## 5 Experimental Results
#### Performance Results
The total cost of allocation for all fragments is determined to be 3204.72 Mbps. The transmission cost per fragment, representing the data transmission between fragments, is 1771.2 Mbps, with a total transmission cost of 5313.6 Mbps. The memory utilization per fragment is 17,712 bytes, resulting in a total memory utilization of 70,848 bytes. The processing time for the air quality dataset is 28.36 s, indicating the time taken to train the RFO-SVM algorithm and perform the data fragmentation. The results highlight the effectiveness of the RFO-SVM algorithm in achieving high accuracy and performance in data fragmentation and allocation. The algorithm effectively distributes the data across fragments, minimizing allocation and transmission costs while ensuring efficient memory utilization. The processing time reflects the algorithm's efficiency in handling large datasets. The RFO-SVM algorithm demonstrates its suitability for data fragmentation and allocation tasks. The performance of the RFO-SVM approach for two datasets with 50 iterations is compared with other existing data fragmentation allocation methods. The proposed RFO-SVM method shows significant improvements in all metrics as shown in Table 6. It achieves an impressive accuracy of 96.21% with a significantly reduced total allocation cost of 3204.72 Mbps. Moreover, it exhibits a remarkably low processing time of 28.36 s and a reduced total transmission cost of 5313.6 Mbps. These results indicate that the proposed RFO-SVM approach outperforms the existing methods, providing higher accuracy, faster processing, and reduced resource utilization in terms of allocation and transmission costs. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**S.NO.**} & \multicolumn{3}{c}{**Evaluation for 50 Iterations**} \\ \cline{2-4}  & **Metrics** & **Air Quality** & **Wine Quality** \\ \hline
1. & Accuracy (\%) & 96.21 & 80.59 \\ \hline
2. & Precision (\%) & 96.23 & 84.69 \\ \hline
3. & Recall (\%) & 95.65 & 90.69 \\ \hline
4. & F1 Score (\%) & 95.93 & 87.59 \\ \hline
5. & Allocation cost per fragment (Mbps) & 801.18 & 2037.4 \\ \hline
6. & Total cost of allocation (Mbps) & 3204.72 & 12,224.4 \\ \hline
7. & Transmission cost per fragment (Mbps) & 1771.2 & 5737.6 \\ \hline
8. & Total transmission cost (Mbps) & 5313.6 & 28,688.0 \\ \hline
9. & Memory utilization per fragment (Bytes) & 17,712 & 57,376 \\ \hline
10. & Total memory utilization (Bytes) & 70,848 & 344,432 \\ \hline
11. & Processing time (Seconds) & 28.36 & 245.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation of RFO-SVM for data fragmentation allocation. Figure 3 shows that the proposed RFO-SVM method exhibited a significant reduction in comparison with all existing methods. Compared with FRAGMENT, the proposed method achieved reductions in allocation costs of 42.15% and for DMA, reduced by 55.93%. Compared with KT-DDE, SS-FONs, DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, the proposed method achieved reductions in allocation costs of 53.27%, 58.79%, 43.32%, 45.36%, 44.16%, 33.94%, 27.21%, and 55.07%, respectively. Figure 4 demonstrates that the proposed method achieved an increase of approximately 15.61% in accuracy when compared with FRAGMENT. Similarly, compared with DMA and KT-DDE, the proposed RFO-SVM method demonstrated increases of 8.91% and 10.81% in accuracy, respectively. In comparison with SS-FONs, DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, the proposed RFO-SVM method achieved increases of 16.61%, 18.81%, 20.41%, 19.21%, 13.91%, 10.81%, and 7.01% in accuracy, respectively.