1,721,029 research outputs found
Target Localization and Tracking in Wireless Sensor Networks
Get PDFThis thesis addresses the target localization problem in wireless sensor networks (WSNs) by employing statistical modeling and convex relaxation techniques. The first and the second part of the thesis focus on received signal strength (RSS)- and RSS-angle of arrival (AoA)-based target localization problem, respectively. Both non-cooperative and cooperative WSNs are investigated and various settings of the localization problem are of interest (e.g. known and unknown target transmit power, perfectly and imperfectly known path loss exponent). For all cases, maximum likelihood (ML) estimation problem is first formulated.
The general idea is to tightly approximate the ML estimator by another one whose
global solution is a close representation of the ML solution, but is easily obtained due to greater smoothness of the derived objective function. By applying certain relaxations, the solution to the derived estimator is readily obtained through general-purpose solvers. Both centralized (assumes existence of a central node that collects all measurements and carries out all necessary processing for network mapping) and distributed (each target determines its own location by iteratively solving a local representation of the derived estimator) algorithms are described. More specifically, in the case of centralized RSS-based localization, second-order cone programming (SOCP) and semidefinite programming (SDP) estimators are derived by applying SOCP and SDP relaxation techniques in non-cooperative and cooperative WSNs, respectively. It is also shown that the derived SOCP estimator can be extended for distributed implementation in cooperative WSNs. In the second part of the thesis, derivation procedure of a weighted least squares (WLS) estimator by converting the centralized non-cooperative RSS-AoA localization problem into a generalized trust region
sub-problem (GTRS) framework, and an SDP estimator by applying SDP relaxations to
the centralized cooperative RSS-AoA localization problem are described. Furthermore, a distributed SOCP estimator is developed, and an extension of the centralized WLS estimator for non-cooperative WSNs to distributed conduction in cooperative WSNs is also presented. The third part of the thesis is committed to RSS-AoA-based target tracking problem. Both cases of target tracking with fixed/static anchors and mobile sensors are investigated. First, the non-linear measurement model is linearized by applying Cartesian to polar coordinates conversion. Prior information extracted from target transition model is then added to the derived model, and by following maximum a posteriori (MAP) criterion, a MAP algorithm is developed. Similarly, by taking advantage of the derived model and the prior knowledge, Kalman filter (KF) algorithm is designed. Moreover, by allowing sensor mobility, a simple navigation routine for sensors’ movement management is described, which significantly enhances the estimation accuracy of the presented algorithms even for a reduced number of sensors.
The described algorithms are assessed and validated through simulation results and
real indoor measurements
Design of multidimensional compact constellations with high power efficiency
Get PDFDissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologi
3-D Hybrid Localization with RSS/AoA in Wireless Sensor Networks: Centralized Approach
Get PDFThis dissertation addresses one of the most important issues present in Wireless Sensor Networks (WSNs), which is the sensor’s localization problem in non-cooperative and cooperative 3-D WSNs, for both cases of known and unknown source transmit power PT .
The localization of sensor nodes in a network is essential data. There exists a large
number of applications for WSNs and the fact that sensors are robust, low cost and do
not require maintenance, makes these types of networks an optimal asset to study or
manage harsh and remote environments. The main objective of these networks is to
collect different types of data such as temperature, humidity, or any other data type,
depending on the intended application. The knowledge of the sensors’ locations is a key feature for many applications; knowing where the data originates from, allows to take particular type of actions that are suitable for each case.
To face this localization problem a hybrid system fusing distance and angle measurements is employed. The measurements are assumed to be collected through received signal strength indicator and from antennas, extracting the received signal strength (RSS) and angle of arrival (AoA) information. For non-cooperativeWSN, it resorts to these measurements models and, following the least squares (LS) criteria, a non-convex estimator is developed. Next, it is shown that by following the square range (SR) approach, the estimator can be transformed into a general trust region subproblem (GTRS) framework. For cooperative WSN it resorts also to the measurement models mentioned above and it is shown that the estimator can be converted into a convex problem using semidefinite programming (SDP) relaxation techniques.It is also shown that the proposed estimators have a straightforward generalization from the known PT case to the unknown PT case. This generalization is done by making use of the maximum likelihood (ML) estimator to compute the value of the PT .
