## 4 Results and Discussion  
To evaluate the performance of the proposed scheme outlined in this paper, we conducted several simulations. Our proposed scheme was compared with the following approaches:  
1. A conventional technique adapted for energy-harvesting nodes (fixed). 2. A technique for randomly selecting cluster heads along a predefined path (random). 3. The cluster configuration approach presented in [14]. 4. [PERSON]'s scheme presented in [24]. The fixed technique involves the UAV identifying a CH at a predefined location with the shortest distance. In the random technique, a fixed number of CHs are chosen at regular intervals, commencing from random points along the UAV's path and are periodically reselected. This technique helps address energy imbalances by periodically changing cluster heads. In our simulations, we use \(\beta\) = 4, a transmission rate of 250 kbps, and a UAV speed of 16 m/s, as presented in [14]. Since we compare our results with those in [14], we adopt the same parameter values to ensure a fair comparison. Table 1 outlines the key parameters employed in the simulation. Figure 3 illustrates the relationship between the number of dead nodes and the number of cluster heads for the considered schemes. In this figure, the fixed and random clustering schemes maintain a predetermined number of clusters, whereas the proposed scheme, [PERSON]'s scheme [14], and [PERSON]'s scheme [24] dynamically adjust the number of clusters based on the network's energy conditions. Consequently, the proposed scheme and [PERSON]'s scheme [14] yielded identical outcomes in certain scenarios due to their  
energy-aware clustering mechanisms. However, [PERSON]'s scheme [24], which employs energy-aware routing, also demonstrated improvements in reducing power outages compared to fixed and random clustering but did not perform as efficiently as our proposed scheme. There was a marginal increase in the number of nodes experiencing power outages as the number of clusters decreased in the fixed, random, and [PERSON]'s [24] techniques. This is attributed to the fact that a reduction in the number of cluster heads results in an increase in transmission hops, leading to higher energy consumption in data relay. The increased energy usage accelerates node depletion, particularly in hotspot areas where nodes relay data over multiple hops. While [PERSON]'s scheme [24] mitigates this effect by optimizing energy-aware routing, it does not incorporate UAV-assisted data collection or WPT, limiting its overall performance compared to the proposed scheme. Moreover, the proposed scheme achieves the lowest number of dead nodes, indicating that it has the longest network lifetime among all the considered schemes. For example, when the number of CHs is four, the percentage of dead nodes in the proposed scheme is 20%, whereas it increases to 88%, 80%, 24%, and 23.5% for the fixed clustering, the random clustering, [PERSON]'s scheme [14], and [PERSON]'s scheme [24], respectively. Figure 4 presents the data collection efficiency as a function of the number of cluster heads for the considered schemes. The results indicate that the proposed scheme achieves \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. of sensors & 1000 \\ \hline Network area & 100 \(\times\) 100 m\({}^{2}\) \\ \hline Efficiency of WPT & 50\% \\ \hline Transmission rate & 250 Kbps \\ \hline Start energy & 0.5 J \\ \hline \(\beta\) & 4 \\ \hline UAV speed & 16 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Used parameters. Figure 3: Number of dead nodes versus number of cluster heads for the fixed clustering, the random clustering, [PERSON]’s scheme [14], and [PERSON]’s scheme [24]. the highest data collection efficiency, benefiting from UAV-assisted data gathering and adaptive clustering which optimizes data transmission and minimizes packet loss.