## 1 Introduction  
During the last few decades, ubiquitous computing, particularly Health Care System and Health Information Technology (HIT), commonly referred to as pervasive/ubiquitous systems, has impacted the world of research. This revolution has materialized through many context-aware applications that have become important in different fields of research in general, and particularly in the medical field. Health Information Technology (HIT) is health technology, particularly information technology, applied to health and health care. It supports health information management across computerized systems and the secure exchange of health information between consumers, providers, payers, and quality monitors. Service level agreement (SLA) was introduced in the 1980. SLA has been used in various fields such as telecommunications, call centers, security, cloud computing and health systems. In effect, remote medical monitoring platforms manipulate large volumes [1] of patient data, and the risk of data loss or poor data quality is real. This situation can be dangerous for chronic obstructive pulmonary disease (COPD) patients in extremely exacerbated cases. Moreover, quality of service (QoS) is important in the field of monitoring systems because context-aware applications must constantly adapt their servicesto the context [2]. The context changes dynamically, and meeting QoS requirements is a challenge. [PERSON] et al. [2] explained that the necessity to meet the quality level requirements of the platforms in variable execution contexts is a major problem. Context-aware applications must constantly adapt their services to the changing context. The question is how to dynamically monitor the service level agreement (SLA) during an adaptation activity in which the context can be changed. [PERSON] et al. [3] state that an SLA document includes negotiable information and unnegotiable constraints. In SLA models, we can identify standard types of metric groupings, such as platform performance metrics, hardware performance metrics, and software performance metrics. [PERSON] argues that context quality of service (CQoS) errors [4], which can occur at any stage of the adaptation process, can have an impact on the quality of service provided by contextual applications. [PERSON] [4] et al., declared that the contextual data fusion is performed from several sensor sources. However, these sensors may have problems of erroneous reading and connection failure. As a result of data acquisition errors and failures, sensors can provide erroneous data. At the inference level, the algorithms on which the calculations are based may be erroneous. All these situations can cause violations of the SLAs between the providers of context-aware applications and users or customers. It is therefore important to have mechanisms to continuously control and improve the quality of service (QoS) on these platforms. Research associated with SLAs for cloud computing is increasing, but in the context of health monitoring, especially in the context of health surveillance, the dynamic QoS control mechanisms of monitoring platforms are not fully addressed [4]. In this article, we propose an ontology that uses SLA information from COPD monitoring platforms with dynamic data from the patient context. The purpose of this article is to propose a dynamic mechanism for evaluating SLA violations. The solution allows us to retrieve the main items from SLA based XML documents and then retrieve the COPD patient context data dynamically from a COPD SLA ontology. These data retrieved in real time allow the calculation of key performance indicator (KPI) targets based on service level objective (SLO). The adaptive SLA model displays the result of SLA control. This model is available on supplier and consumer interfaces in real time. The information on the interface for SLA control changes dynamically depending on the context-aware system data and the SLA document data. The SLA parties can dynamically control their KPI targets.