## 6 Discussion
### Scalability in a Broader Context  
It is crucial to consider the scalability of the proposed architecture in various real-world settings, from both a Cloud and Fog computing perspective, and in the context of smart IoT devices. The ability to increase or decrease the number of smart IoT devices in various network architecture environments is described as scalability. The architecture'sscalability proposed in this study can be applied to other real-world environments. Some of these environments include manufacturing plants and industries, healthcare facilities, such as hospitals and care homes, and smart cities.  
Industrial IoT (IIoT) environments are more complex due to the incorporation of smart industrial machines capable of complex functions with large-scale network sensors and time-sensitive monitoring capabilities [58]. Some of the challenges of the application of IIoT in a Cloud computing environment include security and privacy, data protection regulations, data location restrictions, and high-load-processing demand, which causes communication delays. Scalability challenges include the number of smart IoT devices required to support an industrial application environment and how a system can be designed efficiently to manage energy consumption issues in a scaled-up IIoT environment.  
These challenges can be addressed through the adoption of Fog and edge computing. In the case of scalability in an IIoT environment, adopting Fog computing supports a decentralised network structure with greater security as data are processed locally and in a decentralised manner [59]. Fog computing also supports faster processing speeds and improving data communication for IIoT applications. In the case of smart device scalability, and as shown in this study, Fog computing, compared to Cloud computing, optimises latency, energy consumption, location awareness, real-time connectivity, and security and privacy. As in the smart office building floor plan, the design and implementation of distributed optimisation algorithms can also be adopted in an IIoT environment to manage smart IoT device numbers and the distributed computing resource issue. In addition, cloud computing possesses high interoperability, reliability, and scalability to enhance resource utilisation [60].  
Healthcare environments can include hospitals, care homes, or homes for assisted living. In hospitals and other care home environments, the scalability of smart IoT devices can be used to track the physical location of patients or residents and to monitor their vital signs as an integral part of their care. Furthermore, smart IoT devices can be required to manage facilities and track assets and equipment. Some of the challenges that could be faced in the healthcare environment are the reliability, privacy, and security of patient or resident data, which are classed as sensitive data, as well as providing a good level of QoS. To address these issues, adopting Fog computing, and its ability to manage and process patient data closer to the network edge as a priority and with strict and secure high-level encryption, can strengthen the level of security in these types of healthcare facilities.  
Another environment is smart cities, where the scalability of smart IoT devices is on a larger scale, involving a wide variety and range of smart IoT devices and applications, including the management of utilities, transport, traffic, and security. Some of the challenges involved in this environment involve the management of large data volumes and the management of various interoperable systems. To address these challenges, adopting the Fog computing model, with its hierarchical nature, can address the issue of large data volumes and improve relationships between various solution providers for better sharing and collaboration. The processing and storage of large data volumes is carried out in a scalable manner through Cloud computing storage solutions, while processing occurs at the network edge in the Fog computing layer.