Imagine placing your smartphone on a table in a noisy restaurant and clearly capturing the voices of friends seated around you, or recording a lecturer’s voice with clarity in a reverberant auditorium. We introduce SonicSieve, the first intelligent directional speech extraction system for smartphones using a bio-inspired acoustic microstructure. Our passive design embeds directional cues onto incoming speech without any additional electronics. It attaches to the in-line mic of low-cost wired earphones which can be attached to smartphones. We present an end-to-end neural network that processes the raw audio mixtures in real-time on mobile devices. Our results show that SonicSieve achieves a signal quality improvement of 5.0 dB when focusing on a 30° angular region. Additionally, the performance of our system based on only two microphones exceeds that of conventional 5-microphone arrays.

Wireless sensing literature has long aspired to achieve X-ray-like vision at radio frequencies. Yet, state-of-the-art wireless sensing literature has yet to generate the archetypal X-ray image: one of the bones beneath flesh. In this paper, we explore OssiSense, a penetration-based RF-imaging system for imaging bones at mm-resolution, one that significantly exceeds prior penetration-based RF imaging literature. Indeed the long wavelength, significant attenuation and complex diffraction that occur as RF propagates through flesh, have long limited imaging resolution (to several centimeters at best). We address these concerns through a novel penetration-based synthetic aperture algorithm, coupled with a learning-based pipeline to correct for diffraction-induced artifacts. A detailed evaluation of meat models demonstrates a resolution improvement from sub-decimeter to sub-centimeter over prior art in RF penetrative imaging.

In this paper, we explore a system to sense the roughness of surfaces, even if obstructed from the field of view. We pose this question in the context of robotic grasping and manipulation to explore whether robots can learn the texture of objects prior to grasping them. Importantly, we seek to do so in a completely contact-free fashion (ruling out tactile sensors), despite obstructions (ruling out cameras and lidar). We present mmTexora, a novel roughness sensing system using mmWave radar. mmTexora leverages ambient vibrations that produce temporal phase variations to objects in everyday environments, when perceived by radar. We demonstrate how these phase variations convey valuable information about the structure of bumps and ridges on a surface, thereby revealing details about surface roughness. We then develop a signal processing and deep learning pipeline that extracts surface roughness from the signal’s temporal variations. mmTexora uses this information to classify surface textures when the material type of an object is known. Conversely, mmTexora can also classify material types for objects when they are known to have similar textures. We perform a qualitative study on a robotic arm tested on diverse objects, where either texture or material type is varied individually. Our classification model achieves an average surface classification accuracy of 93.7% on 50 surfaces that are commonly seen in daily life, with an average absolute error of 0.11 mm in roughness measurements.

In this paper, our goal is to enable quantitative feedback on muscle fatigue during exercise to optimize exercise effectiveness while minimizing injury risk. We seek to capture fatigue by monitoring surface vibrations that muscle exertion induces. Muscle vibrations are unique as they arise from the asynchronous firing of motor units, producing surface micro-displacements that are broadband, nonlinear, and seemingly stochastic. Accurately sensing these noise-like signals requires new algorithmic strategies that can uncover their underlying structure. We present GigaFlex the first contactless system that measures muscle vibrations using mmWave radar to infer muscle force and detect fatigue. GigaFlex draws on algorithmic foundations from Chaos theory to model the deterministic patterns of muscle vibrations and extend them to the radar domain. Specifically, we design a radar processing architecture that systematically infuses principles from Chaos theory and nonlinear dynamics throughout the sensing pipeline, spanning localization, segmentation, and learning, to estimate muscle forces during static and dynamic weight-bearing exercises. Across a 23-participant study, GigaFlex estimates maximum voluntary isometric contraction (MVIC) root mean square error (RMSE) of 5.9\%, and detects one to three Repetitions in Reserve (RIR), a key quantitative muscle fatigue metric, with an AUC of 0.83 to 0.86, performing comparably to a contact-based IMU baseline. Our system can enable timely feedback that can help prevent fatigue-induced injury, and opens new opportunities for physiological sensing of complex, non-periodic biosignals.

Metasurfaces, or Reconfigurable Intelligent Surfaces (RISs), have emerged as a transformative technology for next-generation wireless systems, enabling digitally controlled manipulation of electromagnetic wave propagation. By turning the traditionally passive radio environment into a smart, programmable medium, metasurfaces promise advances in communication and sensing. However, metasurfaces also present a new security frontier: both attackers and defenders can exploit them to alter wireless propagation for their own advantage. While prior security research has primarily explored unilateral metasurface applications - empowering either attackers or defenders - this work investigates symmetric scenarios, where both sides possess comparable metasurface capabilities. Using both theoretical modeling and real-world experiments, we analyze how competing metasurfaces interact for diverse objectives, including signal power and sensing perception. Thereby, we present the first systematic study of context-agnostic metasurface-to-metasurface interactions and their implications for wireless security. Our results reveal that the outcome of metasurface "battles" depends on an interplay of timing, placement, algorithmic strategy, and hardware scale. Across multiple case studies in Wi-Fi environments, including wireless jamming, channel obfuscation for sensing and communication, and sensing spoofing, we demonstrate that opposing metasurfaces can substantially or fully negate each other's effects. By undermining previously proposed security and privacy schemes, our findings open new opportunities for designing resilient and high-assurance physical-layer systems in smart radio environments.

With the increasing use of wireless technologies in robotics for communication, sensing, and localization, the potential benefits of how robotics can complement and enhance wireless systems remain underexplored. This paper explores a novel application of the existing inflatable robots for wireless communication systems by forming a shape-programming, reflective waveguide that enhances the received signal quality for wireless devices. Our primary target is enhancing Low-Power Wide-Area Networks (LP-WANs) - where 10-year batterypowered client devices (e.g. energy meters or smart home sensors) connect to cellular-like base stations to deliver data. Devices in these networks often experience significant seasonal variability in battery life - even simple obstructions between the device and base station (e.g. due to construction) can shave off years of battery life. We propose MetaMorph, a programmable robotic reflector attached to base stations that enhances signal quality from client devices by enhancing received signal energy with controlled reflections. We investigate the design of the reflector, and our experiments show the ability to improve the signal quality for LP-WAN (LoRa) communication systems demonstrating signal quality and battery-benefits. To our best knowledge, MetaMorph is the first paper to explore how flexible robotics can serve as virtuous reflectors for wireless communication systems.

