No abstract

Imaging underwater environments is of great importance to marine sciences, sustainability, climatology, defense, robotics, geology, space exploration, and food security. Despite advances in underwater imaging, most of the ocean and marine organisms remain unobserved and undiscovered. Existing methods for underwater imaging are unsuitable for scalable, long-term, in situ observations because they require tethering for power and communication. Here we describe underwater backscatter imaging, a method for scalable, real-time wireless imaging of underwater environments using fully-submerged battery-free cameras. The cameras power up from harvested acoustic energy, capture color images using ultra-low-power active illumination and a monochrome image sensor, and communicate wirelessly at net-zero-power via acoustic backscatter. We demonstrate wireless battery-free imaging of animals, plants, pollutants, and localization tags in enclosed and open-water environments. The method’s self-sustaining nature makes it desirable for massive, continuous, and long-term ocean deployments with many applications including marine life discovery, submarine surveillance, and underwater climate change monitoring.

This paper is motivated by a simple question: 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.To answer this question, 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. We tested our prototype in an emulated marine bioacoustics application, demonstrating the potential to recognize underwater animal sounds without batteries. Through this exploration, we highlight the challenges and opportunities for making underwater batteryfree inference and machine learning ubiquitous. CCS Concepts• Computing methodologies → Supervised learning; • Hardware → Sensor devices and platforms; Renewable energy; Wireless integrated network sensors; • Applied computing → Environmental sciences.

Piezo-acoustic bacskcatter (PAB) is a recentlyintroduced ultra-low-power underwater communication technology. In contrast to traditional underwater acoustic modems, which need to generate power-consuming carrier signals, PAB nodes communicate by simply reflecting (i.e., backscattering) existing acoustic signals in the environment. This reflection-based approach enables them to communicate at net-zero power but also imposes significant constraints on their throughput and modulation schemes.We present PAB-QAM, the first underwater backscatter design capable of achieving higher-order modulation. PAB-QAM exploits the electro-mechanical coupling property of piezoelectric transducers to modulate their reflection coefficients. Specifically, by strategically employing reactive circuit components (inductors), we demonstrate how PAB-QAM nodes can modulate the phase and amplitude of acoustic reflections and realize higher-order and spectrally-efficient modulation schemes such as QAM.We designed and built a prototype of PAB-QAM and empirically evaluated it underwater. Our empirical evaluation demonstrates that PAB-QAM can double the throughput of underwater backscatter without requiring additional power, spectrum, or cost. Looking ahead, such increased throughput paves way for various subsea IoT applications in ocean exploration, underwater climate monitoring, and marine life sensing.

Can we build a battery-free underwater GPS? While underwater localization is a long-studied problem, in this paper, we seek to bring it to battery-free underwater networks [13, 20]. These recentlyintroduced 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 present the design and demonstration of the first underwater backscatter localization (UBL) system. Our design explores various challenges for bringing localization to underwater backscatter, including extreme multipath, acoustic delay spread, and mobility. We describe how an adaptive and context-aware algorithm may address some of these challenges and adapt to diverse underwater environments (such as deep vs shallow water, and high vs low mobility). We also present a prototype implementation and evaluation 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. CCS CONCEPTS • Networks → Network architectures; • Hardware → Wireless integrated network sensors; • Applied computing → Environmental sciences.