Distributed Processing Techniques for Parameter Estimation and Efficient Data Gathering in Wireless Communication and Sensor Networks

This dissertation deals with the distributed processing techniques for parameter estimation and efficient data-gathering in wireless communication and sensor networks. The estimation problem consists in inferring a set of parameters from temporal and spatial noisy observations collected by different nodes that monitor an area or field. The objective is to derive an estimate that is as accurate as the one that would be obtained if each node had access to the information across the entire network. With the aim of enabling an energy aware and low-complexity distributed implementation of the estimation task, several useful optimization techniques that generally yield linear estimators were derived in the literature. Up to now, most of the works considered that the nodes are interested in estimating the same vector of global parameters. This scenario can be viewed as a special case of a more general ...

Bogdanovic, Nikola — University of Patras


Distributed Signal Processing Algorithms for Wireless Networks

Distributed signal processing algorithms have become a key approach for statistical inference in wireless networks and applications such as wireless sensor networks and smart grids. It is well known that distributed processing techniques deal with the extraction of information from data collected at nodes that are distributed over a geographic area. In this context, for each specific node, a set of neighbor nodes collect their local information and transmit the estimates to a specific node. Then, each specific node combines the collected information together with its local estimate to generate an improved estimate. In this thesis, novel distributed cooperative algorithms for inference in ad hoc, wireless sensor networks and smart grids are investigated. Low-complexity and effective algorithms to perform statistical inference in a distributed way are devised. A number of innovative approaches for dealing with node failures, compression of data ...

Xu, Songcen — University of York


Signal processing algorithms for wireless acoustic sensor networks

Recent academic developments have initiated a paradigm shift in the way spatial sensor data can be acquired. Traditional localized and regularly arranged sensor arrays are replaced by sensor nodes that are randomly distributed over the entire spatial field, and which communicate with each other or with a master node through wireless communication links. Together, these nodes form a so-called ‘wireless sensor network’ (WSN). Each node of a WSN has a local sensor array and a signal processing unit to perform computations on the acquired data. The advantage of WSNs compared to traditional (wired) sensor arrays, is that many more sensors can be used that physically cover the full spatial field, which typically yields more variety (and thus more information) in the signals. It is likely that future data acquisition, control and physical monitoring, will heavily rely on this type of ...

Bertrand, Alexander — Katholieke Universiteit Leuven


Wireless Network Localization via Cooperation

This dissertation details two classes of cooperative localization methods for wireless networks in mixed line-of-sight and non-line-of-sight (LOS/NLOS) environments. The classes of methods depend on the amount of prior knowledge available. The methods used for both classes are based on the assumptions in practical localization environments that neither NLOS identification nor experimental campaigns are affordable. Two major contributions are, first, in methods that provide satisfactory localization accuracy whilst relaxing the requirement on statistical knowledge about the measurement model. Second, in methods that provide significantly improved localization performance without the requirement of good initialization. In the first half of the dissertation, cooperative localization using received signal strength (RSS) measurements in homogeneous mixed LOS/NLOS environments is considered for the case where the key model parameter, the path loss exponent, is unknown. The approach taken is to model the positions and the path ...

Jin, Di — Signal Processing Group, Technische Universität Darmstadt


Distributed Signal Processing Algorithms for Acoustic Sensor Networks

In recent years, there has been a proliferation of wireless devices for individual use to the point of being ubiquitous. Recent trends have been incorporating many of these devices (or nodes) together, which acquire signals and work in unison over wireless channels, in order to accomplish a predefined task. This type of cooperative sensing and communication between devices form the basis of a so-called wireless sensor network (WSN). Due to the ever increasing processing power of these nodes, WSNs are being assigned more complicated and computationally demanding tasks. Recent research has started to exploit this increased processing power in order for the WSNs to perform tasks pertaining to audio signal acquisition and processing forming so-called wireless acoustic sensor networks (WASNs). Audio signal processing poses new and unique problems when compared to traditional sensing applications as the signals observed often have ...

