## 5 Conclusions  
In this study, a new localization approach using a metaheuristic system, named the CMLOA-NLA method for WSN, has been introduced. The purpose of the CMLOA-NLA methodology is to define the localization of UNs based on the ANs as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept with the typical LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization errors and transmission ranges. The enhanced accuracy and performance of the CMLOA-NLA technique make it suitable for real-time environmental monitoring, where the accurate localization of nodes is important to assess and respond to environmental changes. Additionally, CMLOA-NLA can be valuable in industrial automation, optimizing data routing in smart factories, and enabling efficient target tracking in surveillance systems. The algorithm's robustness against localization errors and transmission range variations also positions it as a valuable tool in disaster management scenarios, where reliable and accurate localization of sensors can aid in disaster responses and recovery efforts. Further research can explore its potential in emerging IoT applications, contributing to more effective and reliable sensor network deployments. The CMLOA-NLA is based on the existence of ANs as reference points for NL. In scenarios where ANs are sparse or their situation is suboptimal, this method's performance can degrade, resulting in less accurate NL. Although this method can show promise in simulation environments, its efficiency and reliability in real-world utilizations could not be thoroughly tested. The transition from simulation to practical execution uncovers unforeseen limitations and problems. This method's scalability to very large sensor networks can be a concern. As the network size enhances, these methods' computational demands become expensive, making them less appropriate for large-scale deployments.  
Conceptualization, A.M. and A.H.A.H.; Methodology, A.M., A.H.A.H., H.A. ([PERSON]), H.A. ([PERSON]) and F.A.A.; Software, H.A. ([PERSON]); Validation, H.A. ([PERSON]), H.A. ([PERSON]) and F.A.A.; Investigation, A.H.A.H.; Resources, A.S.; Data curation, H.A. ([PERSON]); Writing--original draft, A.M., A.H.A.H., H.A. ([PERSON]), F.A.A. and A.S.; Writing--review & editing, A.M., H.A. ([PERSON]) and A.S.; Visualization, H.A. ([PERSON]); Project administration, A.M.; Funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number (RGP2/159/44). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R361), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Research Supporting Project number (RSPD2023R838), King Saud University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University, project number (PSAU/2023/R/1444). This study is partially funded by the Future University in Egypt (FUE).  
Not applicable.  
Data Availability Statement: Data sharing does not apply to this article as no datasets were generated during the current study.