Underwater backscatter is an emerging technology for low-cost and low-power communication and sensing in underwater environments. However, past underwater backscatter systems have been largely limited to simple on-off modulation schemes, which inherently limit their robustness, adaptivity, and spectral efficiency.

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 low-power anchor features a novel dual-polarization dual-modulation architecture, which enables single-shot 3D localization. Mm Wave 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 90thpercentile less than 15 cm, even in low-light conditions and when the anchor is fully occluded (visually) from the drone. Demo video: voutu.be/LfXfZ26tEok

Localization is a critical task for underwater robots, yet today's underwater localization systems are limited by their accuracy, scalability, and/or energy consumption (i.e., longevity). We present the design, implementation, and evaluation of EchoBLUE- an accurate, scalable, and low-power localization system for underwater robots.

This demo presents BlueTag, a permanently deployed underwater sensor system based on backscatter communication. BlueTag is a battery-powered CTD (conductivity, temperature, depth) sensor that transmits measurements every 15 minutes to a remote base station via underwater backscatter. The base station archives these measurements and publishes them online. Unlike prior underwater backscatter systems limited to short-term laboratory experiments, BlueTag has been deployed in the Charles River in Boston, MA since July 9th, 2025, marking the first long-term underwater backscatter deployment of its kind to sense meaningful environmental data. Live data from this deployment is available publicly at https://sk-exp-server.mit.edu/.

This paper presents the design, implementation, and evaluation of mmNorm, a new and highly-accurate method for non-line-of-sight 3D object reconstruction using millimeter wave (mmWave) signals. In contrast to past approaches for millimeter-wave-based imaging that perform backprojection for 3D object reconstruction, mmNorm reconstructs the surface by estimating the object's surface normals. To do this, it introduces a novel algorithm that directly estimates the surface normal vector field from mmWave reflections. By then inverting the normal field, it can reconstruct structural isosurfaces, then solve for the exact surface through a novel mmWave optimization framework.

Underwater backscatter is a promising technology for ultra-low-power 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.

We present the design, implementation, and evaluation of 3D-BLUE, an ultra-low-power underwater 3D localization system that can be deployed on compact robots to accurately localize them in shallow underwater environments. 3D-BLUE’s design introduces two core components. First, it adapts a recent ultra-low-power underwater acoustic communication technology (called piezo-electric backscatter) to the underwater robotics localization problem; specifically, it integrates backscatter nodes into the underwater robot and uses them for localizing it. Second, it leverages the physical properties of the backscatter technology to efficiently extract spatio-temporal-spectral features from the backscatter signal; using these features, it devises a particle-filter-based algorithm to localize the corresponding robot accurately in challenging shallow-water environments. We implemented an end-to-end prototype of 3D-BLUE on a BlueROV2 robot and custombuilt backscatter localization system, and evaluated it in dozens of experimental trials in a pool. Our results demonstrate that 3D-BLUE can localize the robot with an accuracy of around 0.25m at close range and an accuracy of around 1.4m at a range of 10m. This high localization accuracy opens important commercial, naval, and environmental applications in challenging shallow-water environments such as shores, rivers, pools, and narrow waterways.

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.

This study examines how an airborne device can intercept underwater acoustic signals exchanged between submerged nodes. It challenges the conventional belief that acoustic communications under the water are safe against eavesdropping since acoustics do not cross the water-air boundary. We show that an airborne mmWave radar can detect and decode underwater acoustic signals by picking up minute surface vibrations induced by these signals. The proof-of-concept was tested in controlled (pool) and uncontrolled (lake) environments, proving that an airborne adversary can identify modulation type, bitrate, and decode symbols from an uncooperative underwater transmitter using its radar sensing capabilities. We demonstrate that the secrecy of underwater links depends on modulation type, providing insights into countermeasures to enhance the security of underwater acoustic communications.

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-low-power 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 37X 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.

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.

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.

Acoustic backscatter is a recent communication technology that enables ultra-low power sensing and connectivity in underwater environments. This demo presents an interactive graphical tool (Matlab App) for quickly evaluating the performance of acoustic backscatter in real-world scenarios. Our tool enables users to customize a backscatter system with practical underwater transducers and channel properties, then uses an analytical framework to predict the system's power-up and communication range. Researchers and IoT designers with no acoustics background can leverage our tool to make informed design choices and optimize their system's performance. A demo video of the tool can be found here: https://youtu.be/CJBYtOHUjC8

This demo presents X-AR, an Augmented Reality headset that enables its user to find and retrieve hidden items. X-AR leverages battery-less 3-cent Radio Frequency IDentification (RFID) tags that are already deployed on billions of items. As a user wearing X-AR moves in an environment, the headset transmits RF signals and leverages natural human mobility to locate RFID-tagged items. X-AR then guides the user toward the desired item for retrieval. We built a real-time prototype of this system on a Microsoft Hololens 2 AR headset with a conformal antenna, software radios, and an edge server. Our demo will enable any user to wear our X-AR prototype and use it to find and retrieve hidden items in a warehouse-like setting. Demo Video: youtu.be/bdUN21ft7G0

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.