Szurley, Joseph — KU Leuven


Distributed Signal Processing Algorithms for Acoustic Sensor Networks

In recent years, there has been a proliferation of wireless devices for individual use to the point of being ubiquitous. Recent trends have been incorporating many of these devices (or nodes) together, which acquire signals and work in unison over wireless channels, in order to accomplish a predefined task. This type of cooperative sensing and communication between devices form the basis of a so-called wireless sensor network (WSN). Due to the ever increasing processing power of these nodes, WSNs are being assigned more complicated and computationally demanding tasks. Recent research has started to exploit this increased processing power in order for the WSNs to perform tasks pertaining to audio signal acquisition and processing forming so-called wireless acoustic sensor networks (WASNs). Audio signal processing poses new and unique problems when compared to traditional sensing applications as the signals observed often have ...

Szurley, Joseph C. — KU Leuven


Distributed Signal Processing Algorithms for Multi-Task Wireless Acoustic Sensor Networks

Recent technological advances in analogue and digital electronics as well as in hardware miniaturization have taken wireless sensing devices to another level by introducing low-power communication protocols, improved digital signal processing capabilities and compact sensors. When these devices perform a certain pre-defined signal processing task such as the estimation or detection of phenomena of interest, a cooperative scheme through wireless connections can significantly enhance the overall performance, especially in adverse conditions. The resulting network consisting of such connected devices (or nodes) is referred to as a wireless sensor network (WSN). In acoustical applications (e.g., speech enhancement) a variant of WSNs, called wireless acoustic sensor networks (WASNs) can be employed in which the sensing unit at each node consists of a single microphone or a microphone array. The nodes of such a WASN can then cooperate to perform a multi-channel acoustic ...

Hassani, Amin — KU Leuven


Bayesian Compressed Sensing using Alpha-Stable Distributions

During the last decades, information is being gathered and processed at an explosive rate. This fact gives rise to a very important issue, that is, how to effectively and precisely describe the information content of a given source signal or an ensemble of source signals, such that it can be stored, processed or transmitted by taking into consideration the limitations and capabilities of the several digital devices. One of the fundamental principles of signal processing for decades is the Nyquist-Shannon sampling theorem, which states that the minimum number of samples needed to reconstruct a signal without error is dictated by its bandwidth. However, there are many cases in our everyday life in which sampling at the Nyquist rate results in too many data and thus, demanding an increased processing power, as well as storage requirements. A mathematical theory that emerged ...

Tzagkarakis, George — University of Crete


Cooperative and Cognitive Communication Techniques for Wireless Networks

During the past years wireless communications have been exhibiting an increased growth rendering them the most common way for communication. The continuously increasing demand for wireless services resulted in limited availability of the wireless spectrum. To this end, Cognitive Radio (CR) techniques have been proposed in literature during the past years. The concept of CR approach is to utilize advanced radio and signal-processing technology along with novel spectrum allocation policies to enable new unlicensed wireless users to operate in the existing occupied spectrum areas without degrading the performance of the existing licensed ones. Moreover, the broadcast and fading nature of the wireless channel results in severe degradation on the performance of wireless transmissions. A solution to the problem is the use of multiple-antenna systems so as to achieve spatial diversity. However, in many cases, the communication devices' nature permit the ...

Tsinos, Christos — University of Patras


Learning Transferable Knowledge through Embedding Spaces

The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this thesis, we address the challenges of knowledge transfer through embedding spaces that capture and store hierarchical knowledge. In the first part of the thesis, we focus on the problem of cross-domain knowledge transfer. We first address zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries which align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that ...

