9,502 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
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
As an indispensable personalized service in Location-based Social Networks
(LBSNs), the next Point-of-Interest (POI) recommendation aims to help people
discover attractive and interesting places. Currently, most POI recommenders
are based on the conventional centralized paradigm that heavily relies on the
cloud to train the recommendation models with large volumes of collected users'
sensitive check-in data. Although a few recent works have explored on-device
frameworks for resilient and privacy-preserving POI recommendations, they
invariably hold the assumption of model homogeneity for parameters/gradients
aggregation and collaboration. However, users' mobile devices in the real world
have various hardware configurations (e.g., compute resources), leading to
heterogeneous on-device models with different architectures and sizes. In light
of this, We propose a novel on-device POI recommendation framework, namely
Model-Agnostic Collaborative learning for on-device POI recommendation (MAC),
allowing users to customize their own model structures (e.g., dimension \&
number of hidden layers). To counteract the sparsity of on-device user data, we
propose to pre-select neighbors for collaboration based on physical distances,
category-level preferences, and social networks. To assimilate knowledge from
the above-selected neighbors in an efficient and secure way, we adopt the
knowledge distillation framework with mutual information maximization. Instead
of sharing sensitive models/gradients, clients in MAC only share their soft
decisions on a preloaded reference dataset. To filter out low-quality
neighbors, we propose two sampling strategies, performance-triggered sampling
and similarity-based sampling, to speed up the training process and obtain
optimal recommenders. In addition, we design two novel approaches to generate
more effective reference datasets while protecting users' privacy
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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