Cloud-based hydrophone architecture for real-time detection of surface and underwater vehicles
Researchers from the Information Processing and Telecommunications Center (IPTC) (IPTC) have developed and validated a cloud-based architecture for the acquisition and processing of underwater acoustic data using hydrophone arrays. The system enables the detection and classification of both surface and submerged vessels in real time, combining edge computing, cloud services, and machine learning algorithms to achieve scalable and low-latency performance.
The architecture follows a modular three-layer design, integrating hydrophone data acquisition at the edge, robust communication through message brokers, and cloud-based storage, analytics, and visualization. Field trials at the Port of Valencia demonstrated the platform’s capability to autonomously handle high-volume acoustic streams, correlate them with Automatic Identification System (AIS) data, and generate a multimodal dataset of vessel activity. The approach leverages microservices, containerization, and adaptive scaling to ensure resilience and flexibility across diverse maritime environments.The outcomes show the feasibility of deploying such hybrid edge-cloud systems for continuous maritime monitoring while ensuring efficient resource usage through event-driven data acquisition.
Potential Applications:
This research opens new opportunities in maritime traffic monitoring, harbor security, environmental surveillance, and marine ecosystem protection. The cloud-native design also supports future extensions such as multi-array localization, predictive vessel behavior modeling, and integration into smart port infrastructures. By enabling scalable, adaptive, and resilient underwater acoustic sensing, the system contributes to safer and more sustainable maritime operations.
Bibliographic References:
Pérez Carrasco, F., Fernández García, A., García, A., Ruiz Bejerano, V., Gutiérrez, Á., & Belmonte-Hernández, A. (2025). Cloud-Based Architecture for Hydrophone Data Acquisition and Processing of Surface and Underwater Vehicle Detection. Journal of Marine Science and Engineering, 13(8), 1455 https://doi.org/10.3390/jmse13081455
Alberto Belmonte Hernández, , ORCID / Google Scholar
Forwed information: www.iptc.upm.es
Share this:
