## 6 Conclusions  
This paper proposed an energy-efficient clustering scheme for WSNs, integrating UAV-assisted data collection and WPT to overcome energy limitations and enhance network performance. The proposed scheme demonstrates superior performance in terms of network lifetime, data collection, and energy efficiency. Through various experiments and simulations, it has been established that the scheme effectively mitigates the hotspot problem by adjusting data collection rates based on available energy, resulting in stable moisture levels in smart agriculture applications. By categorizing sensor nodes into standard, advanced, and helper nodes based on their energy reserves, the proposed scheme optimally balances energy consumption and prolongs the network lifetime. The application of the proposed scheme in smart agriculture, as demonstrated by the case study, showcases its tangible benefits. The smart irrigation method, enabled by the scheme, exhibits stable moisture levels and consumes significantly less water compared to the conventional method. This not only ensures better plant quality but also aligns with sustainable agricultural practices.  
While the proposed scheme enhances energy efficiency, network longevity, and data collection in WSN-based smart irrigation, it has certain limitations. Scalability constraints may arise in larger deployments due to an increased communication overhead and UAV energy limitations, potentially reducing the efficiency in extensive sensor networks. The energy constraints of the UAV present another challenge, as a single UAV may struggle to cover large networks effectively, leading to delays in data collection and energy replenishment.  
To overcome these limitations, future research will focus on adaptive clustering mechanisms and multi-UAV coordination to improve scalability in large-scale deployments. Additionally, exploring adaptive mechanisms for UAV trajectories and charging strategies, considering dynamic changes in network topology and energy conditions, could further optimize energy efficiency and network performance. Integration of machine learning algorithms to predict and adapt to changing network conditions, as well as the exploration of alternative energy-harvesting technologies, could contribute to the scheme's resilience and versatility.  
Conceptualization, E.S.H.; Methodology, E.S.H.; Software, E.S.H.; Validation, A.J. and A.A.A.; Formal analysis, E.S.H.; Data curation, E.S.H.; Writing--original draft, E.S.H.; Writing--review & editing, A.J. and A.A.A.; Visualization, A.J. and A.A.A.; Supervision, E.S.H.; Funding acquisition, E.S.H., A.J. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project Number: RG24-M028.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
WSNs  
Wireless Sensor Networks  
Unmanned Aerial Vehicle  
WPT  
Wireless Power Transfer  
IoTs  
Internet-of-Things  
EERPMS  
Energy-Efficient Routing Protocol