47,173 research outputs found
A Framework for Developing Context-Aware Systems
In ubiquitous computing the environment constraints are often regarded as static and software
applications are allowed to function in a mobile ecospace. However, in context-aware
systems the environment attributes of software applications are dynamically changing. This
dynamism of contexts must be accounted for in order to provide the true intended effect
on the application of services. Consequently, context-aware software applications should
perceive their context in a continuous manner and seamlessly adapt to it.
This thesis investigates the process of constructing context-aware applications and identifies
the main challenges in this domain. The two principal requirements are (1) formally
defining what context is and expressing the enclosed semantics, (2) formally defining dynamic
compositions of adaptations and triggering their responses to changes in the environment
context.
This thesis proposes a component-based architecture for a Context-aware Framework
that would be used to bring awareness capabilities into applications. Two languages are
formally designed. One is to formally express situations, leading to a context reasoner, and
another is to formally express workflow, leading to timely triggering of reactions and enforcing
policies. With these formalisms and a component design that can be formalized, the
thesis work fulfills a formal approach to construct context-aware applications. A proof-ofconcept
case study is implemented to examine the expressiveness of the framework design
and test its implementation
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
Using ontologies for modeling context-aware services platforms
This paper discusses the suitability of using ontologies for modeling context-aware services platforms. It addresses the directions of research we are following in the WASP (Web Architectures for Services Platforms) project. For this purpose a simple scenario is considered
An Analysis of Service Ontologies
Services are increasingly shaping the world’s economic activity. Service provision and consumption have been profiting from advances in ICT, but the decentralization and heterogeneity of the involved service entities still pose engineering challenges. One of these challenges is to achieve semantic interoperability among these autonomous entities. Semantic web technology aims at addressing this challenge on a large scale, and has matured over the last years. This is evident from the various efforts reported in the literature in which service knowledge is represented in terms of ontologies developed either in individual research projects or in standardization bodies. This paper aims at analyzing the most relevant service ontologies available today for their suitability to cope with the service semantic interoperability challenge. We take the vision of the Internet of Services (IoS) as our motivation to identify the requirements for service ontologies. We adopt a formal approach to ontology design and evaluation in our analysis. We start by defining informal competency questions derived from a motivating scenario, and we identify relevant concepts and properties in service ontologies that match the formal ontological representation of these questions. We analyze the service ontologies with our concepts and questions, so that each ontology is positioned and evaluated according to its utility. The gaps we identify as the result of our analysis provide an indication of open challenges and future work
Pragmatic interoperability in the enterprise : a research agenda
Eective collaboration among today's enterprises is indispensable. Such collaborative synergy is important to foster the creation of innovative value-added products and services that would have otherwise been dicult to achieve if enterprises work in isolation. However, it is a widely held belief that interoperability problems have been one of the perennial hurdles in achieving such collaboration. This research aims to improve the current state of the art in enterprise interoperability research by zeroing in on the notion of pragmatic interoperability(PI). When enterprise systems collaborate by exchanging information, PI goes beyond the compatibility between the structure and the meaning of shared information, it further ensures that the intended eect of the message exchange is realized. This paper outlines our research agenda to address the analysis, design, development and evaluation of a pragmatically interoperable solution for enterprise collaboration
Context-Aware Information Retrieval for Enhanced Situation Awareness
In the coalition forces, users are increasingly challenged with the issues of information overload and correlation of information from heterogeneous sources. Users might need different pieces of information, ranging from information about a single building, to the resolution strategy of a global conflict. Sometimes, the time, location and past history of information access can also shape the information needs of users. Information systems need to help users pull together data from disparate sources according to their expressed needs (as represented by system queries), as well as less specific criteria. Information consumers have varying roles, tasks/missions, goals and agendas, knowledge and background, and personal preferences. These factors can be used to shape both the execution of user queries and the form in which retrieved information is packaged. However, full automation of this daunting information aggregation and customization task is not possible with existing approaches. In this paper we present an infrastructure for context-aware information retrieval to enhance situation awareness. The infrastructure provides each user with a customized, mission-oriented system that gives access to the right information from heterogeneous sources in the context of a particular task, plan and/or mission. The approach lays on five intertwined fundamental concepts, namely Workflow, Context, Ontology, Profile and Information Aggregation. The exploitation of this knowledge, using appropriate domain ontologies, will make it feasible to provide contextual assistance in various ways to the work performed according to a user’s taskrelevant information requirements. This paper formalizes these concepts and their interrelationships
A Survey on Service Composition Middleware in Pervasive Environments
The development of pervasive computing has put the light on a challenging problem: how to dynamically compose services in heterogeneous and highly changing environments? We propose a survey that defines the service composition as a sequence of four steps: the translation, the generation, the evaluation, and finally the execution. With this powerful and simple model we describe the major service composition middleware. Then, a classification of these service composition middleware according to pervasive requirements - interoperability, discoverability, adaptability, context awareness, QoS management, security, spontaneous management, and autonomous management - is given. The classification highlights what has been done and what remains to do to develop the service composition in pervasive environments
A study of existing Ontologies in the IoT-domain
Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby restricting the possibility of reusing sensor data to
develop new applications. As a solution to this, semantic approaches have been
proposed in the literature to tackle problems related to interoperability of
sensor data. Several ontologies have been proposed to handle different aspects
of IoT-based sensor data collection, ranging from discovering the IoT sensors
for data collection to applying reasoning on the collected sensor data for
drawing inferences. In this paper, we survey these existing semantic ontologies
to provide an overview of the recent developments in this field. We highlight
the fundamental ontological concepts (e.g., sensor-capabilities and
context-awareness) required for an IoT-based application, and survey the
existing ontologies which include these concepts. Based on our study, we also
identify the shortcomings of currently available ontologies, which serves as a
stepping stone to state the need for a common unified ontology for the IoT
domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of
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