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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 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. 

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. 

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. 

The past few years have witnessed a growing interest in wireless and batteryless implants, due to their potential in long-term biomedical monitoring of in-body conditions such as internal organ movements, bladder pressure, and gastrointestinal health. Early proposals for batteryless implants relied on inductive near-field coupling and ultrasound harvesting, which require direct contact between the external power source and the human body. To overcome this near-field challenge, recent research has investigated the use of RF backscatter in wireless micro-implants because of its ability to communicate with wireless receivers that are placed at a distance outside the body (∼0.5 m), allowing a more seamless user experience. Unfortunately, existing far-field backscatter designs remain limited in their functionality: they cannot perform biometric sensing or secure data transmission; they also suffer from degraded harvesting efficiency and backscatter range due to the impact of variations in the surrounding tissues. In this paper, we present the design of a batteryless, wireless and secure system-on-chip (SoC) implant for in-body strain sensing. The SoC relies on four features: 1) employing a reconfigurable in-body rectenna which can operate across tissues adapting its backscatter bandwidth and center frequency; 2) designing an energy efficient 1.37 mmHg strain sensing front-end with an efficiency of 5.9 mmHg·nJ/conversion; 3) incorporating an AES-GCM security engine to ensure the authenticity and confidentiality of sensed data while sharing the ADC with the sensor interface for an area efficient random number generation; 4) implementing an over-the-air closed-loop wireless programming scheme to reprogram the RF front-end to adapt for surrounding tissues and the sensor front-end to achieve faster settling times below 2 s. 

Mechanical search is a robotic problem where a robot needs to retrieve a target item that is partially or fully occluded from its camera. State-of-the-art approaches for mechanical search either require an expensive search process to find the target item, or they require the item to be tagged with a radio frequency identification tag (e.g., RFID), making their approach beneficial only to tagged items in the environment. We present FuseBot, the first robotic system for RF-Visual mechanical search that enables efficient retrieval of both RFtagged and untagged items in a pile. Rather than requiring all target items in a pile to be RF-tagged, FuseBot leverages the mere existence of an RF-tagged item in the pile to benefit both tagged and untagged items. Our design introduces two key innovations. The first is RF-Visual Mapping, a technique that identifies and locates RF-tagged items in a pile and uses this information to construct an RF-Visual occupancy distribution map. The second is RF-Visual Extraction, a policy formulated as an optimization problem that minimizes the number of actions required to extract the target object by accounting for the probabilistic occupancy distribution, the expected grasp quality, and the expected information gain from future actions. We built a real-time end-to-end prototype of our system on a UR5e robotic arm with in-hand vision and RF perception modules. We conducted over 180 real-world experimental trials to evaluate FuseBot and compare its performance to a of-the-art vision-based system named X-Ray. Our experimental results demonstrate that FuseBot outperforms X-Ray’s efficiency by more than 40% in terms of the number of actions required for successful mechanical search. Furthermore, in comparison to X-Ray’s success rate of 84%, FuseBot achieves a success rate of 95% in retrieving untagged items, demonstrating for the first time that the benefits of RF perception extend beyond tagged objects in the mechanical search problem. 

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.

 

We present the design, implementation, and evaluation of RFusion, a robotic system that can search for and retrieve RFID-tagged items in line-of-sight, non-line-of-sight, and fully-occluded settings. RFusion consists of a robotic arm that has a camera and antenna strapped around its gripper. Our design introduces two key innovations: the first is a method that geometrically fuses RF and visual information to reduce uncertainty about the target object's location, even when the item is fully occluded. The second is a novel reinforcement-learning network that uses the fused RF-visual information to efficiently localize, maneuver toward, and grasp target items. We built an end-to-end prototype of RFusion and tested it in challenging real-world environments. Our evaluation demonstrates that RFusion localizes target items with centimeter-scale accuracy and achieves 96% success rate in retrieving fully occluded objects, even if they are under a pile. The system paves the way for novel robotic retrieval tasks in complex environments such as warehouses, manufacturing plants, and smart homes.