Ultra-low-cost and ultra-low-power, miniature acoustic modems using multipath tolerant spread-spectrum techniques

To enable long-term, large-scale, dense underwater sensor networks or Internet of Underwater Things (IoUT) this research investigates new novel waveforms and experimental prototypes for robust communications on ultra-low-cost and ultra-low-power, miniature acoustic modems. Spread-spectrum M-ary orthogonal signalling (MOS) is used with symbols constructed from subsequences of long pseudorandom codes. This decorrelates multipath signals, even when the time-spread spans many symbols, so they present as random noise. A highly cost-engineered and miniaturised prototype acoustic modem implementation was created, for the 24 kHz–32 kHz band, with low receive power consumption (12.5 mW) and transmit power of 3 km in lakes and >2 km in the sea including severe multipath. In lake testing of a 7-node, multi-hop, sensor network with TDA-MAC protocol, packet delivery was near 100% for all nodes. Trials of acoustic sensor nodes in the North Sea achieved 99.5% data delivery over a 3-month period and a wide range of sea conditions. Modulation and hardware have proven reliable in a variety of underwater environments. Competitive range and throughput with low cost and power are attractive for large-scale and long-term battery-operated networks. This research has delivered a viable and affordable communication technology for future IoUT applications

Ad hoc Acoustic Network Aided Localization for micro-AUVs

The navigation of Autonomous Underwater Vehicles (AUVs) is still an open research problem. This is further exacerbated when vehicles can only carry limited sensors as typically the case with micro-AUVs that need to survey large marine areas that can be characterized by high currents and dynamic environments. To address this problem, this work investigates the usage of ad hoc acoustic networks that can be established by a set of cooperating vehicles. Leveraging the network structure makes it possible to greatly improve the navigation of the vehicles and as a result to enlarge the operational envelope of vehicles with limited capabilities. The paper details the design and implementation of the network, and specific details of localization and navigation services made available to the vehicles by the network stack. Results are provided from a sea-trial undertaken in Croatia in October 2019. Results validate the approach, demonstrating the increased flexibility of the system and the navigational performance obtained: the deployed network was able to support long-range navigation of vehicles with no inertial navigation or Doppler Velocity Log (DVL) during a 9.5 km channel crossing, reducing the navigation error from approximately 7% to 0.27% of the distance traveled

System identification-based frequency domain feature extraction for defect detection and characterization

Feature extraction is the key step for defect detection in Non-Destructive Evaluation (NDE) techniques. Conventionally, feature extraction is performed using only the response or output signals from a monitoring device. In the approach proposed in this paper, the NDE device together with the material or structure under investigation are viewed as a dynamic system and the system identification techniques are used to build a parametric dynamic model for the system using the measured system input and output data. The features for defect detection and characterization are then selected and extracted from the frequency response function (FRF) derived from the identified dynamic model of the system. The new approach is validated by experimental studies with two different types of NDE techniques and the results demonstrate the advantage and potential of using control engineering-based approach for feature extraction and quantitative NDE. The proposed approach offers a general framework for selection and extraction of the dynamic property-related features of structures for defect detection and characterization, and provides a useful alternative to the existing methods with a potential of improving NDE performance

Passive Acoustic Detection of Vessel Activity by Low-Energy Wireless Sensors

This paper presents the development of a low-energy passive acoustic vessel detector to work as part of a wireless underwater monitoring network. The vessel detection method is based on a low-energy implementation of Detection of Envelope Modulation On Noise (DEMON). Vessels produce a broad spectrum modulated noise during propeller cavitation, which the DEMON method aims to extract for the purposes of automated detection. The vessel detector design has different approaches with mixtures of analogue and digital processing, as well as continuous and duty-cycled sampling/processing. The detector re-purposes an existing acoustic modem platform to achieve a low-cost and long-deployment wireless sensor network. This integrated communication platform enables the detector to switch between detection/communication mode seamlessly within software. The vessel detector was deployed at depth for a total of 84 days in the North Sea, providing a large data set, which the results are based on. Open sea field trial results have shown detection of single and multiple vessels with a 94% corroboration rate with local Automatic Identification System (AIS) data. Results showed that additional information about the detected vessel such as the number of propeller blades can be extracted solely based on the detection data. The attention to energy efficiency led to an average power consumption of 11.4 mW, enabling long term deployments of up to 6 months using only four alkaline C cells. Additional battery packs and a modified enclosure could enable a longer deployment duration. As the detector was still deployed during the first UK lockdown, the impact of COVID-19 on North Sea fishing activity was captured. Future work includes deploying this technology en masse to operate as part of a network. This could afford the possibility of adding vessel tracking to the abilities of the vessel detection technology when deployed as a network of sensor nodes

Development of smart networks for navigation in dynamic underwater environments

This work describes the development of a network of Autonomous Underwater Vehicles (AUVs) for ocean monitoring and surveillance in dynamic environments. The key component of the system is the ecoSUB vehicle, a small and low cost AUV that acts as a mobile node of the network. Although limited when compared to more traditional assets such as ships or larger and more expensive AUVs, once networked these vehicles can obtain a gain that goes beyond the capabilities of each individual platform, representing a step change in the accessibility to autonomous system use in science and commercial applications. This paper reports results towards the implementation of such a network, describing the communication and localisation infrastructure that it has been built to support it. Experimental results from an initial trail undertaken in Vobster Quay, UK in July 2018 are reported showing the gain in localisation performance that the ecoSUBs can achieve

Longest sediment flows yet measured show how major rivers connect efficiently to deep sea

Here we show how major rivers can efficiently connect to the deep-sea, by analysing the longest runout sediment flows (of any type) yet measured in action on Earth. These seafloor turbidity currents originated from the Congo River-mouth, with one flow travelling >1,130 km whilst accelerating from 5.2 to 8.0 m/s. In one year, these turbidity currents eroded 1,338-2,675 [>535-1,070] Mt of sediment from one submarine canyon, equivalent to 19–37 [>7–15] % of annual suspended sediment flux from present-day rivers. It was known earthquakes trigger canyon-flushing flows. We show river-floods also generate canyon-flushing flows, primed by rapid sediment-accumulation at the river-mouth, and sometimes triggered by spring tides weeks to months post-flood. It is demonstrated that strongly erosional turbidity currents self-accelerate, thereby travelling much further, validating a long-proposed theory. These observations explain highly-efficient organic carbon transfer, and have important implications for hazards to seabed cables, or deep-sea impacts of terrestrial climate change