3,638 research outputs found
Towards dynamic context discovery and composition
Context-awareness has been identified as a key characteristic for pervasive computing systems. As a variety of context-aware environments begin to flourish, pervasive applications shall have to interact different environments well. In this paper we propose extensions to the Strathclyde Context Infrastructure that gives context-aware applications the potential to adapt to unfamiliar environments transparently. We present a vision of a context discovery technique based on automated semantic reasoning about context information and services. The technique will offer higher levels of scalability and of interoperability with new context environments that cannot be achieved with current methods
Smart Geographic object: Toward a new understanding of GIS Technology in Ubiquitous Computing
One of the fundamental aspects of ubiquitous computing is the instrumentation
of the real world by smart devices. This instrumentation constitutes an
opportunity to rethink the interactions between human beings and their
environment on the one hand, and between the components of this environment on
the other. In this paper we discuss what this understanding of ubiquitous
computing can bring to geographic science and particularly to GIS technology.
Our main idea is the instrumentation of the geographic environment through the
instrumentation of geographic objects composing it. And then investigate how
this instrumentation can meet the current limitations of GIS technology, and
offers a new stage of rapprochement between the earth and its abstraction. As
result, the current research work proposes a new concept we named Smart
Geographic Object SGO. The latter is a convergence point between the smart
objects and geographic objects, two concepts appertaining respectively to
Composition and Self-Adaptation of Service-Based Systems with Feature Models
The adoption of mechanisms for reusing software in pervasive systems has not yet become standard practice. This is because the use of pre-existing software requires the selection, composition and adaptation of prefabricated software parts, as well as the management of some complex problems such as guaranteeing high levels of efficiency and safety in critical domains. In addition to the wide variety of services, pervasive systems are composed of many networked heterogeneous devices with embedded software. In this work, we promote the safe reuse of services in service-based systems using two complementary technologies, Service-Oriented Architecture and Software Product Lines. In order to do this, we extend both the service discovery and composition processes defined in the DAMASCo framework, which currently does not deal with the service variability that constitutes pervasive systems. We use feature models to represent the variability and to self-adapt the services during the composition in a safe way taking context changes into consideration. We illustrate our proposal with a case study related to the driving domain of an Intelligent Transportation System, handling the context information of the environment.Work partially supported by the projects TIN2008-05932,
TIN2008-01942, TIN2012-35669, TIN2012-34840 and CSD2007-0004 funded by
Spanish Ministry of Economy and Competitiveness and FEDER; P09-TIC-05231 and
P11-TIC-7659 funded by Andalusian Government; and FP7-317731 funded by EU. Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tec
Semantic-driven Configuration of Internet of Things Middleware
We are currently observing emerging solutions to enable the Internet of
Things (IoT). Efficient and feature rich IoT middeware platforms are key
enablers for IoT. However, due to complexity, most of these middleware
platforms are designed to be used by IT experts. In this paper, we propose a
semantics-driven model that allows non-IT experts (e.g. plant scientist, city
planner) to configure IoT middleware components easier and faster. Such tools
allow them to retrieve the data they want without knowing the underlying
technical details of the sensors and the data processing components. We propose
a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of
automated context-aware configuration of filtering, fusion, and reasoning
mechanisms in IoT middleware according to the problems at hand. We incorporate
semantic technologies in solving the above challenges. We demonstrate the
feasibility and the scalability of our approach through a prototype
implementation based on an IoT middleware called Global Sensor Networks (GSN),
though our model can be generalized into any other middleware platform. We
evaluate CASCoM in agriculture domain and measure both performance in terms of
usability and computational complexity.Comment: 9th International Conference on Semantics, Knowledge & Grids (SKG),
Beijing, China, October, 201
Goal-based analytic composition for on- and off-line execution at scale
Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisationâs engineers and subject matter or domain experts. Previous approaches to this problem have relied on technically adept users with tool-specific training. These approaches have generally not targeted the levels of performance and scalability required to harness the sheer volume and velocity of large-scale data analytics.
In this paper, we present a novel approach to the automated planning of scalable analytics using a semantically rich type system, the use of which requires little programming expertise from the user. This approach is the first of its kind to permit domain experts with little or no technical expertise to assemble complex and scalable analytics, for execution both on- and offline, with no lower-level engineering support. We describe in detail (i) an abstract model of analytic assembly and execution; (ii) goal-based planning and (iii) code generation using this model for both on- and off-line analytics. Our implementation of this model, MENDELEEV, is used to (iv) demonstrate the applicability of our approach through a series of case studies, in which a single interface is used to create analytics that can be run in real-time (on-line) and batch (off-line) environments. We (v) analyse the performance of the planner, and (vi) show that the performance of MENDELEEVâs generated code is comparable with that of hand-written analytics
Cross-Platform Presentation of Interactive Volumetric Imagery
Volume data is useful across many disciplines, not just medicine.
Thus, it is very important that researchers have a simple and
lightweight method of sharing and reproducing such volumetric
data. In this paper, we explore some of the challenges associated
with volume rendering, both from a classical sense and from the
context of Web3D technologies. We describe and evaluate the pro-
posed X3D Volume Rendering Component and its associated styles
for their suitability in the visualization of several types of image
data. Additionally, we examine the ability for a minimal X3D node
set to capture provenance and semantic information from outside
ontologies in metadata and integrate it with the scene graph
Ensuring Cyber-Security in Smart Railway Surveillance with SHIELD
Modern railways feature increasingly complex embedded computing systems for surveillance, that are moving towards fully wireless smart-sensors. Those systems are aimed at monitoring system status from a physical-security viewpoint, in order to detect intrusions and other environmental anomalies. However, the same systems used for physical-security surveillance are vulnerable to cyber-security threats, since they feature distributed hardware and software architectures often interconnected by âopen networksâ, like wireless channels and the Internet. In this paper, we show how the integrated approach to Security, Privacy and Dependability (SPD) in embedded systems provided by the SHIELD framework (developed within the EU funded pSHIELD and nSHIELD research projects) can be applied to railway surveillance systems in order to measure and improve their SPD level. SHIELD implements a layered architecture (node, network, middleware and overlay) and orchestrates SPD mechanisms based on ontology models, appropriate metrics and composability. The results of prototypical application to a real-world demonstrator show the effectiveness of SHIELD and justify its practical applicability in industrial settings
- âŠ