Wireless Sensor Networks for Underwater Localization: A Survey

Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Mobile localization : approach and applications

textLocalization is critical to a number of wireless network applications. In many situations GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., understanding shoppers' behavior within retail spaces by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses. More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we demonstrate the accuracy and robustness of our new schemes. Second, we focus on localizing a single mobile node, which may not be connected with multiple nodes (e.g., without network connectivity or only connected with an access point). We propose trajectory-based localization using Wi-Fi or magnetic field measurements. We show that these measurements have the potential to uniquely identify a trajectory. We then develop a novel approach that leverages multi-level wavelet coefficients to first identify the trajectory and then localize to a point on the trajectory. We show that this approach is highly accurate and power efficient using indoor and outdoor experiments. Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.Computer Science

Exploiting Heterogeneity in Networks of Aerial and Ground Robotic Agents

By taking advantage of complementary communication technologies, distinct sensing functionalities and varied motion dynamics present in a heterogeneous multi-robotic network, it is possible to accomplish a main mission objective by assigning specialized sub-tasks to specific members of a robotic team. An adequate selection of the team members and an effective coordination are some of the challenges to fully exploit the unique capabilities that these types of systems can offer. Motivated by real world applications, we focus on a multi-robotic network consisting off aerial and ground agents which has the potential to provide critical support to humans in complex settings. For instance, aerial robotic relays are capable of transporting small ground mobile sensors to expand the communication range and the situational awareness of first responders in hazardous environments. In the first part of this dissertation, we extend work on manipulation of cable-suspended loads using aerial robots by solving the problem of lifting the cable-suspended load from the ground before proceeding to transport it. Since the suspended load-quadrotor system experiences switching conditions during this critical maneuver, we define a hybrid system and show that it is differentially-flat. This property facilitates the design of a nonlinear controller which tracks a waypoint-based trajectory associated with the discrete states of the hybrid system. In addition, we address the case of unknown payload mass by combining a least-squares estimation method with the designed controller. Second, we focus on the coordination of a heterogeneous team formed by a group of ground mobile sensors and a flying communication router which is deployed to sense areas of interest in a cluttered environment. Using potential field methods, we propose a controller for the coordinated mobility of the team to guarantee inter-robot and obstacle collision avoidance as well as connectivity maintenance among the ground agents while the main goal of sensing is carried out. For the case of the aerial communications relays, we combine antenna diversity with reinforcement learning to dynamically re-locate these relays so that the received signal strength is maintained above a desired threshold. Motivated by the recent interest of combining radio frequency and optical wireless communications, we envision the implementation of an optical link between micro-scale aerial and ground robots. This type of link requires maintaining a sufficient relative transmitter-receiver position for reliable communications. In the third part of this thesis, we tackle this problem. Based on the link model, we define a connectivity cone where a minimum transmission rate is guaranteed. For example, the aerial robot has to track the ground vehicle to stay inside this cone. The control must be robust to noisy measurements. Thus, we use particle filters to obtain a better estimation of the receiver position and we design a control algorithm for the flying robot to enhance the transmission rate. Also, we consider the problem of pairing a ground sensor with an aerial vehicle, both equipped with a hybrid radio-frequency/optical wireless communication system. A challenge is positioning the flying robot within optical range when the sensor location is unknown. Thus, we take advantage of the hybrid communication scheme by developing a control strategy that uses the radio signal to guide the aerial platform to the ground sensor. Once the optical-based signal strength has achieved a certain threshold, the robot hovers within optical range. Finally, we investigate the problem of building an alliance of agents with different skills in order to satisfy the requirements imposed by a given task. We find this alliance, known also as a coalition, by using a bipartite graph in which edges represent the relation between agent capabilities and required resources for task execution. Using this graph, we build a coalition whose total capability resources can satisfy the task resource requirements. Also, we study the heterogeneity of the formed coalition to analyze how it is affected for instance by the amount of capability resources present in the agents

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

A Hybrid Localization Approach in Wireless Sensor Networks by Resolving Flip Ambiguity