In this paper, we design and fabricate cost-effective, electronics-free millimeter-wave (mmWave) fiducial markers that support clutter-free detection on radars. Fiducial markers are widely used in camera-based systems to provide spatial information in robotic navigation applications. QR-code-like fiducial markers can be readily printed on paper - low-cost, easy to produce, and electronics-free. Yet, an analogous mmWave solution is yet to appear, which stems from the unique challenge in the mmWave context: its vulnerability to clutter. Existing solutions either trade hardware simplicity for clutter resilience or stay simple but remain vulnerable to environmental multipath. This paper seeks to solve this dilemma.

Wireless research has advanced in utilizing channel diversity and beamforming for more efficient communication, sensing, and harvesting ambient energy. We demonstrate our wireless robotic platform that utilizes radio-frequency beamforming for robot actuation. The platform delivers a maximum of 60 watts of power accurately towards soft robotic actuators by efficient frequency-aware beamforming. We also engineer soft actuators to absorb microwaves of specific frequencies to enable selective actuation. In this demonstration, we show a simplified version of our system that achieves frequency-selective actuation of two actuators to enable simple robot locomotion.

The Internet today is owned, managed and controlled by a heterogeneous mix of autonomous systems. As a result, there's no one single entity that holds the "Internet kill switch". However, for emerging Low-Earth Orbit satellite Internet services, few gatekeepers control access globally, going against the fundamental principle of the Internet as a distributed and decentralized system. While satellite Internet remains a small part of the Internet today, it is growing exponentially and is often the only connectivity option for regions that are sparsely populated, experience political instability, or are prone to natural disasters that are likely to damage equipment. We first discuss why the satellite Internet world is ripe for monopolies, global ownership, and vertical integration. We then lay out OpenSpace, an architectural roadmap for a more open and heterogeneous satellite Internet paradigm, where many players build, launch, and manage satellites that communicate, to collectively deliver a reliable Internet service. We also discuss several open problems and research challenges in making satellite Internet more interoperable and heterogeneous, facilitating accessibility for big and small firms alike.

The quality of audio recordings in outdoor environments is often degraded by the presence of wind. Mitigating the impact of wind noise on the perceptual quality of single-channel speech remains a significant challenge due to its non-stationary characteristics. Prior work in noise suppression treats wind noise as a general background noise without explicit modeling of its characteristics. In this paper, we leverage ultrasound as an auxiliary modality to explicitly sense the airflow and characterize the wind noise. We propose a multi-modal deep-learning framework to fuse the ultrasonic Doppler features and speech signals for wind noise reduction. Our results show that DeWinder can significantly improve the noise reduction capabilities of state-of-the-art speech enhancement models.

In this paper, we ask, "Can millimeter-wave (mmWave) radars sense objects not directly illuminated by the radar - for instance, objects located outside the transmit beamwidth, behind occlusions, or placed fully behind the radar?" Traditionally, mmWave radars are limited to sense objects that are directly illuminated by the radar and scatter its signals directly back. In practice, however, radar signals scatter to other intermediate objects in the environment and undergo multiple bounces before being received back at the radar. In this paper, we present Hydra, a framework to explicitly model and exploit multi-bounce paths for sensing. Hydra enables standalone mmWave radars to sense beyond-field-of-view objects without prior knowledge of the environment. We extensively evaluate the localization performance of Hydra with an off-the-shelf mmWave radar in five different environments with everyday objects. Exploiting multi-bounce via Hydra provides 2×-10× improvement in the median beyond-field-of-view localization error over baselines.

Microwave ovens have become an essential cooking appliance owing to their convenience and efficiency. However, microwave ovens suffer from uneven distribution of energy, which causes prolonged delays, unpleasant cooking experiences, and even safety concerns. Despite significant research efforts, current solutions remain inadequate. In this paper, we first conduct measurement studies to understand the energy distribution for 10 microwave ovens and show their energy distribution in both 2D and 3D is very skewed, with notably lower energy levels at the center of the microwave cavity, where food is commonly placed. To tackle this challenge, we propose a novel methodology to enhance the performance of microwave ovens. Our approach begins with the development of a measurement driven model of a microwave oven. We construct a detailed 3D model in the High Frequency Structure Simulator (HFSS) and use real temperature measurements from a microwave to derive critical parameters relevant to the appliance's functionality (e.g., operating frequency, waveguide specifications). We then develop a novel approach that optimizes the design and placement of a low-cost passive metasurface for a given heating objective. Using extensive experiments, we demonstrate the efficacy of our approach across diverse food, optimization objectives, and microwave ovens.

Extending the range of RF energy harvesting can revolutionize battery-free/low-power sensing and networking. This paper explores the design space for RF infrastructure to charge battery-free devices (e.g. RFID) or devices with coin-cell batteries (e.g. water and security sensors) over much longer range than the state-of-the-art. Rather than rely completely on ambient RF (e.g. TV towers) or dedicated infrastructure (e.g. RFID readers), we explore a middle path - combine RF energy from (nearly) all available major wireless frequency bands and then supplement this with low-cost specially designed RF charging infrastructure to fill in any gaps.

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, many of which experience significant latency (several hours) from the time information is gathered to the time it is communicated to the ground. This is primarily due to the limited availability of ground infrastructure that is bulky to deploy and expensive to rent. This article explores the opportunity in leveraging the extensive terrestrial LoRa infrastructure as a solution. However, the limited bandwidth and large amount of Doppler on CubeSats precludes these LoRa links to communicate rich satellite Earth images—instead, the CubeSats can at best send short messages. This article details our experience in designing LoRa-based satellite ground infrastructure that requires software-only modifications to receive packets from LoRa-enabled CubeSats recently launched by our team. We present Vista, a communication system that adapts encoding onboard the CubeSat and decoding configuration on commercial LoRa ground stations to allow images to be communicated. We perform a detailed evaluation of Vista by leveraging wireless channel measurements from a recent CubeSat (2021), and show that Vista can achieve 55.55% lower latency in retrieving data with 12.02 dB improvement in packet retrieval in the presence of terrestrial interference. We then evaluate Vista on a case study on land-use classification over images transmitted over the CubeSat link to further demonstrate a 4.56 dB improvement in image PSNR and 1.38× increase in classification accuracy over baseline approaches.

