9,377 research outputs found
Systems Applications of Social Networks
The aim of this article is to provide an understanding of social networks as
a useful addition to the standard tool-box of techniques used by system
designers. To this end, we give examples of how data about social links have
been collected and used in di erent application contexts. We develop a broad
taxonomy-based overview of common properties of social networks, review how
they might be used in di erent applications, and point out potential pitfalls
where appropriate. We propose a framework, distinguishing between two main
types of social network-based user selection-personalised user selection which
identi es target users who may be relevant for a given source node, using the
social network around the source as a context, and generic user selection or
group delimitation, which lters for a set of users who satisfy a set of
application requirements based on their social properties. Using this
framework, we survey applications of social networks in three typical kinds of
application scenarios: recommender systems, content-sharing systems (e.g., P2P
or video streaming), and systems which defend against users who abuse the
system (e.g., spam or sybil attacks). In each case, we discuss potential
directions for future research that involve using social network properties.Comment: Will appear in ACM computing Survey
New Threats to SMS-Assisted Mobile Internet Services from 4G LTE: Lessons Learnt from Distributed Mobile-Initiated Attacks towards Facebook and Other Services
Mobile Internet is becoming the norm. With more personalized mobile devices
in hand, many services choose to offer alternative, usually more convenient,
approaches to authenticating and delivering the content between mobile users
and service providers. One main option is to use SMS (i.e., short messaging
service). Such carrier-grade text service has been widely used to assist
versatile mobile services, including social networking, banking, to name a few.
Though the text service can be spoofed via certain Internet text service
providers which cooperated with carriers, such attacks haven well studied and
defended by industry due to the efforts of research community. However, as
cellular network technology advances to the latest IP-based 4G LTE, we find
that these mobile services are somehow exposed to new threats raised by this
change, particularly on 4G LTE Text service (via brand-new distributed
Mobile-Initiated Spoofed SMS attack which is not available in legacy 2G/3G
systems). The reason is that messaging service over LTE shifts from the
circuit-switched (CS) design to the packet-switched (PS) paradigm as 4G LTE
supports PS only. Due to this change, 4G LTE Text Service becomes open to
access. However, its shields to messaging integrity and user authentication are
not in place. As a consequence, such weaknesses can be exploited to launch
attacks (e.g., hijack Facebook accounts) against a targeted individual, a large
scale of mobile users and even service providers, from mobile devices. Current
defenses for Internet-Initiated Spoofed SMS attacks cannot defend the
unprecedented attack. Our study shows that 53 of 64 mobile services over 27
industries are vulnerable to at least one threat. We validate these
proof-of-concept attacks in one major US carrier which supports more than 100
million users. We finally propose quick fixes and discuss security insights and
lessons we have learnt.Comment: 16 pages, 13 figure
Network Coding Meets Information-Centric Networking
The focus of user behavior in the Internet has changed over the recent years
towards being driven by exchanging and accessing information. Many advances in
networking technologies have utilized this change by focusing on the content of
an exchange rather than the endpoints exchanging the content. Network coding
and information centric networking are two examples of these technology trends,
each being developed largely independent so far. This paper brings these areas
together in an evolutionary as well as explorative setting for a new
internetworking architecture. We outline opportunities for applying network
coding in a novel and performance-enhancing way that could eventually push
forward the case for information centric network itself.Comment: 6 pages, position pape
Analytics for the Internet of Things: A Survey
The Internet of Things (IoT) envisions a world-wide, interconnected network
of smart physical entities. These physical entities generate a large amount of
data in operation and as the IoT gains momentum in terms of deployment, the
combined scale of those data seems destined to continue to grow. Increasingly,
applications for the IoT involve analytics. Data analytics is the process of
deriving knowledge from data, generating value like actionable insights from
them. This article reviews work in the IoT and big data analytics from the
perspective of their utility in creating efficient, effective and innovative
applications and services for a wide spectrum of domains. We review the broad
vision for the IoT as it is shaped in various communities, examine the
application of data analytics across IoT domains, provide a categorisation of
analytic approaches and propose a layered taxonomy from IoT data to analytics.
