51 research outputs found
Cooperative Authentication in Underwater Acoustic Sensor Networks
With the growing use of underwater acoustic communications (UWAC) for both
industrial and military operations, there is a need to ensure communication
security. A particular challenge is represented by underwater acoustic networks
(UWANs), which are often left unattended over long periods of time. Currently,
due to physical and performance limitations, UWAC packets rarely include
encryption, leaving the UWAN exposed to external attacks faking legitimate
messages. In this paper, we propose a new algorithm for message authentication
in a UWAN setting. We begin by observing that, due to the strong spatial
dependency of the underwater acoustic channel, an attacker can attempt to mimic
the channel associated with the legitimate transmitter only for a small set of
receivers, typically just for a single one. Taking this into account, our
scheme relies on trusted nodes that independently help a sink node in the
authentication process. For each incoming packet, the sink fuses beliefs
evaluated by the trusted nodes to reach an authentication decision. These
beliefs are based on estimated statistical channel parameters, chosen to be the
most sensitive to the transmitter-receiver displacement. Our simulation results
show accurate identification of an attacker's packet. We also report results
from a sea experiment demonstrating the effectiveness of our approach.Comment: Author version of paper accepted for publication in the IEEE
Transactions on Wireless Communication
Detecting the presence of sperm whales echolocation clicks in noisy environments
Sperm whales (Physeter macrocephalus) navigate underwater with a series of
impulsive, click-like sounds known as echolocation clicks. These clicks are
characterized by a multipulse structure (MPS) that serves as a distinctive
pattern. In this work, we use the stability of the MPS as a detection metric
for recognizing and classifying the presence of clicks in noisy environments.
To distinguish between noise transients and to handle simultaneous emissions
from multiple sperm whales, our approach clusters a time series of MPS measures
while removing potential clicks that do not fulfil the limits of inter-click
interval, duration and spectrum. As a result, our approach can handle high
noise transients and low signal-to-noise ratio. The performance of our
detection approach is examined using three datasets: seven months of recordings
from the Mediterranean Sea containing manually verified ambient noise; several
days of manually labelled data collected from the Dominica Island containing
approximately 40,000 clicks from multiple sperm whales; and a dataset from the
Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing
with the results of two benchmark detectors, a better trade-off between
precision and recall is observed as well as a significant reduction in false
detection rates, especially in noisy environments. To ensure reproducibility,
we provide our database of labelled clicks along with our implementation code.Comment: 10 pages and 10 figure
Design of an Optimal Testbed for Tracking of Tagged Marine Megafauna
Underwater acoustic technologies are a key component for exploring the
behavior of marine megafauna such as sea turtles, sharks, and seals. The
animals are marked with acoustic devices (tags) that periodically emit signals
encoding the device's ID along with sensor data such as depth, temperature, or
the dominant acceleration axis - data that is collected by a network of
deployed receivers. In this work, we aim to optimize the locations of receivers
for best tracking of acoustically tagged marine megafauna. The outcomes of such
tracking allows the evaluation of the animals' motion patterns, their hours of
activity, and their social interactions. In particular, we focus on how to
determine the receivers' deployment positions to maximize the coverage area in
which the tagged animals can be tracked. For example, an overly-condensed
deployment may not allow accurate tracking, whereas a sparse one, may lead to a
small coverage area due to too few detections. We formalize the question of
where to best deploy the receivers as a non-convex constraint optimization
problem that takes into account the local environment and the specifications of
the tags, and offer a sub-optimal, low-complexity solution that can be applied
to large testbeds. Numerical investigation for three stimulated sea
environments shows that our proposed method is able to increase the
localization coverage area by 30%, and results from a test case experiment
demonstrate similar performance in a real sea environment. We share the
implementation of our work to help researchers set up their own acoustic
observatory.Comment: Submitted for publication in Frontiers in Marine Science, special
topic on Tracking Marine Megafauna for Conservation and Marine Spatial
Plannin
Review of Cetacean's click detection algorithms
The detection of echolocation clicks is key in understanding the intricate
behaviors of cetaceans and monitoring their populations. Cetacean species
relying on clicks for navigation, foraging and even communications are sperm
whales (Physeter macrocephalus) and a variety of dolphin groups. Echolocation
clicks are wideband signals of short duration that are often emitted in
sequences of varying inter-click-intervals. While datasets and models for
clicks exist, the detection and classification of clicks present a significant
challenge, mostly due to the diversity of clicks' structures, overlapping
signals from simultaneously emitting animals, and the abundance of noise
transients from, for example, snapping shrimps and shipping cavitation noise.
