1,297 research outputs found
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks
Prediction-based techniques for the optimization of mobile networks
MenciĂłn Internacional en el tĂtulo de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition
of several random phenomena, most of which, related to human activities, such as mobility,
communications and network usage. However, when observed in their totality, the many individual
components merge into more deterministic patterns and trends start to be identifiable and
predictable.
In this thesis we analyze a recent branch of network optimization that is commonly referred to
as anticipatory networking and that entails the combination of prediction solutions and network
optimization schemes. The main intuition behind anticipatory networking is that knowing in
advance what is going on in the network can help understanding potentially severe problems and
mitigate their impact by applying solution when they are still in their initial states. Conversely,
network forecast might also indicate a future improvement in the overall network condition (i.e.
load reduction or better signal quality reported from users). In such a case, resources can be
assigned more sparingly requiring users to rely on buffered information while waiting for the
better condition when it will be more convenient to grant more resources.
In the beginning of this thesis we will survey the current anticipatory networking panorama
and the many prediction and optimization solutions proposed so far. In the main body of the work,
we will propose our novel solutions to the problem, the tools and methodologies we designed to
evaluate them and to perform a real world evaluation of our schemes.
By the end of this work it will be clear that not only is anticipatory networking a very promising
theoretical framework, but also that it is feasible and it can deliver substantial benefit to current
and next generation mobile networks. In fact, with both our theoretical and practical results we
show evidences that more than one third of the resources can be saved and even larger gain can
be achieved for data rate enhancements.Programa Oficial de Doctorado en IngenierĂa TelemĂĄticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge OrtĂn Gracia.- Vocal: Guevara Noubi
Learning-aided Stochastic Network Optimization with Imperfect State Prediction
We investigate the problem of stochastic network optimization in the presence
of imperfect state prediction and non-stationarity. Based on a novel
distribution-accuracy curve prediction model, we develop the predictive
learning-aided control (PLC) algorithm, which jointly utilizes historic and
predicted network state information for decision making. PLC is an online
algorithm that requires zero a-prior system statistical information, and
consists of three key components, namely sequential distribution estimation and
change detection, dual learning, and online queue-based control.
Specifically, we show that PLC simultaneously achieves good long-term
performance, short-term queue size reduction, accurate change detection, and
fast algorithm convergence. In particular, for stationary networks, PLC
achieves a near-optimal , utility-delay
tradeoff. For non-stationary networks, \plc{} obtains an
utility-backlog tradeoff for distributions that last
time, where
is the prediction accuracy and is a constant (the
Backpressue algorithm \cite{neelynowbook} requires an length
for the same utility performance with a larger backlog). Moreover, PLC detects
distribution change slots faster with high probability ( is the
prediction size) and achieves an convergence time. Our results demonstrate
that state prediction (even imperfect) can help (i) achieve faster detection
and convergence, and (ii) obtain better utility-delay tradeoffs
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
From Wearable Sensors to Smart Implants â Towards Pervasive and Personalised Healthcare
<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p>
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