4,861 research outputs found

    A novel cooperative opportunistic routing scheme for underwater sensor networks

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    Increasing attention has recently been devoted to underwater sensor networks (UWSNs) because of their capabilities in the ocean monitoring and resource discovery. UWSNs are faced with different challenges, the most notable of which is perhaps how to efficiently deliver packets taking into account all of the constraints of the available acoustic communication channel. The opportunistic routing provides a reliable solution with the aid of intermediate nodes’ collaboration to relay a packet toward the destination. In this paper, we propose a new routing protocol, called opportunistic void avoidance routing (OVAR), to address the void problem and also the energy-reliability trade-off in the forwarding set selection. OVAR takes advantage of distributed beaconing, constructs the adjacency graph at each hop and selects a forwarding set that holds the best trade-off between reliability and energy efficiency. The unique features of OVAR in selecting the candidate nodes in the vicinity of each other leads to the resolution of the hidden node problem. OVAR is also able to select the forwarding set in any direction from the sender, which increases its flexibility to bypass any kind of void area with the minimum deviation from the optimal path. The results of our extensive simulation study show that OVAR outperforms other protocols in terms of the packet delivery ratio, energy consumption, end-to-end delay, hop count and traversed distance

    An energy scaled and expanded vector-based forwarding scheme for industrial underwater acoustic sensor networks with sink mobility

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    Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay

    Optimal path shape for range-only underwater target localization using a Wave Glider

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    Underwater localization using acoustic signals is one of the main components in a navigation system for an autonomous underwater vehicle (AUV) as a more accurate alternative to dead-reckoning techniques. Although different methods based on the idea of multiple beacons have been studied, other approaches use only one beacon, which reduces the system’s costs and deployment complexity. The inverse approach for single-beacon navigation is to use this method for target localization by an underwater or surface vehicle. In this paper, a method of range-only target localization using a Wave Glider is presented, for which simulations and sea tests have been conducted to determine optimal parameters to minimize acoustic energy use and search time, and to maximize location accuracy and precision. Finally, a field mission is presented, where a Benthic Rover (an autonomous seafloor vehicle) is localized and tracked using minimal human intervention. This mission shows, as an example, the power of using autonomous vehicles in collaboration for oceanographic research.Peer ReviewedPostprint (author's final draft

    Maximizing the Number of Spatial Nulls with Minimum Sensors

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    In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level

    Analysis of MEMS Piezoelectric Hydrophone at High Sensitivity for underwater application

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    Micro-electromechanical systems (MEMS), is a technology that is used to design small integrated devices that combines both electrical (voltage, resistance) and mechanical components (stress, displacement). These devices are assembled using integrated circuit batch processing methods and their size can vary from Micrometers (μm) to Millimeters (mm). MEMS devices have the capability to sense and control the environment. These devices are fabricated using System Integrated chip technology and micro-level segments are manufactured using silicon. To remove the selective parts of silicon i.e. extra silicon parts, Processes such as back etching, high-aspect-ratio micromachining are used to remove selective parts of silicon or to add the extra layers to form the electromechanical components. The study is concerned to design a T- Shape vector Hydrophone using MEMS Technology and to analyses mechanical properties like induced stresses, deformation and to analyses the piezoelectric hydrophone characteristics like frequency response, sensitivity and Voltage to achieve improved sensitivity. MEMS Directional Hydrophone PZT is simulated. Both the structure resembles the fish lateral line and auditory cilia system which converts acoustic pressure into voltage. © 2017 Elsevier Ltd