This paper provides a survey of the many detection and classification
methodologies of clicks, ranging from 2002 to 2023. We divide the surveyed
techniques into categories by their methodology. Specifically, feature analysis
(e.g., phase, ICI and duration), frequency content, energy based detection,
supervised and unsupervised machine learning, template matching and adaptive
detection approaches. Also surveyed are open access platforms for click
detections, and databases openly available for testing. Details of the method
applied for each paper are given along with advantages and limitations, and for
each category we analyze the remaining challenges. The paper also includes a
performance comparison for several schemes over a shared database. Finally, we
provide tables summarizing the existing detection schemes in terms of
challenges address, methods, detection and classification tools applied,
features used and applications.Comment: 23 pages, 6 tables, 4 figure
Scalable adaptive networking for the Internet of Underwater Things
Internet of Underwater Things (IoUT) systems comprising tens or hundreds of underwater acoustic communication nodes will become feasible in the near future. The development of scalable networking protocols is a key enabling technology for such IoUT systems, but this task is challenging due to the fundamental limitations of the underwater acoustic communication channel: extremely slow propagation and limited bandwidth. The aim of this paper is to propose the JOIN protocol to enable the integration of new nodes into an existing IoUT network without the control overhead of typical state-of-the-art solutions. The proposed solution is based on the capability of a joining node to incorporate local topology and schedule information into a probabilistic model that allows it to choose when to join the network to minimize the expected number of collisions. The proposed approach is tested in numerical simulations and validated in two sea trials. The simulations show that the JOIN protocol achieves fast convergence to a collision-free solution, fast network adaptation to new nodes, and negligible network disruption due to collisions caused by a joining node. The sea trials demonstrate the practical feasibility of this protocol in real UAN deployments and provide valuable insight for future work on the trade-off between control overhead and reliability of the JOIN protocol in a harsh acoustic communication environment
Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning
Effective conservation of maritime environments and wildlife management of
endangered species require the implementation of efficient, accurate and
scalable solutions for environmental monitoring. Ecoacoustics offers the
advantages of non-invasive, long-duration sampling of environmental sounds and
has the potential to become the reference tool for biodiversity surveying.
However, the analysis and interpretation of acoustic data is a time-consuming
process that often requires a great amount of human supervision. This issue
might be tackled by exploiting modern techniques for automatic audio signal
analysis, which have recently achieved impressive performance thanks to the
advances in deep learning research. In this paper we show that convolutional
neural networks can indeed significantly outperform traditional automatic
methods in a challenging detection task: identification of dolphin whistles
from underwater audio recordings. The proposed system can detect signals even
in the presence of ambient noise, at the same time consistently reducing the
likelihood of producing false positives and false negatives. Our results
further support the adoption of artificial intelligence technology to improve
the automatic monitoring of marine ecosystems
A Survey of Techniques and Challenges in Underwater Localization
Underwater Wireless Sensor Networks (UWSNs) are expected to support a variety of civilian and military applications. Sensed data can only be interpreted meaningfully when referenced to the location of the sensor, making localization an important problem. While global positioning system (GPS) receivers are commonly used in terrestrial WSNs to achieve this, this is infeasible in UWSNs as GPS signals do not propagate through water. Acoustic communications is the most promising mode of communication underwater. However, underwater acoustic channels are characterized by harsh physical layer conditions with low bandwidth, high propagation delay and high bit error rate. Moreover, the variable speed of sound and the non-negligible node mobility due to water currents pose a unique set of challenges for localization in UWSNs. In this paper, we provide a survey of techniques and challenges in localization specifically for UWSNs. We categorize them into (i) range-based vs. range-free techniques; (ii) techniques that rely on static reference nodes vs. those who also rely on mobile reference nodes, and (iii) single-stage vs. multi-stage schemes. We compare the schemes in terms of localization speed, accuracy, coverage and communication costs. Finally, we provide an outlook on the challenges that should be, but have yet been, addressed. (C) 2011 Elsevier Ltd. All rights reserved
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