102 research outputs found

    An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

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    The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme (TIN2015-70202-P) and the “Redes de Excelencia” programme (TEC2015-71426-REDT), and from the Regional Government of Valencia (‘Proyectos de I + D para Grupos de Investigación Emergentes’ GV/2016/159). Special thanks to Víctor, Maricarmen, Inma and Daniel who lent their houses for performing the experiments

    Intelligent Personal Assistants Solutions in Ubiquitous Environments in the Context of Internet of Things

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    Internet of Things (IoT) will create the opportunity to develop new types of businesses. Every tangible object, biologic or not, will be identified by a unique address, creating a common network composed by billions of devices. Those devices will have different requirements, creating the necessity of finding new mechanisms to satisfy the needs of all the entities within the network. This is one of the main problems that all the scientific community should address in order to make Internet of Things the Future Internet. Currently, IoT is used in a lot of projects involving Wireless Sensor Networks (WSNs). Sensors are generally cheap and small devices able to generate useful information from physical indicators. They can be used on smart home scenarios, or even on healthcare environments, turning sensors into useful devices to accomplish the goals of many use case scenarios. Sensors and other devices with some reasoning capabilities, like smart objects, can be used to create smart environments. The interaction between the objects in those scenarios and humans can be eased by the inclusion of Intelligent Personal Assistants (IPAs). Currently, IPAs have good reasoning capabilities, improving the assistance they give to their owners. Artificial intelligence (AI), new learning mechanisms, and the evolution assisted in speech technology also contributed to this improvement. The integration of IPAs in IoT scenarios can become a case of great success. IPAs will comprehend the behavior of their owners not only through direct interactions, but also by the interactions they have with other objects in the environment. This may create ubiquitous communication scenarios where humans act as passive elements, being adequately informed of all the aspects of interest that surrounds them. The communication between IPAs and other objects in their surrounding environment may use gateways for traffic forwarding. On ubiquitous environments devices can be mobile or static. For example, in smart home scenarios, objects are generally static, being always on the same position. In mobile health scenarios, objects can move from one place to another. To turn IPAs useful on all types of environments, static and mobile gateways should be developed. On this dissertation, a novel mobile gateway solution for an IPA platform inserted on an IoT context is proposed. A mobile health scenario was chosen. Then, a Body Sensor Network (BSN) is always monitoring a person, giving the real time feedback of his/her health status to another person responsible by him (designated caretaker). On this scenario, a mobile gateway is needed to forward the traffic between the BSN and the IPA of the caretaker. Therefore, the IPA is able to give warnings about the health status of the person under monitoring, in real time. The proposed system is evaluated, demonstrated, and validated through a prototype, where the more important aspects for IPAs and IoT networks are considered

