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Fog Computing is an emerging paradigm, suitable to serve the particular needs of IoT networks. It includes the deployment of computational devices at the edge of the network facilitating faster real-time processing of time-sensitive data. In this article, we present a Fog architecture, which diverges from the traditional hierarchical and centralized Fog model, and adopts a cooperative model, which allows for a federation of Edge networks. In our proposal, the tasks that the nodes are called to complete, are characterized according to their computational nature and are subsequently allocated to the appropriate host. Edge networks communicate through a brokering system with IoT systems in an asynchronous way via the Pub/Sub messaging pattern.

The evolution of manufacturing technologies has allowed for rapid prototype development and easy transfer of ideas from design to production, even in the convenience of the home office (via 3D printers). It has also created an unstable market environment. This situation calls for novel solutions in manufacturing that allow high adaptability and responsiveness. The use of smart, agent-based systems empowered by semantic technologies could provide the solution. The authors' ontology-based smart production system is based on the use of agents. They present both the system architecture and a framework for deployment under a living lab environment. They also give special focus to alleviating obstacles met in the small-series-like ramp-up of highly customized products, such as aircraft.

The rapidly increasing number of health monitoring devices, mainly wearables, will let users monitor their health and habitual parameters in a simple and easy way. However, the diversity of available platforms calls for simplified, standard solutions that can offer integrated tools to users--that is, patients, professionals, and practitioners. The authors present the idea of a health avatar that's used as the electronic equivalent of a human and features a dynamic life profile corresponding to the human owner's physical status, living conditions, and habits. They examine how such avatars can be used to improve the quality of health and life.

The advent of the Internet of Everything, where things and data providers can connect not only to other things and data providers, but to human entities as well, and are enriched with intelligence, calls for sophisticated data handling, storing, and sharing mechanisms. In this chapter, we present an ecosystem built over the idea of Internet of Everything and featuring a collaborative, intelligent service bus, which gathers information from connected devices and uses it to improve quality of life. All aspects related to data collection, processing, protection of privacy, as well as collaboration between users and interfacing between humans and devices are addressed, while mechanisms supporting decision making towards health related goals are presented.

Most scientific applications tend to have a very resource demanding nature and the simulation of such scientific problems often requires a prohibitive amount of time to complete. Distributed computing offers a solution by segmenting the application into smaller processes and allocating them to a cluster of workers. This model was widely followed by Grid Computing. However, Cloud Computing emerges as a strong alternative by offering reliable solutions for resource demanding applications and workflows that are of scientific nature. In this paper we propose a Cloud Platform that supports the simulation of complex electromagnetic problems and incorporates classification (SVM) and resource allocation (Ant Colony Optimization) methods for the effective management of these simulations.

The enormous set of health and wellbeing data sources, as well as the diversity of the data, calls for an effective, time-aware integration paradigm that aids at the manipulation of the information by experts as a whole and not as individual pieces of knowledge. In this paper, we present the Health Service Bus, a service-based platform built on top of the Enterprise Service Bus architecture. Treating new information, either human-generated (e.g., doctors, dieticians, etc.) or device-generated (i.e., smart wristbands or connected scales) as events allows for in-time action and treatment. Platform interoperability is ensured both on service level, since any service irrespective of its specification can be plugged into the Health Service Bus seamlessly, and on data level, since health standards, such as HL7 FHIR and LOINC, are leveraged.

Cloud computing is a paradigm that aims to transform computer, storage and network resources into a utility. As more applications are deployed on cloud environments, one of the main requirements refers to efficient scalability during the application execution. In this paper we present two mechanisms that aim at addressing this requirement: an adaptable two layer monitoring mechanism and an event processing framework. We evaluate the effectiveness of these mechanisms through a set of experiments on a large scale multi-cloud facility. The latter poses challenges with respect to time-constrained execution of applications, since the aforementioned mechanisms need to collect and analyse information from geographically distributed sites, and trigger scaling decisions during the real-time application execution. The experimentation outcomes demonstrate the value of the presented mechanisms, both in terms of efficient scalability and with respect to the introduced overhead on the infrastructure.

