42,319 research outputs found
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
Integrated NFV/SDN Architectures: A Systematic Literature Review
Network Functions Virtualization (NFV) and Software-Defined Networking (SDN)
are new paradigms in the move towards open software and network hardware. While
NFV aims to virtualize network functions and deploy them into general purpose
hardware, SDN makes networks programmable by separating the control and data
planes. NFV and SDN are complementary technologies capable of providing one
network solution. SDN can provide connectivity between Virtual Network
Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as
part of a service function chain. There are many studies designing NFV/SDN
architectures in different environments. Researchers have been trying to
address reliability, performance, and scalability problems using different
architectural designs. This Systematic Literature Review (SLR) focuses on
integrated NFV/SDN architectures, with the following goals: i) to investigate
and provide an in-depth review of the state-of-the-art of NFV/SDN
architectures, ii) to synthesize their architectural designs, and iii) to
identify areas for further improvements. Broadly, this SLR will encourage
researchers to advance the current stage of development (i.e., the
state-of-the-practice) of integrated NFV/SDN architectures, and shed some light
on future research efforts and the challenges faced.Comment: Accepted for publication at ACM Computing Survey
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
201
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
The Internet of Things (IoT) is a ubiquitous system connecting many different
devices - the things - which can be accessed from the distance. The
cyber-physical systems (CPS) monitor and control the things from the distance.
As a result, the concepts of dependability and security get deeply intertwined.
The increasing level of dynamicity, heterogeneity, and complexity adds to the
system's vulnerability, and challenges its ability to react to faults. This
paper summarizes state-of-the-art of existing work on anomaly detection,
fault-tolerance and self-healing, and adds a number of other methods applicable
to achieve resilience in an IoT. We particularly focus on non-intrusive methods
ensuring data integrity in the network. Furthermore, this paper presents the
main challenges in building a resilient IoT for CPS which is crucial in the era
of smart CPS with enhanced connectivity (an excellent example of such a system
is connected autonomous vehicles). It further summarizes our solutions,
work-in-progress and future work to this topic to enable "Trustworthy IoT for
CPS". Finally, this framework is illustrated on a selected use case: A smart
sensor infrastructure in the transport domain.Comment: preprint (2018-10-29
A Review of Error Estimation in Adaptive Quadrature
The most critical component of any adaptive numerical quadrature routine is
the estimation of the integration error. Since the publication of the first
algorithms in the 1960s, many error estimation schemes have been presented,
evaluated and discussed. This paper presents a review of existing error
estimation techniques and discusses their differences and their common
features. Some common shortcomings of these algorithms are discussed and a new
general error estimation technique is presented.Comment: Submitted to ACM Computing Survey
A Survey on the Security of Pervasive Online Social Networks (POSNs)
Pervasive Online Social Networks (POSNs) are the extensions of Online Social
Networks (OSNs) which facilitate connectivity irrespective of the domain and
properties of users. POSNs have been accumulated with the convergence of a
plethora of social networking platforms with a motivation of bridging their
gap. Over the last decade, OSNs have visually perceived an altogether
tremendous amount of advancement in terms of the number of users as well as
technology enablers. A single OSN is the property of an organization, which
ascertains smooth functioning of its accommodations for providing a quality
experience to their users. However, with POSNs, multiple OSNs have coalesced
through communities, circles, or only properties, which make
service-provisioning tedious and arduous to sustain. Especially, challenges
become rigorous when the focus is on the security perspective of cross-platform
OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to
highlight such a requirement and understand the current situation while
discussing the available state-of-the-art. With the modernization of OSNs and
convergence towards POSNs, it is compulsory to understand the impact and reach
of current solutions for enhancing the security of users as well as associated
services. This survey understands this requisite and fixates on different sets
of studies presented over the last few years and surveys them for their
applicability to POSNs...Comment: 39 Pages, 10 Figure
A Survey on Online Judge Systems and Their Applications
Online judges are systems designed for the reliable evaluation of algorithm
source code submitted by users, which is next compiled and tested in a
homogeneous environment. Online judges are becoming popular in various
applications. Thus, we would like to review the state of the art for these
systems. We classify them according to their principal objectives into systems
supporting organization of competitive programming contests, enhancing
education and recruitment processes, facilitating the solving of data mining
challenges, online compilers and development platforms integrated as components
of other custom systems. Moreover, we introduce a formal definition of an
online judge system and summarize the common evaluation methodology supported
by such systems. Finally, we briefly discuss an Optil.io platform as an example
of an online judge system, which has been proposed for the solving of complex
optimization problems. We also analyze the competition results conducted using
this platform. The competition proved that online judge systems, strengthened
by crowdsourcing concepts, can be successfully applied to accurately and
efficiently solve complex industrial- and science-driven challenges.Comment: Authors pre-print of the article accepted for publication in ACM
Computing Surveys (accepted on 19-Sep-2017
Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied
in diverse domains including, climate science, social sciences, neuroscience,
epidemiology, transportation, mobile health, and Earth sciences.
