33 research outputs found

    Understanding human-machine networks: A cross-disciplinary survey

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    © 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends

    Virtual Reality Games for Motor Rehabilitation

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    This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

    Collaborative Augmented Reality

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    Over the past number of years augmented reality (AR) has become an increasingly pervasive as a consumer level technology. The principal drivers of its recent development has been the evolution of mobile and handheld devices, in conjunction with algorithms and techniques from fields such as 3D computer vision. Various commercial platforms and SDKs are now available that allow developers to quickly develop mobile AR apps requiring minimal understanding of the underlying technology. Much of the focus to date, both in the research and commercial environment, has been on single user AR applications. Just as collaborative mobile applications have a demonstrated role in the increasing popularity of mobile devices, and we believe collaborative AR systems present a compelling use-case for AR technology. The aim of this thesis is the development a mobile collaborative augmented reality framework. We identify the elements required in the design and implementation stages of collaborative AR applications. Our solution enables developers to easily create multi-user mobile AR applications in which the users can cooperatively interact with the real environment in real time. It increases the sense of collaborative spatial interaction without requiring complex infrastructure. Assuming the given low level communication and AR libraries have modular structures, the proposed approach is also modular and flexible enough to adapt to their requirements without requiring any major changes

    Collaborative Augmented Reality

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    Over the past number of years augmented reality (AR) has become an increasingly pervasive as a consumer level technology. The principal drivers of its recent development has been the evolution of mobile and handheld devices, in conjunction with algorithms and techniques from fields such as 3D computer vision. Various commercial platforms and SDKs are now available that allow developers to quickly develop mobile AR apps requiring minimal understanding of the underlying technology. Much of the focus to date, both in the research and commercial environment, has been on single user AR applications. Just as collaborative mobile applications have a demonstrated role in the increasing popularity of mobile devices, and we believe collaborative AR systems present a compelling use-case for AR technology. The aim of this thesis is the development a mobile collaborative augmented reality framework. We identify the elements required in the design and implementation stages of collaborative AR applications. Our solution enables developers to easily create multi-user mobile AR applications in which the users can cooperatively interact with the real environment in real time. It increases the sense of collaborative spatial interaction without requiring complex infrastructure. Assuming the given low level communication and AR libraries have modular structures, the proposed approach is also modular and flexible enough to adapt to their requirements without requiring any major changes

    Creation of value with open source software in the telecommunications field

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    Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

    Enabling 5G Edge Native Applications

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    Orchestration of distributed ingestion and processing of IoT data for fog platforms

