# Dwarf Mongoose Optimization-Based Secure Clustering with Routing Technique in Internet of Drones
## 3 Results and Discussion  
In this section, the secure communication performance of the DMOSC-MHRS model is investigated in detail. The proposed model is simulated using MATLAB under three different scenarios based on grid size. * Scenario-1: Grid size of 1000 \(\times\) 1000 m\({}^{2}\)
* Scenario-2: Grid size of 2000 \(\times\) 2000 m\({}^{2}\)
* Scenario-3: Grid size of 3000 \(\times\) 3000 m\({}^{2}\)  
Table 1 and Figure 2 highlight the cluster building time (CBT) of the DMOSC-MHRS approach under varying drones with existing methods such as the hybrid self-organized clustering scheme (HSCS), bio-inspired clustering scheme for FANET (BICSF), and SCA-ITV [22]. The experimental values indicated that the DMOSC-MHRS algorithm has demonstrated enhanced results under all drones. For example, with 15 drones, the DMOSC-MHRS model offered a lower CBT of 0.33 s whereas the SCA-ITV, BICSF, and HSCS algorithms obtained higher CBT of 0.61 s, 0.44 s, and 0.44 s, correspondingly. In line, with 20 drones, the DMOSC-MHRS approach presented a lower CBT of 0.47 s whereas the SCA-ITV, BICSF, and HSCS methods acquired higher CBT of 0.84 s, 0.70 s, and 0.59 s, correspondingly. Along with 25 drones, the DMOSC-MHRS technique has presented a lower CBT of 0.62 s whereas the SCA-ITV, BICSF, and HSCS algorithms gained higher CBT of 1.05 s, 0.86 s, and 0.79 s, correspondingly. Table 2 presents an overall energy consumption (ECM) inspection of the DMOSC-MHRS algorithm with recent models on different scenarios. Figure 3 reports a comparative ECM assessment of the DMOSC-MHRS technique with existing models on Scenario-1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Cluster Building Time (s)**} \\ \hline
**No. of Drones** & **SCA-ITV** & **BICSF** & **HSCS** & **DOC-MHRS** \\ \hline
15 & 0.61 & 0.44 & 0.44 & 0.33 \\ \hline
20 & 0.84 & 0.70 & 0.59 & 0.47 \\ \hline
25 & 1.05 & 0.86 & 0.79 & 0.62 \\ \hline
30 & 1.19 & 1.06 & 0.92 & 0.73 \\ \hline
35 & 1.25 & 1.14 & 1.11 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CBT analysis of DMOSC-MHRS approach with existing algorithms under distinct drones. Figure 2: CBT analysis of DMOSC-MHRS approach under distinct drones. The figure indicates that the DMOSC-MHRS approach shows enhanced results with minimal ECM values. For instance, with 15 drones, the DMOSC-MHRS model gained the least ECM of 0.33 J whereas the SCA-ITV, BICSF, and HSCS models obtained higher ECM of 0.61 J, 0.44 J, and 0.44 J, respectively. In the meantime, with 20 drones, the DMOSC-MHRS approach has achieved the least ECM of 0.47 J whereas the SCA-ITV, BICSF, and HSCS methodologies obtained higher ECM of 0.84 J, 0.70 J, and 0.59 J, correspondingly. In due course, with 25 drones, the DMOSC-MHRS method acquired the least ECM of  
whereas the SCA-ITV, BICSF, and HSCS algorithms reached higher ECM of 1.05 J, 0.86 J, and 0.79 J, correspondingly. Figure 4 reports a brief ECM assessment of the DMOSC-MHRS methodology with existing models in Scenario-2. The figure denoting the DMOSC-MHRS approach shows enhanced results with minimum ECM values. For example, with 15 drones, the DMOSC-MHRS approach gained least ECM of 1.07 J whereas the SCA-ITV, BICSF, and HSCS models obtained higher ECM of 1.73 J, 1.66 J, and 1.39 J, correspondingly. Simultaneously with 20 drones, the DMOSC-MHRS approach attained the least ECM of 1.47 J whereas the SCA-ITV, BICSF, and HSCS techniques obtained higher ECM of 2.81 J, 2.49 J, and 2.25 J, correspondingly. Eventually, with 25 drones, the DMOSC-MHRS method acquired the least ECM of 2.11 J whereas the SCA-ITV, BICSF, and HSCS algorithms reached higher ECM of 3.57 J, 3.30 J, and 2.91 J, correspondingly. Figure 5 reports a comprehensive ECM assessment of the DMOSC-MHRS approach with existing models on Scenario-3. The figure representing the DMOSC-MHRS technique exhibits enhanced results with minimal ECM values.