## 5 Results
### Fixed Simulation Analysis
Furthermore, due to the high demand for processing, computation, and storage in the centralised Cloud computing scenario, the results confirm a direct correlation between the increase in energy consumption and the increase in the number of smart IoT devices. However, due to additional energy required by Fog computing nodes and resources, which are necessary for the core functions of Fog computing and the achievement of the Fog computing benefits, occasionally, the level of energy consumption was higher in the Fog computing scenario compared to the Cloud computing scenario. Additionally, although Cloud computing possesses almost unlimited computation, processing, and storage capabilities and resources, consistent data transmission to the centralised Cloud computing layer can increase energy consumption. Meanwhile, in Fog computing, the results highlight more energy consumption savings compared to Cloud computing. However, the energy savings depend on the number and type of smart IoT devices, as shown by the energy consumption savings of 1.19% for 7 CCTVs and 0.14% for 22 light sensors. This result for the CCTV is negative, therefore highlighting increased energy consumption, which indicates a Fog computing challenge when managing a small number of fixed devices. The 0.14% saving for the 22 light sensors indicates an increased energy consumption saving for Fog computing for specific types of smart IoT devices. The high volume of data generated as the number of devices increases leads to increased network congestion and, subsequently, longer data transmission times. Therefore, the Cloud and Fog computing results highlight a direct correlation between the number of smart IoT devices and increased latency. Furthermore, the results demonstrate a consistently lower latency rate for Fog computing than Cloud computing. This highlights the efficiency benefit of Fog computing, which indicates that applications with real-time response requirements will benefit from Fog computing. Additionally, the results highlight an enhancement in latency for all the smart IoT devices used in this study. The decrease in latency was significant for 7 CCTV cameras at 70.65%, 17 CO2 sensors at 84.41%, and 22 light sensors at 84.13%, indicating the benefits of Fog computing in reducing the request response times, which benefits decision-making and applications with real-time response requirements. Meanwhile, due to the distance between the device layer and the Cloud computing layer, data transmission takes longer, which is unsuitable for applications with real-time requirements. In both the Fog and Cloud scenarios, the scalable increase in the number of smart IoT devices leads to higher quantities of data generation and transmission, leading to higher network bandwidth usage. Meanwhile, the Fog computing scenario highlights the data management efficiency benefits of this approach by consuming slightly lower network bandwidth. Additionally, the results of network bandwidth usage varied, so that the 7 CCTV cameras output difference was \(-67\%\), which subsequently increased for 17 CO\({}_{2}\) sensors to 38.95% and for 22 light sensors to 38.02%, indicating the varied impact of Fog computing on network bandwidth usage, dependent on the volume and type of smart IoT devices.