## 4 Results and Discussion
Figure 8 illustrates the throughput performance, measured as the total amount of successfully transmitted data across the five considered schemes. The results show that the proposed scheme achieves the highest throughput, ensuring more efficient and reliable data collection. Specifically, at the end of the simulation (round 300), the proposed scheme achieves approximately 4.3 Mbps, whereas [PERSON]'s scheme [14] and [PERSON]'s scheme [24] attain 3.01 Mbps and 3.45 Mbps, respectively. In contrast, fixed clustering and random clustering demonstrate significantly lower throughput, with only 1.7 Mbps and 2.2 Mbps, respectively. The higher throughput in the proposed scheme is attributed to the adaptive clustering mechanism and UAV-assisted data gathering, which prevent network congestion and optimize data transmission efficiency. By reducing transmission hops and minimizing the communication overhead, the proposed scheme ensures higher  
Figure 5: Consumed energy versus number of sensors for the fixed clustering, the random clustering, [PERSON]’s scheme [14], and [PERSON]’s scheme [24]. data delivery efficiency, making it a more robust solution for real-time and energy-efficient WSN-based smart irrigation systems. Figure 6: Residual energy against the number of rounds for the considered schemes. Figure 7: Network lifetime comparison for the fixed clustering, the random clustering, [PERSON]’s scheme [PERSON] (2014), and [PERSON]’s scheme ([PERSON], 2012). Table 2 summarizes the obtained results for the considered schemes, comparing key performance metrics such as the percentage of dead nodes, data collection efficiency, energy consumption, residual energy, network lifetime, and throughput. The results clearly demonstrate the superiority of the proposed scheme over traditional clustering methods, including fixed clustering, random clustering, [PERSON]'s scheme [14], and [PERSON]'s scheme [24]. The practical applications of this scheme are extensive. It can be employed in various scenarios, such as environmental monitoring, smart agriculture, and industrial automation. By enhancing the energy efficiency of sensor networks, it allows for extended operational \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Metric**} & **Fixed** & **Random** & **[PERSON]’s Scheme** & **[PERSON]’s** & **Proposed** \\  & **Clustering** & **Clustering** & **[14]** & **[24]** & **Scheme** \\ \hline \% Dead nodes at CHs = 4 & 88\% & 80\% & 24\% & 23.5\% & 20\% \\ \hline Amount of data collection & \multirow{2}{*}{7} & \multirow{2}{*}{6.9} & \multirow{2}{*}{14.7} & \multirow{2}{*}{14.9} & \multirow{2}{*}{16} \\ at CHs = 4 (\(\times 10^{6}\) bit) & & & & \\ \hline \% Energy consumption & 81\% & 81.3\% & 76\% & 74\% & 66\% \\ \hline \% Residual energy at No. & \multirow{2}{*}{25\%} & \multirow{2}{*}{29.5\%} & \multirow{2}{*}{60\%} & \multirow{2}{*}{69\%} & \multirow{2}{*}{75.5\%} \\ of round = 300 & & & & & \\ \hline FDN (Rounds) & 50 & 55 & 68 & 70 & 85 \\ \hline HDN (Rounds) & 80 & 85 & 115 & 120 & 140 \\ \hline LDN (Rounds) & 205 & 225 & 260 & 265 & 285 \\ \hline Throughput at No. of & \multirow{2}{*}{1.7} & \multirow{2}{*}{2.2} & \multirow{2}{*}{3.01} & \multirow{2}{*}{3.45} & \multirow{2}{*}{4.3} \\ round = 300 (Mbps) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of obtained results for the considered schemes. Figure 8: Throughput comparison for the considered schemes. lifetimes, reduced maintenance, and more reliable data collection, which is invaluable in applications requiring long-term, autonomous data gathering and transmission. Finally, the proposed scheme not only contributes to improving the overall performance of WSNs but also has the potential to revolutionize their applicability in diverse real-world scenarios. In the following subsection we present a case study in which we apply the proposed scheme in the smart agriculture sector.