## 6 Discussion  
Evaluating the energy consumption comparison for Cloud and Fog computing across all three smart IoT devices in the smart office building demonstrates that the number of devices directly correlates to the increase in energy consumption. More energy is necessary for the computation, processing, transmission, and storage of data at the centralised Cloud data centre layer as the number of devices increases. However, the results also show instances where energy consumption for data processing, computation, and storage is higher at the Fog layer than at the Cloud layer. These instances of increased energy consumption by Fog computing are due to the infrastructure resource requirements at the network's edge that facilitate operations within the Fog layer, which is what produces the benefits mentioned above. These infrastructure devices require more energy during the data management process at the network's edge. The Fog nodes within the cluster also \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Devices**} & \multirow{2}{*}{**CCTV Fog**} & \multirow{2}{*}{\begin{tabular}{c} **CCTV** \\ **Cloud** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CCTV** \\ **Difference** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CO\({}_{2}\) Fog** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CO\({}_{2}\)** \\ **Difference** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CO\({}_{2}\)** \\ **Difference** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Light** \\ **Cloud** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Light** \\ **Difference** \\ **(\%)** \\ \end{tabular} } \\ \hline
20 & 8.74 & 12.67 & 31.03\% & 9.63 & 15.05 & 35.97\% & 9.64 & 14.78 & 34.78\% \\
40 & 19.41 & 26.23 & 26.00\% & 18.59 & 27.90 & 33.37\% & 18.90 & 28.60 & 33.92\% \\
60 & 30.78 & 39.77 & 22.61\% & 27.52 & 41.60 & 33.85\% & 27.83 & 41.62 & 33.14\% \\
80 & 41.61 & 53.12 & 21.67\% & 37.19 & 54.57 & 31.85\% & 36.90 & 54.52 & 32.31\% \\
100 & 50.84 & 65.79 & 22.73\% & 45.55 & 66.61 & 31.62\% & 45.98 & 66.85 & 31.21\% \\
120 & 59.28 & 76.89 & 22.90\% & 53.72 & 78.80 & 31.82\% & 53.72 & 78.75 & 31.79\% \\
140 & 66.53 & 86.57 & 23.16\% & 60.13 & 87.60 & 31.36\% & 59.81 & 89.07 & 32.85\% \\
160 & 72.37 & 95.95 & 24.57\% & 66.05 & 96.54 & 31.59\% & 66.20 & 96.87 & 31.67\% \\
180 & 78.91 & 103.89 & 24.04\% & 71.39 & 103.91 & 31.30\% & 71.52 & 105.17 & 32.00\% \\
200 & 84.48 & 110.75 & 23.72\% & 76.73 & 111.27 & 31.04\% & 76.18 & 110.76 & 31.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Network bandwidth usage (Gbps) results for light sensor data management. need to be active to receive and process requests from the scheduling node during the offloading and load distribution process. In addition, the scalability analysis demonstrates that Fog computing benefits such as energy efficiency and savings only kick in when the smart IoT device numbers are increased. Therefore, the high energy consumption levels observed for the Fog scenario compared to the Cloud scenario in the smart office floor layout can be theorised to be due to the relatively small number of CCTV cameras used in this study. A review of the latency comparison between Cloud and Fog computing demonstrates that, as the number of devices increases the data generated increases, resulting in increased network congestion and higher data computation and processing times. Therefore, as the number of devices increases, there is an increase in the latency delay for both Cloud and Fog computing. However, the latency level for Fog computing is significantly lower compared to Cloud computing.