From batteryless to scalable

Over the past few years, we have been expanding underwater backscatter to build a scalable and ultra-low power ocean IoT.  In one of our projects, we designed a metamaterial-inspired transducer for underwater backscatter, and algorithms that enable self-interference cancellation and FDMA-based medium access control. Not only does this new design allow us to extend our communication range and to decode backscatter signals in the presence of strong self-interference, but it also enables us to scale underwater backscatter to multiple nodes.

Award-winning

Our technology has been published in over 12 papers and patents and received prestigious awards including:

• ACM MobiCom Best Paper Award, 2023
• Nature Communications Research Highlights, 2023
• Nature Electronics Research Highlights, 2022
• ACM International Conference on Underwater Networks and Systems Best Demo Award, 2022
• Ocean Observing Prize: Winner of Explorer Prize from U.S. Department of Energy and NOAA, 2020
• US Office of Naval Research (ONR) Early Career Grant, 2020
• ACM SIGCOM Best Paper Award, 2019

What are the applications of this technology for sensing and communication ?

Creating an Underwater GPS

While underwater localization is a long-studied problem,  we seek to bring it to battery-free underwater networks. These recently introduced networks communicate by simply backscattering (i.e., reflecting) acoustic signals. While such backscatter-based communication enables them to operate at net-zero power, it also introduces new and unique challenges for underwater localization.  We have designed the first underwater backscatter localization (UBL) system. UBL uses an adaptive and context-aware algorithm that addresses many of these challenges and allows it to adapt to diverse underwater environments (such as deep vs shallow water, and high vs low mobility). We have implemented and evaluated a prototype of UBL in the Charles River in Boston and highlight open problems and opportunities for underwater backscatter localization in ocean exploration, marine-life sensing, and robotics. 

Enabling communication between underwater and air

Did you know that submarines today still cannot wirelessly communicate with airplanes? For decades, communicating between underwater and the air has remained an unsolved problem. Underwater, submarines use acoustic signals (or SONAR) to communicate; in the air, airplanes use radio signals like cellular or WiFi. But neither of these signals can work across both water and air.

We present TARF (Translational Acoustic-RF communication), the first technology that enables communication between underwater and the air. A TARF transmitter sends standard sound (or SONAR signals).  Sound travels as pressure waves; when these waves hit the surface, they cause it to vibrate. To pick up these vibrations, a TARF receiver in the air uses a very sensitive radar. The radar transmits a signal which reflects off the water surface and comes back. As the water surface vibrates, it causes small changes to the received radar signal, enabling a TARF receiver to sense the tiny vibrations caused by the underwater acoustic transmitter.  Because TARF uses acoustic signals underwater and radio signals in air, it is able to achieve the best of both worlds. 

Capturing Battery-free Wireless Underwater Images

More than 95% of the ocean has never been observed by humans, even though the ocean plays the largest role in the world's climate system, has so many undiscovered species, and regulates the world's weather.

We invented the world's first battery-free wireless underwater camera.  The camera is powered by underwater sound, captures color images at ultra-low-power, and transmits them wirelessly to a remote receiver. Our electronic design consumes 100,000 times less power than state-of-the-art low-power underwater imaging systems, allowing us to power it entirely based on the harvested sound energy.  This technology paves the way for massive, continuous, and long-term ocean deployments with many applications including marine life discovery, submarine surveillance,  climatology, maritime archeology, geology, space exploration, aquaculture farming, and underwater climate change monitoring.

Underwater Machine Learning

Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific exploration, and climate/weather prediction. We explore the feasibility of bridging advances from the past decade in two fields: battery-free networking and low-power machine learning. Our exploration demonstrates that it is indeed possible to enable battery-free inference in underwater environments. We designed a device that can harvest energy from underwater sound, power up an ultra-low-power microcontroller and on-board sensor, perform local inference on sensed measurements using a lightweight Deep Neural Network, and communicate the inference result via backscatter to a receiver. 

Theoretical analysis of underwater backscatter

In addition to demonstrating exciting applications of underwater backscatter, we have also rigorously developed the first comprehensive theoretical and empirical analysis of the underwater backscatter channel, including the downlink and uplink of end-to-end backscatter. This work serves as the theoretical basis for designing and implementing long-range underwater backscatter. In this work we show that underwater backscatter communication is possible over kilometer-scale distances, enabling applications in deep-sea exploration, under-ice navigation, and early-warning systems for disaster response. 

Related Conferences and Seminars

MIT Courses

MAS.S62: Ocean IoT: Technologies, Industries, Sustainability