2,623 research outputs found
TS-MUWSN: Time synchronization for mobile underwater sensor networks
Time synchronization is an important, yet challenging, problem in underwater sensor networks (UWSNs). This challenge can be attributed to: 1) messaging timestamping; 2) node mobility; and 3) Doppler scale effect. To mitigate these problems, we present an acoustic-based time-synchronization algorithm for UWSN, where we compare several message time-stamping algorithms in addition to different Doppler scale estimators. A synchronization system is based on a bidirectional message exchange between a reference node and a slave one, which has to be synchronized. Therefore, we take as reference the DA-Sync-like protocol (Liu et al., 2014), which takes into account node's movement by using first-order kinematic equations, which refine Doppler scale factor estimation accuracy, and result in better synchronization performance. In our study, we propose to modify both time-stamping and Doppler scale estimation procedures. Besides simulation, we also perform real tests in controlled underwater communication in a water test tank and a shallow-water test in the Mediterranean Sea.Peer ReviewedPostprint (author's final draft
Bandwidth enhancement : correcting magnitude and phase distortion in wideband piezoelectric transducer systems
Acoustic ultrasonic measurements are widespread and commonly use transducers exhibiting
resonant behaviour due to the piezoelectric nature of their active elements, being designed
to give maximum sensitivity in the bandwidth of interest. We present a characterisation of
such transducers that provides both magnitude and phase information describing the way in
which the receiver responds to a surface displacement over its frequency range. Consequently,
these devices work efficiently and linearly over only a very narrow band of their overall
frequency range. In turn, this causes phase and magnitude distortion of linear signals. To
correct for this distortion, we introduce a software technique, which considers only the input
and the final output signals of the whole systemwhich is therefore generally applicable to any
acoustic system. By correcting for the distortion of the magnitude and phase responses, we
have ensured the signal seen at the receiver replicates the desired signal. We demonstrate a
bandwidth extension on the received signal from 60-130 kHz at -6dB to 40-200 kHz at -1dB
in a test system. The linear chirp signal we used to demonstrate this method showed the
received signal to be almost identical to the desired linear chirp. Such systemcharacterisation
will improve ultrasonic techniques when investigating material properties by maximising the
accuracy of magnitude and phase estimations
Green compressive sampling reconstruction in IoT networks
In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks
Video Quality Assessment in Underwater Acoustic Networks
Fecha de Lectura de Tesis Doctoral: 23 de mayo de 2018.Las imágenes subacuáticas reciben una atención cada vez mayor por parte de la comunidad científica dado que las fotografías y los vídeos son herramientas de gran valor en el estudio del entorno oceánico que cubre el 90% de la biosfera de nuestro planeta. Sin embargo, las Redes de Sensores Submarinas deben enfrentarse al canal hostil que el agua de mar constituye. Las comunicaciones de medio rango son sólo posibles con modems acústicos de capacidades muy limitadas con tasas binarias de pico de unas decenas de kbps. En transmisión de vídeo, estas reducidas tasas binarias fuerzan una compresión elevada que produce niveles de distorsión mucho mayores que en otros entornos. Además, los usuarios de vídeo submarino son oceanógrafos u otros especialistas con una percepción de la calidad diferente a la de un grupo genérico de usuarios. Las peculiaridades descritas exigen un estudio dedicado de la evaluación de calidad de vídeo para redes submarinas.
Esta tesis doctoral aborda el problema de la evaluación de calidad de vídeo y presenta contribuciones en las dos áreas principales de esta disciplina: evaluación subjetiva y evaluación objetiva. La referencia para la percepción de calidad en cualquier servicio es la opinión de los usuarios y, por tanto, un análisis de la calidad subjetiva es el primer paso en este trabajo. Se presentan el diseño experimental y los resultados de un test de acuerdo a métodos psicométricos estándares. Los participantes del test fueron científicos del océano y las secuencias de vídeo utilizadas fueron grabadas en campañas de exploración y procesadas para simular las condiciones de las comunicaciones submarinas. Los resultados experimentales muestran como los vídeos son útiles para tareas científicas incluso en condiciones de muy baja tasa binaria.
Los métodos de evaluación de la calidad objetiva son algoritmos diseñados para calcular puntuaciones de calidad
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
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