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).

Efforts to improve the potential of 5G networks and machine-to-machine (M2M) communications in a way that could help to develop and evolve mobile health (m-health) applications are still in preliminary stages. This is mainly because the specifications and technical solutions for these technologies are still being defined and are under extensive discussion. Thus, this is an excellent opportunity to establish the needs and requirements that future 5G networks must achieve to improve healthcare solutions. Ideally, future m-health solutions should provide services with near-zero latency, advanced quality-of-service capabilities, and data rates on the order of Gbps. This article presents recent research linking the growing m-health field with the need for 5G and M2M technologies. Moreover, this work explores the multiple benefits that these emerging technologies could bring to enable the broad expansion of m-health solutions.

As the subscriber population grows and the network capabilities are enhanced, mobility management and resource management become increasingly critical in (micro-) cellular networks. Moreover, coverage areas are increasingly enlarged, possibly requiring the adoption of partitions to facilitate management activities. Location areas constitute an important strategy of location management, used to reduce signaling traffic caused by location updating and paging messages in cellular networks. Due to the very large state spaces to be searched, the determination of optimal LA's represents a NP-hard combinatorial optimization problem. In this paper, genetic algorithms are used in order to group cells in an efficient way, while preserving bandwidth. Elitism, linear normalization of chromosoma and edge-based crossover are used to speed up the convergence time, allowing near-optimal solutions to be obtained in an acceptable computation time.

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-to-machine architecture for the definition of an interoperable Internet of Medical Things platform.