We are living amidst a pandemic caused by a ravaging coronavirus and an accompanying pandemic of misinformation that has strained our economy and socio-political institutions. A key scientific goal is to examine mechanisms that lead to the widespread propagation of contagions, e.g., misinformation and pathogens, and identify risk factors that can trigger widespread outbreaks. A common phenomenon underlying the spread of disease and misinformation epidemics is the evolution of the contagion as it propagates, leading to the emergence of different strains, e.g., through genetic mutations in pathogens and alterations in the information content. Recent studies have revealed that models that do not account for heterogeneity in transmission risks associated with different strains of the circulating contagion can lead to inaccurate predictions. However, existing results on multi-strain spreading assume that the network has a vanishingly small clustering coefficient, whereas, clustering is widely known to be a fundamental property of real-world social networks. In this work, we investigate spreading processes that entail evolutionary adaptations on random graphs with tunable clustering and arbitrary degree distributions. We derive a mathematical framework that predicts the epidemic threshold and the probability of emergence as functions of the characteristics of the spreading object, the evolutionary pathways of the pathogen/misinformation, and the structure of the underlying network as given by the joint degree distribution of single-edges and triangles. To the best of our knowledge, our work is the first to jointly characterize the impact of clustering and evolution on the emergence of epidemic outbreaks. We supplement our theoretical finding with numerical simulations and case studies, shedding light on how clustering can offer pathways for mutation, thereby altering the course of the epidemic.

This paper explores a machine learning approach on data from a single-chip mmWave radar for generating high resolution point clouds – a key sensing primitive for robotic applications such as mapping, odometry and localization. Unlike lidar and vision-based systems, mmWave radar can operate in harsh environments and see through occlusions like smoke, fog, and dust. Unfortunately, current mmWave processing techniques offer poor spatial resolution compared to lidar point clouds. This paper presents RadarHD, an end-to-end neural network that constructs lidar-like point clouds from low resolution radar input. Enhancing radar images is challenging due to the presence of specular and spurious reflections. Radar data also doesn't map well to traditional image processing techniques due to the signal's sinc-like spreading pattern. We overcome these challenges by training RadarHD on a large volume of raw I/Q radar data paired with lidar point clouds across diverse indoor settings. Our experiments show the ability to generate rich point clouds even in scenes unobserved during training and in the presence of heavy smoke occlusion. Further, RadarHD's point clouds are high-quality enough to work with existing lidar odometry and mapping workflows.

Recent work on battery-free soft robotics has demonstrated the use of liquid crystal elastomers (LCE) to build shape-changing materials activated by applied external heat. However, sources of heat must typically be in direct field-of-view of the robot (i.e. NIR, laser, and visual light EM sources or convective heats guns), be tethered to an external power supply (i.e. thermoelectric heating or resistive joule heaters), or require a heavy on-board battery that limits mobility and range. This paper presents a novel battery-free soft-robotics platform that can crawl through confined, enclosed, and hard-to-reach spaces (e.g. packages, machinery, pipes, etc.), hidden from view of heating infrastructure. This is achieved through the co-design of a soft robotics platform and integrated soft conductive traces that enable wireless (microwave) heating through remote stimulation. We achieve fast actuation through a careful choice of materials and the overall mechanical structure of the robot to maximize heating efficiency. Further, the robot is actively tracked through enclosed spaces using a mm Wave radar to direct heat to its location. We provide a detailed evaluation on the robot's heating efficiency, location-tracking accuracy and crawling speed.

We demonstrate Platypus, a sub-millimeter micro-displacement sensing system presented in [3]. Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery parts can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Platypus enables sub-millimeter level sensing accuracy by using mmWave backscatter tags and their reflection as phase carriers to shift the phase changes due to tiny displacements to clean frequency bins for precise tracking. It then reconstructs the tag phase changes with sub-millimeter level accuracy even from extended ranges (over 100m) or in non-line-of-sight (NLoS) situations where the tag is blocked by other objects. Here, we demonstrate Platypus’s performance by attaching a Platypus tag to a stepper motor-driven motion-stage and demonstrating the micro-displacement detection in real time, and the system robustness against multipath and occlusions.

Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery displace can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Traditionally, such displacements on large structures are measured using visual sensing platforms or advanced surveying equipment. However, they either fall short in varying weather and lighting conditions or require installation and maintenance of high-power sensing platforms that are expensive to deploy at scale, especially if continuous measurements are desired.

This paper introduces RFIMap, a system that aims to inexpensively characterize the spatial and temporal distribution of RF spectrum occupancy of any indoor space at fine granularity (tens of centimeters). RFIMap builds rich wide-band indoor spectrum occupancy maps using low-cost and battery-free commodity RFID tags. RFIMap's spectrum maps have wide-ranging applications such as monitoring ambient interference in smart manufacturing, and smart hospitals. RFIMap relies on the observation that commodity RFID tags naturally reflect ambient transmission at other frequency bands, without any modification. RFIMap uses these reflections to estimate the ambient signal power originally received at these tags. RFIMap further performs a careful modeling of indoor multipath to build a dense spectrum map with fine spatial granularity. Our experiments demonstrate spatial spectrum measurement with 2.15 dB of median error at 2.4 GHz, 4.45 dB of median error at 470-700 MHz TV whitespace band, 2.1 dB of median error at 1.8-1.9 GHz in diverse industrial and university settings.

Millimeter wave radars can perceive through occlusions like dust, fog, smoke and clothes. But compared to cameras and lidars, their perception quality is orders of magnitude poorer. RadarHD [3] tackles this problem of poor quality by creating a machine learning super resolution pipeline trained against high quality lidar scans to mimic lidar. RadarHD ingests low resolution radar and generates high quality lidar-like point clouds even in occluded settings. RadarHD can also make use of the high quality output for typical robotics tasks like odometry, mapping and classification using conventional lidar workflows. Here, we demonstrate the effectiveness of RadarHD's point clouds against lidar in occluded settings.

This paper proposes a new primitive that allows soft robots to be physically controlled in a completely non-line-of-sight context using wireless energy - a process we call wireless actuation. Soft robots, which are composed entirely of soft materials and exclude any rigid components, are highly flexible platforms that can change their shape. This paper considers a specific class of soft robots composed of liquid-crystal elastomers (LCE) that are entirely electronics-free and engineered to change shape when heated to 60 °C. Traditionally, such robotic systems must be in line-of-sight of a light source, such as infrared to be moved, or require an external power supply for Joule heating and often take several tens of seconds to heat. We present WASER, a novel RF-based heating platform that allows electronics-free robots to be actuated rapidly (within a few seconds) and potentially in non-line-of-sight. WASER achieves this through innovations in both wireless systems and material science. On the wireless front, WASER develops a new blind beamforming solution that directs high-power wireless energy at fine spatial granularity without electronics on the robot to provide feedback. On the material science front, WASER exhibits heat-responsive shape-morphing and energy-harvesting material functionalities that allow for rapid wireless heating. We implement and evaluate WASER and demonstrate diverse shape-morphing capabilities.

