It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices' data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices' data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis. 1-IntroductionMost of the current researches are dealing with how medical and health-monitoring devices, clinical wearables, and remote sensors can contribute to better health for patients, and a more efficient healthcare that can drive better systems, population, and patient outcomes [1]. For the past years, medical device manufacturers and suppliers have been facing more challenges in product development to bring their devices faster and easier to the market, facing increasing complexities mainly in how to achieve and gather quicker, more reliable, interoperable and efficient measurements. Current healthcareknown as Healthcare 4.0 [2], is considered as one of the fastest industries to adopt the Internet of Things (IoT) technologies, aiming to help in personalized services, reduce operating costs, and improve patient care and quality of life. Such thing has led to the marriage of the IoT and medicine, generating the concept of Internet of Medical Things (IoMT) [3]. In this domain, IoMT is promising changes in the way that the medical industry delivers healthcare, as almost all of the IoMT devices are linked to cloud platforms, while they are equipped with cellular or wireless communication, thus allowing the machine-to-machine communication.Nevertheless, for most patients and care providers, the promises of the IoT and IoMT have not yet introduced dramatic changes in how these stakeholders are experiencing healthcare. What is required is higher connection speeds that will be transforming the relationship of the patients and the healthcare providers, and integrating electronic communications into medical care, which can be achieved through the arrival of the 5G networks [4]. According to [5], 5G will enable more than $1 trillion in devices and services for the global healthcare sector. However, most of these devices are facing high degrees of heterogeneity, since they are having different capabilities, or ...

Efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become mandatory to guarantee the required service quality in software-defined and 5G networks. To create an operational Network Service, it is highly envisaged to associate it with their network-related parameters that reflect the corresponding quality levels. These are included in policies but while SLAs target usually business users, there is a challenge for mechanisms that bridge this abstraction gap. In this paper, a generic black box approach is used to map high-level requirements expressed by users in SLAs to low-level network parameters included in policies, enabling Quality of Service (QoS) enforcement by triggering the required policies and manage the infrastructure accordingly. In addition, a mechanism for determining the importance of different QoS parameters is presented, mainly used for "relevant" QoS metrics recommendation in the SLA templates.

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In recent years, there has been a lot of focus on how medical and health monitoring devices, clinical wearables, and remote sensors can contribute to better health for patients and more efficient healthcare systems. The fifth generation of mobile, cellular technologies, networks and solutions, promises high bandwidth, low latency and reliability are highly demanded in order to support the needs of healthcare, it is undeniable that what is needed is the transformation of the healthcare providers-patients relationship by integrating richmedia communications into medical care. A key challenge refers to the amount of this information and the way it is transmitted and processed. The different formats, rates and size of datasets (continuously increasing) raise the need for environments able to manage these datasets in an efficient way, while also incorporating and facilitating the requirements of approaches that aim at analyzing them (e.g. through machine learning and artificial intelligence mechanisms) towards efficient healthcare. To this end, network slicing has been envisioned as the promising solution on the heterogeneous medical data requirements and diverse constraints. In this paper we propose an innovative eHealth system powered by 5G network slicing, in order to meet the requirements for establishing an efficient network with high capacity. In this context, healthcare data collection and management inside isolated slices are considered, for creating holistic views of patients, increasing the awareness concerning patients' health condition that leads to the personalization of treatments and enhances health outcomes.

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