## 5 Results
### Fixed Simulation Analysis  
Tables 4-6 further show the comparative percentage differences between Fog and Cloud computing. In addition, Figure 3 depicts a graphical illustration of the Fog/Cloud comparison for all the smart IoT devices, including CCTV, CO\({}_{2}\), and lighting sensors, highlighting the benefits and efficiency of Fog computing compared to Cloud computing. The Fog and Cloud computing comparisons are simulated for (a) energy consumption, (b) latency, and (c) network bandwidth usage. Additionally, for energy consumption and network bandwidth usage, the use of the Fog-only simulation for data management on this floor initially shows that the lower number of devices is higher than that of the Cloud-only simulation. However, as the number of devices increases, the Fog computing paradigm becomes more efficient than the Cloud computing paradigm. This initial increase is due to multiple resources at the network edge, which require more energy overall when activated. Furthermore, it was observed that the Fog-only energy consumption results were higher than their Cloud equivalents for CCTV, which was 1.9% higher than Cloud. However, CO\({}_{2}\) was reduced by 0.06%, and light sensors were decreased by 0.14%, as shown in Table 4. For latency, the Fog simulation results highlight a reduction of 70.65% for CCTV, 84.41% for CO\({}_{2}\), and 84.13% for light sensors compared to their Cloud equivalents, as shown in Table 5. Lastly, analysing the network bandwidth usage, Fog-only simulation results show  
Figure 3: Results for the fixed scenario of smart office building devices: (**a**) Fog/Cloud energy consumption comparison; (**b**) Fog/Cloud latency comparison; (**c**) Fog/Cloud network bandwidth usage comparison. an increase in network use for CCTV data by 67%, while CO2 and light sensors reduced by 38.95% and 38.02%, respectively, as shown in Table 6.  
In the Cloud computing layer, it is theoretically understood that storage and computational processing resources are infinitely available; therefore, data are typically transferred to the Cloud layer for processing [32]. However, some of the costs and consequences associated with constantly forwarding homogeneous and heterogeneous data to the Cloud for processing are increased energy consumption, higher latency, and congested network bandwidth usage. Earlier aspects of this research have demonstrated, through previous studies, that introducing the Fog computing layer to the traditional Cloud computing architecture optimises energy consumption, latency, and network bandwidth usage. Consequently, this study compared the impact on energy consumption, latency, and network bandwidth usage for multiple smart IoT devices in a smart office building. From the results in Tables 4-6 and Figure 3 above, the Fog computing results for energy savings and network bandwidth usage were higher compared to their Cloud scenario equivalents. This highlights the limitations of Fog computing when evaluated in a fixed low-device environment, such as the smart office floor plan. The percentage difference for energy savings ranged between \(-1.19\%\) for 7 CCTV devices and increased to 0.14% for 22 lighting sensors. Meanwhile, the network bandwidth usage results were similar, ranging from \(-67\%\) for 7 CCTV devices, 38.95% for 17 CO2 devices, and 38.02% for 22 lighting sensor devices. However, the benefits of Fog computing were clearly highlighted for latency compared to Cloud computing, ranging from 70.65% for 7 CCTV devices, 84.41% for 17 CO2 devices, and 84.13% for 22 lighting sensor devices. The fixed results highlight a higher energy consumption in the Fog computing simulation with fewer smart IoT devices such as CCTV. Meanwhile, the scalability result indicates that energy consumption efficiency benefits become more pronounced as the number of smart IoT devices increases.