ERI: Towards An AI-Defined Integrated Sensing and Communication Underwater Optical Networking System

There are a growing number of underwater applications, including climate change monitoring, marine biology research, oil rigs exploration, unmanned operations, search and rescue, underwater navigation, and scuba diving. Most of these applications demand reliable, flexible, and high-speed underwater sensing and communication systems. Despite significant advancements in terrestrial and space communication, high-speed underwater wireless communication remains in its infancy due to the harsh environmental conditions, unique signal propagation challenges, and a lack of infrastructure. The most widely used underwater communication methods - acoustic, radio frequency (RF), and optical waves - each face trade-offs. Acoustic signals can travel long distances but suffer from low data rates and high latency. RF signals offer higher data rates but are significantly attenuated in water, limiting their effective range to just a few meters. Optical communication holds great promise for delivering high-speed data transmission, however, it remains underutilized in underwater systems due to issues such as light scattering, absorption, misalignment, and sensitivity to environmental disturbances. To address these limitations, this project aims to develop an AI-defined high-speed underwater optical networking system that integrates sensing and communication into a unified architecture. In such a system, sensing and communication mutually enhance each other: real-time sensing informs more effective communication decisions, while efficient communication ensures timely delivery of sensing data. This synergy enables low-latency, high-reliability, intelligent, and adaptive data exchange in complex underwater environments. The research is organized into four main thrusts. In Thrust 1, we will design novel environment-aware underwater optical sensing techniques by discovering unique received signal patterns. Then, we will develop an innovative efficient framework for multi-agent underwater optical communication in Thrust 2. These two key thrusts will lay the foundation for our efforts to design advanced seamless underwater sensing and communication integration techniques in Thrust 3. Finally, in Thrust 4, we will design and implement an AI-driven software-defined underwater optical wireless networking testbed that natively supports real-time AI/ML-based optimization and decision-making in optical underwater communication, and experimentally validate and further optimize the techniques developed in Thrusts 1-3. Beyond its technical contribution, this project will promote education and workforce development by involving students in cutting-edge STEM research and conducting outreach to K–12 learners. 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.