7 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
Development of a context-aware internet of things framework for remote monitoring services
Asset management is concerned with the management practices necessary to
maximise the value delivered by physical engineering assets. Internet of Things
(IoT)-generated data are increasingly considered as an asset and the data asset
value needs to be maximised too. However, asset-generated data in practice are
often collected in non-actionable form. Moreover, IoT data create challenges for
data management and processing. One way to handle challenges is to introduce
context information management, wherein data and service delivery are
determined through resolving the context of a service or data request.
This research was aimed at developing a context awareness framework and
implementing it in an architecture integrating IoT with cloud computing for
industrial monitoring services. The overall aim was achieved through a
methodological investigation consisting of four phases: establish the research
baseline, define experimentation materials and methods, framework design and
development, as well as case study validation and expert judgment. The
framework comprises three layers: the edge, context information management,
and application. Moreover, a maintenance context ontology for the framework
has developed focused on modelling failure analysis of mechanical components,
so as to drive monitoring services adaptation. The developed context-awareness
architecture is expressed business, usage, functional and implementation
viewpoints to frame concerns of relevant stakeholders. The developed framework
was validated through a case study and expert judgement that provided
supporting evidence for its validity and applicability in industrial contexts.
The outcomes of the work can be used in other industrially-relevant application
scenarios to drive maintenance service adaptation. Context adaptive services
can help manufacturing companies in better managing the value of their assets,
while ensuring that they continue to function properly over their lifecycle.Manufacturin