# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 3 Distributed Computational Framework  
### Health Monitoring Environments  
Modern activities of health monitoring have been revolutionized by development of IoT paradigm. Currently, the new smart sensor devices are changing the way users and professionals can work. In general terms, the combination of sensing technology and smart mobile devices can handle the computing capabilities of new healthcare applications which, not so long ago, had to be performed only in hospital environments.  
Figure 1: General schema of IoT.  
Several devices, such as smartwatches, mobile phones or tablets have been widely adopted to monitor daily sports activities not only for the athletes but also of the general population, obtaining their activity and performance data [79; 80]. In other competitive scenarios, such as professional sports, the medical staff is able to monitor the performance of athletes during times of high stress. For example, in a sporting event such as football match, bicycle or running race. In these cases, the devices usually used are light portable computers like a tablet or laptop. The deployment of powerful workstations or servers are not practical in a real environment. Moreover, the computing aid from a server-side hosted in the cloud is not always available for real-time monitoring. The use of cloud resources is often limited to storing the data for further analysis. These operation conditions limit the type of applications that can be deployed. Thus, nowadays, the most common use by medical staff and sport coaches is just using a monitoring application.  
The proposed framework enhances this health monitoring by means of leveraging the computing capabilities of modern wearables and other IoT devices for computing advanced medical applications. The main idea is to get some devices (sensors or wearables) of the BAN can take part in the application processing and then, to provide high level information to the medical staff's devices to perform further data analysis. Figure 2 shows the general scheme of the proposed computing elements considered by the framework. In contrast with other approaches [76], the criterion to be a part of this BAN is that the device is worn by the user, and not its processing capability. This approach combines the 'things' of the BAN with sensing and computing capabilities in an integrated way. That is, there are thing with only sensing features (i.e., biosensors), there are things with only computing features (i.e., smartphones) and there are things with both sensing and computing features (i.e., smartwatches). All of them have communication capabilities to make a wireless network to share data.  
The computing elements are interconnected creating the IoT communication network. Usually, the set of devices are connected to a Local Area Network (LAN) in a wireless way. Thus, these devices form a wireless local area network (WLAN) among them. The development of the WLAN standards has been driven for bringing connectivity anywhere and improving the performance of the network. Table 2 summarizes some features relating to transfer rate of the latest evolutions of the standard up today [81; 82].  
Figure 2: Diagram of the computing elements of the framework.  
The maximum number of supported devices or stations connected to a WLAN depends on the data rate of the connection since they have to share the available bandwidth. The current transfer rate can be known for each device by simply monitoring the network interface. However, in order to get a relative value, the total bandwidth available for the device is required. In a network where computing nodes are mobile and connected through standard wireless networks, knowing the total free bandwidth is not an easy task. First of all, in standard networks, bandwidth is shared among the interconnected devices, and secondly, the position of the mobile devices affects the available bandwidth. In such cases, one feasible approach is to consider an estimation of the total bandwidth based on the average values retrieved on regular checks by the devices.