2,187 research outputs found
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
The OCarePlatform : a context-aware system to support independent living
Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved
Ambient-aware continuous care through semantic context dissemination
Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data.
Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability.
Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered.
Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results
Self-adaptation via concurrent multi-action evaluation for unknown context
Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project.
Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach.
The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains.
The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason.
The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered.
In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques
Ami-deu : un cadre sémantique pour des applications adaptables dans des environnements intelligents
Cette thĂšse vise Ă Ă©tendre lâutilisation de l'Internet des objets (IdO) en facilitant le dĂ©veloppement dâapplications par des personnes non experts en dĂ©veloppement logiciel. La thĂšse propose une nouvelle approche pour augmenter la sĂ©mantique des applications dâIdO et lâimplication des experts du domaine dans le dĂ©veloppement dâapplications sensibles au contexte. Notre approche permet de gĂ©rer le contexte changeant de lâenvironnement et de gĂ©nĂ©rer des applications qui sâexĂ©cutent dans plusieurs environnements intelligents pour fournir des actions requises dans divers contextes. Notre approche est mise en Ćuvre dans un cadriciel (AmI-DEU) qui inclut les composants pour le dĂ©veloppement dâapplications IdO. AmI-DEU intĂšgre les services dâenvironnement, favorise lâinteraction de lâutilisateur et fournit les moyens de reprĂ©senter le domaine dâapplication, le profil de lâutilisateur et les intentions de lâutilisateur. Le cadriciel permet la dĂ©finition dâapplications IoT avec une intention dâactivitĂ© autodĂ©crite qui contient les connaissances requises pour rĂ©aliser lâactivitĂ©. Ensuite, le cadriciel gĂ©nĂšre Intention as a Context (IaaC), qui comprend une intention dâactivitĂ© autodĂ©crite avec des connaissances colligĂ©es Ă Ă©valuer pour une meilleure adaptation dans des environnements intelligents.
La sĂ©mantique de lâAmI-DEU est basĂ©e sur celle du ContextAA (Context-Aware Agents) â une plateforme pour fournir une connaissance du contexte dans plusieurs environnements. Le cadriciel effectue une compilation des connaissances par des rĂšgles et l'appariement sĂ©mantique pour produire des applications IdO autonomes capables de sâexĂ©cuter en ContextAA. AmI- DEU inclut Ă©galement un outil de dĂ©veloppement visuel pour le dĂ©veloppement et le dĂ©ploiement rapide d'applications sur ContextAA. L'interface graphique dâAmI-DEU adopte la mĂ©taphore du flux avec des aides visuelles pour simplifier le dĂ©veloppement d'applications en permettant des dĂ©finitions de rĂšgles Ă©tape par Ă©tape. Dans le cadre de lâexpĂ©rimentation, AmI-DEU comprend un banc dâessai pour le dĂ©veloppement dâapplications IdO. Les rĂ©sultats expĂ©rimentaux montrent une optimisation sĂ©mantique potentielle des ressources pour les applications IoT dynamiques dans les maisons intelligentes et les villes intelligentes.