We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments. Unlike prior systems that are constrained by the line-of-sight perception of vision and infrared sensors, RF-Grasp employs RF (Radio Frequency) perception to identify and locate target objects through occlusions, and perform efficient exploration and complex manipulation tasks in non-line-of-sight settings.RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest. It introduces two main innovations: (1) an RF-visual servoing controller that uses the RFID’s location to selectively explore the environment and plan an efficient trajectory toward an occluded target, and (2) an RF-visual deep reinforcement learning network that can learn and execute efficient, complex policies for decluttering and grasping.We implemented and evaluated an end-to-end physical prototype of RF-Grasp. We demonstrate it improves success rate and efficiency by up to 40-50% over a state-of-the-art baseline. We also demonstrate RF-Grasp in novel tasks such mechanical search of fully-occluded objects behind obstacles, opening up new possibilities for robotic manipulation. Qualitative results (videos) available at rfgrasp.media.mit.edu

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.

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. 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 present the design, implementation, and evaluation of μmedIC, a fully-integrated wireless and batteryless micro-implanted sensor. The sensor powers up by harvesting energy from RF signals and communicates at near-zero power via backscatter. In contrast to prior designs which cannot operate across various in-body environments, our sensor can self-reconfigure to adapt to different tissues and channel conditions. This adaptation is made possible by two key innovations: a reprogrammable antenna that can tune its energy harvesting resonance to surrounding tissues, and a backscatter rate adaptation protocol that closes the feedback loop by tracking circuit-level sensor hints.

The seismocardiogram (SCG) is a recording of a human heart's mechanical activity. It captures fine-grained cardiovascular events such as the opening and closing of heart valves and the contraction and relaxation of heart chambers. Today, SCG recordings are obtained by strapping an accelerometer at the apex of the heart to measure chest wall vibrations. These recordings can be used to diagnose and monitor various cardiovascular conditions including myocardial infarction (heart attack), coronary heart disease, and ischemia.

We present the design, implementation, and evaluation of U2B, a technology that enables ultra-wideband backscatter in underwater environments. At the core of U2B's design is a novel metamaterialinspired transducer for underwater backscatter, and algorithms that enable self-interference cancellation and FDMA-based medium access control.

We present 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. Its architecture is inspired by radio backscatter which works well in air but cannot work well underwater due to the exponential attenuation of radio signals in water.

Can we sense food quality and safety using wireless signals? In this paper, we explore how we can discover properties of a container's contents without opening it and without any physical contact with its contents. Our idea is to exploit electromagnetic interactions between wireless stickers placed on the container and materials in their immediate vicinity (i.e., inside the container) to determine food quality and safety. We show how a preliminary prototype implementation of our technique enables us to answer questions like: Is a baby formula inside a container pure or tainted? Is the alcohol content of a bottle safe? These capabilities pave way for ubiquitous wireless sensing technologies that can inform their users about the health and safety of their food.

We present IVN (In-Vivo Networking), a system that enables powering up and communicating with miniature sensors implanted or injected in deep tissues. IVN overcomes fundamental challenges which have prevented past systems from powering up miniature sensors beyond superficial depths. These challenges include the significant signal attenuation caused by bodily tissues and the miniature antennas of the implantable sensors.

We consider the problem of wireless communication across medium boundaries, specifically across the water-air interface. In particular, we are interested in enabling a submerged underwater sensor to directly communicate with an airborne node. Today's communication technologies cannot enable such a communication link. This is because no single type of wireless signal can operate well across different media and most wireless signals reflect back at media boundaries.

State-of-the-art RFID localization systems fall under two categories. The first category operates with off-the-shelf narrowband RFID tags but makes restrictive assumptions on the environment or the tag's movement patterns. The second category does not make such restrictive assumptions; however, it requires designing new ultra-wideband hardware for RFIDs and uses the large bandwidth to directly compute a tag's 3D location. Hence, while the first category is restrictive, the second one requires replacing the billions of RFIDs already produced and deployed annually. This paper presents RFind, a new technology that brings the benefits of ultra-wideband localization to the billions of RFIDs in today's world. RFind does not require changing today's passive narrowband RFID tags. Instead, it leverages their underlying physical properties to emulate a very large bandwidth and uses it for localization. Our empirical results demonstrate that RFind can emulate over 220MHz of bandwidth on tags designed with a communication bandwidth of only tens to hundreds of kHz, while remaining compliant with FCC regulations. This, combined with a new super-resolution algorithm over this bandwidth, enables RFind to perform 3D localization with sub-centimeter accuracy in each of the x/y/z dimensions, without making any restrictive assumptions on the tag's motion or the environment.

