## 2 Related Works  
[PERSON] et al. [5] discuss the concept of QoS for the health care system. The authors explain that these systems manipulate large volumes of data and declare that quality control mechanisms are needed to improve the QoS. [PERSON] et al. also [5] explain the importance of adapting the SLA approaches of the systems to patient monitoring. The quality of transmission for data collected from patients is a very promising area of research in the health field according to [PERSON] et al. [4]. Besides, body area networks (BANs) are an emerging field with applications for monitoring vital signs in addition to other contextual information. [PERSON] et al. [4] proposed a solution for the transmission of patient data. The QoS routing protocol for reliability sensitive data in hospital body area networks [4] is an approach that ensures the quality of the transmission of data collected from patients, but it does not guarantee transmission quality when there are a large number of patients to be monitored. Furthermore, this approach does not provide an agreement between the patient and the clinic about monitoring the QoS. The requirements of patients are not addressed in the definition of this approach. In the same concept of service quality approach, [PERSON] et al. [6] and [PERSON] et al. [3] indicate that an SLA model is a document that defines negotiable service level objectives (SLOs) and constraints. Other research projects have used SLA to improve system QoS. This is the case of the SLA predictive monitoringproject [7]. According to [PERSON] et al. [7], this research project presented a new definition of the reliability of SLA forecasts, which included terms related to the variability of the service provision scenario in which a prediction is made. The predictions were based on SLOs, but this later project was limited by the absence of dynamic control of the SLA. Another research project [7] proposed an SLA-based healthcare big data analysis and computing in cloud network by combining big data analysis techniques with data collected from patients. In addition, according to [PERSON] [8], an algorithm is proposed to prioritize the patient emergency work. Despite the contribution of this research project to prioritizing patient emergency work, this project has limitations, such as non-dynamic SLA and absence of an SLA violation control mechanism. The research project name [9], _Cloud SLA terms analysis based on ontology_ focused on the problem of SLA and QoS violation in the context of services offered by the cloud. In the context of service offered by providers, this project allows subscribing consumers to monitor SLA violation, however, the data for calculating the metrics were not extracted from a patient context and no adaptation mechanism was implemented. The authors even indicated the limitations of this approach [7] and declared: _\"For future work, we opt to enhance the process by automatically predicting SLA violation using context-aware systems\"_. Finally, in the same vein, the _AISel_ project [8] aimed to help emergency physicians or practitioners to have relevant information and knowledge about COPD patients to better monitor their health status. This project proposed a virtual organization (VOS) to share knowledge between patient and practitioner actors in compliance with data protection laws. The organization based on SLA, a multi-agent system, explained in [10] that: _\"Conceptual definitions of SLA parameters, metrics, and economic values, as well as resource characteristics, are given in an OWL-DL ontology W3C\"_. The _MORF_ project of [PERSON] et al. [11], was a mobile patient monitoring system with two vital parameters: SAO2(Saturation Oxygen) and HR(Heart Rate). This monitoring system seamlessly integrated vital data from different health services' sensors to identify a patient's condition. The system could then take appropriate measures for timely support. It was an ontology-based system that generated alerts based on the values of vital parameters.