In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

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Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Future generation computer systems.

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This work is partially supported by the European Community under the Information Society Technologies (IST) programme of the 6th Framework Programme for RTD-project ELeGI, contract IST-002205. This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of data appearing therein.

Purpose-The purpose of this paper is to propose an innovative approach for providing an answer to the emerging trends on how to integrate e-learning efficiently in the business value chain in medium and large enterprises. Design/methodology/approach-The proposed approach defines methodologies and technologies for integrating technology-enhanced learning with knowledge and human resources management based on a synergistic use of knowledge models, methods, technologies and approaches covering different steps of the knowledge life-cycle. Findings-The proposed approach makes explicit and supports, from the methodological, technological and organizational points of view, mutual dependencies between the enterprise's organizational learning and the business processes, considering also their integration in order to allow the optimization of employees' learning plans with respect to business processes and taking into account competencies, skills, performances and knowledge available inside the organization. Practical implications-This mutual dependency, bridging individual and organizational learning, enables an improvement loop to become a key aspect for successful business process improvement (BPI) and business process reengineering (BPR), enabling closure of, at the same time, the learning and knowledge loops at individual, group and organization levels. Originality/value-The proposed improvements are relevant with respect to the state of the art and respond to a real need felt by enterprises and further commercial solutions and research projects on the theme.

Summary Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.