Collaborative Research: Elements: Physics-Informed Digitized Cyberinfrustructure Towards Next-G Underwater Networks

Underwater wireless communication and networking are critical for monitoring aquatic environments, improving maritime safety, supporting offshore exploration, and enhancing national security. However, research progress in this field has been slow compared with terrestrial wireless systems because conducting underwater wireless experiments is challenging. Natural underwater environments are uncontrollable, and indoor pools and tanks are static and small, which significantly limits research reproducibility, innovation, and public accessibility. To bridge this gap, this project implements a remotely accessible underwater communication and networking platform in a water tunnel that enables experimentation, dataset collection, and artificial intelligence model building under a range of controlled, reconfigurable, reproducible conditions. The testbed, datasets, and developed software enable new wireless communication technology development without the high cost and complications of natural underwater deployments. By sharing advanced experimental tools, datasets, and software with the research community, this project advances scientific discovery and strengthens national leadership in next-generation underwater communication and networking systems. In addition, this project integrates research with education and actively trains students in communication, networking, sensing, and artificial intelligence to support workforce development and address critical national needs. This project designs and deploys a hybrid underwater acoustic, magnetic, and visible light networking system that integrates a reconfigurable water-tunnel testbed, physics-informed multi-modal deep generative channel models, and a scalable digital twin for dynamic underwater networking simulation and optimal control. First, the remotely accessible, reconfigurable testbed instrument enables the collection of acoustic, magnetic, and visible light communication channel data under dynamic water flow and blockage conditions. Second, the collected datasets are used to train physics-informed deep generative channel models that extend beyond the physical testbed to enable large-scale, measurement-driven simulations. Last, the physical testbed and channel models are integrated to develop a networking digital twin, which allows researchers to evaluate multi-modal scheduling strategies, resource allocation schemes, and networking protocols under realistic dynamic underwater conditions. All software, datasets, models, and documentation will be publicly released through open repositories and public websites. By linking physical experimentation with scalable digital simulation, this project will provide sustainable cyberinfrastructure that accelerates data-driven and artificial intelligence-enabled innovation in underwater wireless communication and networking. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2608506 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Nan Cen | Institution: Saint Louis University, SAINT LOUIS, MO | Award Amount: $257,051 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2608506 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2608506.html