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The authors present recent research linking the growing mobile health (m-health) field with the need for 5G and machine-to-machine technologies. They explore the multiple benefits that these emerging technologies could offer to broadly expand m-health solutions. Since the 1950s, the scientific and industrial communities have proposed using electronic and computer technology in healthcare. In recent decades, society has seen breakthroughs in technology that significantly change peoples' behavior and needs and are poised to revolutionize the healthcare field. These advancements have occurred mainly due to the emergence and evolution of Internet and mobile communications. Such technologies are being widely used to develop medical solutions that make patient information available anytime and anywhere.Computer technologies, modern equipment, and ICT for healthcare solutions have given rise to the electronic health (e-health) field, while the integration of mobile computing, medical sensors, and portable devices in the health environment has enabled the subfield of mobile health (m-health). Although there are similarities between e-health and m-health, they also have some differences. Whereas e-health, in a general way, is using ICT for healthcare, m-health adds to e-health a specific focus on exploiting advances in mobile communications, ubiquitous computing, and wearable technologies.In the past decade, mobile communication technology has achieved significant advancements in coverage, service usage, and transmission rates. Today, mobile networks support more than 3.6

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 manageinerit activities.Location Areas constitute an important strategy of location management, used to reduce signaling lraflic caused by Iocation upclaling and paging messages in cellular net\vorks.. Due to the very large state spaces to be searched, the delerminalion of optinid LA'S rcpresenls a NP -hard cornbhatorial optimization pI.ObleilLIn this paper, Genetic Algorithms are used in order to group cells in an emcient way, wliile preservlng bandwidth.Elitism, linear normalization of chron~oso~na and edge-based crosso~~er are used to speed up the convergence lime, allowing near-optimal solutions to be obtained in an acceptable coniputation time.generation systems ( UMTS, FPLMTS ) : mobility management and resource management ( including bandwidth docation for sigrialing hafEc ).In order to avoid bad resource utilization due to location management activities, two extreme solutions must be avoided : uibiquituous ( global ) paging and systeniatic location updating ( on a cell-by-cell basis >. The riiediurri term solution refers, coiunody, to the use of location wearr ( LA 's) ---as used in the GSM teiminology, or regisfrfztio,u CIYCOS ---as used in IS -41 ( EIA / TIA ), correspoiidjrig to the clustering of cells, subject to some criteria to be presented. Othei solutions c m involve [3], for example, paging areas and grouping of mobile terminals in profiles-based classes.In this paper, Genetic Algorithms are used in order to group cells m an efEcient way, while preserving bandwidth. Its overall organization is as follows : in section 11, we present a brief overview of GA's. In section 111, the location area parationing problem ( LAPP ) is adequately modeled. In section V, perfomiatice-curves of the GA-bnsed solution are shown and resdts are discussed In section VI, ow conclusions are outlined. 11. GENETIC ALGORITHMS Genetic Algorithms ( GA's ' ) constitute an adaptive search technique, inspired on mechanisins of natural evolution. They have succesftdly been applied in a large domain of &fficult optimization problems. In the telecommunication networks Belld, sorue applications have been developed, includq dynamic anticipatory routing in circuit-switched networks [ 1 ] and survivable network design [ 2 1.On the other hand, the dema.rid for new seivices and the number of mobile users have been proven to be monotonically increasing, imposing severe constmints t,o the scarce avadulability of radio resources. Moreover, the reduction of the resource consutnption due to hand06 ( with consequent location updates ) , in special for high or medium mobility users, still represent a challenge to cellular system designers.Tlus point us to two strongly interdependent key issues, of cnicial importance not. only in the already instdlecl syst...

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.