Distributed real-time sound speed profiling in underwater environments

Ahmed, Akram; Younis, Mohamed

doi:10.1109/icc.2017.7997485

Cited by 7 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, the complexity of UWA propagation comes from the irregularity of the sound speed profile (SSP), which shows the speed of sound in water at different vertical levels. However, as marine environment is a typical inhomogeneous medium with strong dynamic characteristics of seasonal changes and day-night temperature variations, there is still no widely accepted method that can effectively and accurately predict the complicated SSP variations [25]. Such complexity further makes it quite challenging to construct accurate and general UWA channel models in an affordable manner.…”

Section: Analysis Of Uwa Channel Modelmentioning

confidence: 99%

Adaptive modulation and coding in underwater acoustic communications: a machine learning perspective

Huang

Zhang

Tan

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

The increasing demand for exploring and managing the vast marine resources of the planet has underscored the importance of research on advanced underwater acoustic communication (UAC) technologies. However, owing to the severe characteristics of the oceanic environment, underwater acoustic (UWA) propagation experiences nearly the harshest wireless channels in nature. This article resorts to the perspective of machine learning (ML) to cope with the major challenges of adaptive modulation and coding (AMC) design in UACs. First, we present an ML AMC framework for UACs. Then, we propose an attention-aided k-nearest neighbor (A-kNN) algorithm with simplicity and robustness, based on which an ML AMC approach is designed with immunity to channel modeling uncertainty. Leveraging its online learning ability, such A-kNN-based AMC classifier offers salient capabilities of both sustainable self-enhancement and broad applicability to various operation scenarios. Next, aiming at higher implementation efficiency, we take strategies of complexity reduction and present a dimensionality-reduced and data-clustered A-kNN (DRDC-A-kNN) AMC classifier. Finally, we demonstrate that these proposed ML approaches have superior performance over traditional model-based methods by simulations using actual data collected from three lake experiments.

show abstract

Section: Analysis Of Uwa Channel Modelmentioning

confidence: 99%

Adaptive modulation and coding in underwater acoustic communications: a machine learning perspective

Huang

Zhang

Tan

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…We further assume that nodes obtain the SSP of the water column either by measuring the water column SSP or referencing data collected by field experiments in the same deployment area [22]. Finally, we have proposed a method to determine the SSP from established communication links where we exploit the fact that the paths of acoustic signals are highly correlated to the SSP of the water column [23]. In the event that the SSP changes, we assume that either such a technique can be leveraged to deduce the changes in the SSP, or new measurements are taken by the node to reflect the latest SSP.…”

Section: Underwater Layering and Sound Speed Profilingmentioning

confidence: 99%

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Ahmed

Younis

2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process.

show abstract

“…Acoustic waves serve as the predominant medium for energy transfer in marine surveying and are extensively utilized across diverse oceanographic domains, including but not limited to underwater acoustic communication, the assessment of marine environmental elements, and the probing of marine resources [1,2]. The propagation of acoustic waves within the oceanic realm is fundamentally determined by the velocity of sound in seawater [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Method for Sound Speed Profile Prediction Based on CNN-BiLSTM-Attention Network

Wei,

Shaohua,

Gang

et al. 2024

JMSE

View full text Add to dashboard Cite

In response to the current challenges in efficiently acquiring sound speed profiles and ensuring their representativeness, considering the need to fully leverage historical sound speed profiles while accounting for their spatiotemporal variability, we introduce a model for sound speed profile prediction based on a CNN-BiLSTM-Attention network, which integrates a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism (AM). The synergy of these components enables the model to extract the spatiotemporal features of sound speed profiles more comprehensively. Utilizing the global ocean Argo grid dataset, the model predicted the sound speed profiles of an experimental zone in the Western Pacific Ocean. In predicting sound speed profiles of a single point, the model achieved a root mean square error (RMSE), relative error (RE), and accuracy (ACC) of 0.72 m/s, 0.029%, and 0.99971, respectively, surpassing comparative models. For regional sound speed profile prediction, the mean RMSE, RE, and ACC of different water layers were 0.919 m/s, −0.016%, and 0.9995, respectively. The experimental outcomes not only confirm the high accuracy of the model, but also highlight its superiority in sound speed profile prediction, particularly as an effective compensatory approach when profile measurements are untimely or contain representational errors.

show abstract

Distributed real-time sound speed profiling in underwater environments

Cited by 7 publications

References 14 publications

Adaptive modulation and coding in underwater acoustic communications: a machine learning perspective

Adaptive modulation and coding in underwater acoustic communications: a machine learning perspective

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

A Method for Sound Speed Profile Prediction Based on CNN-BiLSTM-Attention Network

Contact Info

Product

Resources

About