# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 3  
The fact that SHAs are a superset of IoT, cloud, fog, edge, ML, and other technologies means that they inherit all the well-known characteristics of QoS as well as its difficulties and problems, including delay, latency, energy consumption, and other metrics. Specific QoS metrics have been considered crucial considerations in this domain because of the dynamic nature and strict requirements of smart healthcare systems. In emergency treatment,for instance, patient data must be provided and evaluated swiftly. Even though obtaining QoS in SHA applications is difficult owing to several limitations, it is the most desirable prospect in SHA applications. It is possible to fully utilize the QoS assessment of the technology and get beyond its challenges because SHAs have many uses. We investigated filtered articles in depth to identify the simulation models, QoS metrics, and sensors in SHAs, as shown in Table 4.  
Currently, SHAs are experiencing difficulties with latency, delay, and energy consumption. Because of this, many QoS metrics for SHAs concentrated on ways to lower total energy consumption and boost network throughput while simultaneously prolonging network lifetime and assuring more dependable communication by lowering congestion and collision. We investigate metrics like end-to-end delay, latency, throughput, response time (RT), and energy consumption (EC) because we are primarily interested in network performance. The amount of time a data packet needs to traverse from its origin to its final location is referred to as network latency. The performance and efficiency of medical equipment and systems can be significantly impacted by delays in intelligent healthcare systems [63]. For example, in telemedicine applications, where patients and healthcare providers communicate and interact remotely through video and audio, high latency can result in delays and poor audio and video quality, which can affect quality of care. In remote patient monitoring applications, high latency can also affect the accuracy and timeliness of collected data as well as the ability to respond promptly to alerts and alarms. In general, smart healthcare systems need to have low latency to ensure that medical devices and systems function effectively, and that the quality of care is not compromised. To minimize latency, it is important to optimize the network infrastructure and design, as well as the protocols and algorithms used to transmit data. Moreover, datasets and challenges in adopting smart healthcare are also analyzed. The classification of researched articles is summarized in Figure 17 in terms of the QoS parameters employed over the enabling technologies for developing SHAs that are QoS aware [64]. Different models are applied to analyze QoS factors for efficient smart healthcare applications. Delay, latency, and energy consumption are the QoS metrics that have been the subject of the most research on SHAs, as is clear from the classification above. Furthermore, the top technologies used in SHAs are 5G, IoT, and cloud/fog/edge computing.