Search for: All records

Underwater backscatter is a promising technology for ultra-lowpower underwater networking, but existing systems break down in mobile scenarios. This paper presents EchoRider, the first system to enable reliable underwater backscatter networking under mobility. EchoRider introduces three key components. First, it incorporates a robust and energy-efficient downlink architecture that uses chirp-modulated transmissions at the reader and a sub-Nyquist chirp decoder on backscatter nodes—bringing the resilience of LoRa-style signaling to underwater backscatter while remaining ultra-lowpower. Second, it introduces a NACK-based full-duplex retransmission protocol, enabling efficient, reliable packet delivery. Third, it implements a Doppler-resilient uplink decoding pipeline that includes adaptive equalization, polar coding, and dynamic retraining to combat channel variation. We built a full EchoRider prototype and evaluated it across over 1,200 real-world mobile experiments. EchoRider improves bit error rate by over 125× compared to a state-of-the-art baseline and maintains underwater goodput of 0.8 kbps at speeds up to 2.91 knots. In contrast, the baseline fails at speeds as low as 0.17 knots. Finally, we demonstrate EchoRider in end-to-end deployments involving mobile drones and sensor nodes, showing its effectiveness in practical underwater networked applications. 

We present the design, implementation, and evaluation of MiFly, a self-localization system for autonomous drones that works across indoor and outdoor environments, including low-visibility, dark, and GPS-denied settings. MiFly performs 6DoF self-localization by leveraging a single millimeter-wave (mmWave) anchor in its vicinity- even if that anchor is visually occluded. MiFly’s core contribution is in its joint design of a mmWave anchor and localization algorithm. The lowpower anchor features a novel dual-polarization dual-modulation architecture, which enables single-shot 3D localization. MmWave radars mounted on the drone perform 3D localization relative to the anchor and fuse this data with the drone’s internal inertial measurement unit (IMU) to estimate its 6DoF trajectory. We implemented and evaluated MiFly on a DJI drone. We collected over 6,600 localization estimates across different trajectory patterns and demonstrate a median localization error of 7 cm and a 90th percentile less than 15 cm, even in low-light conditions and when the anchor is fully occluded (visually) from the drone. Demo video: youtu.be/LfXfZ26tEok 

This paper investigates how an airborne node can eavesdrop on the underwater acoustic communication between submerged nodes. Conventionally, such eavesdropping has been assumed impossible as acoustic signals do not cross the water-air boundary. Here, we demonstrate that underwater acoustic communications signals can be picked up and (under certain conditions) decoded using an airborne mmWave radar due to the minute vibrations induced by the communication signals on the water surface. We implemented and evaluated a proof-of-concept prototype of our method and tested it in controlled (pool) and uncontrolled environments (lake). Our results demonstrate that an airborne device can identify the modulation and bitrate of acoustic transmissions from an uncooperative underwater transmitter (victim), and even decode the transmitted symbols. Unlike conventional over-the-air communications, our results indicate that the secrecy of underwater links varies depending on the modulation type and provide insights into the underlying reasons behind these differences. We also highlight the theoretical limitations of such a threat model, and how these results may have a significant impact on the stealthiness of underwater communications, with particular concern to submarine warfare, underwater operations (e.g., oil & gas, search & rescue, mining), and conservation of endangered species. Finally, our investigation uncovers countermeasures that can be used to improve or restore the stealthiness of underwater acoustic communications against such threats. 

We present RL2, a robotic system for efficient and accurate localization of UHF RFID tags. In contrast to past robotic RFID localization systems, which have mostly focused on location accuracy, RL2 learns how to jointly optimize the accuracy and speed of localization. To do so, it introduces a reinforcement learning-based (RL) trajectory optimization network that learns the next best trajectory for a robot-mounted reader antenna. Our algorithm encodes the aperture length and location confidence (using a synthetic-aperture-radar formulation) from multiple RFID tags into the state observations and uses them to learn the optimal trajectory. We built an end-to-end prototype of RL2 with an antenna moving on a ceiling-mounted 2D robotic track. We evaluated RL2 and demonstrated that with the median 3D localization accuracy of 0.55m, it locates multiple RFID tags 2.13x faster compared to a baseline strategy. Our results show the potential for RL-based RFID localization to enhance the efficiency of RFID inventory processes in areas spanning manufacturing, retail, and logistics. 

Underwater backscatter is a recent networking technology that enables net-zero-power communication and sensing in underwater environments. Existing research on underwater backscatter has focused on designing and demonstrating early systems with impressive capabilities; however, what remains critically missing is an end-to-end analysis of the underwater backscatter communication channel, which is necessary to understand the potential of this technology to scale to real-world applications and practical deployments. This paper presents the first comprehensive theoretical and empirical analysis of the underwater backscatter channel, including the downlink and uplink of end-to-end backscatter. We introduce a closed-form analytical model that encompasses the physical properties of piezoelectric materials, electromechanical coupling, electrical impedance, and the underwater acoustic channel. We verify the correctness of this theoretical analysis through both finite-element-model physical simulations and real-world experimental validation in a river, demonstrating that the analytical model matches our real-world experiments with a median deviation of only 0.76 dB. Using this model, we then simulate the theoretical limits of underwater backscatter as a function of different design parameters and identify pathways for pushing underwater backscatter toward its theoretical limits. 

