research

Our research efforts fall into the interdisciplinary field of underwater mobile networks, which integrate knowledge from digital communications, networking, and marine robotics. Due to increased societal interests in the oceans and marine resources, this technical area is attracting more and more attention from governments and industries. Taking an interdisciplinary approach, my team undertakes a wide range of activities, including in-lab tests, field experiments, algorithm design, numerical modeling, and hardware developments.

This project envisions an underwater mobile multi-modal communication network to support ocean sampling and AUV missions.  Through this combined physics- and sampling-based perspective, we see multiple new research questions in the proposed network. We plan to address three research thrusts: 1) mobile acoustic channel modeling, 2) mobile adaptive acoustic communications, 3) integrated sampling, communications, and navigation. We are developing innovative machine learning solutions to address these research problems. Intelligent signal processing techniques will be developed at multiple layers, including learning-based acoustic channel modeling, model-based adaptive acoustic communications, and learning-based multi-objective optimization for integrated sampling, communications, and navigation. These new techniques will demonstrate how the science insights advance our abilities to precisely model the acoustic channel, reliably communicate, and effectively sample in the ocean. With the new tools, we deepen our understanding of how mobile acoustic channels fluctuate in the micro-scale. We will generate knowledge on how to sample the ocean field based on spatial correlation properties.

This project is a collaboration among the University of Alabama, Georgia Tech, City University of New York, and Michigan Technological University. The next frontier in ocean research is to use fleets of aquatic robots, both autonomous surface vehicles (ASVs) and AUVs, to perform distributed sampling in aquatic environments. This project develops a community-shared, open-source, open-architecture infrastructure, mu-Net, to support integrated sensing, communications, and navigation of a fleet of autonomous vehicles in the underwater domain. The overall goal is to support research and education in the joint communities of underwater signal processing, communications, networking, and robotics. Being the first of its kind, the infrastructure will significantly expand research participation in underwater technologies and marine sciences by lowering the barrier for experimental efforts. mu-Net will impact research in many areas within and beyond CISE, including underwater communications and networking, marine robotics, marine biology, food sources, and economic development. It has the potential to inspire more exploration of the earth’s vast water bodies for scientific, commercial, and recreational activities.

Wireless communication technologies in the ocean are critical to scientific, commercial, and national defense operations, and yet are still primitive, especially with respect to their data rates and reliability. This project develops new underwater acoustic communications and networking strategies to improve network efficiency in the harsh ocean environment, through underwater in-band full-duplex (IBFD) acoustic networks. As a joint initiative between the University of Delaware (lead) and the University of Alabama, this NSF project creates a vertically integrated research experience for both undergraduate and graduate students by exposing them to interdisciplinary research.

​Autonomous underwater vehicles (AUVs) are a powerful tool to sample the ocean and the Great Lakes. The project addresses the urgency to effectively transfer data in real-time among a fleet of navigating AUVs. A major technological bottleneck lies in the challenges to achieve high-speed mobile underwater wireless communication. The project develops an underwater mobile testbed using a software-defined networking (SDN) architecture to achieve integrated communications and navigation. In the SDN framework, a fleet of autonomous surface vehicles (ASVs) are directed to follow the sampling AUVs to provide acoustic and magnetic induction (MI) communication services over relatively short ranges. The MRI testbed will be designed to achieve cost-effectiveness, transferability, flexibility, and scalability.

Thousands of miles of pipelines crisscrossed on the Gulf of Mexico seafloor are the veins for offshore oil and gas industry of U.S. or even the whole world, while the leaks and ruptures of those pipelines lead to not only enormous economic loss but also environmental disasters. The goal of this project is thus to effectively monitor the subsea infrastructure such as offshore pipelines, and efficiently deliver the sensed information in the subsea environment. To provide a viable solution, this project will integrate the piezoelectric transducers designs, acoustic communications, stress wave communications (SWC), and hybrid networking, and conduct experiments and field tests to validate the proposed designs. Such a vision needs efforts from both engineering and scientific perspectives, and the success of the proposed transformative research will significantly improve the design, analysis and implementation of subsea infrastructure monitoring and data transmission systems. The research outcomes will potentially contribute to future subsea Internet of things and ocean big data systems, and have impact on offshore oil and gas industry, pollution control, ocean agriculture, disaster rescue, etc. The project will also provide special interdisciplinary training opportunities for both graduate and undergraduate students, particularly women and minority students, across multiple institutions through both research work and related courses.The University of Houston is the lead institution. The University of Alabama is one of the collaborative institutions. The project website is accessible here.