# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 6 Analysis of SLR Results
### Golden Papers' Synopses  
These articles were also selected due to their strong connections to IoT, cloud computing, and machine learning, the three main pillars of smart healthcare. The key themes of the papers are summarized and outlined. The literature reviewed below explores the connections between smart healthcare applications and quality of service. To show how influential each publication is in the subject of smart healthcare, QoS parameters, citation counts, and views are extracted. Simulation tools and datasets are also discussed to evaluate current trends in simulation software in the research community. In [46], service latency is discussed at the network layer, with an emphasis on 5G networks, to meet the demands of service requesters (SRs). Resource allocation schemes are compared to determine the tradeoff between service satisfaction and revenue. Figure 16: This pattern shows that QoS issues in smart healthcare are receiving increased attention from the research community [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56]. Figure 15: Distribution of papers according to research methods. Specifically, a distributed network selection technique is presented for the heterogeneous wireless network environment [47]. The network selection problem has been characterized as an optimization problem to minimize the transmission energy consumption, cost, latency, and distortion. Network selection problems are addressed through the development of optimization strategies to obtain the best network possible within the constraints of resources and latency. The reward and convergence of the objective function are used to assess the method's effectiveness. The positive effects of edge computing for smart alternatives for distributed computation as well as the evaluation of smart IoT medical sensing were underlined in this paper [48] by the authors. This means that by fully or partially educating edge intelligent nodes at the edge of the network level, system latency can be decreased. It is possible to spread additional processing for computationally intensive applications throughout edge-as well as fog-based nodes or perform it in the cloud. Such methods offer considerable advantages in a situation where prevention and the early detection of symptoms are the primary considerations. In a CR-WBAN analytical model based on EH, a primary sensor network and a secondary sensor network coexist [49]. The authors test the hypothesized WBAN of the CR system's throughput performance under the specified parameters using the TSC (time-switching cooperation) and PSC (power-splitting cooperation) protocols. For the considered network, they studied the performance of two EH-based spectrum-sharing protocols, called TSC and PSC protocols, in terms of OP, throughput, and power efficiency over log-normal-distributed fading channels. A new job-offloading approach has been presented [50] to decide whether to offload a job to a certain fog node or to a cloud server at a specific time. When there are no direct connections between IoT nodes and cloud servers, IoT nodes may occasionally offload a task to fog nodes first, and fog nodes may subsequently offload the activity to the cloud. To resolve the issue, a Markov decision process (MDP) is employed. The suggested MDP takes into account two decision-makers: IoT users can decide which fog nodes to assign their work to, and fog nodes can decide whether to split up specific jobs between themselves or to cloud servers to preserve the task balance. The simulation results show that the suggested methodology outperforms competing strategies in terms of minimizing delays, doing more tasks, and distributing the workload. In this study [51], the confident information coverage hole repairing known as the CICHR problem was solved using the algorithms confident information coverage hole predicting abbreviated as CICHP, CEER, and DEER. The CIC (confident information coverage) concept served as an inspiration for these algorithms.