## 5 Experimental Results
#### Performance Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Total Allocation Cost (Mbps)** & \multirow{2}{*}{**Accuracy (\%)**} & **Processing Time (s)** & **Total Transmission Cost (Mbps)** \\ \hline FRAGMENT [28] & 5542.6 & 80.6 & 79.3 & 10,256 \\ \hline DMA [29] & 7256.4 & 87.3 & 82.4 & 11,897 \\ \hline KT-DDE [30] & 6858.2 & 85.4 & 88.2 & 6258 \\ \hline SS-FONs [31] & 7772.5 & 79.6 & 100.5 & 7546 \\ \hline DBE-GAM [32] & 5656.2 & 77.4 & 111.2 & 13,687 \\ \hline PROADAPT [33] & 6112.4 & 75.9 & 98.3 & 8563.9 \\ \hline FTree [34] & 5999.3 & 76.0 & 99 & 10,222.6 \\ \hline MGRM [24] & 4998.8 & 82.3 & 79.8 & 9894 \\ \hline DSGA [25] & 4454.5 & 85.3 & 65.4 & 7791.8 \\ \hline VFAR [35] & 7122.6 & 88.2 & 92.5 & 11,258.2 \\ \hline Proposed RFO-SVM & 3204.72 & 96.21 & 28.36 & 5313.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Comparison of Data fragmentation and Allocation Using Hybrid RFO-SVM for Air Quality Dataset. Figure 3: Total allocation cost. Figure 5 depicts that the processing time of the proposed RFO-SVM method was considerably lower than FRAGMENT by 50.49%, DMA by 65.72%, and KT-DDE by 67.80%. When compared with SS-FONs, DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, the proposed method demonstrated reductions in processing times of 71.50%, 74.48%, 71.28%, 71.35%, 64.41%, 54.16%, and 69.34%, respectively. From Figure 6, the proposed RFO-SVM method achieved reductions in terms of the total transmission cost when compared with FRAGMENT of 49.89%, DMA of 39.46, KT-DDE of 39.35, and SS-FONs of 29.45%. Compared with DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, the proposed method achieved reductions in transmission costs of 60.96%, 37.98%, 41.88%, 49.75%, 42.52%, and 52.87%, respectively. Figure 4: Accuracy results. Figure 5: Processing time. The performance comparison of data fragmentation and allocation methods using the Hybrid RFO-SVM approach for the Wine Quality Dataset, as shown in Table 7, reveals the superiority of the proposed RFO-SVM method across various metrics when compared with the existing methods. Figure 7 shows that the proposed RFO-SVM method achieved a substantial reduction in terms of total allocation cost when compared with FRAGMENT, DMA, KT-DDE, and SS-FONs, and the proposed method achieved reductions in allocation costs of 52.21%, 34.03%, 31.51%, and 38.22%, respectively, and compared with DBE-GAM, PROADAPT, FIree, MGRM, DSGA, and VFAR, achieved reductions in allocation costs of 27.40%, 30.91%, 28.16%, 34.66%, 38.72%, and 40.61%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Fragmentation and** & **Total Allocation** & **Accuracy** & **Processing** & **Total** \\
**Allocation Methods** & **Cost (Mbps)** & **(\%)** & **Time (s)** & **Cost (Mbps)** \\ \hline FRAGMENT [28] & 25,589.5 & 65.9 & 582 & 58,366 \\ \hline DMA [29] & 20,689 & 72.4 & 556 & 49,732 \\ \hline KT-DDE [30] & 19,872 & 70.6 & 645 & 48,679 \\ \hline SS-FONs [31] & 22,645 & 71.4 & 722 & 39,475 \\ \hline DBE-GAM [32] & 18,476 & 75.6 & 765 & 38,666 \\ \hline PROADAPT [33] & 19,005 & 72 & 693 & 43,584 \\ \hline FTee [34] & 18,679.9 & 69.6 & 579 & 48,002 \\ \hline MGRM [24] & 19,834 & 68 & 509 & 36,761 \\ \hline DSGA [25] & 17,264 & 71 & 456 & 53,576 \\ \hline VFAR [35] & 20,579 & 71.3 & 537 & 59,254 \\ \hline
**Proposed RFO-SVM** & **12,224.4** & **80.59** & **245.53** & **28,688.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance Comparison of the Data Fragmentation and Allocation Using Hybrid RFO-SVM for Wine Quality Dataset. Figure 6: Total transmission cost. Figure 8 demonstrates the proposed RFO-SVM method outperformed in terms of accuracy when compared with FRAGMENT and DMA, achieving increases in accuracy of 22.69%, and 8.19%, respectively. When compared with KT-DDE, SS-FONs, DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, the proposed method achieved increases in accuracy of 10.99%, 8.19%, 5.99%, 8.59%, 11.99%, 18.23%, 13.38%, and 12.48%, respectively.