This taxonomy provides us with insights on the appropriateness of analytical
techniques, which in turn shapes a survey of enabling technology and
infrastructure for IoT analytics. Finally, we look at some tradeoffs for
analytics in the IoT that can shape future research
Spatio-Temporal Modeling of Wireless Users Internet Access Patterns Using Self-Organizing Maps
User online behavior and interests will play a central role in future mobile
networks. We introduce a systematic method for large-scale multi-dimensional
analysis of online activity for thousands of mobile users across 79 buildings
over a variety of web domains. We propose a modeling approach based on
self-organizing maps (SOM) for discovering, organizing and visualizing
different mobile users' trends from billions of WLAN records. We find
surprisingly that users' trends based on domains and locations can be
accurately modeled using a self-organizing map with clearly distinct
characteristics. We also find many non-trivial correlations between different
types of web domains and locations. Based on our analysis, we introduce a
mixture model as an initial step towards realistic simulation of wireless
network usage
Privacy Prediction of Images Shared on Social Media Sites Using Deep Features
Online image sharing in social media sites such as Facebook, Flickr, and
Instagram can lead to unwanted disclosure and privacy violations, when privacy
settings are used inappropriately. With the exponential increase in the number
of images that are shared online every day, the development of effective and
efficient prediction methods for image privacy settings are highly needed. The
performance of models critically depends on the choice of the feature
representation. In this paper, we present an approach to image privacy
prediction that uses deep features and deep image tags as feature
representations. Specifically, we explore deep features at various neural
network layers and use the top layer (probability) as an auto-annotation
mechanism. The results of our experiments show that models trained on the
proposed deep features and deep image tags substantially outperform baselines
such as those based on SIFT and GIST as well as those that use "bag of tags" as
features
Cities of the Future: Employing Wireless Sensor Networks for Efficient Decision Making in Complex Environments
Decision making in large scale urban environments is critical for many
applications involving continuous distribution of resources and utilization of
infrastructure, such as ambient lighting control and traffic management.
Traditional decision making methods involve extensive human participation, are
expensive, and inefficient and unreliable for hard-to-predict situations.
Modern technology, including ubiquitous data collection though sensors,
automated analysis and prognosis, and online optimization, offers new
capabilities for developing flexible, autonomous, scalable, efficient, and
predictable control methods. This paper presents a new decision making concept
in which a hierarchy of semantically more abstract models are utilized to
perform online scalable and predictable control. The lower semantic levels
perform localized decisions based on sampled data from the environment, while
the higher semantic levels provide more global, time invariant results based on
aggregated data from the lower levels. There is a continuous feedback between
the levels of the semantic hierarchy, in which the upper levels set performance
guaranteeing constraints for the lower levels, while the lower levels indicate
whether these constraints are feasible or not. Even though the semantic
hierarchy is not tied to a particular set of description models, the paper
illustrates a hierarchy used for traffic management applications and composed
of Finite State Machines, Conditional Task Graphs, Markov Decision Processes,
and functional graphs. The paper also summarizes some of the main research
problems that must be addressed as part of the proposed concep
Using Social Information for Flow Allocation in MANETs
Adhoc networks enable communication between distributed, mobile wireless
nodes without any supporting infrastructure. In the absence of centralized
control, such networks require node interaction, and are inherently based on
cooperation between nodes. In this paper, we use social and behavioral trust of
nodes to form a flow allocation optimization problem. We initialize trust using
information gained from users' social relationships (from social networks) and
update the trusts metric over time based on observed node behaviors. We conduct
analysis of social trust using real data sets and used it as a parameter for
performance evaluation of our frame work in ns-3. Based on our approach we
obtain a significant improvement in both detection rate and packet delivery
ratio using social trust information when compared to behavioral trust alone.
Further, we observe that social trust is critical in the event of mobility and
plays a crucial role in bootstrapping the computation of trust
Knowledge society arguments revisited in the semantic technologies era
In the light of high profile governmental and international efforts to realise the knowledge society, I review the arguments made for and against it from a technology standpoint. I focus on advanced knowledge technologies with applications on a large scale and in open- ended environments like the World Wide Web and its ambitious extension, the Semantic Web. I argue for a greater role of social networks in a knowledge society and I explore the recent developments in mechanised trust, knowledge certification, and speculate on their blending with traditional societal institutions. These form the basis of a sketched roadmap for enabling technologies for a knowledge society
Data Management in Industry 4.0: State of the Art and Open Challenges
Information and communication technologies are permeating all aspects of
industrial and manufacturing systems, expediting the generation of large
volumes of industrial data. This article surveys the recent literature on data
management as it applies to networked industrial environments and identifies
several open research challenges for the future. As a first step, we extract
important data properties (volume, variety, traffic, criticality) and identify
the corresponding data enabling technologies of diverse fundamental industrial
use cases, based on practical applications. Secondly, we provide a detailed
outline of recent industrial architectural designs with respect to their data
management philosophy (data presence, data coordination, data computation) and
the extent of their distributiveness. Then, we conduct a holistic survey of the
recent literature from which we derive a taxonomy of the latest advances on
industrial data enabling technologies and data centric services, spanning all
the way from the field level deep in the physical deployments, up to the cloud
and applications level. Finally, motivated by the rich conclusions of this
critical analysis, we identify interesting open challenges for future research.
The concepts presented in this article thematically cover the largest part of
the industrial automation pyramid layers. Our approach is multidisciplinary, as
the selected publications were drawn from two fields; the communications,
networking and computation field as well as the industrial, manufacturing and
automation field. The article can help the readers to deeply understand how
data management is currently applied in networked industrial environments, and
select interesting open research opportunities to pursue
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