# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 6 Analysis of SLR Results
### Golden Papers' Synopses
The CEER and DEER algorithms have outperformed C-CICHH and D-CICHH, respectively, in terms of MSD, moving energy consumption, residual energy, and coverage ratio in the LS-HWSN based on the prior knowledge of confident information coverage hole that was acquired using the CICHP method. In comparison to C-CICHH and D-CICHH, simulation results show that the suggested algorithms can provide higher network QoS, a longer network lifetime, and reduced time complexity. [PERSON] [52] proposed a hierarchical approach called \"green communication\" for patient monitoring. The energy consumption of each device throughout each of its several states--idle, sleep, awake, and active--is modeled by this methodology. The base station chooses the cluster heads. A centralized routing system chooses the cluster heads at the base station level. The cluster heads no longer require join-request messages from the health monitoring instruments for the clustered to gather. As a result, they use less energy, which increases the network's lifespan. Experimental outcomes demonstrate the effectiveness of our strategy. The implementation of CoT in the context of smart healthcare is examined in this study [53], along with CoT designs and platforms. It is still necessary to find effective ways to achieve energy efficiency in data processing and transmission. The CoT architectures, platforms, and their use in healthcare were surveyed in this research. Instead of focusingon more general recommendations, it largely studied the energy efficiency problems in depth with the more pertinent proposals. In this work [54], a rapid and dependable cloud resource allocation model for body sensor devices to ensure QoS for healthcare applications utilizing smart homes is developed. The suggested resource allocation strategy used Agent-Based Modeling (ABM) and the ABM application NetLogo. Numerous sensors are used by the CABAN platform to collect physiological data. Wearables, mobile devices, cloud platforms, participants, and display terminals make up the system's five core components. It analyzes and saves the data via gateways and the cloud. Utilizing four different sorts of entities--turtles (moving entities), patches (stationary agents), connections, and the listener--provides a context for replicating natural processes. The evaluation is carried out by determining the algorithm's execution time, which determines how resources are distributed based on input data. The findings show that agent-based modeling (ABM) can be used to address complicated challenges. The proposed grey filter Bayesian CNN method [55] is introduced to reduce communication overhead besides response time. In comparison to recent efforts, the suggested GFB-CNN method shortens the end-to-end response time by only storing the best human activity using the Bayesian logistic sigmoid activation function. About the various numbers of instances, the performance of the proposed GFB-CNN approach is assessed in terms of communication time, overhead, and accuracy, and it is then contrasted with two previous works. The simulation results show that, when compared to cutting-edge works, the suggested GFB-CNN technique performs better. The proposed FB-CNN method, however, neglects to take security and privacy into account while analyzing medical data. In [57], static sensors are installed to watch over sleeping patients, while mobile sensors track moving patients to collect data and store it on cloud servers. The proposed SHM consists of the SSOA, BSN, cloud services, and data management domains. An SSOA upholds security and QoS while engaging in activities. Knowledge-based repositories, cloud servers, and semantic information extraction in the data management area are all able to be accessed and exchanged by those with authorization. The mobile sensor recruitment and selection phase assists in obtaining sensors from other clusters for the cluster that lacks enough sensors--first from a neighboring area, then from a nonadjacent area--to improve throughput and reduce latency.