In this paper, we studied people’s smart home privacy-protective behaviors (SH-PPBs), to gain a better understanding of their privacy management do’s and don’ts in this context. We first surveyed 159 participants and elicited 33 unique SH-PPB practices, revealing that users heavily rely on ad hoc approaches at the physical layer (e.g., physical blocking, manual powering off). We also characterized the types of privacy concerns users wanted to address through SH-PPBs, the reasons preventing users from doing SH-PPBs, and privacy features they wished they had to support SH-PPBs. We then storyboarded 11 privacy protection concepts to explore opportunities to better support users’ needs, and asked another 227 participants to criticize and rank these design concepts. Among the 11 concepts, Privacy Diagnostics, which is similar to security diagnostics in anti-virus software, was far preferred over the rest. We also witnessed rich evidence of four important factors in designing SH-PPB tools, as users prefer (1) simple, (2) proactive, (3) preventative solutions that can (4) offer more control.

This paper asks: “Can we detect whether a fragile product, made of porcelain or glass is damaged as it travels along the supply chain, without opening its packaging?” We ask this question in the context of the multi-billion dollar global supply chain industry of fragile products that experience large overheads due to product returns. This paper presents MiLTOn, a novel acoustic and mm-wave based solution for through-box non-invasive product integrity sensing that is sensitive to even minute sub-mm cracks in the object. MiLTOn is inspired by acoustic vibrometry used for instance to monitor cracks in railroads. Unlike traditional vibrometry, MiL-TOn is unique in its ability to sense products non-invasively using an external transducer and microphone, neither of which are in direct physical contact of the object within the box. MiLTOn pro-cesses measurements from the microphone to design a robust and environment-independent product signature that can be used to sense presence of product defects. Our extensive evaluation on a large number of fragile products of diverse materials demonstrates 97% accuracy in identifying product damage.

Real-time access to overhead Low-Earth Orbit (LEO) satellite imagery from a handheld device can have transformative applications: tracking wild-fire, natural disasters and weather events. Today, real-time access to images from LEO satellites overhead is challenging to obtain. LEO satellite ground receivers are bulky, expensive and sparsely deployed in the world. Despite the exponential increase in LEO small satellites orbiting the planet today-there is a significant time gap between an image capture on such a satellite and users who need it the most in remote and ecologically-sensitive regions. This paper presents SelfieStick, a novel satellite receiver system that explores reducing this barrier of access to real-time satellite imagery data using a single low cost (< $ 30) tiny receiver. SelfieStick's core approach takes advantage of the multiplicity of overhead Low-Earth Orbit satellites due to their exponential rise in recent years. While signals from such satellites may be individually weak, especially at a low-cost receiver, SelfieStick stitches together noisy RF captures containing underlying images of the same part of the Earth across many such satellites to generate clean Earth images. This is made possible by combining weak signals in the RF domain (rather than the traditional image domain) after appropriately transforming and aligning the RF signals accounting for different satellite perspectives, their orbits and wireless channels. A detailed experimental evaluation on the RTL-SDR platform on satellite captures from the NOAA constellation demonstrates a PSNR improvement of 5 dB through combining of images across 10 satellites.

This paper presents the WiFi-Sensor-for-Robotics (WSR) open-source toolbox111Code: https://github.com/Harvard-REACT/WSR-Toolbox Dataset: https://github.com/Harvard-REACT/WSR-Toolbox-Dataset Demo: https://github.com/Harvard-REACT/WSR-Toolbox/wiki/Demo. It enables robots in a team to obtain relative bearing to each other, even in nonline-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an open-source toolbox. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for multi-robot mapping and rendezvous, ad-hoc robot networks, and security in multi-robot teams, amongst other applications. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings and for a multi-robot localization use case. Empirical results for hardware experiments show that the bearing estimation from our toolbox achieves accuracy with mean and standard deviation of 1.13 degrees, 11.07 degrees in LOS and 6.04 degrees, 26.4 degrees for NLOS, respectively, in an indoor office environment.

Robotic geo-fencing and surveillance systems require accurate monitoring of objects if/when they violate perimeter restrictions. In this paper, we seek a solution for depth imaging of such objects of interest at high accuracy (few tens of cm) over extended ranges (up to 300 meters) from a single vantage point, such as a pole mounted platform. Unfortunately, the rich literature in depth imaging using camera, lidar and radar in isolation struggles to meet these tight requirements in real-world conditions. This paper proposes Metamoran, a solution that explores long-range depth imaging of objects of interest by fusing the strengths of two complementary technologies: mmWave radar and camera. Unlike cameras, mmWave radars offer excellent cm-scale depth resolution even at very long ranges. However, their angular resolution is at least 10x worse than camera systems. Fusing these two modalities is natural, but in scenes with high clutter and at long ranges, radar reflections are weak and experience spurious artifacts. Metamoran's core contribution is to leverage image segmentation and monocular depth estimation on camera images to help declutter radar and discover true object reflections. We perform a detailed evaluation of Metamoran's depth imaging capabilities in 400 diverse scenarios. Our evaluation shows that Metamoran estimates the depth of static objects up to 90 m away and moving objects up to 305 m away and with a median error of 28 cm, an improvement of 13 x over a naive radar+camera baseline and 23 x compared to monocular depth estimation.

With increasing numbers of nano-satellites (CubeSats) being launched into space in recent years, monitoring their health and debugging become crucial problems. In traditional systems such as big satellites, time-series telemetry is widely used by users to monitor the state of the satellite from the ground stations. However, today smaller CubeSats do not enjoy the benefits of live telemetry due to low throughput and lack of coverage from ground station infrastructure. In this poster, we conduct a motivation study based on data collected from public satellites in low-earth orbit to demonstrate the potential bottlenecks in obtaining live telemetry data from CubeSats. We then describe the design space of possible solutions and opportunities for researchers to improve time-series telemetry for CubeSats.

This paper presents NFCapsule, a light-weight, battery-free, and ingestible biomedical sensor that can potentially enable non-invasive detection of active eosinophilic esophagitis (EoE). EoE is an allergen-induced inflammatory condition of the esophagus; its diagnosis generally involves invasive, wired, and time-consuming endoscopy. In contrast, NFCapsule aims to wirelessly detect active EoE by tracking tissue impedance through an ingestible pill that the patient swallows. Specifically, recent biomedical research has shown that active EoE induces observable changes in the electrochemical impedance of the esophagus tissue due to an increase in its intercellular spacing. We design the NFCapsule pill based on RLC resonant circuits and model the target tissue as an impedance component that changes the resonant properties of the pill circuit. Further, the NFCapsule reader identifies the resonant properties of the pill by consistently monitoring the amount of energy transferred to the pill as it goes through the esophagus, and converts this information to estimates of bio-impedance. We implement NFCapsule pill prototypes with flexible polyimide PCBs and gelatin capsules (27 mm in height and 10 mm in diameter) and evaluated NFCapsule with both ionic agarose hydrogel models and ex vivo porcine esophageal tissues (no human patients involved). We show that NFCapsule maintains high classification accuracy under various practical scenarios (e.g., blockage, bending, movement, etc.) and achieves 85% average accuracy between healthy and unhealthy tissue samples.

