215,327 research outputs found
Methods for Efficient and Accurate Discovery of Services
With an increasing number of services developed and offered in an enterprise setting or the Web, users can hardly verify their requirements manually in order to find appropriate services. In this thesis, we develop a method to discover semantically described services. We exploit comprehensive service and request descriptions such that a wide variety of use cases can be supported. In our discovery method, we compute the matchmaking decision by employing an efficient model checking technique
VOLARE: Adaptive Web Service Discovery Middleware for Mobile Systems
With the recent advent and widespread use of smart mobile devices, the flexibility and versatility offered by Service Oriented Architecture's (SOA) makes it an ideal approach to use in the rapidly changing mobile environment. However, the mobile
setting presents a set of new challenges that service discovery methods developed for nonmobile environments cannot address. The requirements a mobile client device will have
from a Web service may change due to changes in the context or the resources of the client device. In a similar manner, a mobile device that acts as a Web service provider will
have different capabilities depending on its status, which may also change dramatically during runtime. This paper introduces VOLARE, a middleware-based solution that will monitor the resources and context of the device, and adapt service requests accordingly. The same method will be used to adapt the Quality of Service (QoS) levels advertised by service providers, to realistically reflect each provider's capabilities at any given moment. This approach will allow for more resource-efficient and accurate service discovery in mobile systems and will enable more reliable provider functionality in mobile devices
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
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