840 research outputs found
A Framework for Anomaly Diagnosis in Smart Homes Based on Ontology
International audienceSmart homes are pervasive environments to enhance the comfort, the security, the safety and the energy consumption of the residence. An ambient intelligence system uses information of devices to represent the context of the home and its residents. Based on a context database, this system infer the daily life activities of the resident. Hence, abnormal behavior or chronic disease can be detected by the system. Due to the complexity of these systems, a large variety of anomalies may occur and disrupt the functioning of critical and essential applications. To detect anomalies and take appropriate measures, an anomaly management system has to be integrated in the overall architecture. In this paper, we propose an anomaly management framework for smart homes. This framework eases the work of designers in the conception of anomaly detection modules and processes to respond to an anomaly appropriately. Our framework can be used in all heterogeneous environments such as smart home because it uses Semantic Web ontologies to represent anomaly information. Our framework can be useful to detect hardware, software, network, operator and context faults. To test the efficiency of our anomaly management framework, we integrate it in the universAAL middleware. Based on a reasoner, our framework can easily infer some context anomalies and take appropriate measures to restore the system in a full functioning state
Achieving Autonomic Computing through the Use of Variability Models at Run-time
Increasingly, software needs to dynamically adapt its behavior at run-time in response
to changing conditions in the supporting computing infrastructure and in
the surrounding physical environment. Adaptability is emerging as a necessary underlying
capability, particularly for highly dynamic systems such as context-aware
or ubiquitous systems.
By automating tasks such as installation, adaptation, or healing, Autonomic
Computing envisions computing environments that evolve without the need for human
intervention. Even though there is a fair amount of work on architectures
and their theoretical design, Autonomic Computing was criticised as being a \hype
topic" because very little of it has been implemented fully. Furthermore, given that
the autonomic system must change states at runtime and that some of those states
may emerge and are much less deterministic, there is a great challenge to provide
new guidelines, techniques and tools to help autonomic system development.
This thesis shows that building up on the central ideas of Model Driven Development
(Models as rst-order citizens) and Software Product Lines (Variability
Management) can play a signi cant role as we move towards implementing the key
self-management properties associated with autonomic computing. The presented
approach encompass systems that are capable of modifying their own behavior with
respect to changes in their operating environment, by using variability models as if
they were the policies that drive the system's autonomic recon guration at runtime.
Under a set of recon guration commands, the components that make up the architecture
dynamically cooperate to change the con guration of the architecture to a
new con guration.
This work also provides the implementation of a Model-Based Recon guration
Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides
the mechanisms for modifying the system.Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat PolitĂšcnica de ValĂšncia. https://doi.org/10.4995/Thesis/10251/7484Palanci
A Decentralized Personal Data Store based on Ethereum: Towards GDPR Compliance
Sharing personal data with service providers is a fundamental resource for the times we live in. But data sharing represents an unavoidable issue, due to improper data treatment, lack of users\u27 awareness to whom they are sharing with, wrong or excessive data sharing from end users who ignore they are exposing personal information. The problem becomes even more complicate if we try to consider the devices around us: how to share devices we own, so that we can receive pervasive services, based on our contexts and device functionalities. The European Authority has provided the General Data Protection Regulation (GDPR), in order to implement protection of sensitive data in each EU member, throughout certification mechanisms (according to Art. 42 GDPR). The certification assures compliance to the regulation, which represent a mandatory requirement for any service which may come in contact with sensitive data. Still the certification is an open process and not constrained by strict rule. In this paper we describe our decentralized approach in sharing personal data in the era of smart devices, being those considered sensitive data as well. Having in mind the centrality of users in the ownership of the data, we have proposed a decentralized Personal Data Store prototype, which stands as a unique data sharing endpoint for third party services. Even if blockchain technologies may seem fit to solve the issue of data protection, because of the absence of a central authority, they lay to additional concerns especially relating such technologies with specifications described in the regulation. The current work offers a contribution in the advancements of personal data sharing management systems in a distributed environment by presenting a real prototype and an architectural blueprint, which advances the state of the art in order to meet the GDPR regulation. Address those arisen issues, from a technological perspective, stands as an important challenge, in order to empower end users in owning their personal data for real
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Experts on e-learning: insights gained from listening to the student voice!
