Due to significant advances made over the last few decades in the areas of (wireless) networking, communications and microprocessor fabrication, the use of sensor networks to observe physical phenomena is rapidly becoming commonplace. Over this period, many aspects of sensor networks have been explored, yet a thorough understanding of how to analyse and process the vast amounts of sensor data collected remains an open area of research. This work, therefore, aims to provide theoretical, as well as practical, advances this area. In particular, we consider the problem of inferring certain underlying properties of the monitored phenomena, from our sensor measurements. Within mathematics, this is commonly formulated as an inverse problem; whereas in signal processing, it appears as a (multidimensional) sampling and reconstruction problem. Indeed it is well known that inverse problems are notoriously ill-posed and very demanding to solve; meanwhile ...

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 ...

In today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop a data reduction tool called sparse sensing, which consists of a deterministic and structured sensing function (guided by a sparse vector) that is optimally designed ...

Wireless sensor networks consist in a collection of battery-powered sensors able to gather, process and send data. They are typically used to monitor various phenomenons, in a plethora of fields, from environmental studies to smart logistics. Their wireless connectivity and relatively small size allow them to be deployed practically anywhere, even underwater or embedded in everyday clothing, and possibly capture data over a large area for extended periods of time. Their usefulness is therefore tied to their ability to work autonomously, with as little human intervention as possible. This functional requirement directly translates into two design constraints: (i) bandwidth and on-board compute must be used sparingly, in order to extend battery-life as much as possible, and (ii) the system must be resilient to node failures and changing environment. Due to their limited computing capabilities, data processing is usually performed by ...

Future wireless networks will evolve to integrate communication, localization, and sensing capabilities. This evolution is driven by emerging application platforms such as digital twins, on the one hand, and advancements in wireless technologies, on the other, characterized by increased bandwidths, more antennas, and enhanced computational power. Crucial to this development is the application of artificial intelligence (AI), which is set to harness the vast amounts of available data in the sixth-generation (6G) of mobile networks and beyond. Integrating AI and machine learning (ML) algorithms, in particular, with wireless localization offers substantial opportunities to refine communication systems, improve the ability of wireless networks to locate the users precisely, enable context-aware transmission, and utilize processing and energy resources more efficiently. In this dissertation, advanced ML algorithms for enhanced wireless localization are proposed. Motivated by the capabilities of deep neural networks (DNNs) and ...

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 ...

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 ...

The proliferation of technologies like Internet of Things (IoT) and Industrial IoT (IIoT) has led to rapid growth in the number of connected devices and the volume of data associated with IoT applications. It is expected that more than 125 billion IoT devices will be connected to the Internet by 2030. With the plethora of wireless IoT devices, we are moving towards the connected world which is the guiding principle for the IoT. The next generation of IoT network should be capable of interconnecting heterogeneous IoT sensor or devices for effective Device-to-Device (D2D), Machine-to-Machine (M2M) communications as well as facilitating various IoT services and applications. Therefore, the next generation of IoT networks is expected to meet the capacity demand of such a network of billions of IoT devices. The current underlying wireless network is based on Orthogonal Multiple Access (OMA) ...

This thesis develops new mathematical theory and presents novel recovery algorithms for discrete linear dynamical systems (LDS) with sparsity constraints on either control inputs or initial state. The recovery problems in this framework manifest as the problem of reconstructing one or more sparse signals from a set of noisy underdetermined linear measurements. The goal of our work is to design algorithms for sparse signal recovery which can exploit the underlying structure in the measurement matrix and the unknown sparse vectors, and to analyze the impact of these structures on the efficacy of the recovery. We answer three fundamental and interconnected questions on sparse signal recovery problems that arise in the context of LDS. First, what are necessary and sufficient conditions for the existence of a sparse solution? Second, given that a sparse solution exists, what are good low-complexity algorithms that ...

Distributed networks comprising a large number of nodes, e.g., Wireless Sensor Networks, Personal Computers (PC’s), laptops, smart phones, etc., which cooperate with each other in order to reach a common goal, constitute a promising technology for several applications. Typical examples include: distributed environmental monitoring, acoustic source localization, power spectrum estimation, etc. Sophisticated cooperation mechanisms can significantly benefit the learning process, through which the nodes achieve their common objective. In this dissertation, the problem of adaptive learning in distributed networks is studied, focusing on the task of distributed estimation. A set of nodes sense information related to certain parameters and the estimation of these parameters constitutes the goal. Towards this direction, nodes exploit locally sensed measurements as well as information springing from interactions with other nodes of the network. Throughout this dissertation, the cooperation among the nodes follows the diffusion optimization ...

Distributed signal or information processing using Internet of Things (IoT), facilitates real-time monitoring of signals, for example, environmental pollutants, health indicators, and electric energy consumption in a smart city. Despite the promising capabilities of IoTs, these distributed deployments often face the challenge of data privacy and communication rate constraints. In traditional machine learning, training data is moved to a data center, which requires massive data movement from distributed IoT devices to a third-party location, thus raising concerns over privacy and inefficient use of communication resources. Moreover, the growing network size, model size, and data volume combined lead to unusual complexity in the design of optimization algorithms beyond the compute capability of a single device. This necessitates novel system architectures to ensure stable and secure operations of such networks. Federated learning (FL) architecture, a novel distributed learning paradigm introduced by McMahan ...

The envisioned rapid and exponential increase of wireless data traffic demand in the next years imposes rethinking current wireless cellular networks due to the scarcity of the available spectrum. In this regard, three main drivers are considered to increase the capacity of today's most advanced (4G systems) and future (5G systems and beyond) cellular networks: i) use more bandwidth (more Hz) through spectral aggregation, ii) enhance the spectral efficiency per base station (BS) (more bits/s/Hz/BS) by using multiple antennas at BSs and users (i.e. MIMO systems), and iii) increase the density of BSs (more BSs/km2) through a dense and heterogeneous deployment (known as dense heterogeneous cellular networks). We focus on the last two drivers. First, the use of multi-antenna systems allows exploiting the spatial dimension for several purposes: improving the capacity of a conventional point-to-point wireless link, increasing the number ...

Wireless communications and networking are gradually permeating our life and substantially influencing every corner of this world. Wireless devices, particularly those of small size, will take part in this trend more widely, efficiently, seamlessly and smartly. Techniques requiring only limited resources, especially in terms of hardware, are becoming more important and urgently needed. That is why we focus this thesis around analyzing wireless communications and networking based on signal strength (SS) measurements, since these are easy and convenient to gather. SS-based techniques can be incorporated into any device that is equipped with a wireless chip. More specifically, this thesis studies \textbf{SS-based localization} and \textbf{path-loss exponent (PLE) self-estimation}. Although these two research lines might seem unrelated, they are actually marching towards the same goal. The former can easily enable a very simple wireless chip to infer its location. But to solve ...

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 ...

This thesis discusses approaches and techniques to convert Sparsely-Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) devices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid transformation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. ...