Spatio-temporal data differs from relational data for which computational
approaches are developed in the data mining community for multiple decades, in
that both spatial and temporal attributes are available in addition to the
actual measurements/attributes. The presence of these attributes introduces
additional challenges that needs to be dealt with. Approaches for mining
spatio-temporal data have been studied for over a decade in the data mining
community. In this article we present a broad survey of this relatively young
field of spatio-temporal data mining. We discuss different types of
spatio-temporal data and the relevant data mining questions that arise in the
context of analyzing each of these datasets. Based on the nature of the data
mining problem studied, we classify literature on spatio-temporal data mining
into six major categories: clustering, predictive learning, change detection,
frequent pattern mining, anomaly detection, and relationship mining. We discuss
the various forms of spatio-temporal data mining problems in each of these
categories.Comment: Accepted for publication at ACM Computing Survey
What's (Not) Validating Network Paths: A Survey
Validating network paths taken by packets is critical for a secure Internet
architecture. Any feasible solution must both enforce packet forwarding along
endhost-specified paths and verify whether packets have taken those paths.
However, neither enforcement nor verification is supported by the current
Internet. Due likely to a long-standing confusion between routing and
forwarding, only limited solutions for path validation exist in the literature.
This survey article aims to reinvigorate research in to the significant and
essential topic of path validation. It crystallizes not only how path
validation works but also where seemingly qualified solutions fall short. The
analyses explore future research directions in path validation toward improving
security, privacy, and efficiency.Comment: 30 pages with 5 figures, submitted to ACM Computing Survey
Dark Sky Simulations: Early Data Release
The Dark Sky Simulations are an ongoing series of cosmological N-body
simulations designed to provide a quantitative and accessible model of the
evolution of the large-scale Universe. Such models are essential for many
aspects of the study of dark matter and dark energy, since we lack a
sufficiently accurate analytic model of non-linear gravitational clustering. In
July 2014, we made available to the general community our early data release,
consisting of over 55 Terabytes of simulation data products, including our
largest simulation to date, which used
particles in a volume across. Our simulations were
performed with 2HOT, a purely tree-based adaptive N-body method, running on
200,000 processors of the Titan supercomputer, with data analysis enabled by
yt. We provide an overview of the derived halo catalogs, mass function, power
spectra and light cone data. We show self-consistency in the mass function and
mass power spectrum at the 1% level over a range of more than 1000 in particle
mass. We also present a novel method to distribute and access very large
datasets, based on an abstraction of the World Wide Web (WWW) as a file system,
remote memory-mapped file access semantics, and a space-filling curve index.
This method has been implemented for our data release, and provides a means to
not only query stored results such as halo catalogs, but also to design and
deploy new analysis techniques on large distributed datasets.Comment: 26 pages, 9 figures, project website at
http://darksky.slac.stanford.edu, repository at
http://bitbucket.org/darkskysim
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