## 6 Conclusions and Future Work  
This work is mainly aimed at achieving maximum network area coverage and connectivity with strategic deployment and a minimal number of sensor nodes. An efficient node deployment strategy is introduced using the ECSO-OBL algorithm to solve the coverage optimization in WSNs. The OBL strategy is applied to have high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the current solutions and the opposite ones to expand the search space, we achieve higher convergence speed and population diversity. A sequential update evaluation mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. PSO with adaptive inertia weights is integrated with the ECSO-OBL algorithm to enhance the local search capabilities. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes, has minimal redundancy in covering the network area, and attempts to cover the whole ROI with a minimal number of sensor nodes. The simulation results prove the higher efficiency of the ECSO-OBL algorithm in terms of network coverage and convergence speed in WSNs compared with the existing state-of-the-art works. The average coverage rate of the ECSO-OBL algorithm is improved by 14.54%, 8.55%, 19.48%, and 11.73% compared with the ICS-PSO-OBL, CD-CSO, ACSO, and PSO-CSO algorithms, respectively. And the execution speed of the ECSO-OBL algorithm is improved by 56.12%, 48.22%, 51.65%, and 48.98% compared with ICS-PSO-OBL, CD-CSO, ACSO, and PSO-CSO, respectively. This helps to minimize the required sensor nodes to cover the given network area and thus minimize the cost of network deployment. The proposed algorithm can be applied to integrate the node localization and optimal deployment of sensor nodes in WSNs, which will be our future work. As part of future work, the proposed algorithm will be validated through experimental tests for applications including target tracking, healthcare monitoring, and industry monitoring environments [28]. Though the current sensor node placement strategies are adaptive, there is lack of real-time self-learning capabilities. Deep learning techniques, along with neural networks [29], may address the issue of optimal coverage in WSNs. With appropriate training and test data sets, AI/ML techniques can further decrease the number of iterations necessary for achieving more accurate optimal coverage solutions on a real-time basis.  
Conceptualization, R.D. and M.R.R.; methodology, R.D.; software, M.L.R.C.; validation, R.D. and M.R.R.; formal analysis, M.R.R.; investigation, R.D.; resources, M.R.R.; data curation, M.L.R.C.; writing--original draft preparation, M.R.R.; writing--review and editing, R.D.; visualization, M.R.R.; supervision, M.L.R.C. All authors have read and agreed to the published version of the manuscript.  
This research study received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.