## 2 Overview of Modern Technologies and Frameworks in Smart Healthcare Systems
### Cloud Computing Technology for Healthcare  
Cloud computing and its various domains, such as edge and fog computing, offers a collaborative and connected environment for different sectors of the healthcare industry [44]. The smart healthcare ecosystem is vast and faces many critical challenges, including high operational costs, security, privacy, and centralized data storage. The use of cloud technology strengthens the overall workflow of the healthcare sector in terms of accessibility, quicker response time and better-personalized care, and resolves load balancing issues. Furthermore, fog and edge computing are versatile cloud computing techniques that reduce the latency between wearable devices and doctors. This is imperative because an ICU patient requires critical care without any data transmission delay; if his wearable device cannot relay the data quickly to the medical practitioners, his life can be at risk. The fog and edge computing allow the medical devices to process their data locally rather than transmitting it from the global model, resulting in less delay (quick response time), which improves the performance of remote monitoring and strengthens the patient engagement process.  
Researchers have adopted many solutions by employing on-demand computing services [12; 45]. For instance, [PERSON] et al. in [46; 47] presented the problem of delayed response time in the safety-critical system of healthcare. To tackle the issue, they utilized fog computing for reliable data exchange and rapid data processing. However, they observed that fog devices generate an enormous amount of data which is difficult to manage, so they used neighbourhood optimization that enhances the data transmission and fault tolerance of the smart devices of the smart healthcare systems. [PERSON] et al. in [33] proposed a fog and blockchain-based global vaccination scheme to offer a quick and efficient decision-making strategy for vaccination during the COVID-19 pandemic. The proposed prototype was implemented and evaluated using different performance metrics, such as response time, throughput, and data rates where the global vaccination campaign was organized. Then, [48] studied task scheduling challenges (time delay) facing healthcare services, where if the transmission of data to the healthcare providers is delayed, this directly influences the quality of service delivery and patient's life. To address the issue of delayed communication, the authors used cloud computing in the IoT-based healthcare system. They used swarm intelligence algorithms to optimize the data communication between healthcare providers and the patients. Their proposed model outperforms the baseline models in terms of waiting time, makespan, and resource utilization. Then, [PERSON] et al. in [49] discuss the security and privacy issues associated with the traditional medical system that uses precarious communication link between different entities of the healthcare system. Further, they also examined the inefficiency of blockchain technology in terms of computational complexity and its ability to store massive amounts of data. They used cloud computing to store the medical data and blockchain technology to offer privacy and security to the stored medical data. In addition, the authors validated their work using AVISPA tool against different security attacks (e.g., replay attacks). [PERSON] et al. in [50] studied the sensitive nature of medical data targeted by attackers. To overcome this issue, the authors presented a privacy-enabled secure communication framework for a smart healthcare system. Using cloud-based authentication, they provides a robust anonymity to the doctors, patients, and communication between doctors and patients.