Underwater localization using SAR satellite data.

This study delves into the realm of Underwater Wireless Sensor Networks (UWSN) and explores contemporary methods of ocean exploration. It provides an extensive background on UWSN, detailing existing approaches to underwater localization. The study then introduces a novel contribution to this domain by leveraging advanced satellite technology. Employing a pre-trained deep learning model from ArcGIS, static ships within the study area are identified using C-band Synthetic Aperture Radar (SAR) satellite imagery. The identified ship locations serve as reference nodes for underwater localization. Utilizing range-based multilateration in the UnetStack environment, the study achieves precise localization of underwater nodes. The proposed approach demonstrates an error of less than 1% when compared to the actual positions of the underwater nodes, showcasing its effectiveness in enhancing the field of underwater exploration and localization

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Cooperative Authentication in Underwater Acoustic Sensor Networks

With the growing use of underwater acoustic communications (UWAC) for both industrial and military operations, there is a need to ensure communication security. A particular challenge is represented by underwater acoustic networks (UWANs), which are often left unattended over long periods of time. Currently, due to physical and performance limitations, UWAC packets rarely include encryption, leaving the UWAN exposed to external attacks faking legitimate messages. In this paper, we propose a new algorithm for message authentication in a UWAN setting. We begin by observing that, due to the strong spatial dependency of the underwater acoustic channel, an attacker can attempt to mimic the channel associated with the legitimate transmitter only for a small set of receivers, typically just for a single one. Taking this into account, our scheme relies on trusted nodes that independently help a sink node in the authentication process. For each incoming packet, the sink fuses beliefs evaluated by the trusted nodes to reach an authentication decision. These beliefs are based on estimated statistical channel parameters, chosen to be the most sensitive to the transmitter-receiver displacement. Our simulation results show accurate identification of an attacker's packet. We also report results from a sea experiment demonstrating the effectiveness of our approach.Comment: Author version of paper accepted for publication in the IEEE Transactions on Wireless Communication

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Secure positioning in wireless networks

So far, the problem of positioning in wireless networks has been studied mainly in a nonadversarial setting. In this paper, we analyze the resistance of positioning techniques to position and distance spoofing attacks. We propose a mechanism for secure positioning of wireless devices, that we call verifiable multilateration. We then show how this mechanism can be used to secure positioning in sensor networks. We analyze our system through simulations

Sensor Fusion for Mobile Robot Localization using UWB and ArUco Markers

Uma das principais características para considerar um robô autónomo é o facto de este ser capaz de se localizar, em tempo real, no seu ambiente, ou seja saber a sua posição e orientação. Esta é uma área desafiante que tem sido estudada por diversos investigadores em todo o mundo. Para obter a localização de um robô é possível recorrer a diferentes metodologias. No entanto há metodologias que apresentam problemas em diferentes circunstâncias, como é o caso da odometria que sofre de acumulação de erros com a distância percorrida pelo robô. Outro problema existente em diversas metodologias é a incerteza na deteção do robô devido a ruído presente nos sensores. Com o intuito de obter uma localização mais robusta do robô e mais tolerante a falhas é possível combinar diversos sistemas de localização, combinando assim as vantagens de cada um deles. Neste trabalho, será utilizado o sistema Pozyx, uma solução de baixo custo que fornece informação de posicionamento com o auxílio da tecnologia Ultra-WideBand Time-of-Flight (UWB ToF). Também serão utilizados marcadores ArUco colocados no ambiente que através da sua identificação por uma câmara é também possível obter informação de posicionamento. Estas duas soluções irão ser estudadas e implementadas num robô móvel, através de um esquema de localização baseada em marcadores. Primeiramente, irá ser feita uma caracterização do erro de ambos os sistemas, uma vez que as medidas não são perfeitas, havendo sempre algum ruído nas medições. De seguida, as medidas fornecidas pelos sistemas irão ser filtradas e fundidas com os valores da odometria do robô através da implementação de um Filtro de Kalman Extendido (EKF). Assim, é possível obter a pose do robô (posição e orientação), pose esta que é comparada com a pose fornecida por um sistema de Ground-Truth igualmente desenvolvido para este trabalho com o auxílio da libraria ArUco, percebendo assim a precisão do algoritmo desenvolvido. O trabalho desenvolvido mostrou que com a utilização do sistema Pozyx e dos marcadores ArUco é possível melhorar a localização do robô, o que significa que é uma solução adequada e eficaz para este fim.One of the main characteristics to consider a robot truly autonomous is the fact that it is able to locate itself, in real time, in its environment, that is, to know its position and orientation. This is a challenging area that has been studied by several researchers around the world. To obtain the localization of a robot it is possible to use different methodologies. However, there are methodologies that present problems in different circumstances, as is the case of odometry that suffers from error accumulation with the distance traveled by the robot. Another problem existing in several methodologies is the uncertainty in the sensing of the robot due to noise present in the sensors. In order to obtain a more robust localization of the robot and more fault tolerant it is possible to combine several localization systems, thus combining the advantages of each one. In this work, the Pozyx system will be used, a low-cost solution that provides positioning information through Ultra-WideBand Time-of-Flight (UWB ToF) technology. It will also be used ArUco markers placed in the environment that through their identification by a camera it is also possible to obtain positioning information. These two solutions will be studied and implemented in a mobile robot, through a beacon-based localization scheme. First, an error characterization of both systems will be performed, since the measurements are not perfect, and there is always some noise in the measurements. Next, the measurements provided by the systems will be filtered and fused with the robot's odometry values by the implementation of an Extended Kalman Filter (EKF). In this way, it is possible to obtain the robot's pose, i.e position and orientation, which is compared with the pose provided by a Ground-Truth system also developed for this work with the aid of the ArUco library, thus realizing the accuracy of the developed algorithm. The developed work showed that with the use of the Pozyx system and ArUco markers it is possible to improve the robot localization, meaning that it is an adequate and effective solution for this purpose