The results obtained from simulations demonstrate a good estimation accuracy, thus
validating the exceptional performance of the considered approaches for this hybrid
localization system
Posicionamento Cooperativo usando Dispositivos Android
Get PDFRecentemente foram propostas várias soluções de localização indoor baseadas em
WiFi, Bluetooth e UWB. Os ambientes indoor são espaços complexos que apresentam
bastante diversidade, permanecendo aberta a solução para conseguir um sistema de posicionamentobarato e preciso. Embora algumas destas soluções consigam bons resultados, muitas vezes requerem um trabalho de reconhecimento da localização exaustivo ou hardware especializado.
Nesta dissertação é estudado o posicionamento cooperativo de smartphones Android,
explorando os sensores presentes nestes dispositivos e a infra-estrutura sem fios existente, usando a potência do sinal recebido. Inicialmente, os problemas de localização são formulados como a trilateração de um conjunto de medições para estimar a posição relativa dos emissores face ao smartphone.
Para a realização de testes, foi desenvolvida uma aplicação que implementa a odometria do dispositivo usando o acelerómetro, giroscópio, vetor de rotação entre outros.
Tendo uma localização relativa à posição no momento em que se ligou a aplicação (0,0,0), é possível calcular a posição relativa do outro dispositivo. Essas informações são então compartilhadas entre os utilizadores do grupo usando um servidor. No servidor vai ser corrido um algoritmo de localização cooperativa, que permite minimizar o erro da estimação de localização.
A análise teórica e os resultados dos testes realizados com a aplicação demonstram
que esta é uma boa abordagem. Tanto quanto se sabe, esta é a primeira implementação que aborda o problema de localização em dispositivos móveis numa perspetiva relativa, sem necessitar de informação a priori
Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements
Get PDFThis dissertation addresses the target localization problem in wireless sensor networks
(WSNs). WSNs is now a widely applicable technology which can have numerous practical applications and offer the possibility to improve people’s lives. A required feature to many functions of a WSN, is the ability to indicate where the data reported by each sensor was measured. For this reason, locating each sensor node in a WSN is an essential issue that should be considered.
In this dissertation, a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) is presented. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. The measurements are then used to derive a convex estimator, based on second-order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations.
The results obtained in this dissertation confirm the novel algorithms’ performance
and accuracy. Additionally, a change to the algorithms is proposed, allowing the study of a more realistic and challenging scenario where different probabilities of communication failure between neighbor nodes at the broadcast phase are considered. Computational simulations performed in the scope of this dissertation, show that the algorithms’ performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Algoritmos Meta-Heurísticos para Aplicação a Localização baseada em Sistemas Acústicos
No full textTese no âmbito do Doutoramento em Engenharia Eletrotécnica e de Computadores, Ramo de
Especialização em Telecomunicações, apresentada à Faculdade de Ciências e Tecnologia da
Universidade de CoimbraA presente dissertação aborda o problema da localização no espaço de uma fonte acústica, com o recurso a algoritmos meta-heurísticos. Tradicionalmente, as metodologias adotadas neste contexto baseiam-se em aproximações de um estimador estatístico (não convexo), aplicando-se posteriormente métodos de procura heurísticos. Tais métodos implicam normalmente soluções eficientes à custa de elevado poder de computação, podendo no entanto, apenas fornecer soluções subótimas. Através do uso de métodos meta-heurísticos, nomeadamente, métodos baseados
em enxames de partículas, o problema de localização é aqui abordado de forma direta (sem recurso a aproximações), recorrendo-se a uma metodologia de procura global. Embora conceptualmente estes algoritmos sejam desenvolvidos para problemas genéricos, sem explorar particularidades do problema em questão, o presente trabalho analisa, desenvolve e valida novos métodos, de modo a obter resultados ótimos e/ou subótimos com reduzido erro, mas sobretudo, acelerando consideravelmente a convergência dos mesmos. Inicialmente são estudados métodos de otimização global baseados em enxames de partículas, procurando-se ajustar os seus parâmetros de modo a serem obtidas soluções comparáveis ao estado da arte num largo espectro contextual, nomeadamente, ao nível do ruído das medições, do número de sensores e da posição da fonte no espaço. Os resultados obtidos certificam a utilização da metodologia, quer em termos do erro da solução final, quer em termos do tempo de computação. Estes são ainda validados com uma implementação em ambiente real, com medições de campo, obtendo-se resultados em concordância com as simulações computacionais. A segunda parte da dissertação, propõe novas técnicas para a inicialização da população normalmente considerada aleatória neste tipo de cenários. Para esse fim, dois métodos são desenvolvidos e propostos: (1) usando uma estimativa de distância obtida através das observações ruidosas do modelo, gerando pontos aleatórios em torno das interceções em relação aos sensores; (2) pela geração de Cadeias de Markov a partir do algoritmo de Metropolis-Hastings , tendo como ponto inicial o resultado obtido em (1). Em simultâneo, implementa-se uma procura