Analytical Comparison Among Oceanographic Instruments Operations

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Acoustically driven control of mobile robots for source localization in complex ocean environments

Ocean based robotic systems are an opportunity to combine the power of acoustic sensing in the water with sophisticated control schemes. Together these bodies of knowledge could create autonomous systems for mapping acoustic fields and localizing underwater sources. However, existing control schemes have often been designed for land and air robots. This creates challenges for applying these algorithms to complex ocean environments. Acoustic fields are strongly frequency dependent, can rarely be realistically modeled analytically, have complex contours where the feature of interest is not always located at the peak pressure, and include many sources of background noise. This work addresses these challenges for control schemes from three categories: feedback and observer control, gradient ascent control and optimal control. In each case the challenges of applying the control scheme to an acoustic field are enumerated and addressed to create a suite of acoustically driven control schemes. For many of these algorithms, the largest issue is the processing and collection of acoustic data, particularly in the face of noise. Two new methods are developed to solve this issue. The first is the use of Principal Component Analysis as a noise filter for acoustic signals, which is shown to address particularly high levels of noise, while providing the frequency dependent sound pressure levels necessary for subsequent processing. The second method addresses the challenge that an analytical expression of the pressure field is often lacking, due to uncertainties and complexities in the environmental parameters. Basis functions are used to address this. Several candidates are considered, but Legendre polynomials are selected for their low error and reasonable processing time. Additionally, a method of intermediate points is used to approximate high frequency pressure fields with low numbers of collected data points. Following this work, the individual control schemes are explored. A method of observer feedback control is proposed to localize sources by linearizing the acoustic fields. A gradient ascent method for localizing sources in real time is proposed which uses Matched Field Processing and Bayesian filters. These modifications allow the gradient ascent algorithm to be compatible with complex acoustic fields. Finally, an optimal control method is proposed using Pontryagin's Maximum Principle to derive trajectories in real time that balance information gain with control energy. This method is shown to efficiently map an acoustic field, either for optimal sensor placement or to localize sources. The contribution of this work is a new collection of control schemes that use acoustic data to localize acoustically complex sources in a realistic noisy environment, and an understanding of the tradeoffs inherent in applying each of these to the acoustic domain

Intelligent deployment strategies for passive underwater sensor networks

Passive underwater sensor networks are often used to monitor a general area of the ocean, a port or military installation, or to detect underwater vehicles near a high value unit at sea, such as a fuel ship or aircraft carrier. Deploying an underwater sensor network across a large area of interest (AOI), for military surveillance purposes, is a significant challenge due to the inherent difficulties posed by the underwater channel in terms of sensing and communications between sensors. Moreover, monetary constraints, arising from the high cost of these sensors and their deployment, limit the number of available sensors. As a result, sensor deployment must be done as efficiently as possible. The objective of this work is to develop a deployment strategy for passive underwater sensors in an area clearance scenario, where there is no apparent target for an adversary to gravitate towards, such as a ship or a port, while considering all factors pertinent to underwater sensor deployment. These factors include sensing range, communications range, monetary costs, link redundancy, range dependence, and probabilistic visitation. A complete treatment of the underwater sensor deployment problem is presented in this work from determining the purpose of the sensor field to physically deploying the sensors. Assuming a field designer is given a suboptimal number of sensors, they must be methodically allocated across an AOI. The Game Theory Field Design (GTFD) model, proposed in this work, is able to accomplish this task by evaluating the acoustic characteristics across the AOI and allocating sensors accordingly. Since GTFD considers only circular sensing coverage regions, an extension is proposed to consider irregularly shaped regions. Sensor deployment locations are planned using a proposed evolutionary approach, called the Underwater Sensor Deployment Evolutionary Algorithm, which utilizes two suitable network topologies, mesh and cluster. The effects of these topologies, and a sensor\u27s communications range, on the sensing capabilities of a sensor field, are also investigated. Lastly, the impact of deployment imprecision on the connectivity of an underwater sensor field, using a mesh topology, is analyzed, for cases where sensor locations after deployment do not exactly coincide with planned sensor locations

Transmission Scheduling Technique for A Propagation transfer using Sensing Protocol Under water Acoustic Wireless Sensor Networks.

