Radio frequency energy harvesting in wireless sensor networks

As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks (WSNs) has been paid much attention to. This article presents a new approach to making use of electromagnetic energy from useless radio frequency (RF) signals transmitted in WSNs, with a quantitative analysis showing its feasibility. A mechanism to harvest the energy either passively or actively is proposed.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Cross-layer design for MIMO systems over spatially correlated and keyhole Nakagami-m fading channels

Cross-layer design is a generic designation for a set of efficient adaptive transmission schemes, across multiple layers of the protocol stack, that are aimed at enhancing the spectral efficiency and increasing the transmission reliability of wireless communication systems. In this paper, one such cross-layer design scheme that combines physical layer adaptive modulation and coding (AMC) with link layer truncated automatic repeat request (T-ARQ) is proposed for multiple-input multiple-output (MIMO) systems employing orthogonal space--time block coding (OSTBC). The performance of the proposed cross-layer design is evaluated in terms of achievable average spectral efficiency (ASE), average packet loss rate (PLR) and outage probability, for which analytical expressions are derived, considering transmission over two types of MIMO fading channels, namely, spatially correlated Nakagami-m fading channels and keyhole Nakagami-m fading channels. Furthermore, the effects of the maximum number of ARQ retransmissions, numbers of transmit and receive antennas, Nakagami fading parameter and spatial correlation parameters, are studied and discussed based on numerical results and comparisons. Copyright © 2009 John Wiley & Sons, Ltd.

Compensation for HPA nonlinearity and I/Q imbalance in MIMO beamforming systems

In this paper, we investigate the effects of high-power amplifier (HPA) nonlinearity and in-phase and quadrature-phase (I/Q) imbalance on the performance of multiple-input multiple-output (MIMO) transmit beamforming (TB) systems. Specifically, we propose a compensation method for HPA nonlinearity and I/Q imbalance together in MIMO TB systems. The performance of the MIMO TB system under study is evaluated in terms of the average symbol error probability (SEP) and system capacity, considering transmission over uncorrelated frequency-flat Rayleigh fading channels. Numerical results are provided and show the effects of several system parameters, such as the HPA parameters, image-leakage ratio, numbers of transmit and receive antennas, length of pilot symbols, and modulation order of phase-shift keying (PSK), on performance.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Arquitetura para um ambiente de grade computacional direcionado às aplicações distribuídas, móveis e conscientes do contexto da computação pervasiva

Neste início de década, observa-se a transformação das áreas de Computação em Grade (Grid Computing) e Computação Móvel (Mobile Computing) de uma conotação de interesse emergente para outra caracterizada por uma demanda real e qualificada de produtos, serviços e pesquisas. Esta tese tem como pressuposto a identificação de que os problemas hoje abordados isoladamente nas pesquisas relativas às computações em grade, consciente do contexto e móvel, estão presentes quando da disponibilização de uma infra-estrutura de software para o cenário da Computação Pervasiva. Neste sentido, como aspecto central da sua contribuição, propõe uma solução integrada para suporte à Computação Pervasiva, implementada na forma de um middleware que visa criar e gerenciar um ambiente pervasivo, bem como promover a execução, sob este ambiente, das aplicações que expressam a semântica siga-me. Estas aplicações são, por natureza, distribuídas, móveis e adaptativas ao contexto em que seu processamento ocorre, estando disponíveis a partir de qualquer lugar, todo o tempo. O middleware proposto, denominado EXEHDA (Execution Environment for Highly Distributed Applications), é adaptativo ao contexto e baseado em serviços, sendo chamado de ISAMpe o ambiente por este disponibilizado. O EXEHDA faz parte dos esforços de pesquisa do Projeto ISAM (Infra-Estrutura de Suporte às Aplicações Móveis Distribuídas), em andamento na UFRGS. Para atender a elevada flutuação na disponibilidade dos recursos, inerente à Computação Pervasiva, o EXEHDA é estruturado em um núcleo mínimo e em serviços carregados sob demanda. Os principais serviços fornecidos estão organizados em subsistemas que gerenciam: (a) a execução distribuída; (b) a comunicação; (c) o reconhecimento do contexto; (d) a adaptação; (e) o acesso pervasivo aos recursos e serviços; (f) a descoberta e (g) o gerenciamento de recursos No EXEHDA, as condições de contexto são pró-ativamente monitoradas e o suporte à execução deve permitir que tanto a aplicação como ele próprio utilizem essas informações na gerência da adaptação de seus aspectos funcionais e não-funcionais. O mecanismo de adaptação proposto para o EXEHDA emprega uma estratégia colaborativa entre aplicação e ambiente de execução, através da qual é facultado ao programador individualizar políticas de adaptação para reger o comportamento de cada um dos componentes que constituem o software da aplicação. Aplicações tanto do domínio da Computação em Grade, quanto da Computação Pervasiva podem ser programadas e executadas sob gerenciamento do middleware proposto.