## 6 Conclusions  
WSNs have become very popular over the past two decades due to the wide range of applications they have enabled. Their autonomy, simplicity of implementation and miniature appearance have made it possible to integrate them into a wide range of small and large-scale applications. The large-scale deployment of sensor networks raises major challenges, which we cite as: (i) ensuring full connectivity and optimal coverage, (ii) ensuring efficient data dissemination, (iii) reducing interference. The ultimate goal for sensor networks is to best conserve sensor energy in order to extend the life of the network. It is within this framework that our main contributions, summarized below, are made. We first proposed various strategies to maximize the lifetime of LS-WSNs. The first solution is a three-tier sensor deployment architecture maximizing the coverage of the area of interest. The second contribution to extend the life of the LS-WSNs was a modeling and arrangement of sensor activities taking into account the location of the sensors in the three tiers of the proposed deployment architecture.  
Our contributions have been validated by a series of simulations. The various results have shown that our approach provides better performance that helps maximize the life of the network. These performances are in terms of longer network lifetime in terms of the instant of exhaustion of the battery of the first sensor (Figures 9 and 10), of the number of sensors having totally exhausted their energy (Figures 11 and 12) and in terms of the average residual energy (Figures 13 and 14) of the network. These performances are explained by the intelligent modeling of the sensors' activity through the three levels of the architecture. We have been able to show that a significant energy gain can be achieved through the formation of clusters across the different levels of the architecture since it allows the organization of sensor activity, maintenance of topology and efficient, fast and inexpensive routing of the collected data.  
Our experimental studies have revealed the undeniable contribution that can be made by adopting a three-tier architecture combined with efficient modeling of the tasks of the different sensors in comparison with hierarchical methods without management of sensor activities. On the other hand, intelligent modeling of sensor activity across the three levels of the architecture has shown its great interest in our context of large-scale networks because of the significant energy gain they induce since they are based on simple and local calculations. Finally, we managed to show that a significant energy gain can be achieved through the formation of clusters across the different levels of the architecture since it allows to organize the sensor activities, maintain the topology and allows an efficient, fast and inexpensive routing of the collected data. As future work, we continue to investigate ways to improve the quality of our proposed three-tier architecture. Thus we will seek to optimize the deployment of relay sensors in the clusters formed in the different levels of our architecture.  
Conceptualization, A.A.A.A., A.C.D., A.M.G., O.T., A.M. and Z.A.; Formal analysis, A.A.A.A. and A.C.D.; Funding acquisition, A.M.G. and Z.A.; Investigation, A.M.G.; Methodology, A.A.A.A., O.T and A.M.; Supervision, A.M.G. and A.M.; Validation, A.C.D.; Writing --original draft, A.A.A.A. and A.C.D.; Writing --review & editing, A.A.A.A., A.M.G., O.T. and Z.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the PHC-Tassili grant number 18 MDU114.  
**Acknowledgments:** We like to thank the editor and the anonymous reviewers for their valuable remarks that helped us in better improving the content and presentation of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.