58,649 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
A survey on subjecting electronic product code and non-ID objects to IP identification
Over the last decade, both research on the Internet of Things (IoT) and
real-world IoT applications have grown exponentially. The IoT provides us with
smarter cities, intelligent homes, and generally more comfortable lives.
However, the introduction of these devices has led to several new challenges
that must be addressed. One of the critical challenges facing interacting with
IoT devices is to address billions of devices (things) around the world,
including computers, tablets, smartphones, wearable devices, sensors, and
embedded computers, and so on. This article provides a survey on subjecting
Electronic Product Code and non-ID objects to IP identification for IoT
devices, including their advantages and disadvantages thereof. Different
metrics are here proposed and used for evaluating these methods. In particular,
the main methods are evaluated in terms of their: (i) computational overhead,
(ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether
applicable to already ID-based objects and presented in tabular format.
Finally, the article proves that this field of research will still be ongoing,
but any new technique must favorably offer the mentioned five evaluative
parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports,
Wiley, 2020 (Open Access
From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web
In the process of scientific research, many information objects are
generated, all of which may remain valuable indefinitely. However, artifacts
such as instrument data and associated calibration information may have little
value in isolation; their meaning is derived from their relationships to each
other. Individual artifacts are best represented as components of a life cycle
that is specific to a scientific research domain or project. Current cataloging
practices do not describe objects at a sufficient level of granularity nor do
they offer the globally persistent identifiers necessary to discover and manage
scholarly products with World Wide Web standards. The Open Archives
Initiative's Object Reuse and Exchange data model (OAI-ORE) meets these
requirements. We demonstrate a conceptual implementation of OAI-ORE to
represent the scientific life cycles of embedded networked sensor applications
in seismology and environmental sciences. By establishing relationships between
publications, data, and contextual research information, we illustrate how to
obtain a richer and more realistic view of scientific practices. That view can
facilitate new forms of scientific research and learning. Our analysis is
framed by studies of scientific practices in a large, multi-disciplinary,
multi-university science and engineering research center, the Center for
Embedded Networked Sensing (CENS).Comment: 28 pages. To appear in the Journal of the American Society for
Information Science and Technology (JASIST
MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices
The Internet of Things (IoT) is part of Future Internet and will comprise
many billions of Internet Connected Objects (ICO) or `things' where things can
sense, communicate, compute and potentially actuate as well as have
intelligence, multi-modal interfaces, physical/ virtual identities and
attributes. Collecting data from these objects is an important task as it
allows software systems to understand the environment better. Many different
hardware devices may involve in the process of collecting and uploading sensor
data to the cloud where complex processing can occur. Further, we cannot expect
all these objects to be connected to the computers due to technical and
economical reasons. Therefore, we should be able to utilize resource
constrained devices to collect data from these ICOs. On the other hand, it is
critical to process the collected sensor data before sending them to the cloud
to make sure the sustainability of the infrastructure due to energy
constraints. This requires to move the sensor data processing tasks towards the
resource constrained computational devices (e.g. mobile phones). In this paper,
we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT
middleware for mobile devices, that allows to collect and process sensor data
without programming efforts. Our architecture also supports sensing as a
service model. We present the results of the evaluations that demonstrate its
suitability towards real world deployments. Our proposed middleware is built on
Android platform
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