Battery-free sensors, such as RFIDs, are annually attached to billions of items including pharmaceutical drugs, clothes, and manufacturing parts. The fundamental challenge with battery-free sensors is that they are only reliable at short distances of tens of centimeters to few meters. As a result, today's systems for communicating with and localizing battery-free sensors are crippled by the limited range.

This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-the-art emotion recognition systems that require a person to be hooked to an ECG monitor.

The evolution of ubiquitous sensing technologies has led to intelligent environments that can monitor and react to our daily activities, such as adapting our heating and cooling systems, responding to our gestures, and monitoring our elderly. In this paper, we ask whether it is possible for smart environments to monitor our vital signs remotely, without instrumenting our bodies. We introduce Vital-Radio, a wireless sensing technology that monitors breathing and heart rate without body contact. Vital-Radio exploits the fact that wireless signals are affected by motion in the environment, including chest movements due to inhaling and exhaling and skin vibrations due to heartbeats. We describe the operation of Vital-Radio and demonstrate through a user study that it can track users' breathing and heart rates with a median accuracy of 99%, even when users are 8~meters away from the device, or in a different room. Furthermore, it can monitor the vital signs of multiple people simultaneously. We envision that Vital-Radio can enable smart homes that monitor people's vital signs without body instrumentation, and actively contribute to their inhabitants' well-being.

We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subject's body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.

This demo presents Vital-Radio, a wireless sensing technology that monitors breathing remotely, without requiring any body contact. Vital-Radio operates by transmitting a low-power wireless signal and monitoring its reflections off the human body. It uses these reflections to track motion associated with breathing, i.e., the chest movements caused by inhaling and exhaling. The demo will enable any person to sit in front of the device and check that it tracks their inhale and exhale process. The person may hold his/her breath and check that the device detects the breath holding event in real-time.

Recent years have witnessed increasing interest in interference alignment which has been demonstrated to deliver gains for wireless networks both analytically and empirically. Typically, interference alignment is achieved by having a MIMO sender precode its transmission to align it at the receiver. In this paper, we show, for the first time, that interference alignment can be achieved via motion, and works even for single-antenna transmitters. Specifically, this alignment can be achieved purely by sliding the receiver's antenna. Interestingly, the amount of antenna displacement is of the order of one inch which makes it practical to incorporate into recent sliding antennas available on the market. We implemented our design on USRPs and demonstrated that it can deliver 1.98× throughput gains over 802.11n in networks with both single-antenna and multi- antenna nodes.

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches).

Wi-Fi signals are typically information carriers between a transmitter and a receiver. In this paper, we show that Wi-Fi can also extend our senses, enabling us to see moving objects through walls and behind closed doors. In particular, we can use such signals to identify the number of people in a closed room and their relative locations. We can also identify simple gestures made behind a wall, and combine a sequence of gestures to communicate messages to a wireless receiver without carrying any transmitting device. The paper introduces two main innovations. First, it shows how one can use MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. Second, it shows how one can track a human by treating the motion of a human body as an antenna array and tracking the resulting RF beam. We demonstrate the validity of our design by building it into USRP software radios and testing it in office buildings.

GPS is one of the most widely used wireless systems. A GPS receiver has to lock on the satellite signals to calculate its position. The process of locking on the satellites is quite costly and requires hundreds of millions of hardware multiplications, leading to high power consumption. The fastest known algorithm for this problem is based on the Fourier transform and has a complexity of O(n log n), where n is the number of signal samples. This paper presents the fastest GPS locking algorithm to date. The algorithm reduces the locking complexity to O(n√(log n)). Further, if the SNR is above a threshold, the algorithm becomes linear, i.e., O(n). Our algorithm builds on recent developments in the growing area of sparse recovery. It exploits the sparse nature of the synchronization problem, where only the correct alignment between the received GPS signal and the satellite code causes their cross-correlation to spike.

Recent studies show that high-power cross-technology interference is becoming a major problem in today's 802.11 networks. Devices like baby monitors and cordless phones can cause a wireless LAN to lose connectivity. The existing approach for dealing with such high-power interferers makes the 802.11 network switch to a different channel; yet the ISM band is becoming increasingly crowded with diverse technologies, and hence many 802.11 access points may not find an interference-free channel.