## 3 Materials and Methods
### Design of TLBO-MHR Technique
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Network Lifetime (Rounds)**} \\ \hline
**Density of** & **IMD-** & **EACBR** & **SFO Alg.** & **GWO Alg.** & **Genetic** & **ALO Alg.** & **PSO Alg.** \\ \hline
100 & 1719 & 1593 & 1448 & 1415 & 1349 & 1300 \\
150 & 1950 & 1739 & 1653 & 1554 & 1442 & 1380 \\
200 & 2135 & 1956 & 1791 & 1706 & 1547 & 1456 \\
250 & 2438 & 2174 & 2082 & 1818 & 1692 & 1538 \\
300 & 2590 & 2392 & 2286 & 2009 & 1871 & 1687 \\
350 & 2847 & 2656 & 2570 & 2359 & 2062 & 1984 \\
400 & 3092 & 2887 & 2808 & 2643 & 2379 & 2165 \\
450 & 3283 & 3098 & 3045 & 2953 & 2630 & 2354 \\
500 & 3500 & 3501 & 3395 & 3263 & 2966 & 2684 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NLT analysis of the IMD-EACBR technique compared to other approaches under various densities of SNs. Figure 3: NLT analysis of the IMD-EACBR technique under various densities of SNs. Table 3 and Figure 4 demonstrate the number of alive sensor nodes (NOASN) analysis of the IMD-EACBR method and of other models. The results indicate that the IMD-EACBR model accomplished maximum NOASN under all rounds. For instance, with 2000 rounds, the IMD-EACBR approach attained an increased NOASN of 468, whereas the SFO, GWO, GA, ALO, and PSO models obtained reduced NOASNs of 444, 404, 373, 295, and 278, respectively. Simultaneously, with 3250 rounds, the IMD-EACBR prototype achieved the highest NOASN of 315, whereas the SFO, GWO, GA, ALO, and PSO approaches achieved occasional to few NOASNs of 176, 52, 10, 5, and 2, respectively. A comprehensive proportional examination of the IMD-EACBR model compared to current models in terms of numbers of dead sensor nodes (NODSN) is shown in Table 4 and Figure 5. The outcomes exhibit that the IMD-EACBR approach resulted in effective outcomes with negligible values of NODNs [18; 19; 68; 69; 70]. For example, with 2000 rounds, the IMD-EACBR model achieved a decreased NODSN of 215 nodes, whereas the SFO, GWO, GA, ALO, and PSO models resulted in increased NODNs of 367, 478, 500, 500, and 500 nodes, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**No. of Alive Sensor Nodes**} \\ \hline
**No. of** & **IMD-** & \multirow{2}{*}{**SFO Alg.**} & \multirow{2}{*}{**GWO Alg.**} & **Genetic** & \multirow{2}{*}{**ALO Alg.**} & \multirow{2}{*}{**PSO Alg.**} \\
**Rounds** & **EACBR** & & & & & \\ \hline
2000 & 468 & 444 & 404 & 373 & 295 & 278 \\
2250 & 451 & 408 & 322 & 260 & 205 & 154 \\
2500 & 439 & 392 & 205 & 179 & 132 & 54 \\
2750 & 385 & 312 & 176 & 126 & 85 & 33 \\
3000 & 337 & 225 & 70 & 25 & 19 & 8 \\
3250 & 315 & 176 & 52 & 10 & 5 & 2 \\
3500 & 285 & 133 & 22 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NOASN study of the IMD-EACBR procedure compared to other methods for different numbers of rounds. Figure 4: NOASN analysis of the IMD-EACBR technique under varying numbers of rounds. An ephemeral comparative inspection of the IMD-EACBR prototype compared to current algorithms in terms of energy consumption (ECM) is provided in Table 5 and Figure 6. The consequences were that the IMD-EACBR approach outperformed, resulting in effectual outcomes with minimal values of NODSNs. For instance, with 100 SN, the IMD-EACBR approach provided decreased NODSNs of 0.047 mJ, whereas the SFO, GWO, GA, ALO, and PSO models resulted in increased NODSNs of 0.078 mJ, 0.113 mJ, 0.153 mJ, 0.203 mJ, and 0.246 mJ, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**No. of Dead Sensor Nodes**} \\ \hline
**No. of** & **IMD-** & **SO Alg.** & **GWO Alg.** & **Genetic Alg.** & **ALO Alg.** & **PSO Alg.** \\ \hline
2000 & 32 & 56 & 96 & 127 & 205 & 222 \\
2250 & 49 & 92 & 178 & 240 & 295 & 346 \\
2500 & 61 & 108 & 295 & 321 & 368 & 446 \\
2750 & 115 & 188 & 324 & 374 & 415 & 467 \\
3000 & 163 & 275 & 430 & 475 & 481 & 492 \\
3250 & 185 & 324 & 448 & 490 & 495 & 498 \\
3500 & 215 & 367 & 478 & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table}