Project

Oceans Internet of Things

Copyright

MIT

Jimmy Day

Our Oceans IoT technologies enable new applications in climate and ecological monitoring, aquaculture, energy, and robotic navigation. 

Bringing massive connectivity to low-cost, low-power ocean sensors is important for numerous oceanographic applications across climate/weather modeling, marine biology, aquaculture, and defense. However, standard IoT technologies (e.g, Bluetooth, WiFi, GPS) cannot operate underwater, which has left 70% of our planet (the ocean) beyond their reach.  Our research is changing this reality by inventing IoT technologies that are inherently designed for the ocean. By rethinking the entire IoT technology stack in the context of oceans, we introduced low-cost (< $100), net-zero-power, scalable connectivity technologies that seamlessly operate underwater and pave the way for massive underwater sensing, networking, localization, imaging, and machine learning.

How can we enable a batteryless ocean IoT?

Underwater Backscatter

We developed Piezo-Acoustic Backscatter (PAB), the first technology that enables backscatter networking in underwater environments. PAB relies on the piezoelectric e… View full description

Our Oceans IoT technologies enable new applications in climate and ecological monitoring, aquaculture, energy, and robotic navigation. 

Bringing massive connectivity to low-cost, low-power ocean sensors is important for numerous oceanographic applications across climate/weather modeling, marine biology, aquaculture, and defense. However, standard IoT technologies (e.g, Bluetooth, WiFi, GPS) cannot operate underwater, which has left 70% of our planet (the ocean) beyond their reach.  Our research is changing this reality by inventing IoT technologies that are inherently designed for the ocean. By rethinking the entire IoT technology stack in the context of oceans, we introduced low-cost (< $100), net-zero-power, scalable connectivity technologies that seamlessly operate underwater and pave the way for massive underwater sensing, networking, localization, imaging, and machine learning.

How can we enable a batteryless ocean IoT?

Underwater Backscatter

We developed Piezo-Acoustic Backscatter (PAB), the first technology that enables backscatter networking in underwater environments. PAB relies on the piezoelectric effect to enable underwater communication and sensing at near-zero power. Check out the video below and our paper to see how it works.

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.

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. 

The Future of Underwater Sensing

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. 

Copyright

MIT

Related Conferences and Seminars