Localization has received considerable attention because many wireless sensor network applications require accurate knowledge of the locations of the sensors in the network. In the process the location calculation is achieved by either distance measurements or angle-of‐arrival measurement. However, the former technique suffers from flip ambiguity due to either the presence of insufficient reference points or uncertainties in the inter‐nodal distance measurements in a triangular network structure. A recently proposed quadrilateral structure (an extended complex version of a trilateration structure) can resolve flip ambiguity of a node in dense deployments under restricted orientations for anchors. However, the technique leaves open issues to consider imprecise inter‐nodal distances between all pairs of nodes as complexity increases due to measurement uncertainties in determining the locations. Moreover, both the structures (trilateral and quadrilateral) completely fail to resolve flip ambiguity in sparse node deployments as sufficient nodes are not available in order to determine the signs in calculated angles. On the other hand, AOA can provide the sign of the angles but requires expensive hardware calibration to provide a high‐level of accuracy in the measured angles. Therefore, there is a need of a localization technique that is cheaper, less complex, and robust by considering measurement uncertainties between all pair of nodes and more importantly, involves fewer reference nodes. The primary contributions of this thesis include a hybrid technique that uses low‐accuracy (cheap) AOA measurements along with erroneous distance measurements between each pair of nodes in a much simpler triangular network that corresponds to a sparse deployment. In our initial phase we develop mathematical models involving only two reference nodes that are able to resolve flip ambiguity a unknown node with a high probability of success even with an RMS error as high as 150 in the line‐of‐bearing estimate, which avoids the need for calibration in many practical situations. In later phases, we modelled our hybrid localization technique to accommodate imprecise inter‐nodal measurements between all pairs of nodes. In the final phase, we intend our localization technique to solve ambiguity in extremely sparse scenarios with non‐closed network structure that are yet to be solved by existing localizations approaches. Resolution of flip ambiguity is useful, not only to develop lower‐complexity localization techniques, but also for many lower‐layer network functionalities such as geographic routing, topology control, coverage and tracking, and controlled mobility when a large number of these nodes have to be deployed

CALIBRATION OF AN ULTRASONIC TRANSMISSIVE COMPUTED TOMOGRAPHY SYSTEM

Tato dizertace je zaměřena na medicínskou zobrazovací modalitu – ultrazvukovou počítačovou tomografii – a algoritmy zlepšující kvalitu zobrazení, zejména kalibraci USCT přístroje. USCT je novou modalitou kombinující ultrazvukový přenos signálů a principy tomografické rekonstrukce obrazů vyvíjených pro jiné tomografické systémy. V principu lze vytvořit kvantitativní 3D obrazové objemy s vysokým rozlišením a kontrastem. USCT je primárně určeno pro diagnózu rakoviny prsu. Autor spolupracoval na projektu Institutu Zpracování dat a Elektroniky, Forschungszentrum Karlsruhe, kde je USCT systém vyvíjen. Jeden ze zásadních problémů prototypu USCT v Karlsruhe byla absence kalibrace. Tisíce ultrazvukových měničů se liší v citlivosti, směrovosti a frekvenční odezvě. Tyto parametry jsou navíc proměnné v čase. Další a mnohem závažnější problém byl v pozičních odchylkách jednotlivých měničů. Všechny tyto aspekty mají vliv na konečnou kvalitu rekonstruovaných obrazů. Problém kalibrace si autor zvolil jako hlavní téma dizertace. Tato dizertace popisuje nové metody v oblastech rekonstrukce útlumových obrazů, kalibrace citlivosti měničů a zejména geometrická kalibrace pozic měničů. Tyto metody byly implementovány a otestovány na reálných datech pocházejících z prototypu USCT z Karlsruhe.This dissertation is centered on a medical imaging modality – the ultrasonic computed tomography (USCT) – and algorithms which improve the resulting image quality, namely the calibration of a USCT device. The USCT is a novel imaging modality which combines the phenomenon of ultrasound and image reconstruction principles developed for other tomographic systems. It is capable of producing quantitative 3D image volumes with high resolution and tissue contrast and is primarily aimed at breast cancer diagnosis. The author was involved in a joint research project at the Institute of Data Processing and Electronics, Forschungszentrum Karlsruhe (German National Research Center), where a USCT system is being developed. One of the main problems in the Karlsruhe USCT prototype was the absence of any calibration. The thousands of transducers used in the system have deviations in sensitivity, directivity, and frequency response. These parameters change over time as the transducers age. Also the mechanical positioning of the transducer elements is not precise. All these aspects greatly affect the overall quality of the reconstructed images. The problem of calibration of a USCT system was chosen as the main topic for this dissertation. The dissertation thesis presents novel methods in the area of reconstruction of attenuation images, sensitivity calibration, and mainly geometrical calibration. The methods were implemented and tested on real data generated by the Karlsruhe USCT device.

Bearing rigidity theory and its applications for control and estimation of network systems: Life beyond distance rigidity

Distributed control and location estimation of multiagent systems have received tremendous research attention in recent years because of their potential across many application domains [1], [2]. The term agent can represent a sensor, autonomous vehicle, or any general dynamical system. Multiagent systems are attractive because of their robustness against system failure, ability to adapt to dynamic and uncertain environments, and economic advantages compared to the implementation of more expensive monolithic systems

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field