322 research outputs found
Surface Reconstruction from Scattered Point via RBF Interpolation on GPU
In this paper we describe a parallel implicit method based on radial basis
functions (RBF) for surface reconstruction. The applicability of RBF methods is
hindered by its computational demand, that requires the solution of linear
systems of size equal to the number of data points. Our reconstruction
implementation relies on parallel scientific libraries and is supported for
massively multi-core architectures, namely Graphic Processor Units (GPUs). The
performance of the proposed method in terms of accuracy of the reconstruction
and computing time shows that the RBF interpolant can be very effective for
such problem.Comment: arXiv admin note: text overlap with arXiv:0909.5413 by other author
CLEVER: a cooperative and cross-layer approach to video streaming in HetNets
We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (eNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of eNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After optimally formulating the problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer aswell as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE)
Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization
The inference of novel knowledge, the discovery of hidden patterns, and the
uncovering of insights from large amounts of data from a multitude of sources
make Data Science (DS) to an art rather than just a mere scientific discipline.
The study and design of mathematical models able to analyze information
represents a central research topic in DS. In this work, we introduce and
investigate a novel model for influence maximization (IM) on graphs using ideas
from kernel-based approximation, Gaussian process regression, and the
minimization of a corresponding variance term. Data-driven approaches can be
applied to determine proper kernels for this IM model and machine learning
methodologies are adopted to tune the model parameters. Compared to stochastic
models in this field that rely on costly Monte-Carlo simulations, our model
allows for a simple and cost-efficient update strategy to compute optimal
influencing nodes on a graph. In several numerical experiments, we show the
properties and benefits of this new model
analysis of a data flow in a financial iot system
Abstract Data retrieving, analysis e management are usually known as complex task in financial contexts. In an Internet of Things (IoT) system data-flow processes represent the knowledge base used in mathematical models for credits and financial products. Several sources such as distributed database systems, portals and local information are generally used as input of inferring models. In this paper we describe an overview of software tools, methodologies and strategies in real data-flow system
remarks on a computational estimator for the barrier option pricing in an iot scenario
Abstract The importance of derivatives in financial markets has known an exponential growth in the last decades, especially in risk management and speculation fields: this explains researchers' interest in answering questions about this kind of contracts. In particular, in this paper we restrict our attention on European vanilla and barrier options, and we propose a statistical procedure to solve efficiently the problem of determining the no arbitrage price of this type of derivatives in an IoT context: starting form an Internet of Things (IoT) data flow, an IoT system takes information from several sources and stores it into a suitable database; this information is used in our estimation problem. Our scheme is based on some strong assumptions about the market model, in particular the completeness of the market, the log-normality of the underlying asset with a constant volatility. We conclude this paper with an application of our framework to a real case
A Numerical Approach for Assigning a Reputation to Users of an IoT Framework
AbstractNowadays, in the Internet of Things (IoT) society, the massive use of technological devices available to the people makes possible to collect a lot of data describing tastes, choices and behaviours related to the users of services and tools. These information can be rearranged and interpreted in order to obtain a rating (i.e., evaluation) of the subjects (i.e., users) interacting with specific objects (i.e., items). Generally, reputation systems are widely used to provide ratings to products, services, companies, digital contents and people. Here, we focus on this issue, adopting a Collaborative Reputation System (CRS) to evaluate the visitors' behaviour in a real cultural event. The results obtained, compared with those obtained by other methods (i.e., classification), have confirmed the reliability and the usefulness of CRSes for deeply understand dynamics related to visiting styles
HIRO-NET.Heterogeneous intelligent robotic network for internet sharing in disaster scenarios
This article describes HIRO-NET, an Heterogeneous Intelligent
Robotic Network. HIRO-NET is an emergency infrastructure-less
network that aims to address the problem of providing connectivity in
the immediate aftermath of a natural disaster, where no cellular or
wide area network is operational and no Internet access is available.
HIRO-NET establishes a two-tier wireless mesh network where the
Lower Tier connects nearby survivors in a self-organized mesh via
Bluetooth Low Energy (BLE) and the Upper Tier creates long-range
VHF links between autonomous robots exploring the disaster stricken
area. HIRO-NET’s main goal is to enable users in the disaster area to
exchange text messages to share critical information and request help
from first responders. The mesh network discovery problem is analyzed
and a network protocol specifically designed to facilitate the exploration
process is presented. We show how HIRO-NET robots successfully
discover, bridge and interconnect local mesh networks. Results show
that the Lower Tier always reaches network convergence and the Upper
Tier can virtually extend HIRO-NET functionalities to the range of a
small metropolitan area. In the event of an Internet connection still being
available to some user, HIRO-NET is able to opportunistically share and
provide access to low data-rate services (e.g., Twitter, Gmail) to the
whole network. Results suggest that a temporary emergency network
to cover a metropolitan area can be created in tens of minutes.
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