An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

FracBot: Design of wireless underground sensor networks for mapping hydraulic fractures and determining reservoir parameters in unconventional systems

Wireless underground sensor networks (WUSNs) enable a wide variety of emerging applications that are not possible with current underground monitoring techniques, which require miniaturized wireless sensor systems for mapping hydraulic fractures, monitoring unconventional reservoirs and measuring other wellbore parameters. We call these devices FracBots (Fracture Robots), an extension of RFID (Radio Frequency IDentifcation) tags that realize WUSNs for mapping and characterization of hydraulic fractures in unconventional reservoirs. The objective of this thesis is to design fully integrated magnetic induction (MI)-based FracBots (WUSNs) that enable reliable and e fficient wireless communications in underground oil reservoirs for performing the in-situ monitoring of oil reservoirs. This is very crucial for determining the sweet spot of oil and natural gas reserves. To this end, we have contributed in four areas as follows: fi rst, we develop a novel cross-layer communication framework for MI-based FracBot networks in dynamically changing underground environments. The framework combines a joint selection of modulation, channel coding, power control and a geographic forwarding paradigm. Second, we develop a novel MI-based localization framework that exploits the unique properties of MI- eld to determine the locations of the randomly deployed FracBot nodes in oil reservoirs. Third, we develop an accurate energy framework of a linear FracBot network topology that generates feasible nodes' transmission rates and network topology while always guaranteeing su fficient energy. Then, we design, develop, and fabricate MI-based FracBot nodes. Finally, to validate the performance of our solutions in our produced prototype of FracBot nodes, we develop a physical MI-based WUSN testbed.Ph.D

A Survey on Subsurface Signal Propagation

Wireless Underground Communication (WUC) is an emerging field that is being developed continuously. It provides secure mechanism of deploying nodes underground which shields them from any outside temperament or harsh weather conditions. This paper works towards introducing WUC and give a detail overview of WUC. It discusses system architecture of WUC along with the anatomy of the underground sensor motes deployed in WUC systems. It also compares Over-the-Air and Underground and highlights the major differences between the both type of channels. Since, UG communication is an evolving field, this paper also presents the evolution of the field along with the components and example UG wireless communication systems. Finally, the current research challenges of the system are presented for further improvement of the WUCs

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Underwater simulation and mapping using imaging sonar through ray theory and Hilbert maps

Mapping, sometimes as part of a SLAM system, is an active topic of research and has remarkable solutions using laser scanners, but most of the underwater mapping is focused on 2D maps, treating the environment as a floor plant, or on 2.5D maps of the seafloor. The reason for the problematic of underwater mapping originates in its sensor, i.e. sonars. In contrast to lasers (LIDARs), sonars are unprecise high-noise sensors. Besides its noise, imaging sonars have a wide sound beam effectuating a volumetric measurement. The first part of this dissertation develops an underwater simulator for highfrequency single-beam imaging sonars capable of replicating multipath, directional gain and typical noise effects on arbitrary environments. The simulation relies on a ray theory based method and explanations of how this theory follows from first principles under short-wavelegnth assumption are provided. In the second part of this dissertation, the simulator is combined to a continous map algorithm based on Hilbert Maps. Hilbert maps arise as a machine learning technique over Hilbert spaces, using features maps, applied to the mapping context. The embedding of a sonar response in such a map is a contribution. A qualitative comparison between the simulator ground truth and the reconstucted map reveal Hilbert maps as a promising technique to noisy sensor mapping and, also, indicates some hard to distinguish characteristics of the surroundings, e.g. corners and non smooth features.O mapeamento, às vezes como parte de um sistema SLAM, é um tema de pesquisa ativo e tem soluções notáveis usando scanners a laser, mas a maioria do mapeamento subaquático é focada em mapas 2D, que tratam o ambiente como uma planta, ou mapas 2.5D do fundo do mar. A razão para a dificuldade do mapeamento subaquático origina-se no seu sensor, i.e. sonares. Em contraste com lasers (LIDARs), os sonares são sensores imprecisos e com alto nível de ruído. Além do seu ruído, os sonares do tipo imaging têm um feixe sonoro muito amplo e, com isso, efetuam uma medição volumétrica, ou seja, sobre todo um volume. Na primeira parte dessa dissertação se desenvolve um simulador para sonares do tipo imaging de feixo único de alta frequência capaz de replicar os efeitos típicos de multicaminho, ganho direcional e ruído de fundo em ambientes arbitrários. O simulador implementa um método baseado na teoria geométrica de raios, com todo seu desenvolvimento partindo da acústica subaquática. Na segunda parte dessa dissertação, o simulador é incorporado em um algoritmo de reconstrução de mapas contínuos baseado em Hilbert Maps. Hilbert Maps surge como uma técnica de aprendizado de máquina sobre espaços de Hilbert, usando mapas de características, aplicadas ao contexto de mapeamento. A incorporação de uma resposta de sonar em um tal mapa é uma contribuição desse trabalho. Uma comparação qualitativa entre o ambiente de referência fornecido ao simulador e o mapa reconstruído pela técnica proposta, revela Hilbert Maps como uma técnica promissora para mapeamento atráves de sensores ruidosos e, também, aponta para algumas características do ambiente difíceis de se distinguir, e.g. cantos e regiões não suaves

Collective excitations in low dimensional systems and stochastic control of population growth in a fluctuating environment

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2007.Vita.Includes bibliographical references.In this thesis, I study several problems in the following areas: collective excitations in condensed matter physics, noise in gene network and stochastic control in biophysics. In the first area, I construct an effective field theory to describe Bose-Einstein Condensate (BEC) realized in an external potential. This theory explicitly explores the idea of spontaneous symmetry breaking and its application in the description of phase transitions of confined systems. Based on the effective lagrangian, I calculate the excitation spectrum and Matsubara Green's functions using the method of functional integrals. The theory also shows that in one dimension the collective excitation of a bosonic system can be unified with that of a fermionic system, which is described by Luttinger liquid theory. The unified theory of collective excitations of low dimensional quantum systems motivates my study of collective excitations of interacting classical particles confined in one dimension. It is shown in my paper that the structure of Hamiltonian or Lagrangian for one dimensional constrained systems is uniquely determined by conservation laws. Therefore the excitations of bosonic, fermionic and classical particles are strikingly similar in one dimension.(cont.) In the second area, i. e., noise in gene networks and phenotypic switching in a fluctuating environment, I study the noise propagation in a gene network cascade using the method of master equations which examines the validity of the more popular methods such as the Langevin equation. To further explore the applications of stochastic processes for complex systems, I study phenotypic switches in a fluctuating environment. By combining the techniques of stochastic differential equation and stochastic dynamical programming, I propose a simple framework which can be used to study phenotypic growth dynamics. Another work is to explore the influence of environment on the dynamical properties of small systems is directed to the unusual blinking statistics of semiconductor quantum dots. I show in a model system that a broad spectrum of decay rates is possible when disorder is present in the environment.by Xiang Xia.Ph.D

Sensing physical fields: Inverse problems for the diffusion equation and beyond

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 viewing it as the latter also presents several technical challenges. In particular, the monitored field is usually nonbandlimited, the sensor placement is typically non-regular and the space-time dimensions of the field are generally non-homogeneous. Furthermore, although sensor production is a very advanced domain, it is near impossible and/or extremely costly to design sensors with no measurement noise. These challenges therefore motivate the need for a stable, noise robust, yet simple sampling theory for the problem at hand. In our work, we narrow the gap between the domains of inverse problems and modern sampling theory, and in so doing, extend existing results by introducing a framework for solving the inverse source problems for a class of some well-known physical phenomena. Some examples include: the reconstruction of plume sources, thermal monitoring of multi-core processors and acoustic source estimation, to name a few. We assume these phenomena and their sources can be described using partial differential equation (PDE) and parametric source models, respectively. Under this assumption, we obtain a well-posed inverse problem. Initially, we consider a phenomena governed by the two-dimensional diffusion equation -- i.e. 2-D diffusion fields, and assume that we have access to its continuous field measurements. In this setup, we derive novel exact closed-form inverse formulae that solve the inverse diffusion source problem, for a class of localized and non-localized source models. In our derivation, we prove that a particular 1-D sequence of, so called, generalized measurements of the field is governed by a power-sum series, hence it can be efficiently solved using existing algebraic methods such as Prony's method. Next, we show how to obtain these generalized measurements, by using Green's second identity to combine the continuous diffusion field with a family of well-chosen sensing functions. From these new inverse formulae, we therefore develop novel noise robust centralized and distributed reconstruction methods for diffusion fields. Specifically, we extend these inverse formulae to centralized sensor networks using numerical quadrature; conversely for distributed networks, we propose a new physics-driven consensus scheme to approximate the generalized measurements through localized interactions between the sensor nodes. Finally we provide numerical results using both synthetic and real data to validate the proposed algorithms. Given the insights gained, we eventually turn to the more general problem. That is, the two- and three-dimensional inverse source problems for any linear PDE with constant coefficients. Extending the previous framework, we solve the new class of inverse problems by establishing an otherwise subtle link with modern sampling theory. We achieved this by showing that, the desired generalized measurements can be computed by taking linear weighted-sums of the sensor measurements. The advantage of this is two-fold. First, we obtain a more flexible framework that permits the use of more general sensing functions, this freedom is important for solving the 3-D problem. Second, and remarkably, we are able to analyse many more physical phenomena beyond diffusion fields. We prove that computing the proper sequence of generalized measurements for any such field, via linear sums, reduces to approximating (a family of) exponentials with translates of a particular prototype function. We show that this prototype function depends on the Green's function of the field, and then derive an explicit formula to evaluate the proper weights. Furthermore, since we now have more freedom in selecting the sensing functions, we discuss how to make the correct choice whilst emphasizing how to retrieve the unknown source parameters from the resulting (multidimensional) Prony-like systems. Based on this new theory we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify the performance of our proposed schemes.Open Acces

Measurements, Models, Systems and Design

531 s.