# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 4  
There are many QoS-aware technologies which are employed in healthcare applications, such as network virtualization and resource allocation. In [46], resource allocation is used to maximize SPs' income while considering SR priority and interference in the 5G network. Using cognitive radio technology, [PERSON] [49] primarily focused on maximizing QoS through intelligent spectrum sharing, allowing different sensor nodes in the network infrastructure to coexist without jeopardizing QoS standards. For on-demand access to a shared collection of resources such as networks, servers, storage, and services to maintain QoS requirements, authors in [53] used the cloud computing concept. In [54], BANs are integrated with CC to fulfill the QoS requirements of caregivers. Traffic management regulates the flow of data in the network to ensure that critical traffic is delivered promptly and to prevent network congestion. Traffic management in healthcare refers to the process of controlling and directing the flow of patients, staff, and resources within a healthcare facility. This can include scheduling appointments, managing patient flow in clinics and hospitals, and coordinating the movement of medical equipment and supplies. The goal of traffic management is to improve the efficiency and effectiveness of healthcare delivery, as well as to enhance patient satisfaction and safety. This can be achieved with technology such as electronic medical records, computerized appointment scheduling systems, and real-time location tracking systems [65]. To handle the data traffic of healthcare applications and achieve the necessary QoS levels, researchers used a variety of routing algorithms in [51; 59]. Quality of experience (QoE) measurement involves using specialized tools and techniques to measure the perceived quality of a healthcare service, including factors such as latency, reliability, and usability. In healthcare, QoE measurement can be used to evaluate the effectiveness of telemedicine systems, electronic health records, and other digital health tools. This can assist developers and healthcare practitioners in identifying problem areas and making the required corrections to improve user satisfaction [66]. In [46; 51], QoE is addressed along with QoS to measure the technical aspects as well as the user's perception of the service. Machine learning involves using machine learning algorithms to analyze and optimize QoS in healthcare applications, such as by predicting and preventing network congestion. Some of the most promising areas of research in machine learning for healthcare include [67]. Computer-aided diagnosis, in which machine learning algorithms are used to analyze medical images to identify signs of disease. Predictive analytics uses machine learning algorithms to predict future health outcomes based on a patient's medical history and other factors. This involves using data and analytics to predict and prevent issues that could affect QoS, such as network congestion or device failures [68]. Natural language processing (NLP) uses machine learning algorithms to extract information from unstructured data, such as electronic medical records. Currently, various ML algorithms and models are used in SHAs from underlying network selection to disease detection and even prognosis. Methods that are quality-of-service-aware guarantee QoS while giving end users effective medical treatment. Research articles review a variety of methodologies, and they choose the most recent and widely used methodologies from the reviewed research publications. The prioritization method involves assigning higher priority to critical traffic,  
Figure 17: The QoS parameters related to SHAs that have received the most research include delay, latency, and energy consumption. such as real-time medical data, to ensure that it is delivered promptly. Bandwidth allocation reserves a certain amount of bandwidth for critical applications and devices to ensure that they have the resources they need to function effectively.