## 9 Performance Analysis  
In this section, we compare two SSS-RRNS solutions: well-known Asmuth-Bloom and DT-RRNS. To measure encoding time, decoding time, and redundancy, we transmit data from 6 MB to 146 MB across a network of 16-24 nodes with 4 neighboring nodes, using a number of moduli from 4 to 6. The secret key used for the schemes is 2147483659. Table 3 and Figure 7 show the encoding and decoding time, and redundancy for the Asmuth-Bloom and DT-RRNS schemes with varying data sizes and the number of moduli. We see that the encoding and decoding times increase linearly for both Asmuth-Bloom and DT-RRNS. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**(\(k\), \(n\))/Number of Nodes**} & \multicolumn{2}{c}{**Pr\(\quad\)**} \\ \cline{2-3}  & **Pr\({}_{\text{node}}\) = 0.9** & **Pr\({}_{\text{node}}\) = 0.99** \\ \hline \((2\), \(3)/6\) & 0.905 & 0.998827731 \\ \hline \((3\), \(4)/8\) & 0.976 & 0.999968948 \\ \hline \((4\), \(5)/10\) & 0.994 & 0.999999228 \\ \hline \((5\), \(6)/12\) & 0.998 & 0.99999982 \\ \hline \((6\), \(7)/14\) & 0.9997 & 0.99999999 \\ \hline \((7\), \(8)/16\) & 0.9999 & 0.99999999 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Probability \(Pr\) of secure data transmission at different numbers of routes and the total number of nodes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Mohali**} & **Data Size** & **Conling Time** & **Redundancy** & **Deconing Time (nm)** \\ \hline Asmuth-Bloom & & 6076 & 52 & 5.33072 & 57 \\ \hline DLRNS & & 9 & 1.07728 & 12 \\ \hline Asmuth-Bloom & & 129 & 5.33445 & 136 \\ \hline DLRNS & & 27 & 1.06782 & 32 \\ \hline Asmuth-Bloom & & 519 & 5.33085 & 494 \\ \hline DT-RRNS & & 107 & 1.06697 & 116 \\ \hline Asmuth-Bloom & & 547 & 5.33054 & 523 \\ \hline DT-RRNS & & 113 & 1.06699 & 119 \\ \hline Asmuth-Bloom & & 111,108 & 589 & 5.3348 & 560 \\ \hline DT-RRNS & & 129 & 1.06693 & 127 \\ \hline DLRNS & & 137,016 & 719 & 5.33045 & 675 \\ \hline DT-RRNS & & 148 & 1.0665 & 156 \\ \hline \multirow{2}{*}{Asmuth-Bloom} & & 146,133 & 776 & 5.33044 & 779 \\ \hline DT-RRNS & & 158 & 1.06696 & 165 \\ \hline Asmuth-Bloom & & 6076 & 44 & 6.67215 & 38 \\ \hline DT-RRNS & & 9 & 1.06692 & 12 \\ \hline Asmuth-Bloom & & 23,974 & 158 & 6.68005 & 166 \\ \hline DLRNS & & 24 & 1.03854 & 29 \\ \hline Asmuth-Bloom & & 90,927 & 632 & 6.66694 & 590 \\ \hline DT-RRNS & & 102 & 1.0529 & 117 \\ \hline Amuth-Bloom & & 642 & 6.66692 & 644 \\ \hline DT-RRNS & & 107 & 1.05293 & 119 \\ \hline Asmuth-Bloom & & 111,108 & 708 & 6.6668 & 668 \\ \hline DT-RRNS & & 115 & 1.05285 & 129 \\ \hline Asmuth-Bloom & & 157,016 & 872 & 6.66681 & 869 \\ \hline DT-RRNS & & 142 & 1.05297 & 151 \\ \hline DT-RRNS & & 146,133 & 903 & 6.6668 & 866 \\ \hline DT-RRNS & & 152 & 1.05288 & 145 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The coding and decoding time and redundancy. DT-RRNS shows better runtime results than Asmuth-Bloom. DT-RNS for the largest data size has less time than Asmuth-Bloom for the smallest data size when using 6 moduli. The redundancy for DT-RRNS is approximately the same for all moduli sets and data sizes. The redundancy of the Asmuth-Bloom is increasing with the number of moduli increasing. Table 4 contains the moduli used in the experiment. Figure 7 shows the encoding time (a) and decoding time (b) versus the number of moduli and data size. Figure 8 shows the redundancy versus the number of moduli and data size. DT-RRNS has lower redundancy close to 1. We see that the redundancy of both Asmuth-Bloom and DT-RRNS weakly depends on the input data. It varies with scheme parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Moduli** & **Moduli** \\ \hline
4 & (2147483693, 2147483713, 2147483743, 2147483777) \\ \hline
5 & (2147483693, 2147483713, 2147483743, 2147483777, 2147483783) \\ \hline
6 & (2147483693, 2147483713, 2147483743, 2147483777, 2147483783, 2147483813) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of moduli and their meanings.