This paper describes an ICT platform aiming to support the well-being of frail elderly people and facilitate them to stay longer and more healthily in their own home. Its principal characteristic is the combination of Telehealth and Telecare monitoring in a unified way, allowing the simultaneous health, mental and psychological status evaluation of an elderly person. For this purpose the platform enables the deployment of services to follow-up the patient’s health status based on a set of monitored parameters per disease, to track the suitability of the in-house environmental conditions and finally to profile user’s habits and diagnose deviations from their usual activities. The inCASA project implements such platform based on a Service-Oriented Architecture which relies on the Hydra Middleware. Hydra is receiving measurements from proprietary Telehealth and Telecare gateways deployed in the home premises and transforms them into Health Level 7 (HL7) compliant data. Platform developers may add business logic and create healthcare applications on top of the Middleware without getting involved with low-level communication issues with the various types of sensor devices and their protocols. Another core module of the architecture is the Smart Personal Platform (SPP) in which the patient data are forwarded from Hydra, stored and analyzed. SPP includes a reasoning mechanism responsible for the comparison of retrieved measurements with specified thresholds per monitored parameter and per patient. Furthermore, this mechanism detects deviations from the stored habits profile of each user which is dynamically built based on history data. Either in the case of thresholds exceeding or in the case of habits profile deviation, alerts are generated and classified based on their severity. Both data and alerts are available in the back-end user interface of the platform, the so-called Consumer Application interface which is the single point of access for the inCASA operators. In this Web Application, there is an integrated view of Telecare (e.g. movement, habits) and Telehealth (e.g. body weight, blood pressure) data offering also graphical and statistical facilities. Forwarded from the SPP alerts are presented real-time on screen by the Consumer Applications and, if this is the case, other relevant actions take place too, like SMS sending to relatives, doctors and/or operators. inCASA is an EU co-funded pilot project with a combination of industry and academic partners and has already deployed its pre-mature solution to five European pilots (hospitals or social services). The primary measurable indicators during the pilots include the overall elderly patient satisfaction with the provided services, enhancement of their self-reliance and living conditions and the added value of this service model (i.e. reduced hospitalization of patients and/or response times to emergencies). First results are already collected and satisfy the doctors/operators aim for Telehealth-Telecare integration. One of their main targets that can be supported by the aforementioned integration is the early detection of health deterioration triggered by deviation in user’s habits. In our presentation, we will report on latest results and discuss challenges and benefits of the Telehealth and Telecare monitoring combination.

As the number of incidents of bacterial infections continues to rise around the globe, simpler, faster, and more sensitive diagnostic techniques are required to improve the safety of the food supply and to screen for potential bacterial infections in humans. We present here direct and indirect approaches for the detection of bacteria, which are based upon a combination of immunofluorescent staining and capillary electrophoresis. In the direct approach, Escherichia coli O157:H7 bacteria stained with fluorescein-tagged specific antibodies are detected by CE, while in the indirect approach fluorescein-tagged specific antibodies to E. coli are first captured by E. coli O157:H7 bacteria and then released and detected by CE. We have identified suitable bacteria staining and CE protocols, which involved a 10 mM Tris-borate-EDTA (TBE) buffer, 0.25 micro g antibody/1 million bacteria, and capillaries dynamically coated with poly-N-hydroxyethylacrylamide (polyDuramide). We have also successfully detected the presence of E. coli O157:H7 in contaminated meat. The total time required for analysis was 6-8 h, which is less than that realized in most commercial assays presently available.

It has been postulated that one of the important biological functions of the alpha-2 globulin, ceruloplasmin, is the regulation of the biliary excretion of inorganic copper(1). However, there is little experimental evidence to support this theory(2,3). With the availability of an experimental technique for modifying the blood ceruloplasmin levels(4), it became feasible to reexamine the problem as to possible correlations between serum ceruloplasmin, liver uptake, and biliary excretion of Cu64 in the rabbit.Three groups containing 9 to 12 male albino rabbits (12-13 weeks old) were employed. One group was maintained for 10–15 days on a commercial dry ration containing molybdate-sulfate, a diet known to reduce ceruloplasmin levels in the rabbit and greatly elevate total blood copper(4,5). A second group, which served as controls, was maintained on the commercial ration without added salts. A third group was maintained on a ration mixed with inorganic copper, 0.005% copper as Cu2SO4 for 4 weeks in order to el...