61,243 research outputs found
USING EMBEDDED TECHNOLOGY IN END-USER PROGRAMMING OF SMART SPACES WITH MOBILE DEVICES
A recent shift in computing paradigm from stand-alone microcomputers and mainframes towards entirely pervasivecomputing where billions of miniature, ubiquitous inter-connected computing elements weave themselves into thefabric of everyday life. Embedded systems run the computing devices hidden inside every object and appliance suchas cell phones, toys, handheld PDAs, cameras, microwave ovens, cars, airplanes, etc. These numerous, easilyaccessible devices connected to each other and to network infrastructure exhibit context-awareness of anenvironment in order to optimize their operation in that environment. In this paper, we examined embedded systemsin end-user programming of smart spaces with mobile devices. We designed and implemented a microcontrollerbasedsystem capable of monitoring and controlling the electronic appliances in a home from any location. Weadopted a task-driven computing approach of the composition of the semantic web. The end user uses thefunctionality of the networked devices in the home as semantic web services to arbitrarily form his request whichinvolves the typing of SMS through the user-friendly interface of a Java enabled mobile phone. An Arduinomicrocontroller for generating the timing and control signals programmed using Wiring language was used. TheGSM wireless technology was used for transmission and reception of the data. Our work addresses the problem ofenergy wastage and domestic accidents by enabling end-users to easily use their mobile devices to monitor andinstruct their home devices from any location over a wireless network.Keywords: Embedded Technology, Smart Spaces, End-User Programming, Mobile Devices, Pervasive Networkin
Programming patterns and development guidelines for Semantic Sensor Grids (SemSorGrid4Env)
The web of Linked Data holds great potential for the creation of semantic applications that can combine self-describing structured data from many sources including sensor networks. Such applications build upon the success of an earlier generation of 'rapidly developed' applications that utilised RESTful APIs. This deliverable details experience, best practice, and design patterns for developing high-level web-based APIs in support of semantic web applications and mashups for sensor grids. Its main contributions are a proposal for combining Linked Data with RESTful application development summarised through a set of design principles; and the application of these design principles to Semantic Sensor Grids through the development of a High-Level API for Observations. These are supported by implementations of the High-Level API for Observations in software, and example semantic mashups that utilise the API
IRS II: a framework and infrastructure for semantic web services
In this paper we describe IRSâII (Internet Reasoning Service) a framework and implemented infrastructure, whose main goal is to support the publication, location, composition and execution of heterogeneous web services, augmented with semantic descriptions of their functionalities. IRSâII has three main classes of features which distinguish it from other work on semantic web services. Firstly, it supports one-click publishing of standalone software: IRSâII automatically creates the appropriate wrappers, given pointers to the standalone code. Secondly, it explicitly distinguishes between tasks (what to do) and methods (how to achieve tasks) and as a result supports capability-driven service invocation; flexible mappings between services and problem specifications; and dynamic, knowledge-based service selection. Finally, IRSâII services are web service compatible â standard web services can be trivially published through the IRSâII and any IRSâII service automatically appears as a standard web service to other web service infrastructures. In the paper we illustrate the main functionalities of IRSâII through a scenario involving a distributed application in the healthcare domain
Ontology-based Classification and Analysis of non- emergency Smart-city Events
Several challenges are faced by citizens of urban centers while dealing with
day-to-day events, and the absence of a centralised reporting mechanism makes
event-reporting and redressal a daunting task. With the push on information
technology to adapt to the needs of smart-cities and integrate urban civic
services, the use of Open311 architecture presents an interesting solution. In
this paper, we present a novel approach that uses an existing Open311 ontology
to classify and report non-emergency city-events, as well as to guide the
citizen to the points of redressal. The use of linked open data and the
semantic model serves to provide contextual meaning and make vast amounts of
content hyper-connected and easily-searchable. Such a one-size-fits-all model
also ensures reusability and effective visualisation and analysis of data
across several cities. By integrating urban services across various civic
bodies, the proposed approach provides a single endpoint to the citizen, which
is imperative for smooth functioning of smart cities
Managing contextual information in semantically-driven temporal information systems
Context-aware (CA) systems have demonstrated the provision of a robust solution for personalized information delivery in the current content-rich and dynamic information age we live in. They allow software agents to autonomously interact with users by modeling the userâs environment (e.g. profile, location, relevant public information etc.) as dynamically-evolving and interoperable contexts. There is a flurry of research activities in a wide spectrum at context-aware research areas such as managing the userâs profile, context acquisition from external environments, context storage, context representation and interpretation, context service delivery and matching of context attributes to usersâ queries etc. We propose SDCAS, a Semantic-Driven Context Aware System that facilitates public services recommendation to users at temporal location. This paper focuses on information management and service recommendation using semantic technologies, taking into account the challenges of relationship complexity in temporal and contextual information
Automated syntactic mediation for Web service integration
As the Web Services and Grid community adopt Semantic Web technology, we observe a shift towards higher-level workflow composition and service discovery practices. While this provides excellent functionality to non-expert users, more sophisticated middleware is required to hide the details of service invocation and service integration. An investigation of a common Bioinformatics use case reveals that the execution of high-level workflow designs requires additional processing to harmonise syntactically incompatible service interfaces. In this paper, we present an architecture to support the automatic reconciliation of data formats in such Web Service worklflows. The mediation of data is driven by ontologies that encapsulate the information contained in heterogeneous data structures supplying a common, conceptual data representation. Data conversion is carried out by a Configurable Mediator component, consuming mappings between \xml schemas and \owl ontologies. We describe our system and give examples of our mapping language against the background of a Bioinformatics use case
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Bridging between sensor measurements and symbolic ontologies through conceptual spaces
The increasing availability of sensor data through a variety of sensor-driven devices raises the need to exploit the data observed by sensors with the help of formally specified knowledge representations, such as the ones provided by the Semantic Web. In order to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the potential infinite variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an ontology for CS which allows to refine symbolic concepts as CS and to ground instances to so-called prototypical members described by vectors. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of prototypical members, the most similar instance can be identified. In that, we provide a means to bridge between the real-world as observed by sensors and symbolic representations. We also propose an initial implementation utilizing our approach for measurement-based Semantic Web Service discovery
PowerAqua: fishing the semantic web
The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources
Flexible coordination techniques for dynamic cloud service collaboration
The provision of individual, but also composed services is central in cloud service provisioning. We describe a framework for the coordination of cloud services, based on a tupleâspace architecture which uses an ontology to describe the services. Current techniques for service collaboration offer limited scope for flexibility. They are based on statically describing and compositing services. With the open nature of the web and cloud services, the need for a more flexible, dynamic approach to service coordination becomes evident. In order to support open communities of service providers, there should be the option for these providers to offer and withdraw their services to/from the community. For this to be realised, there needs to be a degree of selfâorganisation. Our techniques for coordination and service matching aim to achieve this through matching goalâoriented service requests with providers that advertise their offerings dynamically. Scalability of the solution is a particular concern that will be evaluated in detail
Semantic model-driven development of service-centric software architectures
Service-oriented architecture (SOA) is a recent architectural paradigm that has received much attention. The prevalent focus on platforms such as Web services, however, needs to be complemented by appropriate software engineering methods. We propose the model-driven development of service-centric software systems. We present in particular an investigation into the role of enriched semantic modelling for a modeldriven development framework for service-centric software systems. Ontologies as the foundations of semantic modelling and its enhancement
through architectural pattern modelling are at the core of the proposed approach. We introduce foundations and discuss the benefits and also the challenges in this context
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