## 9 Conclusions  
In conclusion, the results of the data derived from the simulation in this study, with regard to the analysis of energy consumption, make it evident that Fog computing can be more efficient, with core services running energy consumption optimisation. An example was the result of the CCTV energy consumption scalability analysis, where the energy consumption for Fog computing was slightly higher than for Cloud computing at 20 devices. However, from 40 to 200 devices, the Fog computing energy consumption levels steadily decreased compared to Cloud computing. For CO\({}_{2}\), the Fog computing energy consumption levels were again higher than the Cloud up to 40 devices, subsequently reducing as the number of devices increased to 200. The light sensor results highlight a different trend, with Fog computing energy levels starting lower than the Cloud computing levels at 20 devices, increasing to become higher at 40 devices, and then decreasing steadily from 60 to 200 devices. From the fixed scenario results in Section 4, the results for CCTV demonstrate a 1.9% higher energy saving for Fog compared to Cloud computing. Meanwhile, CO\({}_{2}\) was lower at 0.06%, and the lighting sensor energy consumption was lower at 0.14% for Fog compared to Cloud computing. Regarding latency, the Fog computing results, compared to Cloud computing, were significantly lower by 70.65% for CCTV, 84.41% for CO\({}_{2}\), and 84.13% for lighting sensors. The network bandwidth usage results for Fog computing compared to the Cloud scenario showed a significant increase for CCTV by 67%. However, the CO\({}_{2}\) and lighting sensors exhibited reduced usage by 38.95% and 38.02%, respectively. In the scalability analysis section, the Fog computing results for CCTV, compared to the Cloud results, showed lower energy savings by \(-0.47\%\) for 20 devices. Then, the energy savings increased to 4.35% for 180 devices. The CO\({}_{2}\) results showed a similar trend of lower energy savings at \(-0.26\%\) for 20 devices and an increase to between 4.20% and 4.01% for 140 to 200 devices. The lighting sensor results also showed a similar trend of a \(-0.58\%\) saving at 40 devices increasing to 3.89% for 180 devices. For latency, the CCTV Fog computing results compared to Cloud computing showed a 70.97% difference at 20 devices and a 82.10% difference at 200 devices. Meanwhile, the CO\({}_{2}\) results showed significantly less latency by 84.78% for 20 devices and 87.15% less for 200 devices. The lighting sensor results showed significantly less latency, by 85.28% for 20 devices and by 87.33% for 200 devices. Compared to Cloud computing, the Fog computing results showed substantially less network bandwidth usage by 31.02% for 20 devices and by 23.72% for 200 devices. This trend indicated a shift in the threshold as the number of devices increased before the benefits of Fog computing energy consumption kicked in. Then, energy consumption started to decline as expected compared to Cloud computing. These results vary due to the type of IoT smart device, device numbers, their respective device parameter values set in the iFogSim2 network simulation toolkit, and the infrastructure parameter value settings when using real-world data. Furthermore, the results indicate that the Fog computing layer is better suited and equipped to deal with many volatile random devices than the Cloud computing layer. Conversely, Fog computing could be more energy efficient generally for a low-data bandwidth and fixed, rigid infrastructure. In this study, however, the benefits are derived from the extra volatility of additional smart IoT devices and the accompanying data load.