# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 4
To ensure that vital traffic is delivered quickly and to avoid network congestion, traffic shaping modulates the data flow throughout the network. Error correction uses techniques such as forward error correction (FEC) to detect and correct errors in transmitted data to ensure that it is received correctly. Load balancing distributes traffic across multiple network resources to ensure that no single resource becomes overloaded and to improve overall system performance. The electronic medical records (EMR) dataset [69] contains electronic medical records for many patients. It can be used to evaluate and optimize QoS in healthcare applications, such as by analyzing the effect of network latency on the precision and timeliness of data collection and analysis. Clinical trial datasets are datasets containing data collected during clinical trials, including data on patient outcomes and treatment effectiveness. They can be used to evaluate and optimize QoS in healthcare applications, such as by analyzing the impact of network latency on the accuracy and timeliness of data collection and analysis. Wearable device datasets are collected from wearable devices, such as fitness trackers and smart watches. They can be used to evaluate and optimize QoS in healthcare applications, such as by investigating the influence of network latency on the correctness and timeliness of data collection and analysis. Numerous simulation technologies can be employed in applications for smart healthcare. Several scenarios were found in recent literature. A popular programming language used to create applications for smart healthcare is Java. Platform independence, security features, and the capacity to process enormous sizes of data are some of the characteristics of Java that make it suitable for healthcare applications. Java-based healthcare applications can be used to gather, store, and analyze patient data and can run on a range of devices, including smartphones, tablets, and laptops. Medical imaging software, telemedicine platforms, and electronic health record systems are a few examples of Java-based healthcare applications [46; 59]. Healthcare applications for the numerical computing environment and programming language MATLAB (Matrix Laboratory) include image and signal processing, data analysis, and method development. MATLAB can be used in smart healthcare to create and analyze medical pictures, separate characteristics from signals, create prediction models, and create control schemes for medical equipment. To facilitate more complex analysis and decision-making, MATLAB can also be combined with other tools and libraries, such as those for machine learning and deep learning. Image-guided surgery, computer-assisted diagnostics, and patient monitoring are a few examples of smart healthcare applications that make use of MATLAB [47; 50; 52; 56]. C++ is a sophisticated programming language that is popular in many industries, including in applications of smart healthcare. It is frequently employed in the creation of medical software, including clinical decision support systems, medical imaging software, and electronic health record systems. Additionally, C++ is used to create embedded systems and gadgets, such as wearable tech and medical sensors that may gather and transmit patient data for tracking and analysis. Additionally, C++ is frequently used to create simulations and other kinds of software for medical research. It is a preferred option for creating complicated and resource-intensive healthcare applications because of its flexibility and performance advantages, as mentioned in [51; 58]. Healthcare applications can benefit from the usage of TensorFlow, a potent machine learning software framework that is available for free and open source. Predictive modeling, natural language processing, and image and signal analysis are some of the activities that TensorFlow can be used for in the healthcare industry [59].