# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 4 Case Study
### Results and Discussion
This data represents the flexibility of the framework to handle an increasing workload by distributing the tasks and using different computing platforms. The previous tables allow to draw some conclusions on how the framework opens computing possibilities to handle monitoring applications in different operation contexts and verify the variable use of resources in mobile environments. For training monitoring of a single athlete (application context (A)), as shown in Table 4, all the devices (b\({}_{2}\), m\({}_{1}\) and m\({}_{2}\)) can provide a real-time processing of the healthcare application. It is the classic scheme for self-monitoring. In a training session where a group of athletes must be monitored for healthcare and/or performance in sport purposes (application context (B)), the wearable devices of the BAN cannot provide all information since the data needs to be centralized and be compared with information from databases. In this case, the computation is distributed using a combination of devices of BAN and devices of medical staff and/or sport coach, for example the tablet device. In this case, as shown in Table 5, the configurations Figure 3c,d can provide the results in time for all the players. For Figure 3c, with just the m\({}_{1}\) device the data from all athletes can be provided. Finally, in a football match with 26 on-field athletes (application context (C)), \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Smartwatch (b\({}_{2}\))** & **Table (m\({}_{1}\))** & **Portable Computer (m\({}_{2}\))** \\ \hline \(t_{2}\) & 0.0000228 & 0.0000058 & 0.0000050 \\ \(t_{3}\) & 0.0001824 & 0.0000464 & 0.0000400 \\ \(t_{4}\) & 0.020000 & 0.0050891 & 0.0043860 \\ \(t_{5}\) & 0.180000 & 0.0458015 & 0.0026316 \\ \(t_{6}\) & 0.0223440 & 0.0056855 & 0.0049000 \\ Total & 0.2225492 & 0.0566283 & 0.0119625 \\ \hline \hline \end{tabular}  
* Units in seconds (s).  
\end{table} 
Table 4: Time estimation for SD detection in application context (A). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Application Context (B) \(-\) 11 Players** & **Application Context (C) \(-\) 22 Players \(+\) 4 Referees** \\ \hline
**Task** & _Figure 3b_ & _Figure 3c_ & _Figure 3d_ & _Figure 3b *_ & _Figure 3c *_ & _Figure 3d_ \\ \hline \(t_{2}\) & 0.0000228 & 0.0000228 & 0.0000228 & 0.0000228 & 0.0000228 & 0.0000228 \\ \(t_{3}\) & 0.0001824 & 0.0001824 & 0.0001824 & 0.0001824 & 0.0001824 \\ \(t_{4}\) & 0.0559796 & 0.0200000 & 0.0559796 & 0.1140351 & 0.020000 & 0.0735235 \\ \(t_{5}\) & 0.5038168 & 0.1800000 & 0.0289474 & 0.0684211 & 0.1800000 & 0.0684211 \\ \(t_{6}\) & 0.0625405 & 0.0625405 & 0.0539000 & 0.1274000 & 0.1274000 & 0.1274000 \\ Total & 0.6225421 & **0.2627457** & **0.1390322** & 0.3100613 & 0.3276052 & **0.2695498** \\ \hline \hline \end{tabular}  
* The computing is outsourced to the portable device; Units in seconds (s).  
\end{table} 
Table 5: Time estimation for SD detection in application contexts (B) and (C). the m\({}_{1}\) device is not sufficient to give enough computing power and the only configuration to supply the data on time is Figure 3c. Other data sources and computing devices could produce different performance results, and, of course, more hardware resources can be deployed in order to provide more computing power. However, the main idea is to make the best of the potential of _things_ for healthcare monitoring and distributing the processing among the available surrounding devices. Not only in sensing, but also, in computing the health applications. Thus, the involvement of the BAN devices enhances processing performance and reduces the need for external hardware, thereby leveraging the network resources more effectively. The flexibility provided by the system allows it to be run on heterogeneous IoT environments where the numbers of users and devices are changing. For single users, personal trainers and amateurs, the main tasks of the application can be computed both by external resources and mobile things.