### Related Work  
In recent times, significant attention has been devoted to developing healthcare services and addressing various technical and architectural challenges in the healthcare sector. Researchers and professionals have actively engaged in designing and implementing healthcare solutions to enhance patient care, improve healthcare delivery processes, and foster better communication and collaboration among healthcare providers. Various technologies have been introduced by researchers and developers, providing new approaches for creating more efficient, effective, and secure healthcare platforms. These technologies, based on existing literature with methods and purposes in the healthcare domain, are shown in Table 1. For instance, in [4], artificial intelligence (AI) and machine learning (ML) solutions were utilized to enhance COVID-19 analysis and screening, enabling faster diagnosis and customized treatment and control measures. In [5], deep learning (DL) was employed to successfully screen COVID-19 patients using X-radiography (X-rays) and Computed Tomography (CT) scans with 100% accuracy. In [6], Layered Recurrent Neural Networks (L-RNN) are used to predict missing data in Hepatocellular Carcinoma data. In [7], several ML algorithms are compared with detect brain tumors from magnetic resonance (MR) images, with Partial Tree (PART) outperforming other techniques. AI and ML techniques for monitoring cardiovascular diseases highlight the challenge of interpreting black box models [8]. Furthermore, Ref. [9] applies deep reinforcement learning and neural networks (NN) to optimize spectrum access. The articles emphasize the need for big data analytics to address issues such as sustainable information and communication technology (ICT) [10; 11]. Additionally, AI/ML techniques are employed for security and privacy in networks [12], proposing an AI-driven hybrid framework for intrusion detection in IoT-enabled e-health. Authors in [13; 14; 15; 16; 17] discuss the use of blockchain and cryptography in the e-healthcare domain to enhance security and privacy. For contact tracing in crowded areas while maintaining COVID patients' privacy, a proposed physical unclonable function (PUF)-based host tracking system is introduced [18]. PUF-based sensors secure physical measurements [19] and utilize PUF-based sensor devices for secure monitoring of COVID-19 patients [20]. The use of Software Defined Networking (SDN) in the IoMT for network management and slicing is exemplified in [21; 22; 23; 24]. These works showcase the potential of SDN in IoMT for efficient network management and improved service provisioning. Additionally, IoMT applications addressing COVID-19 challenges are presented in [25], while Ref. [26] discusses the challenges of data-intensive ecosystems in cyber-physical systems (CPS), particularly in the context of mobile healthcare environmental monitoring. A wireless sensor network (WSN)-based system has been designed to track the heart rate and movement of elderly individuals within their residences. The system can send alerts to healthcare professionals, caregivers, or family members via a smartphone if there are significant changes in physiological indicators such as falls, high heart rate, or low heart rate [27]. Researchers are actively working on enhancing healthcare systems through advanced technologies such as AI and IoT [28; 29]. Several studies have also explored the integration of various computing technologies, including IoT and cloud computing, to develop smart healthcare solutions [30]. These solutions hold the potential to tackle healthcare issues and improve patient care by leveraging the capabilities of these advanced technologies. For instance, a smart healthcare framework based on cloud and edge computing has been proposed [31], which can be implemented in smart cities for progressive developments. Similarly, a framework has been developed for individuals with voice disorders using edge computing and cloud frameworks [32].