Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).
The development of information and telecommunication technologies has given rise to new platforms for e-Health. However, some difficulties have been detected since each manufacturer implements its communication protocols and defines their data formats. A semantic incongruence is observed between platforms since no common healthcare domain vocabulary is shared between manufacturers and stakeholders. Despite the existence of standards for semantic and platform interoperability (e.g. openEHR for healthcare, Semantic Sensor Network for Internet of Medical Things platforms, and machine-to-machine standards), no approach has combined them for granting interoperability or considered the whole integration of legacy Electronic Health Record Systems currently used worldwide. Moreover, the heterogeneity in the large volume of health data generated by Internet of Medical Things platforms must be attenuated for the proper application of big data processing techniques. This article proposes the joint use of openEHR and Semantic Sensor Network semantics for the achievement of interoperability at the semantic level and use of a machine-tomachine architecture for the definition of an interoperable Internet of Medical Things platform.
Summary
The technological development and dissemination of IoT equipment have led to large volumes of environmental data which, in some cases, are incomplete, follow different formats of representation, and even have different semantic approaches. All such aspects and the heterogeneity of different IoT components (e.g., network interfaces, communication protocols, data structure, and data semantics) have caused interoperability issues which might hamper the effectiveness of support decision systems for smart cities, where the use of big data and machine learning techniques has been considered, in addition to the exploration of smart city data. This article proposes an environment IoT‐based platform for smart cities that grants interoperability from data capture to knowledge extraction and visualization through the use of Semantic Web Technologies and the definition of an ontology for environment indicators. The components of the platform include IoT devices, gateways, cloud, and fog computing, which are used for a better application of big data techniques. A real environment quality monitoring use case was considered for the validation of the platform. Metrics, such as latency and resources consumption, were analyzed for three communication protocols, namely, MQTT, CoAP, and REST. CoAP adapter provided the best results regarding latency, RAM, and CPU consumption.
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