IoTA: IoT Automated SIP-based Emergency Call Triggering System for general eHealth purposes

The expansion of Internet of Things (IoT) and the evolution in communication technologies have enabled homes, cars even whole cities to be network connected. However, during an emergency incident, IoT devices have not been used to trigger emergency calls directly to healthcare providers mainly due to their constrained capabilities and lack of support session-oriented communications. Moreover, emergency services are currently offered by public safety stakeholders that do not support call triggering by IoT devices. This paper proposes IoTA framework which enables IoT devices to generate automatically emergency calls and support bi-directional communication sessions between healthcare providers and end users. The IoTA framework incorporates intelligent algorithms for processing and evaluating emergency events from various devices and performs emergency calls immediately after the occurrence of an event. The healthcare providers can interact with the IoTA framework requesting continuous real-time sensor data. A prototype implementation and initial evaluation results are presented as a proof of concept for people suffering from diverse chronic diseases. Experimental results have shown that the proposed framework can be considered as a promising solution for detecting, reporting emergency events, eliminating the hoax calls and responding swiftly saving lives

Unified representation of monitoring information across federated cloud infrastructures

Nowadays one of the issues hindering the potential of federating cloud-based infrastructures to reach much larger scales is their standard management and monitoring. In particular, this is true in cases where these federated infrastructures provide emerging Future Internet and Smart Cities-oriented services, such as the Internet of Things (IoT), that benefit from cloud services. The contribution of this paper is the introduction of a unified monitoring architecture for federated cloud infrastructures accompanied by the adoption of a uniform representation of measurement data. The presented solution is capable of providing multi-domain compatibility, scalability, as well as the ability to analyze large amounts of monitoring data, collected from datacenters and offered through open and standardized APIs. The solution described herein has been deployed and is currently running on a community of 5 infrastructures within the framework of the European Project XIFI, to be extended to 12 more infrastructures

Queue Management in Network Processors

Abstract: -One of the main bottlenecks when designing a network processing system is very often its memory subsystem. This is mainly due to the state-of-the-art network links operating at very high speeds and to the fact that in order to support advanced Quality of Service (QoS), a large number of independent queues is desirable. In this paper we analyze the performance bottlenecks of various data memory managers integrated in typical Network Processing Units (NPUs). We expose the performance limitations of software implementations utilizing the RISC processing cores typically found in most NPU architectures and we identify the requirements for hardware assisted memory management in order to achieve wire-speed operation at gigabit per second rates. Furthermore, we describe the architecture and performance of a hardware memory manager that fulfills those requirements. This memory manager, although it is implemented in a reconfigurable technology, it can provide up to 6.2Gbps of aggregate throughput, while handling 32K independent queues

Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

Medical Informatics (MI) and Bioinformatics (BI) are two interdisciplinary areas located at the intersection between computer science and medicine and biology, respectively. Historically, they have been separated and only occasionally have researchers of both disciplines collaborated. The completion of the Human Genome Project has brought about in this post genomic era the need for a synergy of these two disciplines to further advance in the study of diseases by correlating essential genotypic information with expressed phenotypic information. Biomedical Informatics (BMI) is the emerging technology that aims to put these two worlds together in the new rising genomic medicine. In this regard, institutions such as the European Commission have recently launched several initiatives to support a new combined research agenda, based on the potential for synergism of both disciplines. In this paper we review the results the BIOINFOMED study one of these projects funded by the E

Reviewing the integration of patient data: how systems are evolving in practice to meet patient needs

<p>Abstract</p> <p>Background</p> <p>The integration of Information Systems (IS) is essential to support shared care and to provide consistent care to individuals – patient-centred care. This paper identifies, appraises and summarises studies examining different approaches to integrate patient data from heterogeneous IS.</p> <p>Methods</p> <p>The literature was systematically reviewed between 1995–2005 to identify articles mentioning patient records, computers and data integration or sharing.</p> <p>Results</p> <p>Of 3124 articles, 84 were included describing 56 distinct projects. Most of the projects were on a regional scale. Integration was most commonly accomplished by messaging with pre-defined templates and middleware solutions. HL7 was the most widely used messaging standard. Direct database access and web services were the most common communication methods. The user interface for most systems was a Web browser. Regarding the type of medical data shared, 77% of projects integrated diagnosis and problems, 67% medical images and 65% lab results. More recently significantly more IS are extending to primary care and integrating referral letters.</p> <p>Conclusion</p> <p>It is clear that Information Systems are evolving to meet people's needs by implementing regional networks, allowing patient access and integration of ever more items of patient data. Many distinct technological solutions coexist to integrate patient data, using differing standards and data architectures which may difficult further interoperability.</p

Integrating IoT and Fog Computing for Healthcare Service Delivery

Internet of Things (IoT) technologies provide many opportunities for providing healthcare applications such as home based assisted living and well-being application solutions. Nowadays, numerous IoT devices are used to monitor users’ healthcare status and transmit the data directly to remote data centers through the cloud computing paradigm. This direct interconnection of the large amount of devices for remote storage, processing, and retrieval of medical records in the cloud demands a reliable network connection imposing many challenges related to network connectivity and traffic. This chapter deals with the transfer of the computing intelligence from cloud to the edge network. Fog computing operates closer to the user, on network edge, enabling accurate service delivery with low response time avoiding delays and network failures that may interrupt or delay the decision process and healthcare service delivery. An architectural model is proposed and a set of use cases illustrate the benefits of the IoT and fog computing integration

Independent 3D Motion Detection Based on Depth Elimination in Normal Flow Fields

This paper considers a spec @ problem of visual percep-tion of motion, namely the problem of visual detection of independent 30 motion. Most of the existing techniques for solving this problem rely on restrictive assumptions about the environment, the observer’s motion, or both. Moreovel; they are based on the computation of optical jow, which amounts to solving the ill-posed correspondence problem. In this work, independent motion detection is formulated as robust parameter estimation applied to the visual input ac-quired by a binoculal; rigidly moving observer Depth and motion measurements are combined in a linear model. The parameters of this model are related to the parameters of self-motion (egomotion) and the parameters of the stereo-scopic configuration of the observer The robust estimation of this model leads to a segmentation of the scene based on 30 motion. The method avoids the correspondence problem by employing only normaljow fields. Experimental results demonstrate the effectiveness of this method in detecting independent motion in scenes with large depth variations, without any constraints imposed on observer motion. 1

Exploiting Shared-Access Passive Optical Networks for Building Distributed Datacenters

We propose a novel optical cloud architecture where IT and Telecom resources are used interchangeably as common infrastructure. Key assets are the MAC controlled passive networks for distributed multiplexing and grooming and a node architecture integrating transmission and switching