The Student Experience of e-Learning Laboratory (SEEL) project at the University of Greenwich was designed to explore and then implement a number of approaches to investigate learnersâ experiences of using technology to support their learning. In this paper members of the SEEL team present initial findings from a University-wide survey of nearly a 1000 students. A selection of 90 âcameosâ, drawn from the survey data, offer further insights into personal perceptions of e-learning and illustrate the diversity of students experiences. The cameos provide a more coherent picture of individual student experience based on the
totality of each personâs responses to the questionnaire. Finally, extracts from follow-up case studies, based
on interviews with a small number of students, allow us to âhearâ the student voice more clearly. Issues arising from an analysis of the data include student preferences for communication and social networking tools, views on the âsmartnessâ of their tutorsâ uses of technology and perceptions of the value of e-learning. A primary finding and the focus of this paper, is that students effectively arrive at their own individualised selection, configuration and use of technologies and software that meets their perceived needs. This âpersonalisationâ does not imply that such configurations are the most efficient, nor does it automatically suggest that effective learning is occurring. SEEL reminds us that learners are individuals, who approach
learning both with and without technology in their own distinctive ways. Hearing, understanding and responding to the student voice is fundamental in maximising learning effectiveness. Institutions should consider actively developing the capacity of academic staff to advise students on the usefulness of particular online tools and resources in support of learning and consider the potential benefits
of building on what students already use in their everyday lives. Given the widespread perception that students tend to be âdigital nativesâ and academic staff âdigital immigrantsâ (Prensky, 2001), this could represent a considerable cultural challenge
Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain
New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders' participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS
HIDE: User centred Domotic evolution toward Ambient Intelligence
Pervasive Computing and Ambient Intelligence (AmI) visions are still far from being achieved, especially with regard to Domotics and home applications. According to the vision of Ambient Intelligence (AmI), the most advanced technologies are those that disappear: at maturity, computer technology should become invisible. All the objects surrounding us must possess sufficient computing capacity to interact with users, the surroundings and each other. The entire physical environment in which users are immersed should thus be a hidden computer system equipped with the appropriate software in order to exhibit intelligent behavior. Even though many implementations have started to
appear in several contexts, few applications have been made available for the home environment and the general public. This is mainly due to the segmentation of standards and proprietary solutions, which are currently confusing the market with a sparse offer of uninteroperable devices and systems. Although modern houses are equipped with smart technological appliances, still very few of these appliances can be seamlessly connected to each other.
The objective of this research work is to take steps in these directions by proposing, on the one hand, a software system designed to make todayâs heterogeneous, mostly incompatible domotic systems fully interoperable and, on the other hand, a feasible software application able to learn the behavior and habits of home inhabitants in order to actively contribute to anticipating user needs, and preventing emergency situations for his health. By applying machine learning techniques, the system offers a complete, ready-to-use practical application that learns through interaction with the user in order to improve life quality in a technological living environment, such as a house, a smart city and so on.
The proposed solution, besides making life more comfortable for users without particular needs, represents an opportunity to provide greater autonomy and safety to disabled and elderly occupants, especially the critically ill ones.
The prototype has been developed and is currently running at the Pisa CNR laboratory, where a home environment has been faithfully recreated
Personalized City Tours - An Extension of the OGC OpenLocation Specification
A business trip to London last month , a day visit in Cologne next saturday and romantic weekend in Paris in autumn â this example exhibits one of the central characteristics of todayâs tourism. People in the western hemisphere take much pleasure in frequent and repeated short term visits of cities. Every city visitor faces the general problems of where to go and what to see in the diverse microcosm of a metropolis. This thesis presents a framework for the generation of personalized city tours - as extension of the Open Location Specification of the Open Geospatial Consortium. It is founded on context-awareness and personalization while at the same time proposing a combined approach to allow for adaption to the user. This framework considers TimeGeography and its algorithmic implementations to be able to cope with spatio-temporal constraints of a city tour. Traveling salesmen problems - for which a heuristic approache is proposed â are subjacent to the tour generation. To meet the requirements of todayâs distributed and heterogeneous computing environments, the tour framework comprises individual services that expose standard-compliant interfaces and allow for integration in service oriented architectures
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