## 2 Literature Review
### Related Work  
According to [25], many research publications in IoT and Fog computing focus on real-world data collection, which can be achieved securely and reliably. The use of real-world application data in Fog computing research and technology is increasing exponentially; therefore, there is a need to understand the challenges and future direction of Fog computing. Furthermore, the demand for smart IoT devices in various environments, including smart homes, is also rising. The data gathered from these smart IoT devices are processed and analysed to address real-world issues and challenges in real time. Conversely, synthetic data may be used to simulate real-world scenarios; however, they cannot be used to address real-world issues and challenges in real-time [26; 27]. The most effective way to evaluate service management policy is through the use of real-world data [16]. However, the time it takes to implement a real-world environment is considerable and modifying system parameters is difficult, especially during the simulation [28]. Most network simulator toolkits do not have set benchmarks to validate management usability policies in this research area. Instead, simulation toolkits use synthetic data without a functional grounding, which leads to errors and biased performance. To deal with the issue of bias, iFogSim2 was developed to exploit and mimic various forms of real-world data [16]. Furthermore, other network simulation toolkits, like FogBus, use real-world data to achieve effective analysis and critical decision-making, which may not be possible when using synthetic data [29]. According to [16], many Fog computing network simulation toolkits do not integrate real-world data. These simulation toolkits rely on synthetic data, which are quite rigid in their variability and do not replicate simulations in various contexts. Furthermore, the lack of standardised benchmark policies with synthetic data makes these simulation toolkits unsuitable for effective performance comparison and validation accuracy of resource management for policies in Fog computing environments. According to [30], the use of synthetic data provides room for variability control that might not be possible with real-world data, which benefits a research study when trying to isolate certain factors in a simulation. However, the use of synthetic data may only factor in some of the complexities of and unpredictable variations in real-world data, which creates discrepancies and makes the results less accurate compared to the use of real-world data. Although synthetic data are quite effective, they depend on three primary characteristics: relevance, coherence, and diversity. Therefore, in order for synthetic data to remain effective, the data need to be reliable [31]. While real-world data tend to be reliable, thereare potential issues with privacy. However, in this study, the privacy issue is non-existent as the data type excludes any personal data. In addition, a major issue with synthetic data is that they might not capture unpredictability, correlation, and other intricate details found in real-world data. This can skew model results performed with synthetic data and, therefore, cannot be used to generalise in real-world environments [31]. Table 1 summarises the research literature on data management optimisation in traditional Cloud infrastructure while introducing the Fog computing layer in a smart home environment with IoT smart devices at the device layer. The measures focused on energy consumption, latency, and network bandwidth usage in a smart building's Cloud- and Fog-based computing environment. Ref. [32] investigated the use of Fog computing in a Cloud computing network architecture to enhance sustainability in smart cities using real-world application data and smart IoT devices to measure energy consumption in the transportation area. The research paper did not use real-world infrastructure data, data optimisation, latency, or bandwidth usage measurements. Ref.