# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 5 Conclusions and Future Work  
The computing requirements for monitoring and advanced analysis of the data acquired by IoT environments can overcome the capabilities of the sensors and personal computers deployed by specialists. Additional specialized hardware is usually required to meet the application requirements. In this work, a distributed framework that combines sensing and processing at different levels of the network to share the computing load among the available devices has been proposed to address this challenge. The IoT environments composed of wearables and other biosensors may benefit of it by allowing the processing of advanced applications with real-time constraints in a collaborative way. The main advantages and novelties of the proposed system is the flexibility in the application execution by using resources from different available devices. In this way, the devices of BAN can provide shared computing resources that enables real-time monitoring and analysis of all acquired data. The health monitoring and SD detection in sport is a good scenario where the proposed system demonstrates its capacity for handling intensive data processing applications. In this case study, the real-time needs for cardiac information of athletes can be solved by applying a real-time heart telemetry system capable of collecting the main cardiac parameters. Then, our approach broadcasting alerts when any of those parameters exhibit different behavior compared to normal cardiac activity in situations that require athletes to perform under physical effort. In those situations where cardiac disorders exist and the cardiologist team has only a few seconds to make the right decisions to provide the best medical care for the athlete, our system provides them with precise information for doing that. This proposal provides two main research contributions: a strategy to optimize the use of biomedical sensors and computing resources to deliver advanced applications to the user, and the design of a distributed computational framework that uses the available computing capabilities in the smart devices for sharing the whole processing of the health monitoring applications. The framework will need to be adjusted when used in real situations. It appears to work in sports but it would be necessary to test this in a real situation. The technology and formalization of the domain knowledge are the limitations and we need to do an in-depth study of these issues for future work. To this end, we have already established contact with and had several meetings with the directors of sport centres and gyms who are interested in evaluating of our framework. This proposal can be applied to other IoT scenarios, especially those where intensive data acquisition and high processing needs take place. Further work must be invested in building a proper predictive model and exploit the off-line possibilities of processing the information gathered. In this way, interesting issues come from analyzingathletes' achievements, movements and each other interactions. This could be made by computing the differences between current and stored athlete profile, as well as making the classifier that detects dangerous patterns. **Acknowledgments:** This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educacion, Investigacion, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by Vicerrectorado de Innovacion, University of Alicante, Spain (Vigrob). **Author Contributions:** All authors were involved in the foundation items. All authors wrote the paper and read and approved the final manuscript. **Conflicts of Interest:** The authors declare no conflict of interest.