    SeMoM: a semantic middleware for IoT healthcare applications

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    De nos jours, l'internet des objets (IoT) connaît un intérêt considérable tant de la part du milieu universitaire que de l'industrie. Il a contribué à améliorer la qualité de vie, la croissance des entreprises et l'efficacité dans de multiples domaines. Cependant, l'hétérogénéité des objets qui peuvent être connectés dans de tels environnements, rend difficile leur interopérabilité. En outre, les observations produites par ces objets sont générées avec différents vocabulaires et formats de données. Cette hétérogénéité de technologies dans le monde IoT rend nécessaire l'adoption de solutions génériques à l'échelle mondiale. De plus, elle rend difficile le partage et la réutilisation des données dans d'autres buts que ceux pour lesquels elles ont été initialement mises en place. Dans cette thèse, nous abordons ces défis dans le contexte des applications de santé. Pour cela, nous proposons de transformer les données brutes issues de capteurs en connaissances et en informations en s'appuyant sur les ontologies. Ces connaissances vont être partagées entre les différents composants du système IoT. En ce qui concerne les défis d'hétérogénéité et d'interopérabilité, notre contribution principale est une architecture IoT utilisant des ontologies pour permettre le déploiement d'applications IoT sémantiques. Cette approche permet de partager les observations des capteurs, la contextualisation des données et la réutilisation des connaissances et des informations traitées. Les contributions spécifiques comprennent : * Conception d'une ontologie " Cognitive Semantic Sensor Network ontology (CoSSN) " : Cette ontologie vise à surmonter les défis d'interopérabilité sémantiques introduits par la variété des capteurs potentiellement utilisés. CoSSN permet aussi de modéliser la représentation des connaissances des experts. * Conception et mise en œuvre de SeMoM: SeMoM est une architecture flexible pour l'IoT intégrant l'ontologie CoSSN. Elle s'appuie sur un middleware orienté message (MoM) pour offrir une solution à couplage faible entre les composants du système. Ceux-ci peuvent échanger des données d'observation sémantiques de manière flexible à l'aide du paradigme producteur/consommateur. Du point de vue applicatif, nous sommes intéressés aux applications de santé. Dans ce domaine, les approches spécifiques et les prototypes individuels sont des solutions prédominantes ce qui rend difficile la collaboration entre différentes applications, en particulier dans un cas de patients multi-pathologies. En ce qui concerne ces défis, nous nous sommes intéressés à deux études de cas: 1) la détection du risque de développement des escarres chez les personnes âgées et 2) la détection des activités de la vie quotidienne (ADL) de personnes pour le suivi et l'assistance à domicile : * Nous avons développé des extensions de CoSSN pour décrire chaque concept en lien avec les deux cas d'utilisation. Nous avons également développé des applications spécifiques grâce à SeMoM qui mettent en œuvre des règles de connaissances expertes permettant d'évaluer et de détecter les escarres et les activités. * Nous avons mis en œuvre et évaluer le framework SeMoM en se basant sur deux expérimentations. La première basée sur le déploiement d'un système ciblant la détection des activités ADL dans un laboratoire d'expérimentation pour la santé (le Connected Health Lab). La seconde est basée sur le simulateur d'activités ADLSim développé par l'Université d'Oslo. Ce simulateur a été utilisé pour effectuer des tests de performances de notre solution en générant une quantité massive de données sur les activités d'une personne à domicile.Nowadays, the adoption of the Internet of Things (IoT) has received a considerable interest from both academia and industry. It provides enhancements in quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these "Things" are made available with heterogeneous vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were originally set up. In this thesis, we address these challenges in the context of healthcare applications considering how we transform raw data to cognitive knowledge and ontology-based information shared between IoT system components. With respect to heterogeneity and integration challenges, our main contribution is an ontology-based IoT architecture allowing the deployment of semantic IoT applications. This approach allows sharing of sensors observations, contextualization of data and reusability of knowledge and processed information. Specific contributions include: * Design of the Cognitive Semantic Sensor Network ontology (CoSSN) ontology: CoSSN aims at overcoming the semantic interoperability challenges introduced by the variety of sensors potentially used. It also aims at describing expert knowledge related to a specific domain. * Design and implementation of SeMoM: SeMoM is a flexible IoT architecture built on top of CoSSN ontology. It relies on a message oriented middleware (MoM) following the publish/subscribe paradigm for a loosely coupled communication between system components that can exchange semantic observation data in a flexible way. From the applicative perspective, we focus on healthcare applications. Indeed, specific approaches and individual prototypes are preeminent solutions in healthcare which straighten the need of an interoperable solution especially for patients with multiple affections. With respect to these challenges, we elaborated two case studies 1) bedsore risk detection and 2) Activities of Daily Living (ADL) detection as follows: * We developed extensions of CoSSN to describe each domain concepts and we developed specific applications through SeMoM implementing expert knowledge rules and assessments of bedsore and human activities. * We implemented and evaluated the SeMoM framework in order to provide a proof of concept of our approach. Two experimentations have been realized for that target. The first is based on a deployment of a system targeting the detection of ADL activities in a real smart platform. The other one is based on ADLSim, a simulator of activities for ambient assisted living that can generate a massive amount of data related to the activities of a monitored person

    Fog computing for sustainable smart cities: a survey

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    The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities

    Utilizing industry 4.0 on the construction site : challenges and opportunities

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    In recent years a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organisations alike. This paper discusses the current state of the art in the adoption of industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of industry 4.0 technologies. This paper discusses the relevance of the following key industry 4.0 technologies to construction: data analytics and artificial intelligence; robotics and automation; buildings information management; sensors and wearables; digital twin and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This paper also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector; a three-phase use of intelligent assets from the point of manufacture up to after build and a four staged R&D process for the implementation of smart wearables in a digital enhanced construction site

    Smart Sensing Technologies for Personalised Coaching

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    People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and can age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people toward healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support but also gives us an incentive to set goals and to do more. This book presents some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes

    The Applications of the Internet of things in the Medical Field

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    The Internet of Things (IoT) paradigm promises to make “things” include a more generic set of entities such as smart devices, sensors, human beings, and any other IoT objects to be accessible at anytime and anywhere. IoT varies widely in its applications, and one of its most beneficial uses is in the medical field. However, the large attack surface and vulnerabilities of IoT systems needs to be secured and protected. Security is a requirement for IoT systems in the medical field where the Health Insurance Portability and Accountability Act (HIPAA) applies. This work investigates various applications of IoT in healthcare and focuses on the security aspects of the two internet of medical things (IoMT) devices: the LifeWatch Mobile Cardiac Telemetry 3 Lead (MCT3L), and the remote patient monitoring system of the telehealth provider Vivify Health, as well as their implementations

    Data semantic enrichment for complex event processing over IoT Data Streams

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    This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation
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