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Cloud-Based Architecture for Hydrophone Data Acquisition and Processing of Surface and Underwater Vehicle Detection

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Pubblicato in:Journal of Marine Science and Engineering vol. 13, no. 8 (2025), p. 1455-1476
Autore principale: Pérez, Carrasco Francisco
Altri autori: Fernández García Anaida, García, Alberto, Ruiz, Bejerano Verónica, Gutiérrez Álvaro, Belmonte-Hernández, Alberto
Pubblicazione:
MDPI AG
Soggetti:
Data acquisition
Ingestion
Bandwidths
Propellers
Signal processing
Hydrophones
Modularity
Machine learning
Localization
Fault tolerance
Underwater vehicles
Acoustic data
Vehicles
Propagation
Acoustics
Target recognition
Classification
Network latency
Arrays
Algorithms
Latency
Real time
Cloud computing
Aquatic environment
Harbors
Ports
Data processing
Data transmission
Monitoring
Distributed processing
Acoustic imagery
Modular systems
Sensors
Neural networks
Design
Environmental
Accesso online:Citation/Abstract
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Abstract:This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance.
ISSN:2077-1312
DOI:10.3390/jmse13081455
Fonte:Engineering Database