    Vector sensors for underwater : acoustic communications

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    Acoustic vector sensors measure acoustic pressure and directional components separately. A claimed advantage of vector sensors over pressure-only arrays is the directional information in a collocated device, making it an attractive option for size-restricted applications. The employment of vector sensors as a receiver for underwater communications is relatively new, where the inherent directionality, usually related to particle velocity, is used for signal-to-noise gain and intersymbol interference mitigation. The fundamental question is how to use vector sensor directional components to bene t communications, which this work seeks to answer and to which it contributes by performing: analysis of acoustic pressure and particle velocity components; comparison of vector sensor receiver structures exploring beamforming and diversity; quanti cation of adapted receiver structures in distinct acoustic scenarios and using di erent types of vector sensors. Analytic expressions are shown for pressure and particle velocity channels, revealing extreme cases of correlation between vector sensors' components. Based on the correlation hypothesis, receiver structures are tested with simulated and experimental data. In a rst approach, called vector sensor passive time-reversal, we take advantage of the channel diversity provided by the inherent directivity of vector sensors' components. In a second approach named vector sensor beam steering, pressure and particle velocity components are combined, resulting in a steered beam for a speci c direction. At last, a joint beam steering and passive time-reversal is proposed, adapted for vector sensors. Tested with two distinct experimental datasets, where vector sensors are either positioned on the bottom or tied to a vessel, a broad performance comparison shows the potential of each receiver structure. Analysis of results suggests that the beam steering structure is preferable for shorter source-receiver ranges, whereas the passive time-reversal is preferable for longer ranges. Results show that the joint beam steering and passive time-reversal is the best option to reduce communication error with robustness along the range.Sensores vetoriais acústicos (em inglês, acoustic vector sensors) são dispositivos que medem, alem da pressão acústica, a velocidade de partícula. Esta ultima, é uma medida que se refere a um eixo, portando, esta associada a uma direção. Ao combinar pressão acústica com componentes de velocidade de partícula pode-se estimar a direção de uma fonte sonora utilizando apenas um sensor vetorial. Na realidade, \um" sensor vetorial é composto de um sensor de pressão (hidrofone) e um ou mais sensores que medem componentes da velocidade de partícula. Como podemos notar, o aspecto inovador está na medição da velocidade de partícula, dado que os hidrofones já são conhecidos.(...)This PhD thesis was supported by the Brazilian Navy Postgraduate Study Abroad Program Port. 227/MB-14/08/2019

    A stateless opportunistic routing protocol for underwater sensor networks

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    Routing packets in Underwater Sensor Networks (UWSNs) face different challenges, the most notable of which is perhaps how to deal with void communication areas. While this issue is not addressed in some underwater routing protocols, there exist some partially state-full protocols which can guarantee the delivery of packets using excessive communication overhead. However, there is no fully stateless underwater routing protocol, to the best of our knowledge, which can detect and bypass trapped nodes. A trapped node is a node which only leads packets to arrive finally at a void node. In this paper, we propose a Stateless Opportunistic Routing Protocol (SORP), in which the void and trapped nodes are locally detected in the different area of network topology to be excluded during the routing phase using a passive participation approach. SORP also uses a novel scheme to employ an adaptive forwarding area which can be resized and replaced according to the local density and placement of the candidate forwarding nodes to enhance the energy efficiency and reliability. We also make a theoretical analysis on the routing performance in case of considering the shadow zone and variable propagation delays. The results of our extensive simulation study indicate that SORP outperforms other protocols regarding the routing performance metrics

    ON-ICE DETECTION, CLASSIFICATION, LOCALIZATION AND TRACKING OF ANTHROPOGENIC ACOUSTIC SOURCES WITH MACHINE LEARNING

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    Arctic acoustics have been of concern in recent years for the US navy. First-year ice is now the prevalent factor in ice coverage in the Arctic, which changes the previously understood acoustic properties. Due to the ice melting each year, anthropogenic sources in the Arctic region are more common: military exercises, shipping, and tourism. For the navy, it is of interest to detect, classify, localize, and track these sources to have situational awareness of these surroundings. Because the sources are on-water or on-ice, acoustic radiation propagates at a longer distance and so acoustics are the method by which the sources are detected, classified, localized, and tracked. These methods are all part of sound navigation and ranging (SONAR). This dissertation describes algorithms which will better SONAR results without modification of the sensors or the environment and the process by which to arrive to this point. The focus is to use supervised machine learning algorithms to facilitate such technological enhancements. Specifically, neural networks analyze labeled experimental data from a first-year, shore-fast, shallow and narrow water environment. The experiments were conducted over the span of three years from 2019 to 2022, mostly during the months from January to March where ice formed over the Keweenaw Waterway at the Michigan Technological University. All experiments were conducted to analyze a passive acoustic source; that is, the source was non-cooperative and did not send any localizing pings for active SONAR. The experiments were recorded using an underwater pa-type acoustic vector sensor (AVS). The data and analysis were done intermittently to update any upcoming experiments with discrepancies found in the analysis to create a more generalized algorithm. The work in this dissertation focuses on two topics for passive SONAR: localization and classification. Because of the ``black box nature in machine learning, tracking the target source is an extension of localization and thought of as the same goal within machine learning. To introduce and verify the complexity of the testing environment, an underwater acoustic simulation is shown with Ray tracing and bathymetry data to compare with the experimental results used in machine learning. The focus of the algorithms is to produce the best results for the experiments and compare the results with traditional methods, such as a simulation or a linear Gaussian localization with a Kalman filter. Experiments studying neural network types have shown that the Vision Transformer (ViT) produces excellent results. The ViT is capable of analyzing acoustic intensity azimuthal spectrogram (azigram) data and localizing a moving target at high accuracy, and the ViT is capable of classifying multiple acoustic sources with the acoustic intensity magnitude spectrogram at high accuracy as well
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