## 6 Experiment and Result
### Data  
For testing, we used artificial data that were related to the attributes of the entities defined in the data model. These data were divided into two groups. The first group was created manually and included data such as the patient identification, the name of a patient, home address, and other attributes of medical interest such as the date of birth of a patient. These attributes have static behavior. Although they are important in the system, they are not of crucial use in testing the healthcare environment. In our research, we used this type of data to test some system microservices, such as entering a new patient's data into the system. The second group of attributes has dynamic behavior, and they are related to the patient's medical attributes, such as body temperature or blood pressure. A data generator can use some probability distribution to create values from this data type.  
To perform tests and validations of some system components, we can use values obtained through the generation of artificial data. For example, to test and validate the monitoring of the vital sign data of an elderly person as close as possible to the real world using values with a valid format and range [71; 72; 73]. Although data simulation techniques were applied in this paper, the vital sign values could look unrealistic. However, we propose that the artificial data used are valid for the experiments presented in this paper.  
The tests carried out were used to technically evaluate the performance and response times of system components and not the accuracy of the vital sign data used. The physiological data of the patient were modeled by Gaussian probability density functions. The Gaussian probability density function allows the generation of values around a mean and a specific value for the standard deviation [74].  
Furthermore, the Ptolemy II simulator was used as an open-source software framework for modeling and simulation [75]. The simulator of the vital signs of a patient was built on a MacBook Pro. Figure 12 shows the model of the data generator that simulated the signal of vital signs. Figure 13 shows the patient monitor model that simulated the real-time behavior by simulates the amount of time given by the sampling period (in seconds) before producing an output in JSON format. Additionally, the simulator of vital signs uses the actual incoming serial port of the MacBook Pro to send simulation data to a real smartphone that acts as a gateway. For additional details on the JSON message structure of the patient's vital signs data, see Appendix G.  
Figure 12: (a) The vital sign simulation model uses Gaussian distributions to generate artificial data. Some controls for the parameterization of the simulator are available to simulate certain health conditions. (b) The patient monitor model simulates the use of sensors that capture vital sign data. The sampling phases and the structuring of the messages in JSON format are included in the model.  
It is necessary to point out that a more exact simulation of the behavior of the patient and the WBAN is outside the limits of our work. Therefore, we have modeled the simulator considering only the sampling stages, integration of the sampled data, and the sending of the detected data from the MAC to the patient's smartphone. Despite the technological advances in medical sensors, microelectronics, and low-power miniaturization and communications, there are some open issues around WBANs that must be taken into consideration when designing and building this type of network. One of these challenges is the limitation of memory, processing, and power in the nodes of a WBAN [76].