## 4 Performance Validation  
The presented technique is implemented in a PC i5-8600k processor, GeForce 1050 Ti, 4 GB RAM, 16 GB OS Storage and 250 GB SSD File Storage. This section discusses the analysis of the result of the presented GEER-DLAD model interms of energy efficiency, lifetime, throughput, average delay, and detection performance. For validating the energy-efficient performance, a series of comparative results analysis takes place with grasshopper optimization algorithm (GOA), glowworm swarm optimization algorithm (GSOA), multi-objective particle swarm optimization algorithm (MOPSOA), and particle swarm optimization algorithm (PSOA). Table 1 and Figure 5 analyses the energy consumption results of the GEER-DLAD with existing methods with respect to distinct IoT devices. The resultant figure reported that the PSOA technique has resulted in inferior performance by exhibited higher energy consumption. Followed by, the MOPSOA has revealed slightly reduced energy consumption over PSOA. At the same time, the GSOA and GOA models have demonstrated closer energy consumption. However the presented GEER-DLAD model has accomplished better performance by obtaining reduced energy consumption over all the other compared methods. For instance, on the applied 10 IoT devices, the GEER-DLAD model has exploited a minimum energy consumption of 35 mJ whereas the GSOA, GOA, MOPSOA, and PSOA models have depicted higher energy consumption of 40 mJ, 43 mJ, 46 mJ, and 55 mJ respectively. Simultaneously, on the given 50 IoT devices, the GEER-DLAD model has applied a minimum energy consumption of 105 mJ whereas the GSOA, GOA, MOPSOA, and PSOA methods have depicted higher energy consumption of 126 mJ, 135 mJ, 146 mJ, and 165 mJ respectively. Concurrently, on the applied 100 IoT devices, the GEER-DLAD model has employed low power consumption of 160 mJ whereas the GSOA, GOA, MOPSOA, and PSOA models have illustrated maximum energy consumption of 174 mJ, 195 mJ, 213 mJ, and 222 mJ respectively. Table 2 and Figure 6 investigates the network lifetime analysis of the GEER-DLAD model under distinct IoT devices. From the figure, it is showcased that the PSOA and MOPSOA models have demonstrated least network lifetime over the other methods. At the same time, GOA and GSOA models have exhibited even better network lifetime, but not superior to GEER-DLAD model. However, the GEER-DLAD model has exhibited improved network lifetime when compared with other models. For sample, on the applied 10 IoT devices, the GEER-DLAD model has obtained a higher network lifetime of 5600 rounds whereas the GSOA, GOA, MOPSOA, and PSOA models have exhibited reduced network lifetime of 5500, 5200, 4800, and 4500 rounds. Eventually, on the applied 50 IoT tools, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of IoT**} & \multicolumn{5}{c}{**Energy Consumption (m))**} \\ \cline{2-6}  & **GEER-DLAD** & **GSOA** & **GOA** & **MOPSOA** & **PSOA** \\ \hline
10 & 35 & 40 & 43 & 46 & 55 \\ \hline
20 & 56 & 64 & 68 & 71 & 80 \\ \hline
30 & 72 & 85 & 92 & 98 & 107 \\ \hline
40 & 81 & 99 & 107 & 111 & 140 \\ \hline
50 & 105 & 126 & 135 & 146 & 165 \\ \hline
60 & 119 & 137 & 147 & 163 & 174 \\ \hline
70 & 125 & 150 & 164 & 177 & 186 \\ \hline
80 & 134 & 161 & 177 & 192 & 197 \\ \hline
90 & 145 & 166 & 189 & 198 & 211 \\ \hline
100 & 160 & 174 & 195 & 213 & 222 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Result Analysis of Existing with Proposed GEER-DLAD Method in terms of Energy Consumption (m)). the GEER-DLAD approach has gained a higher network lifetime of 4900 rounds whereas the GSOA, GOA, MOPSOA, and PSOA models have shown limited network lifetime of 4700, 4500, 4000, and 3600 rounds. Along with that, on the applied 100 IoT devices, the GEER-DLAD scheme has achieved a maximum network lifetime of 3800 rounds while the GSOA, GOA, MOPSOA, and PSOA schemes have showcased limited network lifetime of 3300, 3100, 2900, and 2800 rounds. Table 3 and Figure 7 scrutinizes the throughput analysis of the GEER-DLAD model under various IoT devices.