Optimising Performance Metrics Through Data Management: Leveraging Real-World Data in Cloud and Fog Computing Scenarios for IoT Networks in Smart Building Environments
Authors:   , Lawal Kelvin Nojeem 
Published: September 2024

The increased use of heterogeneous and homogeneous smart devices in smart homes increases the rate of energy consumption. Among the plethora of smart devices within a smart home, one of the critical smart devices that account for high data volume and data transmission rate is the Closed-Circuit Television (CCTV) system. Researchers have made mul5ple attempts to use Cloud computing solutions to address the issue of storage and computa5on processing power capabilities. However, there are challenges associated with Cloud compu5ng, such as increased energy consumption, high latency, and network bandwidth usage bottlenecks. The introduction of Fog computing aims to address these Cloud computing challenges and improve mobility, security, and on-demand requests. This research thesis presents a series of studies, one of which delves into how Fog computing offers a significant solution for managing data generated by CCTV camera systems. The study further demonstrates the efficiency and optimisation of these parameters using the iFogSim2 network simulation toolkit, showcasing Fog-based scenarios’ benefits in efficiently managing data from the CCTV camera system. Another study examined user scalability simulations conducted in a Cloud and Fog-based scenario, demonstrating an average percentage difference of 3.10%, 90.33% and 33.03% for energy consumption, latency and network usage, respectively. This underscores the superior efficiency of Fog computing compared to Cloud computing. The user scalability further demonstrates that as the number of users increases, the energy consumption, latency and network usage rise more slowly in a Fog-based approach than in a Cloud-based approach, leading to potential energy and cost savings. A further study in this research focuses on Fog computing in a smart building environment where heterogeneous IoT devices and sensors are standardly placed in a smart building. The study validated these findings using the CRISP-DM methodology and achieved a maximum percentage saving of 1.66% and 39% for energy consumption and network bandwidth utilisation, respectively. These findings have practical implications for designing and implementing smart building architectures, offering potential energy and cost savings. While current literature relies on synthetic data in the iFogSim2 simulation toolkit, a further study in this research bridges the gap by using real-world data. A comparative analysis was done on a fixed and scalable Cloud and Fog computing environment scenario. The findings demonstrated that Fog computing was more efficient than Cloud computing, particularly with increased data volume through smart IoT device scalability. This indicates a direct correlation between the increase in devices and efficiency within a scalable scenario. The fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage within a fixed architecture. In comparison, scalability analysis demonstrates improvements of up to 4%, 91.38%, and 34.78% for energy, latency, and network usage, respectively.