which is a special characteristic of LOA. As expected, the End-to-End delay is decreasing when the communication range of the sensor nodes is increases because of the reduced number of intermediate nodes required to deliver a data packet. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of Pipeline**} & \multicolumn{4}{c}{**End-to-End Delay (in msec.)**} \\ \cline{2-5}  & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 0.7870 & 0.7461 & 0.7437 & 0.7301 \\
1000 & 0.8036 & 0.7483 & 0.7465 & 0.7321 \\
1500 & 0.8176 & 0.7498 & 0.7479 & 0.7346 \\
2500 & 0.8243 & 0.7537 & 0.7498 & 0.7362 \\
3500 & 0.8385 & 0.7553 & 0.7512 & 0.7378 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparion of delay (Range 100 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of Pipeline**} & \multicolumn{4}{c}{**End-to-End Delay (in msec.)**} \\ \cline{2-5}  & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 1.5913 & 1.4554 & 1.4548 & 1.4318 \\
1000 & 1.6039 & 1.4560 & 1.4554 & 1.4378 \\
1500 & 1.6140 & 1.4570 & 1.4563 & 1.4386 \\
2500 & 1.6261 & 1.4586 & 1.4572 & 1.4398 \\
3500 & 1.6334 & 1.4610 & 1.4597 & 1.4413 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Delay (Range 25 m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of Pipeline**} & \multicolumn{4}{c}{**End-to-End Delay (in msec.)**} \\ \cline{2-5}  & **Greedy Approach** & **ACO** & **GA** & **Proposed** \\ \hline
500 & 1.5913 & 1.4554 & 1.4548 & 1.4318 \\
1000 & 1.6039 & 1.4560 & 1.4554 & 1.4378 \\
1500 & 1.6140 & 1.4570 & 1.4563 & 1.4386 \\
2500 & 1.6261 & 1.4586 & 1.4572 & 1.4398 \\
3500 & 1.6334 & 1.4610 & 1.4597 & 1.4413 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of delay (Range 50 m). Figure 3: Comparison of End-to-End delay (Range 25 m). It is evident from the simulation results that the delay in the greedy approach is increasing with the length of the pipeline whereas this trend is not consistent in the case of the other three meta-heuristic techniques. A possible reason for this phenomenon may be that the number of nodes also increases when the length of the pipeline is increased. In the case of meta-heuristic techniques, when the number of nodes in the system is higher, the meta-heuristic techniques can be applied more efficiently which may offset the effect of increased length of the pipeline on the end to end delay in some cases. The proposed algorithm outperforms greedy approach as far as throughput is concerned. Additionally, the throughput of our technique is slightly better than the other two considered techniques namely GA and ACO. In the presented approach, the location has been optimized based upon drop ratio and delay whereas the authors in [24] try to optimize the location based upon delay only. This factor is the reason for the better throughput of the proposed algorithm.