17,567 research outputs found
A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment
The singular value decomposition (SVD) is a widely used matrix factorization
tool which underlies plenty of useful applications, e.g. recommendation system,
abnormal detection and data compression. Under the environment of emerging
Internet of Things (IoT), there would be an increasing demand for data analysis
to better human's lives and create new economic growth points. Moreover, due to
the large scope of IoT, most of the data analysis work should be done in the
network edge, i.e. handled by fog computing. However, the devices which provide
fog computing may not be trustable while the data privacy is often the
significant concern of the IoT application users. Thus, when performing SVD for
data analysis purpose, the privacy of user data should be preserved. Based on
the above reasons, in this paper, we propose a privacy-preserving fog computing
framework for SVD computation. The security and performance analysis shows the
practicability of the proposed framework. Furthermore, since different
applications may utilize the result of SVD operation in different ways, three
applications with different objectives are introduced to show how the framework
could flexibly achieve the purposes of different applications, which indicates
the flexibility of the design.Comment: 24 pages, 4 figure
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
End-to-End Privacy for Open Big Data Markets
The idea of an open data market envisions the creation of a data trading
model to facilitate exchange of data between different parties in the Internet
of Things (IoT) domain. The data collected by IoT products and solutions are
expected to be traded in these markets. Data owners will collect data using IoT
products and solutions. Data consumers who are interested will negotiate with
the data owners to get access to such data. Data captured by IoT products will
allow data consumers to further understand the preferences and behaviours of
data owners and to generate additional business value using different
techniques ranging from waste reduction to personalized service offerings. In
open data markets, data consumers will be able to give back part of the
additional value generated to the data owners. However, privacy becomes a
significant issue when data that can be used to derive extremely personal
information is being traded. This paper discusses why privacy matters in the
IoT domain in general and especially in open data markets and surveys existing
privacy-preserving strategies and design techniques that can be used to
facilitate end to end privacy for open data markets. We also highlight some of
the major research challenges that need to be address in order to make the
vision of open data markets a reality through ensuring the privacy of
stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special
Issue Cloud Computing and the La
Understanding the Internet of Things: A Conceptualisation of Business-to-Thing (B2T) Interactions
The Internet of Things is widely regarded as one of the most disruptive technologies as it integrates Internet-enabled physical objects into the networked society and makes these objects increasingly autonomous partners in digitised value chains. After transforming internal processes and enhancing efficiency, the Internet of Things yields the potential to transform traditional business-to-customer interactions in a way previously not thought of. Remote patient monitoring, predictive maintenance, and automatic car repair are only some innovative examples. This paper contributes to the conceptualisation of the emerging business relationships based on such empowered smart things by proposing a series of core and advanced business-to-thing (B2T) interaction patterns. The core patterns named C2T-Only, B2T-Only, Customer-Centred, Business-Centred, Thing-Centred, and All-In B2T classify alternative interactions between businesses, customers, and smart things, using the connected car as an ongoing case and Uber as an example to demonstrate how patters can be composed. The proposed patterns demonstrate the affordances of integrating smart things into the networked society and sensitise for the emergence of B2T interactions
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
An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform
With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations
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