# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 1 Introduction
### Review Contributions  
IoT, AI, cloud computing, machine learning, and other cutting-edge technologies are all part of the smart healthcare ecosystem. However, only a small number of articles on smart healthcare have considered QoS optimization. Our foremost contribution is to provide a thorough overview of all the key enabling technologies, covering everything from the fundamentals of smart healthcare to the most cutting-edge systems and services, such as delivering reliable levels of service for such applications. This article presents a systematic literature review (SLR). Along with it, the following list includes the areas on which our review focused:  
1. An innovative method of describing pre-SLR activities that enables readers to judge whether an issue has undergone thorough research before examining it. To report on previous multidisciplinary investigations on this new paradigm, the study chose smart healthcare as the subject for conducting SLR. The metefacto approach was used to produce keyword co-occurrence maps, and VOS viewer was used to extract pertinent research papers and analyze them. In MS Excel, thesaurus files were used to  
Figure 1: QoS requirements at different layers of smart healthcare applications [7].  
remove misspellings, synonyms, abbreviations, and elaborations. Section 2 elaborates on the entire strategy.
2. A systematic review of recent research on QoS optimization in smart healthcare applications is conducted, providing valuable insights for researchers and academics. The state of the art in QoS optimization for healthcare applications is revealed by this review, which incorporates articles, studies, datasets, and technology. Section 3 delineates the SLR.
3. By concentrating on how ML, cloud computing, and IoT are employed in smart healthcare, a study of the application of enabling technologies, smart healthcare, and QoS is offered. Secondary research analyzes primary studies using methods such as systematic mapping, reviews, and SLRs. The study highlights the lack of comprehensive studies and shortcomings in current methods utilized in smart healthcare applications. These investigations are presented in Sections 4 and 5.
4. Smart healthcare applications encounter challenges with QoS optimization due to network congestion, interoperability problems, real-time demand, and cost limitations. Low latency is required by real-time requirements, network congestion produces unstable connectivity, and interoperability provides seamless system and equipment operation. Section 6 discusses and presents solutions to these issues.
5. Articles are chosen for their relatedness to cloud computing, machine learning, and IoT in the context of smart healthcare. Reviewing the literature and extracting QoS parameters, citation counts, and views, we analyzed in the result section how smart healthcare applications and service quality are related.  
To our knowledge, no other SLR has been written with such a theme or concentration. Moreover, to validate our claim we conducted a topic-level pre-SLR search elaborated upon in Section 2. In Section 3, SLR is delineated. Preliminary concepts are presented in Section 4. Related work is highlighted in Section 5. Section 6 explains SLR results and analysis. Section 7 is about responses to posed research questions. Section 8 presents the challenges faced by SHAs and Section 9 elaborates recommendations and lessons learned. In the end, Section 10 concludes the SLR with future work.