## 5 Conclusions  
This paper introduced the IK-MACHES protocol, an advanced clustering algorithm designed to enhance the lifetime and operational efficiency of WSNs, particularly in autonomous vehicular environments. By integrating an improved K-means clustering algorithm with a MACHES mechanism, the proposed protocol addresses the limitations of LEACH-based approaches. The IK-MACHES algorithm employs a comprehensive CH scoring strategy that incorporates residual energy, distance to the BS and cluster centroid, and relative node mobility. This multi-metric approach ensures optimal CH placement and load distribution, minimizing energy consumption and cluster instability in both static and mobile WSN scenarios. The simulation results demonstrate that IK-MACHES significantly outperforms classical LEACH, LEACH-B, Improved-LEACH, LEACH-K, LEACH-GK, and PSO-based clustering protocols in terms of the FND and 80% alive node metrics. Under both no-mobility and mobility conditions, IK-MACHES achieves superior energy efficiency and network stability, ensuring a prolonged operational lifetime even in scenarios with high node mobility. This is particularly evident in scenarios with centrally located BSs, where the proposed protocol achieves a balanced residual energy distribution among nodes. These findings highlight the critical role of integrating mobility-aware mechanisms with clustering protocols to address the challenges of dynamic WSN environments, such as vehicular networks. The IK-MACHES protocol provides a scalable and energy-efficient solution, paving the way for future advancements in adaptive WSN clustering methodologies.  
Figure 9: Comparison of residual energy of nodes after 100 rounds, 500 rounds, and 1000 rounds for Scenario 2 (BS = 100 m, 100 m) with mobility: (**a**) no mobility and (**b**) maximum mobility range (10 m/round), depicting how energy sustainability varies by scenario.  
**Author Contributions:** Conceptualization, S.C. and Y.L.; methodology, S.C.; software, C.Y.; validation, C.Y., S.C. and Y.L.; formal analysis, S.C.; investigation, C.Y.; resources, S.C.; data curation, Y.L.; writing--original draft preparation, S.C.; writing--review and editing, Y.L.; visualization, C.Y.; supervision, Y.L.; project administration, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research and its APC were funded by the Institute for Information Communications Technology Planning Evaluation (IITP) grant number (MSIT, Korea, No. 2021-0-01352).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data recorded in the current study are available in all tables and figures of the manuscript.  
**Acknowledgments:** This work was supported by the Institute for Information Communications Technology Planning Evaluation (IITP) grant funded by the Ministry of Science and ICT (MSIT, Korea, No.2021-0-01352, Development of technology for validating autonomous driving services in perspective of laws and regulations).  
**Conflicts of Interest:** The authors declare no conflicts of interest.