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

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

First wide-angle view of channelized turbidity currents links migrating cyclic steps to flow characteristics

Field observations of turbidity currents remain scarce, and thus there is continued debate about their internal structure and how they modify underlying bedforms. Here, I present the results of a new imaging method that examines multiple surge-like turbidity currents within a delta front channel, as they pass over crescent-shaped bedforms. Seven discrete flows over a 2-h period vary in speed from 0.5 to 3.0 ms−1. Only flows that exhibit a distinct acoustically attenuating layer at the base, appear to cause bedform migration. That layer thickens abruptly downstream of the bottom of the lee slope of the bedform, and the upper surface of the layer fluctuates rapidly at that point. The basal layer is inferred to reflect a strong near-bed gradient in density and the thickening is interpreted as a hydraulic jump. These results represent field-scale flow observations in support of a cyclic step origin of crescent-shaped bedforms

RRS Discovery Cruise 360, 19 Jan-02 Feb 2011. Trials of the Autosub LR AUV, HyBIS, PELAGRA, Ellsworth Camera and MYRTLE-X Lander systems

There were five main objectives for the trials cruise: The first tests of the Autosub Long Range AUV, testing of the HyBIS video guided grab system, testing of the MYRTLE-X Lander systems, testing of a deep camera system for the Lake Ellsworth probe and test deployments of the PELAGRA neutrally buoyant sediment capture drifters.The working area was about 300 miles south west of the Canary Islands, in international waters, over benthic plains of 4000 m depth, with some tests of the video systems over a isolated sea mount rising to 1200 m depth. Most of the objectives of the cruise where met, with successful diving and control of the Autosub LR, tests of the HyBIS and Ellsworth camera systems, and 3 deployments and recoveries of two PELAGRA floats. Several wire tests of MYRTLE-X systems were carried out, predominantly successful, but concerns over the release system prevented a deployment of the lander