## 4 Experimental Setup and Result Analysis
### Results and Discussion
evaluated the security measures of IoT devices implemented in an intelligent building context whose value can be high, medium, or low. The energy optimization score is evaluated in terms of percentages ranging from 0 to 1. The occupant comfort rating is analyzed in the range of 1 to 5. Energy optimization of intelligent buildings is evaluated by utilizing the power of IoT devices and niche genetic algorithms. The result of our proposed method is thoroughly compared with very similar work found in the literature such as the work proposed by [PERSON] et al. [12] and [PERSON] et al. [13], as shown in Figure 7. It is clearly seen in the figure that our proposed approach optimized the energy level in intelligent buildings context more than already proposed approaches. The different zonal areas of the building are taken into consideration for energy optimization by different researchers. They used different approaches toward the energy optimization of these areas. The figure clearly states that our proposed method optimized the resources of all mentioned areas of intelligent buildings at a rate of higher than 80%. Figure 8 shows the occupant comfortability level in the intelligent buildings' environment after the energy optimization. The basic aim of checking the occupant comfort level is to check whether the energy optimization and security measure of IoT devices affects their comfort or not. We compared our proposed work with very similar work proposed by [PERSON] et al. [12] and [PERSON] et al. [13]. We measure occupant comfortability on a scale of 1 to 5, whereas previous studies measured it on a scale of 1 to 10. We adjusted our scale to make comparisons easier. It can be seen from Figure 8 that in most of the zonal areas \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Building Zone**} & \multirow{2}{*}{**IoT Security Level**} & \multicolumn{2}{c}{**Energy**} & \multicolumn{1}{c}{**Occupant Comfort**} \\  & & **Optimization Score** & **Rating** \\ \hline Living room & High & 0.85 & 4.2 \\ \hline Bedroom & Medium & 0.75 & 4.5 \\ \hline Kitchen & Low & 0.82 & 4 \\ \hline Office & High & 0.78 & 4.3 \\ \hline Bathroom & Medium & 0.77 & 4.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Intelligent building zonal areas analysis. Figure 7: Energy optimization score of intelligent buildings. of intelligent buildings, we are the winner. However, in some areas, the score of the prior work is higher. The occupant comfort level is evaluated in different areas of the building including the bedroom, living room, kitchen, office, and washroom by different authors. They utilized different optimization approaches for this and obtained quite satisfactory results. It can be seen in Figure 8 that the level of comfortability of all authors' proposed methods had a rate greater than 4. However, none of them are clearly winning, as one approach shows a higher comfortability level in one area, while the other in another area. The value of the fitness function over 90 generations is depicted in Figure 9 for the niche genetic algorithm. In this study, we used a specialized evolutionary algorithm to identify the best possible solution to the problem of energy consumption in intelligent buildings. The lowest value in the range (zero), if the fitness function monitors energy consumption that we want to reduce, would be considered the \"best\" value. In this scenario, better performance or a more optimal solution is indicated by a lower fitness rating. However, we also want to make sure that the occupants of intelligent buildings are comfortable, so our goal is not just to reduce the energy consumption of these buildings. Therefore, the midpoint between the maximum and minimum would be the optimal value. Table 4 shows the results of the partial optimization calculation after using the adaptive optimization control model.