## 7 Conclusions and Future Scope  
One of the most pressing concerns with WSNs is localization. In this research, an individual anchor node is used to conduct localization, with its projection at six different angles within the circle utilizing mLebTLBO. When a target node comes inside the span of the anchor node, two virtual anchor nodes are picked along with the anchor node (as at least three nodes is mandatory to locate the 2D parameters values of the target node). BBO, PSO, FA, HPSO, and mLebTLBO meta-heuristic optimization algorithms are used to compute the localization of 20 randomly deployed target nodes. These algorithms are then run for five iterations to calculate the localization error. In comparison to competitive methods, the Table 4 reveals that the mLebTLBO algorithm outperforms others with respect to average localization error as it has the least average localization error in locating the target nodes. This algorithm can be employed in a diversity of applications, including animal tracking, logistics, personnel tracking in coal mines, and other industrial applications. Another meta-heuristic approach can be implemented in the future for improved accuracy and faster convergence.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **No. of** & **Transmission** & **Localization Error** & **Localization** & **Average Error** & **Localized** \\
**Used** & **Movements** & **Range** & **(Maximum)** & **(Minimum Error)** & **Average Error** & **Target Nodes** \\ \hline PSO & 1 & 10 & 1.8874 & 0.1431 & 0.6944 & 20 \\  & 2 & 10 & 3.7233 & 0.2142 & 1.1234 & 20 \\  & 3 & 10 & 2.8678 & 0.1241 & 0.8132 & 20 \\  & 4 & 10 & 1.8914 & 0.2132 & 0.5878 & 20 \\  & 5 & 10 & 1.3497 & 0.1698 & 0.7432 & 20 \\ HPSO & 1 & 10 & 0.7827 & 0.1088 & 0.2345 & 20 \\  & 2 & 10 & 0.9823 & 0.0863 & 0.3432 & 20 \\  & 3 & 10 & 0.5636 & 0.0381 & 0.3234 & 20 \\  & 4 & 10 & 0.6891 & 0.2089 & 0.3387 & 20 \\  & 5 & 10 & 0.5371 & 0.2087 & 0.2105 & 20 \\ BBO & 1 & 10 & 1.4313 & 0.0221 & 0.3734 & 20 \\  & 2 & 10 & 1.4647 & 0.0879 & 0.8123 & 20 \\  & 3 & 10 & 1.4634 & 0.0263 & 0.6822 & 20 \\  & 4 & 10 & 1.4654 & 0.0342 & 0.7880 & 20 \\  & 5 & 10 & 1.5454 & 0.0482 & 0.9213 & 20 \\ FA & 1 & 10 & 4.5985 & 0.3734 & 2.3490 & 20 \\  & 2 & 10 & 5.7875 & 0.5765 & 3.0434 & 20 \\  & 3 & 10 & 4.7452 & 0.0223 & 2.4308 & 20 \\  & 4 & 10 & 5.1564 & 0.2354 & 3.1256 & 20 \\  & 5 & 10 & 4.5788 & 0.1880 & 2.5920 & 20 \\ mLebTLBO & 1 & 10 & 0.5409 & 0.0927 & 0.2138 & 20 \\  & 2 & 10 & 0.6121 & 0.0698 & 0.3114 & 20 \\  & 3 & 10 & 0.5987 & 0.0272 & 0.2871 & 20 \\  & 4 & 10 & 0.6096 & 0.1898 & 0.2915 & 20 \\  & 5 & 10 & 0.4851 & 0.1793 & 0.1958 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of localization Algorithms in calculating localization error.  
**Author Contributions:** [PERSON] performed the experimentation and wrote the manuscript. [PERSON] did the investigation. [PERSON] designed the methodology, [PERSON] did the supervision. [PERSON] did the administration. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this manuscript.