## 4 Results Analysis
In addition, on a CR of 15 m, the CMLOA-NLA method provides a decreasing ALE of 5.77% while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN models offer an increased ALE of 48.53%, 25.07  
and EOFFONLWN models offer an enhanced ALE of 34.71%, 23.08%, 11.37%, and 7.82%, correspondingly. Furthermore, on a CR of 35 m, the CMLOA-NLA technique achieves a lesser ALE of 2.09% while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN models offer an increased ALE of 27.38%, 18.98%, 9.50%, and 4.47%, correspondingly. In Table 4 and Figure 6, an LT outcome of the CMLOA-NLA methodology is compared on a distinct number of CRs. The outcome highlights that the CMLOA-NLA algorithm offers reduced LT values over all CR values. On a CR of 5 m, the CMLOA-NLA system provides a lesser LT of 0.200 min while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN approaches attain an improved LT of 1.219 min, 2.391 min, 0.489 min, and 0.391 min, correspondingly. Also, on a CR of 15 m, the CMLOA-NLA method offers a lesser LT of 0.149 min while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN algorithm offer an enhanced LT of 0.87 min, 2.335 min, 0.474 min, and 0.367 min, correspondingly. Figure 5: ALE outcome of CMLOA-NLA approach under varying CR. \begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Average Localization Error (\%)**} \\ \hline
**Communication Radius (m)** & **WND-DV-Hop** & **MGDV-Hop** & **VPDC** & **EOFFONLWN** & **CMLOA-NLA** \\ \hline
5 & 48.53 & 25.07 & 15.44 & 6.93 & 4.84 \\
10 & 37.92 & 27.95 & 14.55 & 9.31 & 7.02 \\
15 & 34.71 & 23.08 & 11.37 & 7.82 & 5.77 \\
20 & 31.07 & 25.79 & 12.22 & 7.58 & 5.68 \\
25 & 28.82 & 23.08 & 10.78 & 6.71 & 4.58 \\
30 & 27.22 & 24.30 & 9.77 & 5.39 & 3.29 \\
35 & 27.38 & 18.98 & 9.50 & 4.47 & 2.09 \\ \hline \end{tabular}
\end{table} 
Table 3: ALE outcome of CMLOA-NLA approach with the recent system on distinct CR. Furthermore, on a CR of 35 m, the CMLOA-NLA methodology provides a lesser LT of 0.158 min while the WND-DV-Hop, MGDV-Hop, VPDC, and EOFFONLWN systems attain a higher LT of 0.631 min, 2.348 min, 0.396 min, and 0.342 min, correspondingly. These results stated the improved NL results of the CMLOA-NLA technique over other models on the WSN. The presented CMLOA-NLA offers many benefits that make it depict recent localization models. Initially, the CMLOA-NLA approach leverages the model of chaotic mapping that establishes a dynamic and unpredictable element in the optimizer method. This dynamic nature of chaotic mapping can support the method to escape local optima more efficiently, permitting it to converge faster and determine more accurate performances. This is particularly useful in WSNs, but the accuracy of NL is essential for proficient network function and data fusion. By integrating this chaotic mapping model into the standard LOA, the CMLOA-NLA significantly improves the convergence speed and precision of NL. Second, the CMLOA-NLA method concentrates on defining the localization of unknown nodes and relies on ANs that serve as reference points. By optimizing the localization process utilizing LOA, it is optimum to exploit the spatial data provided by the ANs, leading to more particular NL. Widespread simulations and comparative outcomes with existing localization methods show that the CMLOA-NLA system excels in terms of localization \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Localization Time (min)**} \\ \hline
**Communication Radius (m)** & **WND-DV-Hop** & **MGDV-Hop** & **VPDC** & **EOFFONLWN** & **CMLOA-NLA** \\ \hline
5 & 1.219 & 2.391 & 0.489 & 0.391 & 0.200 \\
10 & 0.936 & 2.364 & 0.493 & 0.418 & 0.208 \\
15 & 0.87 & 2.335 & 0.474 & 0.367 & 0.149 \\
20 & 0.811 & 2.329 & 0.476 & 0.342 & 0.161 \\
25 & 0.732 & 2.304 & 0.434 & 0.337 & 0.138 \\
30 & 0.697 & 2.282 & 0.428 & 0.362 & 0.157 \\
35 & 0.631 & 2.348 & 0.396 & 0.342 & 0.158 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LT outcome of CMLOA-NLA approach with the recent system under varying CR. Figure 6: LT outcome of CMLOA-NLA approach under varying CR.