We present Peekaboo, a new privacy-sensitive architecture for smart homes that leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before sending it to external cloud servers. Peekaboo's key innovations are (1) abstracting common data preprocessing functionality into a small and fixed set of chainable operators, and (2) requiring that developers explicitly declare desired data collection behaviors (e.g., data granularity, destinations, conditions) in an application manifest, which also specifies how the operators are chained together. Given a manifest, Peekaboo assembles and executes a pre-processing pipeline using operators pre-loaded on the hub. In doing so, developers can collect smart home data on a need-to-know basis; third-party auditors can verify data collection behaviors; and the hub itself can offer a number of centralized privacy features to users across apps and devices, without additional effort from app developers. We present the design and implementation of Peekaboo, along with an evaluation of its coverage of smart home scenarios, system performance, data minimization, and example built-in privacy features.

The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands – LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4× and battery life of up to 3.5 years.

No abstract available.

This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.

LoRa is a popular Low-Power Wide-Area Networking (LP-WAN) technology that allows devices powered by a ten year AA battery to connect to radio infrastructure miles away. One of the most promising features of LoRa is the ability to track the location of radios from a distance, enabling applications ranging from inventory tracking, smart infrastructure monitoring and structural health sensing. Yet, state-of-the-art LoRa localization systems experience errors of several tens or even hundreds of meters in location tracking, owing to the narrow bandwidth and limited battery life of LoRa devices.

This paper builds a Near-field Communication (NFC) based localization system that allows ordinary surfaces to locate surrounding objects with high accuracy in the near-field. While there is rich prior work on device-free localization using far-field wireless technologies, the near-field is less explored. Prior work in this space operates at extremely small ranges (a few centimeters), leading to designs that sense close proximity rather than location.

In this paper, we demonstrate Adaptive Millimetro as an extension of Millimetro, an ultra-low power millimeter-wave (mmWave) retro-reflector presented in [1], for high mobility scenarios. Adaptive Millimetro makes use of automotive radars and enables communication with and accurate localization of roadside infrastructure overextended distances (i.e. >100m). Millimetro achieves this by designing ultra-low-power retro-reflective tags that operate in the mmWave frequency band and can be embedded in road signs, pavements, bi-cycles, or even the clothing of pedestrians. Millimetro addresses the severe path loss problem of mmWave signals by combining coding gain and retro-reflective antenna front-end to achieve long-range operation. However, highly mobile scenarios may still experience unreliable performance due to the Doppler effect changing the received signals. In this paper, we demonstrate a simple solution for robust localization in high mobility by implementing a Moving Target Indication (MTI) filter and an adaptive Kalman filter. We also present an augmented reality app, as an in-car AR platform, that uses Adaptive Millimetro’s algorithms to estimate the tag positions and overlay a virtual box at the estimated locations.

Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely to significantly exceed your launch costs. While space launch costs have lowered significantly with innovative launch vehicles, private players, and smaller payloads, access to ground infrastructure remains a luxury. This is especially true for smaller LEO satellites that are only visible at any location for a few tens of minutes a day and whose signals are extremely weak, necessitating bulky and expensive ground station infrastructure.

This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Millimetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current ultra-low-power localization systems struggle to operate accurately at extended ranges under strict latency requirements. Millimetro addresses this challenge by re-using existing automotive radars that operate at mmWave frequency where plentiful bandwidth is available to ensure high accuracy and low latency. We address the crucial free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Our experimental results indoors and outdoors demonstrate a scalable system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (< 3 uW).

Imagine when you line up in a store, the person in front of you can make you pay her bill by using a passive wearable device that forces a scan of your credit card or mobile phones without your awareness. An important assumption of today's near-field communication (NFC)-enabled cards is the limited communication range between the commercial reader and the NFC cards. Previous approaches effectively used mobile phones and active relays to break the range limit of NFC propagation for the NFC attack. However, these approaches require a power supply and protocol modification when mobile phones or active relays transmit NFC signals. We propose ReCoil, a system that uses passive relays to attack NFC-enabled mobile phones or cards by expanding the communication range of NFC to 49.6 cm, an obvious improvement over its intended commercial distance. ReCoil is a magnetically coupled resonant wireless power transfer system, which optimizes the energy transfer by searching the optimal geometry parameters. Specifically, we first narrow down the feasible area reasonably and design the ReCoil-greedy algorithm such that the relays absorb the maximum energy from the reader. In order to reroute the signal to pass over the surface of the human body, we then design a half waistband by carefully analyzing the impact of the distance and orientation between two coils on the mutual inductance. Then, three more coils are added to the system to keep enlarging the communication range. Finally, extensive experiment results validate our analysis, showing that our passive relays consisting of common copper wires and tunable capacitors can expand the range of NFC to 49.6 centimeters.

Today, industry practitioners (e.g., data scientists, developers, product managers) rely on formal privacy reviews (a combination of user interviews, privacy risk assessments, etc.) in identifying potential customer acceptance issues with their organization’s data practices. However, this process is slow and expensive, and practitioners often have to make ad-hoc privacy-related decisions with little actual feedback from users. We introduce Lean Privacy Review (LPR), a fast, cheap, and easy-to-access method to help practitioners collect direct feedback from users through the proxy of crowd workers in the early stages of design. LPR takes a proposed data practice, quickly breaks it down into smaller parts, generates a set of questionnaire surveys, solicits users’ opinions, and summarizes those opinions in a compact form for practitioners to use. By doing so, LPR can help uncover the range and magnitude of different privacy concerns actual people have at a small fraction of the cost and wait-time for a formal review. We evaluated LPR using 12 real-world data practices with 240 crowd users and 24 data practitioners. Our results show that (1) the discovery of privacy concerns saturates as the number of evaluators exceeds 14 participants, which takes around 5.5 hours to complete (i.e., latency) and costs 3.7 hours of total crowd work ( $80 in our experiments); and (2) LPR finds 89% of privacy concerns identified by data practitioners as well as 139% additional privacy concerns that practitioners are not aware of, at a 6% estimated false alarm rate.