Notre approche favorise l'adoption de la technologie pour amĂ©liorer le bienĂȘtre et la qualitĂ© de vie des personnes. Cette thĂšse se termine par des orientations de recherche que le cadriciel AmI-DEU dĂ©voile pour rĂ©aliser des environnements intelligents omniprĂ©sents fournissant des adaptations appropriĂ©es pour soutenir les intentions des personnes.Abstract: This thesis aims at expanding the use of the Internet of Things (IoT) by facilitating the development of applications by people who are not experts in software development. The thesis proposes a new approach to augment IoT applicationsâ semantics and domain expert involvement in context-aware application development. Our approach enables us to manage the changing environment context and generate applications that run in multiple smart environments to provide required actions in diverse settings. Our approach is implemented in a framework (AmI-DEU) that includes the components for IoT application development. AmI- DEU integrates environment services, promotes end-user interaction, and provides the means to represent the application domain, end-user profile, and end-user intentions. The framework enables the definition of IoT applications with a self-described activity intention that contains the required knowledge to achieve the activity. Then, the framework generates Intention as a Context (IaaC), which includes a self-described activity intention with compiled knowledge to be assessed for augmented adaptations in smart environments. AmI-DEU framework semantics adopts ContextAA (Context-Aware Agents) â a platform to provide context-awareness in multiple environments. The framework performs a knowledge compilation by rules and semantic matching to produce autonomic IoT applications to run in ContextAA. AmI-DEU also includes a visual tool for quick application development and deployment to ContextAA. The AmI-DEU GUI adopts the flow metaphor with visual aids to simplify developing applications by allowing step-by-step rule definitions. As part of the experimentation, AmI-DEU includes a testbed for IoT application development. Experimental results show a potential semantic optimization for dynamic IoT applications in smart homes and smart cities. Our approach promotes technology adoption to improve peopleâs well-being and quality of life. This thesis concludes with research directions that the AmI-DEU framework uncovers to achieve pervasive smart environments providing suitable adaptations to support peopleâs intentions
Towards formalisation of situation-specific computations in pervasive computing environments
We have categorised the characteristics and the content of pervasive computing
environments (PCEs), and demonstrated why a non-dynamic approach to
knowledge conceptualisation in PCEs does not fulfil the expectations we may have from them. Consequently, we have proposed a formalised computational model,
the FCM, for knowledge representation and reasoning in PCEs which, secures the
delivery of situation and domain specific services to their users. The proposed
model is a user centric model, materialised as a software engineering solution,
which uses the computations generated from the FCM, stores them within software
architectural components, which in turn can be deployed using modern software
technologies. The model has also been inspired by the Semantic Web (SW) vision
and provision of SW technologies. Therefore, the FCM creates a semantically rich situation-specific PCE based on SWRL-enabled OWL ontologies that allows
reasoning about the situation in a PCE and delivers situation specific service.
The proposed FCM model has been illustrated through the example of remote
patient monitoring in the healthcare domain. Numerous software applications
generated from the FCM have been deployed using Integrated Development
Environments and OWL-API
A Context-Oriented Extension of F#
Context-Oriented programming languages provide us with primitive constructs
to adapt program behaviour depending on the evolution of their operational
environment, namely the context. In previous work we proposed ML_CoDa, a
context-oriented language with two-components: a declarative constituent for
programming the context and a functional one for computing. This paper
describes the implementation of ML_CoDa as an extension of F#.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694
Supporting Management lnteraction and Composition of Self-Managed Cells
Management in ubiquitous systems cannot rely on human intervention or centralised
decision-making functions because systems are complex and devices
are inherently mobile and cannot refer to centralised management applications
for reconfiguration and adaptation directives. Management must be devolved,
based on local decision-making and feedback control-loops embedded in autonomous
components. Previous work has introduced a Self-Managed Cell (SMC)
as an infrastructure for building ubiquitous applications. An SMC consists
of a set of hardware and software components that implement a policy-driven
feedback control-loop. This allows SMCs to adapt continually to changes in
their environment or in their usage requirements. Typical applications include
body-area networks for healthcare monitoring, and communities of unmanned
autonomous vehicles (UAVs) for surveillance and reconnaissance operations.
Ubiquitous applications are typically formed from multiple interacting autonomous
components, which establish peer-to-peer collaborations, federate and
compose into larger structures. Components must interact to distribute management
tasks and to enforce communication strategies. This thesis presents
an integrated framework which supports the design and the rapid establishment
of policy-based SMC interactions by systematically composing simpler abstractions
as building elements of a more complex collaboration. Policy-based
interactions are realised â subject to an extensible set of security functions â
through the exchanges of interfaces, policies and events, and our framework
was designed to support the specification, instantiation and reuse of patterns of
interaction that prescribe the manner in which these exchanges are achieved.
We have defined a library of patterns that provide reusable abstractions for
the structure, task-allocation and communication aspects of an interaction,
which can be individually combined for building larger policy-based systems in
a methodical manner. We have specified a formal model to ensure the rigorous
verification of SMC interactions before policies are deployed in physical devices.
A prototype has been implemented that demonstrates the practical feasibility
of our framework in constrained resources
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