RadiaLE: A framework for designing and assessing link quality estimators in wireless sensor networks

Stringent cost and energy constraints impose the use of low-cost and low-power radio transceivers in large-scale wireless sensor networks (WSNs). This fact, together with the harsh characteristics of the physical environment, requires a rigorous WSN design. Mechanisms for WSN deployment and topology control, MAC and routing, resource and mobility management, greatly depend on reliable link quality estimators (LQEs). This paper describes the RadiaLE framework, which enables the experimental assessment, design and optimization of LQEs. RadiaLE comprises (i) the hardware components of the WSN testbed and (ii) a software tool for setting-up and controlling the experiments, automating link measurements gathering through packets-statistics collection, and analyzing the collected data, allowing for LQEs evaluation. We also propose a methodology that allows (i) to properly set different types of links and different types of traffic, (ii) to collect rich link measurements, and (iii) to validate LQEs using a holistic and unified approach. To demonstrate the validity and usefulness of RadiaLE, we present two case studies: the characterization of low-power links and a comparison between six representative LQEs. We also extend the second study for evaluating the accuracy of the TOSSIM 2 channel model.

Enhancing the real-time capabilities of the Linux Kernel

The mainline Linux Kernel is not designed forhard real-time systems; it only fits the requirements of soft realtimesystems. In recent years, a kernel developer communityhas been working on the PREEMPT-RT patch. This patch(that aims to get a fully preemptible kernel) adds some realtimecapabilities to the Linux kernel. However, in terms ofscheduling policies, the real-time scheduling class of Linux islimited to the First-In-First-Out (SCHED_FIFO) and Round-Robin (SCHED_RR) scheduling policies. These scheduling policiesare however quite limited in terms of realtime performance.Therefore, in this paper, we report one importantcontribution for adding more advanced real-time capabilitiesto the Linux Kernel. Specifically, we describe modificationsto the (PREEMPT-RT patched) Linux kernel to supportreal-time slot-based task-splitting scheduling algorithms. Ourpreliminary evaluation shows that our implementation exhibitsa real-time performance that is superior to the schedulingpolicies provided by the current version of PREMPT-RT. Thisis a significant add-on to a widely adopted operating system.

Demo abstract: RadiaLE: a framework for benchmarking link quality estimators

Link quality estimation is a fundamental building block for the design of several different mechanisms and protocols in wireless sensor networks (WSN). A thorough experimental evaluation of link quality estimators (LQEs) is thus mandatory. Several WSN experimental testbeds have been designed ([1–4]) but only [3] and [2] targeted link quality measurements. However, these were exploited for analyzing low-power links characteristics rather than the performance of LQEs. Despite its importance, the experimental performance evaluation of LQEs remains an open problem, mainly due to the difficulty to provide a quantitative evaluation of their accuracy. This motivated us to build a benchmarking testbed for LQE - RadiaLE, which we present here as a demo. It includes (i.) hardware components that represent the WSN under test and (ii.) a software tool for the set up and control of the experiments and also for analyzing the collected data, allowing for LQEs evaluation.

Run-time monitoring approach for the shark kernel

Typically common embedded systems are designed with high resource constraints. Static designs are often chosen to address very specific use cases. On contrast, a dynamic design must be used if the system must supply a real-time service where the input may contain factors of indeterminism. Thus, adding new functionality on these systems is often accomplished by higher development time, tests and costs, since new functionality push the system complexity and dynamics to a higher level. Usually, these systems have to adapt themselves to evolving requirements and changing service requests. In this perspective, run-time monitoring of the system behaviour becomes an important requirement, allowing to dynamically capturing the actual scheduling progress and resource utilization. For this to succeed, operating systems need to expose their internal behaviour and state, making it available to the external applications, usually using a run-time monitoring mechanism. However, such mechanism can impose a burden in the system itself if not wisely used. In this paper we explore this problem and propose a framework, which is intended to provide this run-time mechanism whilst achieving code separation, run-time efficiency and flexibility for the final developer.

Renewable power forecast to scheduling of thermal units

In this work is discussed the importance of the renewable production forecast in an island environment. A probabilistic forecast based on kernel density estimators is proposed. The aggregation of these forecasts, allows the determination of thermal generation amount needed to schedule and operating a power grid of an island with high penetration of renewable generation. A case study based on electric system of S. Miguel Island is presented. The results show that the forecast techniques are an imperative tool help the grid management.

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

Hausman tests for inefficient estimators: application to demand for health care service (revised)

The Hausman (1978) test is based on the vector of differences of two estimators. It is usually assumed that one of the estimators is fully efficient, since this simplifies calculation of the test statistic. However, this assumption limits the applicability of the test, since widely used estimators such as the generalized method of moments (GMM) or quasi maximum likelihood (QML) are often not fully efficient. This paper shows that the test may easily be implemented, using well-known methods, when neither estimator is efficient. To illustrate, we present both simulation results as well as empirical results for utilization of health care services.

Estimation of dynamic latent variable models using simulated nonparametric moments

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

The Difference, System and ‘Double-D’ GMM Panel Estimators in the Presence of Structural Breaks

The effects of structural breaks in dynamic panels are more complicated than in time series models as the bias can be either negative or positive. This paper focuses on the effects of mean shifts in otherwise stationary processes within an instrumental variable panel estimation framework. We show the sources of the bias and a Monte Carlo analysis calibrated on United States bank lending data demonstrates the size of the bias for a range of auto-regressive parameters. We also propose additional moment conditions that can be used to reduce the biases caused by shifts in the mean of the data.

Estimation of dynamic latent variable models using simulated nonparametric moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

Beurling-Landau densities of weighted Fekete sets and correlation kernel estimates

Vegeu el resum a l'inici del document del fitxer adjunt.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Exploração da Implementação Actual de IPv6 no Kernel do Linux

A presente dissertação visa retratar a exploração do suporte do protocolo internet versão seis (IPv6) no kernel do Linux, conjuntamente com a análise detalhada do estado de implementação dos diferentes aspectos em que se baseia o protocolo. O estudo incide na experimentação do funcionamento em geral do stack, a identificação de inconsistências deste em relação RFC’s respectivos, bem como a simulação laboratorial de cenários que reproduzam casos de utilização de cada uma das facilidades analisadas. O objecto desta dissertação não é explicar o funcionamento do novo protocolo IPv6, mas antes, centrar-se essencialmente na exploração do IPv6 no kernel do Linux. Não é um documento para leigos em IPv6, no entanto, optou-se por desenvolver uma parte inicial onde é abordado o essencial do protocolo: a sua evolução até à aprovação e a sua especificação. Com base no estudo realizado, explora-se o suporte do IPv6 no kernel do Linux, fazendo uma análise detalhada do estudo de implementação dos diferentes aspectos em que se baseia o protocolo. Bem como a realização de testes de conformidade IPv6 em relação aos RFC’s.