Full duplex communication has been well explored in the past decade, with several physical layer designs proposed for effectively doubling the throughput of mobile devices. However, these systems are far from being well-deployed in the Wi-Fi and cellular applications, where most of the research has been done. In this paper, we investigate some of the fundamental pragmatic challenges in deploying full duplex that explain the reluctance from industry players in deploying this feature in commercial end-user devices and base stations at a large scale. Upon doing so, we identify that the problems lie not quite at the radio layer -- but at layers below and above it. At the hardware layer, we find that the power and complexity of IC-implementations of full duplex far exceeds that of other existing technologies that achieve similar throughput gain, such as multi-user MIMO. We further identified how higher-layer cellular and Wi-Fi traffic patterns and MAC protocols are fundamentally ill-suited to the full duplex paradigm. We report empirical analysis from the power consumption of an IC-implementation of a full duplex cancellation circuit, demonstrating that full duplex cancellation circuitry would consume at least 57 percent more power than a MIMO system achieving identical throughput gains. We also show how current traffic patterns reduce the benefits of full duplex to 1.25X instead of the expected 2X.

Low-Power Wide-Area Networks (LP-WANs) are seeing wide-spread deployments connecting millions of sensors, each powered by a ten-year AA battery to radio infrastructure, often miles away. By design, iteratively querying all sensors in an LP-WAN may take several hours or even days, given the stringent battery limits of client radios. This precludes obtaining even an approximate real-time view of sensed information across LP-WAN devices over a large area, say in the event of a disaster, fault or simply for diagnostics.This paper presents QuAiL1, a system that provides a coarse aggregate view of sensed data across LP-WAN devices over a wide- area within a time span of just one LP-WAN packet. QuAiL achieves this by coordinating multiple LP-WAN radios to transmit their information synchronously in time and frequency despite their power constraints. We design each client’s transmission so that the base station can retrieve an approximate heatmap of sensed data by exploiting the spatial correlation of this data across clients. We further show how our system can be optimized for statistical and machine learning queries, all while maintaining the security and privacy of sensed data from individual clients. Our deployment over a 3 sq. km. LP-WAN deployment around CMU campus in Pittsburgh demonstrates a 4x faster information retrieval versus the state-of- the-art statistical methods to retrieve the spatial sensor heatmap at a desired resolution.

mm-Wave has emerged as an attractive high-speed wireless communication paradigm owing to the high available bandwidth at mm-wave frequencies. Full-Duplex has the potential to double the available capacity in the mm-wave bands by enabling simultaneous radio transmission and reception. While full-duplex has been extensively studied in sub-6 GHz bands, this paper exposes the unique challenges in porting this capability to mm-wave frequencies.

Wireless sensors have enabled a number of key applications. Due to their energy constraints, wireless sensors today communicate occasional short samples or pre-determined summary statistics of the data they collect. This means that computing every additional statistic at high fidelity incurs additional communication and energy overhead. This paper presents Joltik, a framework enabling general, future-proof, and energy-efficient analytics for low power wireless sensors. Joltik is general in that it summarizes sensed data from low-power devices without making assumptions on which specific statistical metric(s) are desired at the cloud and is future-proof, meaning it supports new, unforeseen metrics. Joltik is built upon recent theoretical advances in universal sketching, which can enable a Joltik sensor node to report a compact summary of observed data to enable a large class of statistical summaries. We address key system design and implementation challenges with respect to communication, memory, and computation bottlenecks that arise in practically realizing the potential benefits of universal sketching in the low-power regime. We present a proof-of-concept testbed evaluation of Joltik in LoRaWAN NUCLEO-L476RG boards and sensors. Across a range of realistic datasets, Joltik provides up to a 24.6× reduction in energy cost compared to transmitting raw data and outperforms many natural alternatives (e.g., sub-sampling, custom sketches, compressed sensing, and lossy compression) in terms of energy-accuracy trade-offs.

Conventional wireless communication systems are typically designed assuming a single transmitter-receiver pair for each link. In Low-Power Wide-Area Networks (LP-WANs), this one-to-one design paradigm is often overly pessimistic in terms of link budget because client packets are frequently detected by multiple gateways (i.e. one-to-many). Prior work has shown massive improvement in performance when specialized hardware is used to coherently combine signals at the physical layer.

Recent years have witnessed novel designs of battery-free IoT tags using RF backscatter. Traditionally, they require a dedicated transmitter to excite the tag. However, such a deployment is infeasible at large scale. To counter this problem, researchers have proposed using ambient RF energy to power up the battery-free tag. In this poster, we evaluate if this ambient RF energy is sufficient to meet the power requirements of a battery-free tag in today's urban and rural areas. We also compare available ambient RF energy across different frequencies. Finally, we discuss open challenges in realising ambient backscatter systems in real-world.

Tire wear is a leading cause of automobile accidents globally. Beyond safety, tire wear affects performance and is an important metric that decides tire replacement, one of the biggest maintenance expense of the global trucking industry. We believe that it is important to measure and monitor tire wear in all automobiles. Current approach to measure tire wear is manual and extremely tedious. Embedding sensor electronics in tires to measure tire wear is challenging, given the inhospitable temperature, pressure and dynamics of the tire. Further, off-tire sensors placed in the well such as laser range-finders are vulnerable to road debris that may settle in tire grooves.

In this paper, we demonstrate Osprey, a tire wear sensor presented in [4]. Osprey makes use of commodity automotive, mmWave RADAR, places it in the tire well of automobiles to image the tire and then measures the tire wear. Osprey measures accurate tire wear continuously while being resilient to road debris and without embedding any electronics in tires. Osprey achieves this by building a super resolution algorithm based on Inverse Synthetic Aperture RADAR imaging and by embedding thin metallic strips along coded patterns in the grooves to combat debris. Here, we implement Osprey on a tire rotation rig and demonstrate the ability to measure tire wear (with and without debris) accurately and detect potentially harmful foreign objects.

Coronavirus disease 2019, known as COVID-19, has spread rapidly and infected millions of people around the world. In addition to respiratory droplet spreading, a common mode of contraction of this virus is when individuals touch their face after coming into contact with a contaminated surface. In this poster, we leverage near-field communication (NFC) and propose a system design, NoFaceContact, which can promptly warn users when they attempt to touch their face with the aim of helping to reduce the spread of COVID-19 and improve overall hygiene. A proof-of-concept experiment shows that NoFaceContact can achieve an average communication distance of 8.07 cm and can potentially detect a wide range of face touching poses.

