## 4 Conclusions  
IoMT is one of the novel approaches for enhancements in current healthcare services. Its use, combined with other paradigms, such as cloud computing, data mining, and machine learning, will empower physicians to make better decisions and improve knowledge of  
Figure 18: Latencies of binding protocols over LTE (for Gateway\(<\)>Cloud).  
healthcare. However, several technical issues, addressed in this paper, hamper its application in the healthcare domain.  
Our IoMT platform is based on an OneM2M architecture for e-Health applications. It enables the integration of multiple healthcare data sources and IoMT devices and sharing of healthcare knowledge according to the state-of-the-art tools for online analytic processing, big data, machine learning and healthcare data interchange.  
Differently from other works, the proposed platform considers standardized data formats for the storage and transmission of data related to healthcare observations, which promotes interoperability regarding data representation formats, IoMT platforms and knowledge-extraction techniques.  
In particular, OpenEHR helped the development of tools for the transformation of data in simple formats, such as SenML (almost raw data), into others, e.g., FHIR and OpenEHR itself. Since a sensor is attached to an OpenEHR observation definition, the correctness of an observation can be automatically evaluated according to a professional set of concepts. Moreover, a variation of the SenML format enabled the proposal of a transmission technique that avoids the sending of duplicated messages.  
The platform automatically prepares the data for the application of OLAP through the anonymization of records, thus granting patient's privacy. The integration of a Hadoop distributed file system simplifies the application of data mining, knowledge-extraction techniques and training of machine learning models based on pre-processed anonymized records resulting from the application of Map/Reduce algorithms over the healthcare repository.  
A development framework was also implemented for the IoMT device, gateway, and fog server, for the integration of new sensors to the platform, extension of AE functionalities in the gateway, and implementation of new binding protocols. It enables the development of BP focused on data interchange with healthcare systems rather than isolated devices.  
Finally, the performance tests yielded good results and validated the platform in the multiple scenarios considered. CoAP-based BP showed the lowest latency. Regarding MQTT and REST BPs, latency was shown as acceptable and considering the functional advantages of MQTT and the simplicity of REST, they can also be considered to be appropriate protocols.  
Future studies will involve the analyses of other healthcare scenarios, such as Smart Cities (SC), where healthcare data play a vital role in the creation of statistical indicators of life quality and city performance. Moreover, analytical models for calculating throughput, mean time for data delivering and energy efficiency should be developed, as in [47; 48].  
Conceptualization, J.N.S.R. and P.R.L.G.; methodology, J.N.S.R. and P.R.L.G.; software, J.N.S.R.; validation, J.N.S.R. and P.R.L.G.; formal analysis, J.N.S.R. and P.R.L.G.; investigation, J.N.S.R. and P.R.L.G.; resources, J.N.S.R. and P.R.L.G.; data curation, J.N.S.R. and P.R.L.G.; writing-original draft preparation, J.N.S.R. and P.R.L.G.; writing-review and editing, J.N.S.R. and P.R.L.G.; visualization, J.N.S.R. and P.R.L.G.; supervision, P.R.L.G.; project administration, P.R.L.G.; funding acquisition, J.N.S.R. and P.R.L.G. This research was funded by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES). Conflicts of Interest: The authors declare no conflict of interest.