We present the design, implementation, and evaluation of SeaScan, an energy-efficient camera for 3D imaging of underwater environments. At the core of SeaScan’s design is a trinocular lensing system, which employs three ultra-lowpower monochromatic image sensors to reconstruct color images. Each of the sensors is equipped with a different filter (red, green, and blue) for color capture. The design introduces multiple innovations to enable reconstructing 3D color images from the captured monochromatic ones. This includes an ML-based cross-color alignment architecture to combine the monochromatic images. It also includes a cross-refractive compensation technique that overcomes the distortion of the wide-angle imaging of the low-power CMOS sensors in underwater environments.We built an end-to-end prototype of SeaScan, including color filter integration, 3D reconstruction, compression, and underwater backscatter communication. Our evaluation in real-world underwater environments demonstrates that SeaScan can capture underwater color images with as little as 23.6 mJ, which represents 37× reduction in energy consumption in comparison to the lowest-energy state-of-the-art underwater imaging system.We also report qualitative and quantitative evaluation of SeaScan’s color reconstruction and demonstrate its success in comparison to multiple potential alternative techniques (both geometric and ML-based) in the literature. SeaScan’s ability to image underwater environments at such low energy opens up important applications in long-term monitoring for ocean climate change, seafood production, and scientific discovery. 

There is much interest in fine-grained RFID localization systems. Existing systems for accurate localization typically require infrastructure, either in the form of extensive reference tags or many antennas (e.g., antenna arrays) to localize RFID tags within their radio range. Yet, there remains a need for fine-grained RFID localization solutions that are in a compact, portable, mobile form, that can be held by users as they walk around areas to map them, such as in retail stores, warehouses, or manufacturing plants. We present the design, implementation, and evaluation of POLAR, a portable handheld system for fine-grained RFID localization. Our design introduces two key innovations that enable robust, accurate, and real-time localization of RFID tags. The first is complex-controlled polarization (CCP), a mechanism for localizing RFIDs at all orientations through software-controlled polarization of two linearly polarized antennas. The second is joint tag discovery and localization (JTDL), a method for simultaneously localizing and reading tags with zero-overhead regardless of tag orientation. Building on these two techniques, we develop an end-to-end handheld system that addresses a number of practical challenges in self-interference, efficient inventorying, and self-localization. Our evaluation demonstrates that POLAR achieves a median accuracy of a few centimeters in each of the x/y/z dimensions in practical indoor environments. 

We present the design, implementation, and evaluation of Van Atta Acoustic Backscatter (VAB), a technology that enables long-range, ultra-low-power networking in underwater environments. At the core of VAB is a novel, scalable underwater backscatter architecture that bridges recent advances in RF backscatter (Van Atta architectures) with ultra-low-power underwater acoustic networks. Our design introduces multiple innovations across the networking stack, which enable it to overcome unique challenges that arise from the electro-mechanical properties of underwater backscatter and the challenging nature of low-power underwater acoustic channels. We implemented our design in an end-to-end system, and evaluated it in over 1,500 real-world experimental trials in a river and the ocean. Our evaluation in stationary setups demonstrates that VAB achieves a communication range that exceeds 300m in round trip backscatter across orientations (at BER of 10−3). We compared our design head-to-head with past state-of-the-art systems, demonstrating a 15× improvement in communication range at the same throughput and power. By realizing hundreds of meters of range in underwater backscatter, this paper presents the first practical system capable of coastal monitoring applications. Finally, our evaluation represents the first experimental validation of underwater backscatter in the ocean. 

The majority of existing RFID readers rely on circularly polarized or switched polarization antennas for powering and communicating with tags.In this paper, we argue that a new form of software-controlled polarization brings important benefits to the tasks of powering, communicating with, and localizing RFID tags. Using only two linearly polarized antennas, we demonstrate how one could generate an arbitrarily linear polarization in the same plane relying entirely on software control. We incorporate this approach into a protocol that automatically discovers RFID orientations in the environment and show how this approach increases the range(or alternatively reduces the transmit power) of RFID readers. We also demonstrate this approach in an end-to-end RFID localization application. 

Locating RFID-tagged items in the environment and guiding humans to retrieve the tagged items is an important problem in the RFID community. This paper explores how to exploit synergies between Augmented Reality (AR) headsets and RFID localization to help solve this problem by improving both user experience and localization accuracy. Using fundamental mathematical formulations for RFID localization, we derive confidence metrics and display guidance to the user to improve their experience and enable them to retrieve items faster. We build our primitives into an end - to-end system, RF - AR, and show that it achieves 8.6 cm median localization accuracy within 76 seconds and enables 55% faster retrieval than state-of-the-art past systems. Our results demonstrate that AR-based “human-in-the-loop” designs can make the localization task more accurate and efficient, and thus holds the potential to improve processes where items need to be retrieved quickly, such as in manufacturing, retail, and warehousing.