Network Function Virtualization has demonstrated how general purpose infrastructure in the cloud can match the performance of specialized hardware while providing greater flexibility and scalability. Yet, specialized hardware remains the preferred implementation choice for the wireless physical layer. This is primarily due to the tight latency requirements and high bandwidth demanded by operations at the wireless PHY, as opposed to the network-layer. This poster argues that in the era of low-power Internet of Things, virtualization of physical layer functions deserves a revisit. We specifically study low-power Internet of Things technologies that present a ripe opportunity for cloudification of wireless network functions, with their lower bandwidths and lax latency constraints. We present our vision of how physical layer function virtualization should be structured to maximize benefits and performance. We present a feasibility study on the latency and scalability limits of such virtualization for various physical layer functions that could leverage such an architecture.

Despite widespread popularity, today's microwave ovens are limited in their cooking capabilities, given that they heat food blindly, resulting in a non-uniform and unpredictable heating distribution. We present SDC (software-defined cooking), a low-cost closed-loop microwave oven system that aims to heat the food in a software-defined thermal trajectory. SDC achieves this through a novel high-resolution heat sensing and actuation system that uses microwave-safe components to augment existing microwaves. SDC first senses thermal gradient by using arrays of neon lamps that are charged by the Electromagnetic (EM) field a microwave produces. SDC then modifies the EM-field strength to desired levels by accurately moving food on a programmable turntable towards sensed hot and cold spots. To create a more skewed arbitrary thermal pattern, SDC further introduces two types of programmable accessories: microwave shield and susceptor. We design and implement one experimental test-bed by modifying a commercial off-the-shelf microwave oven. Our evaluation shows that SDC can programmatically create temperature deltas at a resolution of 21 degrees with a spatial resolution of 3 cm without accessories and 183 degrees with the help of accessories. We further demonstrate how a SDC-enabled microwave can be enlisted to perform unexpected cooking tasks: cooking meat and fat in bacon discriminatively and heating milk uniformly.

We present a demonstration of SDC, a low-cost closed-loop microwave oven system that aims to heat the food in a software-defined thermal trajectory. SDC achieves this through a novel high-resolution heat sensing and actuation system that uses microwave-safe components to augment existing microwaves. In this demo, we demonstrate our experimental test-bed, a modified commercial off-the-shelf microwave oven, and show a SDC-enabled microwave can be enlisted to perform unexpected cooking tasks: cooking meat and fat in bacon discriminatively and heating rice uniformly.

Wireless sensing has demonstrated the potential of using Wi-Fi signals to track people and objects, even behind walls.Yet, prior work in this space aims to merely detect the presence of objects around corners, rather than their type. In this paper, we explore the feasibility of the following re-search question: ?Can commodity Wi-Fi radios detect both the location and type of moving objects around them?". We present IntuWition, a complementary sensing system that can sense the location and type of material of objects in the environment, including those out of line-of-sight. It achieves this by sensing wireless signals reflected off surrounding objects using commodity Wi-Fi radios, whose signals penetrate walls and occlusions. At the core of IntuWition is the idea that different materials reflect and scatter polarized waves in different ways. We build upon ideas from RADAR Polarimetry to detect the material of objects across spatial locations, despite mobility of the sensing device and the hardware non-idealities of commodity Wi-Fi radios. A detailed feasibility study reveals an average accuracy of 95% in line-of-sight and 92% in non-line-of-sight in classifying five types of materials:copper, aluminum, plywood, birch, and human. Finally, we present a proof-of-concept application of our system on an autonomous UAV that uses its onboard Wi-Fi radios to sense whether an occlusion is a person versus another UAV.

Robust, wide-area sensing of human environments has been a long-standing research goal. We present Sozu, a new low-cost sensing system that can detect a wide range of events wirelessly, through walls and without line of sight, at whole-building scale. To achieve this in a battery-free manner, Sozu tags convert energy from activities that they sense into RF broadcasts, acting like miniature self-powered radio stations. We describe the results from a series of iterative studies, culminating in a deployment study with 30 instrumented objects. Results show that Sozu is very accurate, with true positive event detection exceeding 99%, with almost no false positives. Beyond event detection, we show that Sozu can be extended to detect richer signals, such as the state, intensity, count, and rate of events.

Several years ago, software radios were seen as the future of commercial wireless infrastructure, given that they could flexibly decode any wireless technology with a simple software update. Yet, despite their numerous advantages, the industry tilted in favor of dedicated chips for wireless infrastructure sacrificing generality for performance. Today, the advent of the Internet of Things (IoT) and the resulting fragmentation of wireless technologies has led to a new billion-dollar industry: multi-technology gateways that support many radio technologies. Predictably, commercial gateways do this through multiple radio chips each decoding dedicated wireless technologies.

At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.

In this demonstration, we present OpenChirp, an open-source Low-Power Wide-Area Networking (LPWAN) infrastructure. OpenChirp is a management framework that provides data context, storage, visualization, and access control over the web. At the physical layer of the system, we present LPRAN, a low-cost high-performance software-defined radio hardware platform that can receive signals up to -30 dB below the noise floor. Using our LPRAN hardware, it is possible to operate on raw I/Q streams in the cloud to perform collaborative tasks across multiple gateways such as jointly decoding weak signals and localization.

Low-Power Wide-Area Networks (LPWANs) are an emerging wireless platform which can support battery-powered devices lasting 10-years while communicating at low data-rates to gateways several kilometers away. Not all such devices will experience the promised 10 year battery life despite the high density of LPWAN gateways expected in cities. Transmission from devices located deep within buildings or in remote neighborhoods will suffer severe attenuation forcing the use of slow data-rates to reach even the closest gateway, thus resulting in battery drain. This paper presents Charm, a system that enhances both the battery life of client devices and the coverage of LPWANs in large urban deployments. Charm allows multiple LoRaWAN gateways to pool their received signals in the cloud, coherently combining them to detect weak signals that are not decodable at any individual gateway. Through a novel hardware and software design at the gateway, Charm carefully detects which chunks of the received signal need to be sent to the cloud, thereby saving uplink bandwidth. We present a scalable solution to decoding weak transmissions at city-scale by identifying the set of gateways whose signals need to be coherently combined over time. In evaluations over a test network and from simulations using traces from a large LoRaWAN deployment in Pittsburgh, Pennsylvania, Charm demonstrates a gain of up to 3x in range and 4x in client battery-life.

Future urban spaces are expected to see Unmanned Aerial Vehicles (UAVs) deployed for wide-ranging applications including product delivery, imaging and search-and-rescue. Yet today's UAVs rely critically on inertial sensors and GPS to remain stable in-flight, meaning they struggle to penetrate urban canyons where GPS is unavailable. This poster presents LoRaTilt, a system that maintains stability of a UAV using a Low-Power Wide-Area Network (LP-WAN) transmitter. We mount a light-weight and ten-year battery-powered LP-WAN transmitter on a UAV and process its signals from base stations up to hundreds of meter away. We demonstrate how our system estimates and corrects for UAV drift in GPS-denied settings with minimal impact on the cost and flight life of a UAV. Our proof-of-concept experiments show a promising median accuracy of 1.2326 mm and 0.0164 radians in estimating the displacement and orientation of a drone with a LoRa LP-WAN radio.

