## 4 Results and Discussion
In contrast, the fixed and random clustering schemes exhibit lower efficiency due to inefficient cluster head selection and increased node failures, which disrupt communication and reduce the amount of successfully collected data. For example, when the number of CHs is four, the amount of data collection in the proposed scheme is 16 \(\times\) 10\({}^{6}\) bit, whereas it decreases to 7 \(\times\) 10\({}^{6}\) bit, 6.9 \(\times\) 10\({}^{6}\) bit, 14.7 \(\times\) 10\({}^{6}\) bit, and 14.9 \(\times\) 10\({}^{6}\) bit for the fixed clustering, the random clustering, [PERSON]'s scheme [14], and [PERSON]'s scheme [24], respectively. The fixed and random clustering schemes suffer from high packet losses due to inefficient CH selection and frequent node failures, leading to lower data collection efficiency. [PERSON]'s scheme [14] improves efficiency through hierarchical clustering, but lacks UAV-assisted path optimization, which limits its performance. Similarly, [PERSON]'s scheme [24] enhances routing efficiency but does not dynamically optimize CH selection based on real-time energy conditions, making it less effective than the proposed scheme. Figure 5 illustrates the proportion of energy consumption across all schemes. It is evident that the energy consumption increases as the number of nodes grows. However, the energy consumption of the proposed scheme is lower than that of the other schemes. This variation is primarily due to the smaller number of nodes experiencing blackouts in the proposed scheme. A reduced number of nodes experiencing blackouts mean that the nodes within hotspots must expend more energy since they need to relay data from other nodes. Additionally, the helper nodes consume more energy as they receive energy from the mobile sink and utilize it. Consequently, the proposed scheme exhibits lower energy consumption in contrast to the other schemes. For example, when the number of sensors is 400, the percentage of consumed energy in the proposed scheme is 66%, whereas it increases to 81%, 81.3%, 76%, and 74% for the fixed clustering, the random clustering, [PERSON]'s scheme [14], and [PERSON]'s scheme [24], respectively. Figure 4: Amount of data collected versus number of cluster heads for the fixed clustering, the random clustering, [PERSON]’s scheme [14], and [PERSON]’s scheme [24]. Figure 6 presents the residual energy levels of sensor nodes across different simulation rounds for the five considered schemes: the proposed scheme, the fixed clustering, the random clustering, [PERSON]'s scheme [14], and [PERSON]'s scheme [24]. The results demonstrate that the proposed scheme maintains the highest residual energy, outperforming all other approaches. Specifically, at the end of the simulation (round 300), the proposed scheme retains approximately 75.5% of residual energy, whereas [PERSON]'s scheme [14] and [PERSON]'s scheme [24] retain 60% and 69%, respectively. In contrast, fixed clustering and random clustering exhibit a steeper decline, with only 25% and 29.5% remaining, respectively. This improvement is attributed to the proposed cluster formation and UAV-assisted WPT, which optimizes energy distribution and prevents the rapid depletion of nodes. Moreover, the proposed scheme minimizes transmission hops and optimizes the UAV's path, reducing energy consumption and achieving the highest residual energy level among the considered schemes. Figure 7 compares the network lifetime among the considered schemes by presenting the first dead node (FDN), the half dead node (HDN), and the last dead node (LDN). The results indicate that the proposed scheme significantly extends the network lifespan as it achieves a slower rate of node depletion compared to other schemes. This enhancement is due to efficient cluster head selection, energy-aware data collection, and UAV-based wireless charging through an optimized path, which collectively mitigate the hotspot problem and prevent early node failures.