## 6 Scheduling Algorithm Performance Test Using TLS-VaD
### Results and Discussion  
TLS-VaD was used in the process of testing and retrieving data for performance comparison of IRByTSA, FTSA, and FLSA. The use of TLS-VaD eases the process. The TLS-VaD outputs are the data of active timeslots (\(\lambda\)), duty cycles (D), number of cycles (C), and total channel offsets (ChOffs) for IRByTSA, FTSA, and FLSA, collected in Tables 4-7. Furthermore, the data are displayed in graphical form to ease the process of performance comparison. As a reference for performance comparison, as stated in Section 4.2, the desired TSCH scheduling algorithm is one that can produce a minimum value of \(\lambda\) and minimum number of cycles (C). Figure 36: Scenarios I and II in algorithm testing using TLS-VaD. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**IRByTSA**} & \multicolumn{2}{c}{**FTSA**} & \multicolumn{2}{c}{**FLSA**} \\ \cline{2-5}  & **Horiz.** & **Vert.** & **Horiz.** & **Vert.** & **Horiz.** & **Vert.** \\ \hline
10 & 6 & 6 & 5 & 5 & 4 & 4 \\
20 & 8 & 8 & 9 & 9 & 6 & 6 \\
30 & 8 & 8 & 11 & 11 & 7 & 7 \\
40 & 11 & 11 & 11 & 11 & 7 & 7 \\
50 & 11 & 12 & 12 & 11 & 8 & 8 \\
60 & 12 & 12 & 15 & 16 & 9 & 9 \\
70 & 12 & 13 & 15 & 17 & 9 & 10 \\
80 & 13 & 14 & 17 & 18 & 10 & 11 \\
90 & 14 & 15 & 17 & 18 & 11 & 11 \\
100 & 14 & 15 & 19 & 21 & 11 & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cycle (C) data of IRByTSA, FTSA, and FLSA. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**IRByTSA**} & \multicolumn{2}{c}{**FTSA**} & \multicolumn{2}{c}{**FLSA**} \\ \cline{2-5}  & **Horiz.** & **Vert.** & **Horiz.** & **Vert.** & **Horiz.** & **Vert.** \\ \hline
10 & 9 & 9 & 9 & 10 & 10 \\
20 & 19 & 19 & 22 & 22 & 22 \\
30 & 30 & 30 & 35 & 35 & 35 \\
40 & 40 & 40 & 51 & 51 & 49 & 49 \\
50 & 51 & 50 & 64 & 66 & 59 & 60 \\
60 & 60 & 60 & 73 & 82 & 73 & 75 \\
70 & 70 & 70 & 86 & 93 & 79 & 90 \\
80 & 80 & 82 & 110 & 114 & 87 & 105 \\
90 & 90 & 101 & 126 & 137 & 101 & 126 \\
100 & 100 & 112 & 137 & 135 & 118 & 133 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Active timeslot (\(\lambda\)) data of IRByTSA, FTSA, and FLSA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**IRByTSA**} & \multicolumn{2}{c}{**FTSA**} & \multicolumn{2}{c}{**FLSA**} \\ \cline{2-5}  & **Horiz. ** & **Vert. ** & **Horiz. ** & **Vert. ** & **Horiz. ** & **Vert. ** \\ \hline
10 & 0.045 & 0.045 & 0.045 & 0.045 & 0.050 & 0.050 \\
20 & 0.095 & 0.095 & 0.110 & 0.110 & 0.110 & 0.110 \\
30 & 0.150 & 0.150 & 0.175 & 0.175 & 0.175 & 0.175 \\
40 & 0.200 & 0.200 & 0.255 & 0.255 Based on the \(\lambda\) data in Figure 37, with an increasing number of nodes, IRByTSA always produced a lower value of \(\lambda\) than FTSA (\(\lambda_{\text{FTSA}}\)) and FLSA (\(\lambda_{\text{FLSA}}\)). The value of \(\lambda\) in IRByTSA (\(\lambda_{\text{IRByTSA}}\)) remained the smallest when the network dimension increased. Only at the network dimension of 10 nodes was the value of \(\lambda_{\text{IRByTSA}}\) the same as \(\lambda_{\text{FTSA}}\). However, this did not indicate anything significant, because the network dimension was still too small. The performance of the actual algorithm will only be seen on large networks, on which will arise more problems, such as processing time, processing delay, memory capacity, and so on. Efficient algorithms are mostly needed in large networks. In addition to being the smallest, \(\lambda_{\text{IRByTSA}}\) also had a distance value that tended to get further away from \(\lambda_{\text{FTSA}}\) and \(\lambda_{\text{FLSA}}\) as network dimensions increased. This was indicated by the difference data between \(\lambda_{\text{IRByTSA}}\) and \(\lambda\) of FTSA and FLSA, as shown in Table 8. In scenario I (Horiz.