###### Abstract  
It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud-Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while 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, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices. f +
Footnote †: journal: _sciences_  
0000-0001-919-22000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-0000  
Fog computing [9]. Addressing these challenges is achieved by integrating the Fog layer into the traditional Cloud computing architecture between the Cloud and device layers, bringing the IoT device processing requests closer to the network edge [10]. Additionally, one of the characteristics of Cloud network infrastructure is the geographical distribution of IoT smart devices, which can generate large amounts of data per unit time [11]. In the current trend within the Fog computing literature, researchers frequently use default synthetic data in the iFogSim2 network simulation toolkit [12; 13; 14; 15; 16]. At the same time, a small group of researchers use real-world data to represent the network infrastructure Cloud, Fog nodes, gateway, and proxy servers using Arduino and Raspberry Pi and other IoT devices [17; 18]. Notably, research has yet to use real-world data throughout a smart building network across all the network architecture, Cloud, Fog, and device layers. Therefore, this has created a gap in the literature with the use of real-world data across both the device and infrastructure network layers within a real-world smart building. Consequently, this study's motivation was founded on the need to use real-world data across the real-world smart building network architecture, including using real-world parameter values of both the infrastructure and the device in the simulation, to bridge this research gap. The method used to conduct this study's simulation experiment in a real-world smart office building was based on data being collected from the standardly placed smart IoT devices. Then, the smart IoT device placements were modified to enhance their efficiency, based on the usage frequency of certain areas in the smart building floor plan. Subsequently, the iFogSim2 network simulation toolkit was used to run the simulation for the standard and enhanced scenarios in both Cloud and Fog computing environments. The results were then processed, analysed, evaluated, and compared to highlight the benefits of Fog computing and the novel contribution of demonstrating further improvements to the state-of-the-art Fog computing paradigm in energy consumption reduction, higher latency, and reduced network bandwidth usage.