This paper presents WiSh, a solution that makes ordinary surfaces shape-aware, relaying their real-time geometry directly to a user's handheld device. WiSh achieves this using inexpensive, light-weight and battery-free RFID tags attached to these surfaces tracked from a compact single-antenna RFID reader. In doing so, WiSh enables several novel applications: shape-aware clothing that can detect a user's posture, interactive shape-aware toys or even shape-aware bridges that report their structural health.

Low-Power Wide Area Networks (LP-WANs) are an attractive emerging platform to connect the Internet-of-things. LP-WANs enable low-cost devices with a 10-year battery to communicate at few kbps to a base station, kilometers away. But deploying LP-WANs in large urban environments is challenging, given the sheer density of nodes that causes interference, coupled with attenuation from buildings that limits signal range. Yet, state-of-the-art techniques to address these limitations demand inordinate hardware complexity at the base stations or clients, increasing their size and cost.

This paper focuses on a simple, yet fundamental question: ``Can a node infer the wireless channels on one frequency band by observing the channels on a different frequency band?'' This question arises in cellular networks, where the uplink and the downlink operate on different frequencies. Addressing this question is critical for the deployment of key 5G solutions such as massive MIMO, multi-user MIMO, and distributed MIMO, which require channel state information.

Adaptive HTTP video streaming over LTE has been gaining popularity due to LTE's high capacity. Quality of adaptive streaming depends highly on the accuracy of client's estimation of end-to-end network bandwidth, which is challenging due to LTE link dynamics. In this paper, we present piStream, that allows a client to efficiently monitor the LTE basestation's PHY-layer resource allocation, and then map such information to an estimation of available bandwidth. Given the PHY-informed bandwidth estimation, piStream uses a probabilistic algorithm to balance video quality and the risk of stalling, taking into account the burstiness of LTE downlink traffic loads. We conduct a real-time implementation of piStream on a software-radio tethered to an LTE smartphone. Comparison with state-of-the-art adaptive streaming protocols demonstrates that piStream can effectively utilize the LTE bandwidth, achieving high video quality with minimal stalling rate.

The time-of-flight of a signal captures the time it takes to propagate from a transmitter to a receiver. Time-of-flight is perhaps the most intuitive method for localization using wireless signals. If one can accurately measure the time-of-flight from a transmitter, one can compute the transmitter's distance simply by multiplying the time-of-flight by the speed of light. Today, GPS, the most widely used outdoor localization system, localizes a device using the time-of-flight of radio signals from satellites. However, applying the same concept to indoor localization has proven difficult. Systems for localization in indoor spaces are expected to deliver high accuracy (e.g., a meter or less) using consumer-oriented technologies (e.g., Wi-Fi on one's cellphone). Unfortunately, past work could not measure time-of-flight at such an accuracy on Wi-Fi devices. As a result, over the years, research on accurate indoor positioning has moved towards more complex alternatives such as employing large multi-antenna arrays to compute the angle-of-arrival of the signal. These new techniques have delivered highly accurate indoor localization systems. Despite these advances, time-of-flight based localization has some of the basic desirable features that state-of-the-art indoor localization systems lack. In particular, measuring time-of-flight does not require more than a single antenna on the receiver. In fact, by measuring time-of-flight of a signal to just two antennas, a receiver can intersect the corresponding distances to locate its source. Thus, a receiver can locate a wireless transmitter with no support from the surrounding infrastructure. This is quite unlike current indoor localization systems, which require multiple access points at known locations, to find the distance between a pair of mobile devices. Furthermore, each of these access points need to have many antennas -- far beyond what is supported in commercial Wi-Fi devices.

Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption.

Despite the rapid growth of next-generation cellular networks, researchers and end-users today have limited visibility into the performance and problems of these networks. As LTE deployments move towards femto and pico cells, even operators struggle to fully understand the propagation and interference patterns affecting their service, particularly indoors. This paper introduces LTEye, the first open platform to monitor and analyze LTE radio performance at a fine temporal and spatial granularity. LTEye accesses the LTE PHY layer without requiring private user information or provider support. It provides deep insights into the PHY-layer protocols deployed in these networks. LTEye's analytics enable researchers and policy makers to uncover serious deficiencies in these networks due to inefficient spectrum utilization and inter-cell interference. In addition, LTEye extends synthetic aperture radar (SAR), widely used for radar and backscatter signals, to operate over cellular signals. This enables businesses and end-users to localize mobile users and capture the distribution of LTE performance across spatial locations in their facility. As a result, they can diagnose problems and better plan deployment of repeaters or femto cells. We implement LTEye on USRP software radios, and present empirical insights and analytics from multiple AT&T and Verizon base stations in our locality.

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.

Recent years have seen major innovations in cross-layer wireless designs. Despite demonstrating significant throughput gains, hardly any of these technologies have made it into real networks. Deploying cross-layer innovations requires adoption from Wi-Fi chip manufacturers. Yet, manufacturers hesitate to undertake major investments without a better understanding of how these designs interact with real networks and applications.

This paper introduces CarSpeak, a communication system for autonomous driving. CarSpeak enables a car to query and access sensory information captured by other cars in a manner similar to how it accesses information from its local sensors. CarSpeak adopts a content-centric approach where information objects -- i.e., regions along the road -- are first class citizens. It names and accesses road regions using a multi-resolution system, which allows it to scale the amount of transmitted data with the available bandwidth. CarSpeak also changes the MAC protocol so that, instead of having nodes contend for the medium, contention is between road regions, and the medium share assigned to any region depends on the number of cars interested in that region.

We present joint multi-user beamforming (JMB), a system that enables independent access points (APs) to beamform their signals, and communicate with their clients on the same channel as if they were one large MIMO transmitter. The key enabling technology behind JMB is a new low-overhead technique for synchronizing the phase of multiple transmitters in a distributed manner. The design allows a wireless LAN to scale its throughput by continually adding more APs on the same channel. JMB is implemented and tested with both software radio clients and off-the-shelf 802.11n cards, and evaluated in a dense congested deployment resembling a conference room. Results from a 10-AP software-radio testbed show a linear increase in network throughput with a median gain of 8.1 to 9.4x. Our results also demonstrate that JMB's joint multi-user beamforming can provide throughput gains with unmodified 802.11n cards.