## 4 Results Analysis  
The proposed model was simulated using the MATLAB tool. The parameter settings are given as follows. Number of nodes: 100, beacon nodes: 35, communication radius: 35 m, and node deployment: random. The NL results of the CMLOA-NLA technique are examined in terms of different metrics. In Table 1 and Figure 3, an average localization error (ALE) result of the CMLOA-NLA technique is compared under a distinct number of beacon nodes (NBNs) [28]. ALE measures the accuracy of the node localization. It determines the mean error between the estimated positions and the ground truth positions of the nodes. The results highlightthat the CMLOA-NLA technique offers reduced ALE values over all NBN values. On an NBN of 5, the CMLOA-NLA technique provides a decreasing ALE of 5.46% while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN models offer an increased ALE of 44.4%, 66.79%, 16%, and 8.73%, respectively. The proposed model quantifies that it offers closer estimated positions of nodes to their actual positions. Also, on an NBN of 15, the CMLOA-NLA method gains a lesser ALE of 4.59% while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN models offer a bigger ALE of 32.17%, 25.86%, 12.73%, and 7.67%, correspondingly. Furthermore, on an NBN of 35, the CMLOA-NLA system attained a lesser ALE of 2.55% while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN approaches achieved a better ALE of 25.86%, 14.71%, 8.34%, and 4.74%, correspondingly. In Table 2 and Figure 4, a localization time (LT) study of the CMLOA-NLA algorithm is compared under a distinct NBN. LT represents the time taken by the algorithm to complete the localization process. This is an essential factor, especially in real-time applications where timely and efficient localization is critical. The outcome demonstrates that the CMLOA-NLA system attains lesser LT values over all NBN values. On an NBN of 5, the CMLOA-NLA approach achieves a lesser LT of 0.123 min while the WND-DV-Hop, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Average Localization Error (\%)**} \\ \hline
**No. of Beacon Nodes** & **WND-DV-Hop** & **MGDV-Hop** & **VPDC** & **EOFFONLWN** & **CMLOA-NLA** \\ \hline
5 & 44.40 & 66.79 & 16.00 & 8.73 & 5.46 \\
10 & 35.04 & 56.54 & 14.61 & 8.25 & 5.60 \\
15 & 32.17 & 25.86 & 12.73 & 7.67 & 4.59 \\
20 & 36.62 & 23.46 & 12.34 & 7.72 & 3.95 \\
25 & 29.44 & 23.98 & 10.33 & 7.54 & 3.97 \\
30 & 28.33 & 21.39 & 9.39 & 5.82 & 3.01 \\
35 & 25.86 & 14.71 & 8.34 & 4.74 & 2.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ALE outcome of CMLOA-NLA approach with the recent system under varying NBNs. Figure 3: ALE outcome of CMLOA-NLA approach under varying NBNs. MGDV-Hop, VPDC, and EOFFONLWN approaches offer an enhanced LT of 0.912 min, 2.839 min, 0.341 min, and 0.297 min, correspondingly. Likewise, on an NBN of 15, the CMLOA-NLA technique provides a decreasing LT of 0.162 min while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN approaches offer an increased LT of 0.803 min, 2.543 min, 0.405 min, and 0.327 min, correspondingly. Furthermore, on an NBN of 35, the CMLOA-NLA method attains a minimal LT of 0.150 min while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN models offer an increased LT of 0.799 min, 2.246 min, 0.475 min, and 0.324 min, correspondingly. The minimal CT values portrayed that the proposed model is suitable for real-time or near real-time applications. In Table 3 and Figure 5, an ALE outcome of the CMLOA-NLA methodology is compared on distinct numbers of communication radius (CR). The outcome represented that the CMLOA-NLA algorithm attains lesser ALE values over all CR values. On a CR of 5 m, the CMLOA-NLA technique provides a decreasing ALE of 4.84% while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN models offer an increased ALE of 48.53%, 25.07%, 15.44%, and 6.93%, correspondingly.