39,945 research outputs found
Grids and the Virtual Observatory
We consider several projects from astronomy that benefit from the Grid paradigm and
associated technology, many of which involve either massive datasets or the federation
of multiple datasets. We cover image computation (mosaicking, multi-wavelength
images, and synoptic surveys); database computation (representation through XML,
data mining, and visualization); and semantic interoperability (publishing, ontologies,
directories, and service descriptions)
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
Predicting Network Attacks Using Ontology-Driven Inference
Graph knowledge models and ontologies are very powerful modeling and re
asoning tools. We propose an effective approach to model network attacks and
attack prediction which plays important roles in security management. The goals
of this study are: First we model network attacks, their prerequisites and
consequences using knowledge representation methods in order to provide
description logic reasoning and inference over attack domain concepts. And
secondly, we propose an ontology-based system which predicts potential attacks
using inference and observing information which provided by sensory inputs. We
generate our ontology and evaluate corresponding methods using CAPEC, CWE, and
CVE hierarchical datasets. Results from experiments show significant capability
improvements comparing to traditional hierarchical and relational models.
Proposed method also reduces false alarms and improves intrusion detection
effectiveness.Comment: 9 page
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
Link Before You Share: Managing Privacy Policies through Blockchain
With the advent of numerous online content providers, utilities and
applications, each with their own specific version of privacy policies and its
associated overhead, it is becoming increasingly difficult for concerned users
to manage and track the confidential information that they share with the
providers. Users consent to providers to gather and share their Personally
Identifiable Information (PII). We have developed a novel framework to
automatically track details about how a users' PII data is stored, used and
shared by the provider. We have integrated our Data Privacy ontology with the
properties of blockchain, to develop an automated access control and audit
mechanism that enforces users' data privacy policies when sharing their data
across third parties. We have also validated this framework by implementing a
working system LinkShare. In this paper, we describe our framework on detail
along with the LinkShare system. Our approach can be adopted by Big Data users
to automatically apply their privacy policy on data operations and track the
flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on
Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE
International Conference on Big Data (IEEE BigData 2017) December 14, 2017,
Boston, MA, US
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