## 5 Performance Evaluation and Results  
In this section, the simulation has been performed using the \(MB-NO\) algorithm for energy conserved node optimization, and the results are compared with ACO and ABC optimization. The simulation coverage has been conducted with the below experimental setup for various C-shaped, H-shaped and S-shaped networks, as shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **C-, H-, S-Shaped Networks** \\ \hline Simulation area & 250 \(\times\) 250 m \\ \hline Frequency band & 700 MHz \\ \hline Sensor nodes & 30 \\ \hline Anchor and other unknown nodes & 250 \\ \hline Channel bandwidth & 4 MHz \\ \hline Packet size (Control) & 150 bit \\ \hline Packet size (Data) & 4 Kbytes \\ \hline Energy during transmission & 0.1 \\ \hline Transmitter energy & 40 \(\times\) 10\({}^{-8}\) \\ \hline Receiver energy & 40 \(\times\) 10\({}^{-8}\) \\ \hline Maximum lifetime & 6 \(\times\) 10\({}^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation settings. Figure 7 estimates the objective function for an H-shaped network with optimized nodes. The estimation shows that the Bat Algorithm establishes a good optimal solution with proper energy conservation, with the optimized sensor and the adjacent nodes maintained throughout. Table 5 shows the conservative energy analysis using the above metrics, and the parameters are established within the conservative column. The \(MB-NO\) performs the best approach in energy conservation using the 30 sensor nodes with 250 adjacent nodes. The 30 sensor nodes use the best efficiency for the summarized time stamp. The \(MB-NO\) outperforms when compared with the ACO and ABC optimization. In the H-shaped network, the performance measurement is estimated with the rise in transmission time in ms. The H-shaped network is subject to parallel communication estimation in the fixed sensor nodes. The optimal values suggest that the efficient node optimization performs well with the H-shape based on the energy equilibrium, and the average solution attained here works well for the H-shaped network. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{3}{c}{**Objective Function Values (ms)**} \\ \cline{2-4}  & \(MB-NO\) & **ACO** & **ABC** \\ \hline
10 & 0.3 & 0.2 & 0.1 \\ \hline
15 & 0.4 & 0.35 & 0.2 \\ \hline
20 & 0.6 & 0.54 & 0.3 \\ \hline
25 & 0.85 & 0.62 & 0.42 \\ \hline
30 & 1 & 0.7 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Objective function estimation for a C-shaped network with optimized sensor nodes. Figure 6: Objective function estimation for a C-shaped network with optimized nodes. Figure 8 estimates the objective function for an S-shaped network with optimized nodes. The estimation shows that the Bat Algorithm establishes a good optimal solution with proper energy conservation, with the optimized sensor and the adjacent nodes maintained throughout. Table 6 shows the conservative energy analysis using the above metrics, and the parameters are established within the conservative column. The \(MB-NO\) performs the best approach in energy conservation using the 30 sensor nodes with 250 adjacent nodes. The 30 sensor nodes use the best efficiency for the summarized time stamp. The \(MB-NO\) outperforms when compared with the ACO and ABC optimization. In an S-shaped network, the performance measurement is estimated with the raise in transmission time in ms compared to the H- and C-shaped networks. The optimal values suggest that the efficient node optimization performs well with the S-shape based on the energy equilibrium, and the average solution attained here works well for the S-shaped network. At the offset, all the H- and C-shaped networks are assumed to be off, with the best solution, even in compressed mode transmission. Comparatively, the average solution implies a node estimation in the proper assumptions for the sensor and adjacent node communication using the \(MB-NO\) algorithm.