## 4 Comparative Analysis and Results
When the mobility speed is 1, the packet transmission rates of ETGSA, SEROMP, TC-SSA, the proposed system, ECATS, and EATSRA are 98.2, 99.2, 97.1, 99.3, 98, and 99.3, respectively. When the motility speed changed to 2, the corresponding packet transmission rates of ETGSA, SEORMP, TC-SSA, the proposed system, ECATS, and EATSRA were 96.4, 99, 95, 99.1, 94.2, and 98.5, respectively. If the mobility speed increases to 5, the transmission rates of packets in ETGSA, SEORMP, TC-SSA, the proposed system, ECATS, and EATSRA are 94.5, 98.9, 94.3, 99, 90.7, and 97.8, respectively. The packet transmission rates of ETGSA, SEORMP, TC-SSA, the proposed system, ECATS, and EATSRA when the mobility speed is 10 are 89.3, 98.5, 92.4.98.8, 88.4, and 97.1, respectively. If the mobility speed increases to 20, then the packet transmission rates of ETGSA, SEORMP, TC-SSA, the proposed system, ECATS, and EATSRA are 88.7, 98.2, 90.3, 98.6, 87.8, and 94.2, respectively. Table 6 explains that the proposed system achieves a higher packet transmission rate based on the mobility speed compared to the other methods. A graphical representation of the comparative analysis of the proposed method with other techniques such as RPL, EESRA, E-ALWO, MDSMAP, and GWO based on the computational time is illustrated in Figure 7. With the proposed technique, less computation time for data transfer is achieved compared to GWO [30], EESRA [31], MDS-MAP [32], RPL and E-ALWO [17]. This shows that the proposed method is energy efficient, as the more the computational power used, the more the model consumes energy for the process. Here, GWO implemented alone suffered from slow convergence and failed to find the local optimum effectively, having to search repeatedly and thus increasing the time to find the optimum solution. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mobility Speed** & **ETGSA** & **SEORMP** & **TC-SSA** & **Proposed** & **ECATS** & **EATSRA** \\ \hline
1 & 98.2 & 99.2 & 97.1 & 99.3 & 98 & 99.3 \\
2 & 96.4 & 99 & 95 & 99.1 & 94.2 & 98.5 \\
5 & 94.5 & 98.9 & 94.3 & 99 & 90.7 & 97.8 \\
10 & 89.3 & 98.5 & 92.4 & 98.8 & 88.4 & 97.1 \\
20 & 88.7 & 98.2 & 90.3 & 98.6 & 87.8 & 94.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of the transmission rate of a packet concerning speed among various methods. The most efficient algorithms are those with low computational time and minimum steps. The computational time is defined as the time taken by an algorithm to compute a given task. the algorithm with minimum steps accomplishes the task with minimal computation time. The same issue can be solved by many algorithms with different computation times, but the steps implemented by different routing protocols differ, which leads to high computation time in some cases. The algorithm with the minimum number of steps to resolve an issue has the lowest computational time. The computational times of the proposed technique and various techniques such as RPL, MSD-MAP, EESRA, E-ALWO, and GWO are calculated and categorized in Table 7.  
Table 7 states that the computational time required for the proposed system is the lowest, 6 s, while the computational time taken by Grey Wolf Optimizer is 11 s. The computational time needed by the Routing Protocol for Low-Power and Lossy Networks is 7 s and the computational time of the Multidimensional-Scaling Map is 8 s. The computational time needed by an Efficient Energy Scaling Routing Algorithm is 9.5 s, and finally, the computational time required for the Exponentially Ant Lion Whale Optimization algorithm is 6.5 s. Therefore, our proposed system is efficient in terms of computational time since it provides the lowest computational time of the methods. The algorithm E-ALWO consumes less computational time than other existing methods such as GWO, MDS-MAP, RPL, and EESRA, but our proposed method beats the E-ALWO algorithm and secures the first position in the least computational time consumed by an algorithm.