local baseada no gradiente discreto do modelo. Além disso, através da aplicação de uma nova condição de paragem dos métodos que normalmente consideram um valor pré-definido de gerações de população, é demonstrado que as propostas no âmbito desta dissertação aceleram consideravelmente a convergência dos mesmos. Tal situação implica um cômputo bastante reduzido do número de gerações de população e, consequentemente, do tempo de processamento. Este trabalho introduz várias inovações importantes no campo de estudo: (1) incorporação de informações do modelo em estudo nos métodos de otimização baseados em enxames de partículas; (2) abordagens inovadoras para inicialização de populações para métodos baseados em enxames de partículas; (3) critérios de paragem mais eficientes de modo a interromper o processo iterativo, normalmente considerado com valores pré-determinados; (4) uma estratégia de procura local, sem a necessidade do cálculo analítico do gradiente do modelo. Tais inovações, resultam em novas metodologias com desempenho aprimorado da localização em termos de precisão, complexidade computacional e taxa de convergência. As inovações introduzidas no estado da arte para resolução do problema de localização de uma fonte acústica, abrem caminho para implementações "embedded" , em processadores de baixa complexidade, fator de forma e consumo. Deste modo, surgem novas possibilidades de investigação ao nível da computação de borda ou em nuvem (do inglês Edge ou Fog Computing ), assim como a implementação de métodos de localização distribuídos ou sequenciais, quer pela simplicidade de implementação, quer pela menor exigência do esforço computacional.The present dissertation addresses the problem of locatization of an acoustic source,
using metaheuristic algorithms. Typically, the existing methodologies adopted for
the considered localization problem are based on a sequence of approximations
and/or relaxations of some proposed (non-convex) statistical estimator, whose
solutions are possibly used later as the starting point for local search, applying
heuristic search methods. Although solutions entailed from such methods are
relatively accurate, they come at the expense of high computational cost, and
are only sub-optimal.
In this thesis, through the use of metaheuristic, namely swarm-based methods,
the localization problem is tackled directly (without resorting to any approximations
/relaxations), using a global optimization methodology. Although conceptually
these algorithms are intended to address generic problems, without exploiting
particularities of the problem at hand, the present work shows that they can
be adopted for problem-specific applications. More specifically, the present work
develops novel frameworks for metaheuristic algorithms which result in enhanced
localization performance, both in terms of localization accuracy and algorithm
convergence.
In the first part of the dissertation, global optimization methods based on
particle swarms are studied, aiming to adjust their parameters in order to match
the localization accuracy of state-of-the-art solutions, in a wide range of settings
of practical interest, namely in terms of power measurement noise, number of
sensors and the position of the source in a search space. The simulation results
obtained validate the use of the particle swarm methodology, both in terms of
localization accuracy and execution time. These results are also validated through
a real-world implementation using field measurements, achieving results in line
with the computer simulations.
The second part of the thesis proposes new techniques for population initialization,
which is performed completely randomly in the traditional approach. To this end,
two methods are developed: (1) we use distance estimates from noisy model
observations to form circles with centers at the known reference locations and
radii equal to the respective distance estimates to generate random points in
the neighborhood of the intersections of the circles; (2) by generating Markov Chains and employing Metropolis-Hastings algorithm, taking as their starting point
the result obtained in (1). Simultaneously, a local search based on a discrete
gradient of the model is implemented. Moreover, by adopting a different stopping
criterion in comparison with the traditional one (a predefined value a number of
function evaluations), it is demonstrated that the proposed approaches in this thesis
considerably accelerate the convergence of the method. This result implies a reduced
number of population generations and, consequently, a reduced processing time.
Therefore, this work introduces several important innovations in the field of
study, namely: (1) incorporation of model problem particularity under study into
global optimization methods; (2) new novel approaches for population initialization
for swarm-based methods; (3) more efficient stopping criteria to interpose the
iteration process, usually considered as a predetermined values; (4) a local search
methodology without the need of analytic gradient calculations.
Such innovations result in new methodologies, with enhanced localization
performance in terms of localization accuracy, computational complexity, and
convergence rate. The proposed state-of-the-art innovations introduced for solving
the localization problem of an acoustic source paves the way for embedded implementations
on low complexity processors, with small form factor, and consumption.
This way, new research paths arise at the level of Edge or Cloud computing, as well
as distributed implementation or sequential localization schemes. This is mainly due
to the simplicity of implementation, which comes at a low computational effort
- …
