## 2 Literature Review
### Related Work
[33] proposed a framework for leveraging Fog computing in a Cloud architecture to enhance a car parking solution. The paper covered data optimisation through latency and network bandwidth usage measurements; however, the paper excluded energy consumption measurements and the use of real-world device and infrastructure data. Ref. [34] discussed using the Cloud and Fog computing paradigm to manage smart IoT devices and sensors in a smart home environment through efficient resource allocation and router optimisation. Although the paper used real-world device data, it used synthetic infrastructure data. Ref. [35] investigates smart home security optimisation through smart IoT devices. The research paper also covers data optimisation and Cloud and Fog computing. The use of real-world device and infrastructure data, including performance metrics, was excluded. Ref. [36] delves into smart home system automaton, managing the smart IoT devices through design functionality using real-world device data, energy consumption measurements, but excluding real-world infrastructure data, data optimisation, Fog computing, latency, and network bandwidth usage performance metrics. Ref. [37] explores decentralised transactive energy concepts in buildings and electric vehicles within an edge computing environment focusing on sustainable energy. The research covers data optimisation, IoT devices, Cloud and Fog computing, but excludes real-world device and infrastructure data and all identified performance metrics. Ref. [38] focuses on using Fog computing to optimise data aggregation security in a smart building environment. The paper covers the IoT, Cloud and Fog computing, but excludes the use of real-world data for both devices and infrastructure, though it does include identified performance metrics. In ref. [39], the paper explores how personalised smart IoT devices can optimise energy efficiency in a smart building environment. The paper focused on data optimisation, Cloud and Fog computing, but excluded the use of real-world device and infrastructure data, latency, and network bandwidth usage performance metrics. Ref. [40] investigates the use of Fog computing in managing application microservices and data management through patient monitoring. Ref. [32], encouraged the use of real-world data at the device layer with data analysis at the network's edge, to be combined with enterprise-level IoT solutions to realise real Fog computing potentials in a smart home network architecture and other environments. Meanwhile, ref. [34] focused on a novel resource management technique to accurately predict smart IoT device performance metrics and resource utilisation efficiency using real-world device parameter values. In addition, ref. [36] developed an adaptable and user-friendly home automation system that allows smart building users to control smart IoT devices using real-world device data through the various case studies presented in their research. These research studies focused on device layer real-world data parameters only. Conversely, ref. [40] focused on the simulated system topologies and application infrastructure models responsible for multisensory processing at the Cloud and Fog computing infrastructure layers. Overall, the limitation of these existing works is the lack of consideration for real-world infrastructure data gaps. Although a few related studies did consider real-world infrastructure data, they lacked consideration of device layer real-world data. These gaps observed in existing research studies led to the design of this study's proposed approach of adopting both device and infrastructure layer real-world data and using a single floor plan in a real-world smart building network architecture environment as the primary novel contribution. Real-world infrastructure data, data optimisation, Cloud and Fog computing, IoT devices, and performance metrics were used in the paper, but energy consumption performance metrics and real-world device data were not included.