368 research outputs found

    Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

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    The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions

    Ubiquitous Computing

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    The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

    Steps towards adaptive situation and context-aware access: a contribution to the extension of access control mechanisms within pervasive information systems

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    L'évolution des systèmes pervasives a ouvert de nouveaux horizons aux systèmes d'information classiques qui ont intégré des nouvelles technologies et des services qui assurent la transparence d'accès aux resources d'information à n'importe quand, n'importe où et n'importe comment. En même temps, cette évolution a relevé des nouveaux défis à la sécurité de données et à la modélisation du contrôle d'accès. Afin de confronter ces challenges, differents travaux de recherche se sont dirigés vers l'extension des modèles de contrôles d'accès (en particulier le modèle RBAC) afin de prendre en compte la sensibilité au contexte dans le processus de prise de décision. Mais la liaison d'une décision d'accès aux contraintes contextuelles dynamiques d'un utilisateur mobile va non seulement ajouter plus de complexité au processus de prise de décision mais pourra aussi augmenter les possibilités de refus d'accès. Sachant que l'accessibilité est un élément clé dans les systèmes pervasifs et prenant en compte l'importance d'assurer l'accéssibilité en situations du temps réel, nombreux travaux de recherche ont proposé d'appliquer des mécanismes flexibles de contrôle d'accès avec des solutions parfois extrêmes qui depassent les frontières de sécurité telle que l'option de "Bris-de-Glace". Dans cette thèse, nous introduisons une solution modérée qui se positionne entre la rigidité des modèles de contrôle d'accès et la flexibilité qui expose des risques appliquées pendant des situations du temps réel. Notre contribution comprend deux volets : au niveau de conception, nous proposons PS-RBAC - un modèle RBAC sensible au contexte et à la situation. Le modèle réalise des attributions des permissions adaptatives et de solution de rechange à base de prise de décision basée sur la similarité face à une situation importanteÀ la phase d'exécution, nous introduisons PSQRS - un système de réécriture des requêtes sensible au contexte et à la situation et qui confronte les refus d'accès en reformulant la requête XACML de l'utilisateur et en lui proposant une liste des resources alternatives similaires qu'il peut accéder. L'objectif est de fournir un niveau de sécurité adaptative qui répond aux besoins de l'utilisateur tout en prenant en compte son rôle, ses contraintes contextuelles (localisation, réseau, dispositif, etc.) et sa situation. Notre proposition a été validé dans trois domaines d'application qui sont riches des contextes pervasifs et des scénarii du temps réel: (i) les Équipes Mobiles Gériatriques, (ii) les systèmes avioniques et (iii) les systèmes de vidéo surveillance.The evolution of pervasive computing has opened new horizons to classical information systems by integrating new technologies and services that enable seamless access to information sources at anytime, anyhow and anywhere. Meanwhile this evolution has opened new threats to information security and new challenges to access control modeling. In order to meet these challenges, many research works went towards extending traditional access control models (especially the RBAC model) in order to add context awareness within the decision-making process. Meanwhile, tying access decisions to the dynamic contextual constraints of mobile users would not only add more complexity to decision-making but could also increase the possibilities of access denial. Knowing that accessibility is a key feature for pervasive systems and taking into account the importance of providing access within real-time situations, many research works have proposed applying flexible access control mechanisms with sometimes extreme solutions that depass security boundaries such as the Break-Glass option. In this thesis, we introduce a moderate solution that stands between the rigidity of access control models and the riskful flexibility applied during real-time situations. Our contribution is twofold: on the design phase, we propose PS-RBAC - a Pervasive Situation-aware RBAC model that realizes adaptive permission assignments and alternative-based decision-making based on similarity when facing an important situation. On the implementation phase, we introduce PSQRS - a Pervasive Situation-aware Query Rewriting System architecture that confronts access denials by reformulating the user's XACML access request and proposing to him a list of alternative similar solutions that he can access. The objective is to provide a level of adaptive security that would meet the user needs while taking into consideration his role, contextual constraints (location, network, device, etc.) and his situation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile Geriatric Teams, (ii) Avionic Systems and (iii) Video Surveillance Systems

    Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

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    Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

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