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    In recent years there has been an extraordinary growth of the Internet of Things (IoT) and its protocols. The increasing diffusion of electronic devices with identification, computing and communication capabilities is laying ground for the emergence of a highly distributed service and networking environment. The above mentioned situation implies that there is an increasing demand for advanced IoT data management and processing platforms. Such platforms require support for multiple protocols at the edge for extended connectivity with the objects, but also need to exhibit uniform internal data organization and advanced data processing capabilities to fulfill the demands of the application and services that consume IoT data. One of the initial approaches to address this demand is the integration between IoT and the Cloud computing paradigm. There are many benefits of integrating IoT with Cloud computing. The IoT generates massive amounts of data, and Cloud computing provides a pathway for that data to travel to its destination. But today’s Cloud computing models do not quite fit for the volume, variety, and velocity of data that the IoT generates. Among the new technologies emerging around the Internet of Things to provide a new whole scenario, the Fog Computing paradigm has become the most relevant. Fog computing was introduced a few years ago in response to challenges posed by many IoT applications, including requirements such as very low latency, real-time operation, large geo-distribution, and mobility. Also this low latency, geo-distributed and mobility environments are covered by the network architecture MEC (Mobile Edge Computing) that provides an IT service environment and Cloud-computing capabilities at the edge of the mobile network, within the Radio Access Network (RAN) and in close proximity to mobile subscribers. Fog computing addresses use cases with requirements far beyond Cloud-only solution capabilities. The interplay between Cloud and Fog computing is crucial for the evolution of the so-called IoT, but the reach and specification of such interplay is an open problem. This thesis aims to find the right techniques and design decisions to build a scalable distributed system for the IoT under the Fog Computing paradigm to ingest and process data. The final goal is to explore the trade-offs and challenges in the design of a solution from Edge to Cloud to address opportunities that current and future technologies will bring in an integrated way. This thesis describes an architectural approach that addresses some of the technical challenges behind the convergence between IoT, Cloud and Fog with special focus on bridging the gap between Cloud and Fog. To that end, new models and techniques are introduced in order to explore solutions for IoT environments. This thesis contributes to the architectural proposals for IoT ingestion and data processing by 1) proposing the characterization of a platform for hosting IoT workloads in the Cloud providing multi-tenant data stream processing capabilities, the interfaces over an advanced data-centric technology, including the building of a state-of-the-art infrastructure to evaluate the performance and to validate the proposed solution. 2) studying an architectural approach following the Fog paradigm that addresses some of the technical challenges found in the first contribution. The idea is to study an extension of the model that addresses some of the central challenges behind the converge of Fog and IoT. 3) Design a distributed and scalable platform to perform IoT operations in a moving data environment. The idea after study data processing in Cloud, and after study the convenience of the Fog paradigm to solve the IoT close to the Edge challenges, is to define the protocols, the interfaces and the data management to solve the ingestion and processing of data in a distributed and orchestrated manner for the Fog Computing paradigm for IoT in a moving data environment.En els últims anys hi ha hagut un gran creixement del Internet of Things (IoT) i els seus protocols. La creixent difusió de dispositius electrònics amb capacitats d'identificació, computació i comunicació esta establint les bases de l’aparició de serveis altament distribuïts i del seu entorn de xarxa. L’esmentada situació implica que hi ha una creixent demanda de plataformes de processament i gestió avançada de dades per IoT. Aquestes plataformes requereixen suport per a múltiples protocols al Edge per connectivitat amb el objectes, però també necessiten d’una organització de dades interna i capacitats avançades de processament de dades per satisfer les demandes de les aplicacions i els serveis que consumeixen dades IoT. Una de les aproximacions inicials per abordar aquesta demanda és la integració entre IoT i el paradigma del Cloud computing. Hi ha molts avantatges d'integrar IoT amb el Cloud. IoT genera quantitats massives de dades i el Cloud proporciona una via perquè aquestes dades viatgin a la seva destinació. Però els models actuals del Cloud no s'ajusten del tot al volum, varietat i velocitat de les dades que genera l'IoT. Entre les noves tecnologies que sorgeixen al voltant del IoT per proporcionar un escenari nou, el paradigma del Fog Computing s'ha convertit en la més rellevant. Fog Computing es va introduir fa uns anys com a resposta als desafiaments que plantegen moltes aplicacions IoT, incloent requisits com baixa latència, operacions en temps real, distribució geogràfica extensa i mobilitat. També aquest entorn està cobert per l'arquitectura de xarxa MEC (Mobile Edge Computing) que proporciona serveis de TI i capacitats Cloud al edge per la xarxa mòbil dins la Radio Access Network (RAN) i a prop dels subscriptors mòbils. El Fog aborda casos d?us amb requisits que van més enllà de les capacitats de solucions només Cloud. La interacció entre Cloud i Fog és crucial per a l'evolució de l'anomenat IoT, però l'abast i especificació d'aquesta interacció és un problema obert. Aquesta tesi té com objectiu trobar les decisions de disseny i les tècniques adequades per construir un sistema distribuït escalable per IoT sota el paradigma del Fog Computing per a ingerir i processar dades. L'objectiu final és explorar els avantatges/desavantatges i els desafiaments en el disseny d'una solució des del Edge al Cloud per abordar les oportunitats que les tecnologies actuals i futures portaran d'una manera integrada. Aquesta tesi descriu un enfocament arquitectònic que aborda alguns dels reptes tècnics que hi ha darrere de la convergència entre IoT, Cloud i Fog amb especial atenció a reduir la bretxa entre el Cloud i el Fog. Amb aquesta finalitat, s'introdueixen nous models i tècniques per explorar solucions per entorns IoT. Aquesta tesi contribueix a les propostes arquitectòniques per a la ingesta i el processament de dades IoT mitjançant 1) proposant la caracterització d'una plataforma per a l'allotjament de workloads IoT en el Cloud que proporcioni capacitats de processament de flux de dades multi-tenant, les interfícies a través d'una tecnologia centrada en dades incloent la construcció d'una infraestructura avançada per avaluar el rendiment i validar la solució proposada. 2) estudiar un enfocament arquitectònic seguint el paradigma Fog que aborda alguns dels reptes tècnics que es troben en la primera contribució. La idea és estudiar una extensió del model que abordi alguns dels reptes centrals que hi ha darrere de la convergència de Fog i IoT. 3) Dissenyar una plataforma distribuïda i escalable per a realitzar operacions IoT en un entorn de dades en moviment. La idea després d'estudiar el processament de dades a Cloud, i després d'estudiar la conveniència del paradigma Fog per resoldre el IoT prop dels desafiaments Edge, és definir els protocols, les interfícies i la gestió de dades per resoldre la ingestió i processament de dades en un distribuït i orquestrat per al paradigma Fog Computing per a l'IoT en un entorn de dades en moviment

    Actas da 10ª Conferência sobre Redes de Computadores

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    Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio
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