## 1 Introduction  
Recent innovative works and research progress in the area of integrated circuits, wireless communications and sensors have allowed the creation of smart devices. Many fields now use a number of these devices, for example ubiquitous healthcare systems. The omnipresence of heterogeneous technologies has opened the way for better approaches to solving many current issues related to connected devices, and has highlighted the importance of the Internet of Things. Smart connected sensors are principal components of wireless body area networks (WBANs) and biosensors, and are fast becoming a key aspect of the Internet of Things. A WBAN is an emerging technology consisting of tiny sensors and medical devices placed on the bodies of humans or animals. It connects wirelessly to sense vital signs, and allows the remote, real-time diagnosis of health issues. Technologies used in association with wireless sensor networks and the Internet of Things are closely linked to many fields, such as e-health [1], gaming, sports [2], military [3] and many applications are built in protected agriculture [4]. A recent medical study reports that the average age of the populations of developing countries is increasing [5]. Healthcare organizations are faced with the issue of a significantly larger elderly populace [6; 7], which is becoming a major worldwide health problem. Ubiquitous systems offer the possibility of simplifying and easing access to health services [8], especially at the end of intensive care. Through collaboration between these smart systems, WBANs offer a number of medical sensors that are capable of gathering vital physiological information such as blood pressure, heart rate, oxygen saturation, etc. and then transmitting these data wirelessly to be analyzed for a given process and task. In order to ensure the wellbeing of patients with chronic diseases, hospitals, smart homes and medical centers need to be equipped with a wide assortment of e-health solutions and connected medical devices. Hypertension as an example is a frequent health issue that has no evident symptoms; It is therefore important to monitor blood pressure regularly, as the only way to control its changes is to be on medications. The classification of medical abnormalities such as hypertension involves detecting and classifying data samples that present a problem or a danger to a patient's health. The use of multi-agent systems is a research trend that can help to solve problems that are difficult for a monolithic system to deal with. The aim of our approach is to help medical staff to diagnose health disorders, and to detect or predict anomalies and seizures. The system proposed in this paper uses a specific classification model for each patient, meaning that decision making is specific, and on a case-by-case basis. The Gaussian distribution applied in this work takes into account various detected anomalies in order to analyze them. This facilitates learning of the behavior and habits of a patient while guiding the medical treatments that can alleviate any medical conditions. Statistics is used in many everyday applications, and many machine learning and data mining systems assume that data fed to these models follow a normal (Gaussian) distribution, allowing inferences to be made from a sample to a parent population. To support the results obtained, a comparative study between different classification techniques is illustrated in the results section. Despite continuous research and improvement efforts from industry, wireless sensor networks face many issues associated with network topologies, energy usage and restricted resources. These sensors are placed in, on or around a patient's body in order to gather and transmit medical information such as electrocardiograms (ECGs), blood pressure (BP), movement and so on [9]. Figure 1 illustrates a WBAN architecture applied to healthcare detailing different communications types.