A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications

Liu, Lanjun; Liao, Zhibo; Chen, Caiyi; Chen, Jialin; Niu, Jiong; Jia, Yangwen; Guo, Xinglin; Chen, Zhaowei; Deng, Li; Xu, Haibo; Liu, Tao

doi:10.3390/s19051255

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Figure 8. shows that C-DNN can achieve similar performance under channels that have never appeared in the training dataset.…”

mentioning

confidence: 89%

“…One challenge is underwater information need cross the water-air interface. Fortunately, using buoy node to exchange seabed observation information with satellite control informa- tion is a very typical and extremely important application for ocean observation [8]. The emerging ocean of things (OoT) based on low-cost floating devices [4] [5], will provide a feasible way for water and air information interaction.…”

Section: Introductionmentioning

confidence: 99%

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Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Zhao¹,

Ji²,

Guan³

et al. 2021

Preprint

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Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air. In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Internet of things (IoT) and Internet of underwater things (IoUT). OoT device not only can collect data through underwater methods, but also can utilize radio frequence over the air. For underwater communications, underwater acoustic communications (UWA COMMs) is the most effective way for OoT devices to exchange information, but it is always tormented by doppler shift and synchronization errors. In this paper, in order to overcome UWA tough conditions, a deep neural networks based receiver for underwater acoustic chirp communication, called C-DNN, is proposed. Moreover, to improve the performance of DL-model and solve the problem of model generalization, we also proposed a novel federated meta learning (FML) enhanced acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer. Particularly, tractable expressions are derived for the convergence rate of FML in a wireless setting, accounting for effects from both scheduling ratio, local epoch and the data amount on a single node. From our analysis and simulation results, it is shown that, the proposed C-DNN can provide a better BER performance and lower complexity than classical matched filter (MF) in underwater acoustic communications scenario. The ARC/FML framework has good convergence under a variety of channels than federated learning (FL). In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.

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“…Figure 8. shows that C-DNN can achieve similar performance under channels that have never appeared in the training dataset.…”

mentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Zhao¹,

Ji²,

Guan³

et al. 2021

Preprint

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“…Its implementation requires that the master control system has a series of characteristics such as real-time communication, embedded algorithms and data processing. With the developments of microprocessors, system-on-chip and sensor detection, the ARM microprocessor system presents integrated and intelligent application advantages in atmospheric detection and marine environment monitoring [ 14 ]. Moreover, previous work [ 15 , 16 ] proved the feasibility of building a radiometer system on the ARM platform to reduce energy consumption and size.…”

Section: Introductionmentioning

confidence: 99%

Optomechanical Design and Application of Solar-Skylight Spectroradiometer

Zhaoyang

Wang

et al. 2021

Sensors

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Using a solar radiometer is an effective approach for improving the remote sensing of solar irradiance distribution and atmospheric composition. Long-term development of a solar scanning radiometer enables frequent and reliable measurement of atmospheric parameters such as the water vapor column and aerosol optical properties. However, the discrete wavelength radiometer has encountered a bottleneck with respect to its insufficient spectral resolution and limited observation waveband, and it has been unable to satisfy the needs of refined and intelligent on-site experiments. This study proposes a solar-skylight spectroradiometer for obtaining visible and near-IR fine spectrum with two types of measurement: direct-sun irradiance and diffuse-sky radiance. The instrument adopts distributed control architecture composed of the ARM-Linux embedded platform and sensor networks. The detailed design of the measuring light-path, two-axis turntable, and master control system will be addressed in this study. To determine all coefficients needed to convert instrument outputs to physical quantities, integrating sphere and Langley extrapolation methods are introduced for diffuse-sky and direct-sun calibration, respectively. Finally, the agreement of experimental results between spectroradiometers and measuring benchmarks (DTF sun-photometer, microwave radiometer, and Combined Atmospheric Radiative Transfer simulation) verifies the feasibility of the spectroradiometer system, and the radiation information of feature wavelengths can be used to retrieve the characteristics of atmospheric optics.

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“…These sensors are connected by long distance cables to a coastal base station. The Ocean University of China has developed a buoy that communicates with sea bed sensors using acoustic transceivers, which in turn relays the data via Beidou Satellite to a server [5]. The use of satellites enables wireless communication with the buoy anywhere in the world.…”

Section: Introductionmentioning

confidence: 99%

Cellular network Marine Sensor Buoy

Przybysz

Duarte

Geraldi

et al. 2020

2020 IEEE Sensors Applications Symposium (SAS)

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Studies in the marine environment require devices to gather data in remote locations. These devices commonly use some form of data logging that require the user to retrieve the device after a period of time. This is not only a large effort, but often presents an unnecessary delay and risk in the event that the device malfunctions, goes missing or if the data collected is redundant. We propose a modular, remote and autonomous sensing solution that enables multi-sensor readout and wireless data communication, providing scientists with real time data. This sensor buoy is comprised of a primary module which is a data logger that connects to an online server via mobile network. The secondary portion is the hub that connects to an array of sensors that are customized to the needs of the marine scientists.

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A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications

Cited by 12 publications

References 12 publications

Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Optomechanical Design and Application of Solar-Skylight Spectroradiometer

Cellular network Marine Sensor Buoy

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