 As detector nodes square measure typically powered devices, the vital aspects to face concern the way to cut back the energy consumption of nodes, so the network lifespan may be extended to cheap times. Mobile underwater networks with acoustic communications square measure faced with many distinctive challenges like high transmission power utilization, giant propagation delay and node quality. In which Protocol multichip wireless network that uses multiple channel and dynamic channel choice technique. The comparison is conceded out by means that of analytical models, that square measure wont to confine the activities of a node that acts in line with either thought-about specifically for the underwater acoustic surroundings. The delay-aware opportunist transmission planning rule has been principally designed for underwater mobile detector networks. It uses passively obtained native info to reinforce the probabilities of synchronic transmissions whereas reducing collisions. Together with that, a straightforward performance mechanism that allows multiple outstanding packets at the sender facet, facultative multiple transmission sessions has been projected, that successively considerably improves the turnout. Every node learns neighboring node’s propagation delay info and their expected transmission schedules by passively overhearing packet transmissions through the institution of the new developed Macintosh protocol referred to as DOTS. This protocol principally aspires to attain higher channel utilization by harnessing each temporal and spatial recycle. The simulation results exemplify that DOTS provides truthful, medium access even with node quality. Thence this protocol additionally saves transmission energy by avoiding collisions whereas increasing turnout. It additionally achieves a turnout many times over that of the Slotted FAMA, whereas providing connected savings in energy. understanding that protocol is additional suited to given network setting and square measure expected to be of facilitate in planning novel protocol that presumably surmount presently out there solutions. Node monitor native underwater activities and report collected detector knowledge exploitation acoustic multi-hop routing to alternative mobile nodes for collaboration or just to a far off knowledge assortment center

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

Green underwater wireless communications using hybrid optical-acoustic technologies

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions

Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation

El concepto de entorno inteligente concibe un mundo donde los diferentes tipos de dispositivos inteligentes colaboran para conseguir un objetivo común. En este concepto, inteligencia hace referencia a la habilidad de adquirir conocimiento y aplicarlo de forma autónoma para conseguir el objetivo común, mientras que entorno hace referencia al mundo físico que nos rodea. Por tanto, un entorno inteligente se puede definir como aquel que adquiere conocimiento de su entorno y aplicándolo permite mejorar la experiencia de sus habitantes. La computación ubicua o generalizada permitirá que este concepto de entorno inteligente se haga realidad. Normalmente, el término de computación ubicua hace referencia al uso de dispositivos distribuidos por el mundo físico, pequeños y de bajo precio, que pueden comunicarse entre ellos y resolver un problema de forma colaborativa. Cuando esta comunicación se lleva a cabo de forma inalámbrica, estos dispositivos forman una red de sensores inalámbrica o en inglés, Wireless Sensor Network (WSN). Estas redes están atrayendo cada vez más atención debido al amplio espectro de aplicaciones que tienen, des de soluciones para el ámbito militar hasta aplicaciones para el gran consumo. Esta tesis se centra en las redes de sensores inalámbricas y subacuáticas o en inglés, Underwater Wireless Sensor Networks (UWSN). Estas redes, a pesar de compartir los mismos principios que las WSN, tienen un medio de transmisión diferente que cambia su forma de comunicación de ondas de radio a ondas acústicas. Este cambio hace que ambas redes sean diferentes en muchos aspectos como el retardo de propagación, el ancho de banda disponible, el consumo de energía, etc. De hecho, las señales acústicas tienen una velocidad de propagación cinco órdenes de magnitud menor que las señales de radio. Por tanto, muchos algoritmos y protocolos necesitan adaptarse o incluso rediseñarse. Como el despliegue de este tipo de redes puede ser bastante complicado y caro, se debe planificar de forma precisa el hardware y los algoritmos que se necesitan. Con esta finalidad, las simulaciones pueden resultar una forma muy conveniente de probar todas las variables necesarias antes del despliegue de la aplicación. A pesar de eso, un nivel de precisión adecuado que permita extraer resultados y conclusiones confiables, solamente se puede conseguir utilizando modelos precisos y parámetros reales. Esta tesis propone un ecosistema para UWSN basado en herramientas libres y de código abierto. Este ecosistema se compone de un modelo de recolección de energía y unmodelo de unmódemde bajo coste y bajo consumo con un sistema de activación remota que, junto con otros modelos ya implementados en las herramientas, permite la realización de simulaciones precisas con datos ambientales del tiempo y de las condiciones marinas del lugar donde la aplicación objeto de estudio va a desplegarse. Seguidamente, este ecosistema se utiliza con éxito en el estudio y evaluación de diferentes protocolos de transmisión aplicados a una aplicación real de monitorización de una piscifactoría en la costa del mar Mediterráneo, que es parte de un proyecto de investigación español (CICYT CTM2011-2961-C02-01). Finalmente, utilizando el modelo de recolección de energía, esta plataforma de simulación se utiliza para medir los requisitos de energía de la aplicación y extraer las necesidades de hardware mínimas.Climent Bayarri, JS. (2014). Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3532