2,261 research outputs found
Deep learning for internet of underwater things and ocean data analytics
The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes
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
Deep Learning and the Oceans
Machine and deep learning (DL) offer significant opportunities for exploring and monitoring oceans and for tackling important problems ranging from litter and oil spill detection to marine biodiversity estimation. Reasonably priced hardware platforms, in the form of autonomous (AUV) and remote operated (ROV) underwater vehicles, are also becoming available, fuelling the growth of data and offering new types of application areas. DL not only supports emerging applications that harness this data but offers support for operating such platforms. This article presents a research vision for DL in the oceans, collating applications and use cases, identifying opportunities, constraints, and open research challenges. We conduct experiments on underwater marine litter detection to demonstrate the benefits DL can bring to underwater environments. Our results show that integrating DL in underwater explorations can automate and scale-up monitoring, and highlight practical challenges in enabling underwater operations. We also provide a research roadmap for the path forward.Peer reviewe
A Communication Interface for Multilayer Cloud Computing Architecture for Low Cost Underwater Vehicles
To enable high computational loads for low cost underwater drones, a cloud based architecture is proposed to take advantage of recent development in machine learning and computer vision. The processing power made available will benefit vehicles with limited onboard processing capacity. The rapid development of cloud computing services have made servers with significant computational resources easier to access. In this paper, a communication interface for cloud based multilayer architecture is proposed to enable real time performance by distributing the workload to networked processing devices. It adopts a publish-subscribe model for efficient communication between the layers. The latency and workload distribution are evaluated to assess the efficiency of the proposed method. An application to semantic segmentation of under-water scenes is also tested to measure the framework capabilities for real-time operation using more resource-demanding tools. The conducted experiments resulted in time and performance gains through offloading the underwater vehicle, and forwarding the computations to the cloud based layer
SI-Lab Annual Research Report 2020
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020
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