## 6 Conclusions  
In this paper, we presented a heterogeneity-aware, threshold-based hybrid clustering and routing algorithm that significantly improves the lifetime and the stability period of wireless sensor networks (WSNs) as compared to existing routing protocols, such as threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), hybrid centralized clustering path planning algorithm (HADCC), LEACH-centralized and energy efficient sensor network (TEEN). Our proposed model contains a hybrid approach that provides a cluster head selection at two levels. Our hybrid clustering and routing algorithm adds flexibility in cluster formation based on the sensor node's position, initial energy, residual energy, and cluster head selection history. These critical selection metrics for WSN provide a better cluster head selection and controlled network traffic. Moreover, our proposed technique addresses the challenges of centralized as well as distributed WSNs. Our proposed model utilized various thresholds, prevents redundant transmission of the same information, and improves the reliability of mission-critical WSNs.  
Figure 16: Performance evaluation of the average end-to-end delay for different network densities.  
Figure 17: Performance evaluation of the average end-to-end delay for different network areas.  
The performance of the proposed model also depends upon the threshold values. It exhibits better results when the networks are stable as compared to the networks with frequent changes in their topology. Its performance depends upon the threshold values adjusted by the base station. We extended our proposal by introducing multi-hoping to achieve 10% to 20% better network scalability.  
Although our proposed models have higher computational complexity as compared to the other distributed routing protocols, the performance evaluation shows that the proposed approach outperforms popular alternatives concerning the network lifetime, energy consumption, and overall network stability.  
In our future work, we focus on its practical implementation for small and medium-sized farming areas to monitor the environment that often remains stable or gradual change is observed. We are focusing to collect the least information from member nodes to improve stability. We are expecting the enhanced performance of our proposed model that can acknowledge with the least communication traffic and time-critical features when unexpected or quick change occurs in the system.  
Conceptualization, M.B.; Formal analysis, M.B. and F.K.A.; Investigation, E.U.M.; Methodology, M.B.; Project administration, E.U.M.; Resources, E.U.M.; Validation, E.U.M.; Visualization, F.K.A.; Writing--original draft, M.B.; Writing--review and editing, E.U.M. and F.K.A. 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 Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group No. RG-21-51-01.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.