Mohammad Rostami — University of Pennsylvania


Adaptive Algorithms and Variable Structures for Distributed Estimation

The analysis and design of new non-centralized learning algorithms for potential application in distributed adaptive estimation is the focus of this thesis. Such algorithms should be designed to have low processing requirement and to need minimal communication between the nodes which would form a distributed network. They ought, moreover, to have acceptable performance when the nodal input measurements are coloured and the environment is dynamic. Least mean square (LMS) and recursive least squares (RLS) type incremental distributed adaptive learning algorithms are first introduced on the basis of a Hamiltonian cycle through all of the nodes of a distributed network. These schemes require each node to communicate only with one of its neighbours during the learning process. An original steady-steady performance analysis of the incremental LMS algorithm is performed by exploiting a weighted spatial-temporal energy conservation formulation. This analysis confirms that ...

Li, Leilei — Loughborough University


Robust Wireless Localization in Harsh Mixed Line-of-Sight/Non-Line-of-Sight Environments

This PhD thesis considers the problem of locating some target nodes in different wireless infrastructures such as wireless cellular radio networks and wireless sensor networks. To be as realistic as possible, mixed line-of-sight and non-line-of-sight (LOS/NLOS) localization environment is introduced. Both the conventional non-cooperative localization and the new emerging cooperative localization have been studied thoroughly. Owing to the random nature of the measurements, probabilistic methods are more advanced as compared to the old-fashioned geometric methods. The gist behind the probabilistic methods is to infer the unknown positions of the target nodes in an estimation process, given a set of noisy position related measurements, a probabilistic measurement model, and a few known reference positions. In contrast to the majority of the existing methods, harsh but practical constraints are taken into account: neither offline calibration nor non-line-of-sight state identification is equipped in ...

Yin, Feng — Technische Universität Darmstadt


Cooperative Techniques for Interference Management in Wireless Networks

In the last few years, wireless devices have evolved to unimaginable heights. Current forecasts suggest that, in the near future, every device that may take advantage of a wireless connection will have one. In addition, there is a gradual migration to smart devices and high-speed connections, and, as a consequence, the overall mobile traffic is expected to experience a tremendous growth in the next years. The multiuser interference will hence become the main limiting factor and the most critical point to address. As instrumental to efficiently manage interference between different systems, this thesis provides a thorough study on cooperative techniques. That is, users share information and exploit it to improve the overall performance. Since multiuser cooperation represents a very broad term, we will focus on algorithm design and transceiver optimization for three cooperative scenarios that capture some of the main ...

Lameiro, Christian — University of Cantabria


Algorithms for Energy-Efficient Adaptive Wireless Sensor Networks

In this thesis we focus on the development of energy-efficient adaptive algorithms for Wireless Sensor Networks. Its contributions can be arranged in two main lines. Firstly, we focus on the efficient management of energy resources in WSNs equipped with finite-size batteries and energy-harvesting devices. To that end, we propose a censoring scheme by which the nodes are able to decide if a message transmission is worthy or not given their energetic condition. In order to do so, we model the system using a Markov Decision Process and use this model to derive optimal policies. Later, these policies are analyzed in simplified scenarios in order to get insights of their features. Finally, using Stochastic Approximation, we develop low-complexity censoring algorithms that approximate the optimal policy, with less computational complexity and faster convergence speed than other approaches such as Q-learning. Secondly, we ...

Fernandez-Bes, Jesus — Universidad Carlos III de Madrid


Cooperative Positioning based on Array Processing and Information Fusion

We are in the middle of the digital era, with more and more amazing features becoming available even in entry-level consumer devices (smartphones, tablets, wearable devices such as smart watches, etc.). This pervasive almost ubiquitous availability of interconnected devices, unconceivable until only a decade ago, is opening the doors to unprecedented applications, for which location awareness is an essential need. Unfortunately, none of the current positioning technologies alone is able to provide anywhere and anytime location capabilities, that is, to ensure service coverage in heterogeneous environments (e.g., outdoor, indoor) while offering adequate positioning accuracy. In response to such demand, this thesis investigates novel localization algorithms that can offer ubiquitous positioning capabilities. For this purpose, a novel holistic framework is proposed, based on the combined use of four dimensions of design, each focusing on a specific aspect of the whole localization ...

Fascista, Alessio — University of Salento

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