Abstract
The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.
Highlights
The Internet of Underwater Things (IoUT) is the network of interconnected underwater systems, which is envisioned to facilitate a variety of applications, such as oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, and tactical surveillance [1]
Since the speed of sound in an ocean environment is mainly affected by the temperature, salinity, and pressure, the distribution of these oceanographic features determines the attenuation, reflection, refraction, and scattering of underwater acoustic (UWA) waveforms, which results in a complex
We propose DeepOcean, a deep learning framework for predicting oceanographic feature distributions, which consists of a generative module and a prediction module
Summary
The Internet of Underwater Things (IoUT) is the network of interconnected underwater systems, which is envisioned to facilitate a variety of applications, such as oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, and tactical surveillance [1]. Due to the challenging nature of communication in the ocean environment, underwater acoustic (UWA) communication plays an essential role in the networking of various underwater systems in an IoUT. Since the speed of sound in an ocean environment is mainly affected by the temperature, salinity, and pressure, the distribution of these oceanographic features determines the attenuation, reflection, refraction, and scattering of UWA waveforms, which results in a complex. The complex distribution of convergence zones and shadow zones determines the connectivity and communication performance between IoUT nodes, as well as the network topologies. According to the acoustic velocity, the ocean can be vertically divided into three layers: the surface layer (0 to ∼100m), the thermocline layer (∼100 to ∼600m), and the deep isothermal layer (600+m) [2]. As the refraction rate of acoustic waveform changes with the depth, and total reflection could happen when the acoustic wave is propagating at the boundary area between layers from a specific
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
