Cardiovascular diseases remain the leading cause of death worldwide, and Doppler blood flow indices play a crucial role in their early detection and diagnosis. Existing ultrasound devices, though effective, are costly, bulky, and unsuitable for daily or at-home use. In this work, we introduce Phonotonos, a system that transforms commodity smartphones into portable and accessible tools for through-skin ultrasonic sensing of blood flow velocity. We demonstrate that despite smartphones' inherent limitations—including low ultrasound power, coarse spatial resolution, and lack of beamforming—blood flow velocity waveforms can still be recovered using novel phase-based signal processing, including nonlinearity cancellation and baseline drift removal. We further propose a triple-modality framework that fuses Doppler ultrasound, arterial sound, and IMU for robust artery localization and interfering motion (e.g., involuntary hand motion) rejection. Extensive simulations, phantom experiments, and IRB-approved human studies validate that our proposed system can measure four key Doppler indices (AT, S/D ratio, RI, PI) with accuracy comparable to dedicated medical devices. These results highlight the potential of smartphones to democratize vascular health monitoring and enable continuous cardiovascular screening in everyday environments.

Wireless sensing is an emerging technology with a wide range of applications, but most existing systems capture only the motion of a single point, such as in respiration monitoring. This limitation is critical for tasks requiring multi-point data, such as respiratory volume measurement, where different body points provide distinct information, and a single point cannot represent them all. In this paper, we propose LiSen, a smartphone-integrated LiDAR system for multi-point wireless sensing, and demonstrate its contact-free capability for measuring respiratory volume. LiSen uses smartphone LiDAR to track multiple chest and abdominal points, enabling the first ranging-based spirometer system that captures the full volume curve without new-user calibration. We leverage the unique feature of multi-point sensing to address challenges such as body interference, diverse breathing patterns, and pressure differences. Tests with 35 examinees show that LiSen accurately estimates both instantaneous forced expiratory and inspiratory volume, achieving mean absolute errors below 0.24 L and 0.30 L, respectively, and an 8.93% error for four common pulmonary function indices.

Eavesdropping poses a critical threat to the confidentiality and integrity of voice communications. In recent years, techniques have advanced beyond traditional microphone-based methods toward more intelligent approaches, such as leveraging millimeter-wave sensing to detect the subtle vibrations induced by speakers and reconstruct voice information without direct audio capture. Despite their technical feasibility, these methods remain constrained by limited working ranges—typically only several meters—rendering them impractical for real-world stealthy eavesdropping. In this work, we propose MoiréEar, the first long-range passive eavesdropping system based on moiré patterns. The key idea is to exploit the amplification capability of moiré patterns, which amplify the minute vibrations induced by acoustic signals by hundreds of times, enabling long-range eavesdropping. To make the proposed method even more practical and stealthy, we develop new theoretical foundations that relax the strict requirements for generating moiré patterns. Specifically, our approach enables the use of irregular stripe structures (e.g., commonly seen barcodes) instead of standard moiré gratings to generate moiré patterns. We implement our design using a low-cost photodiode instead of cameras, achieving real-time eavesdropping with lightweight signal processing. Comprehensive experiments show that the system can extract intelligible audio at a distance of up to 90 m, outperforming the state of the art by an order of magnitude in range. We believe this new eavesdropping modality can inspire a wide range of IoT applications.

Low Earth Orbit (LEO) Synthetic Aperture Radar (SAR) satellites conventionally rely on dedicated communication links, which impose prohibitive hardware, spectrum, and power overhead as satellite constellations scale. This paper presents CommSAR, a novel system that reuses existing SAR imaging waveforms to enable bidirectional communication without modifying satellite hardware or compromising imaging performance. For the downlink, data are embedded by modulating the starting frequency offset of the imaging waveform, preserving the waveform structure and imaging quality. For the uplink, we propose a compact, low-cost programmable metasurface to replace conventional large, expensive antennas, significantly lowering the barrier for dense ground station deployment. To handle extreme satellite dynamics, we employ an opposite-slope waveform as a pilot to compensate for mobility-induced effects. We implement a ground station prototype of CommSAR and validate its performance using an in-orbit commercial SAR satellite and a UAV SAR platform. Experimental results show that CommSAR preserves imaging performance without degradation while achieving downlink and uplink data rates of up to 105 kbps and 112 kbps, respectively, significantly outperforming the state of the art and demonstrating utility-grade performance.

Millimeter-wave (mmWave) communication offers a promising solution for high-throughput, low-latency unmanned aerial vehicle (UAV) networks. However, maintaining strong received signal strength (RSS) remains a challenge due to UAV mobility. While existing studies have largely focused on beam alignment, they often overlook another critical issue: polarization mismatch caused by UAV orientation changes. This problem is particularly severe in cost-sensitive commercial off-the-shelf (COTS) mmWave devices, which typically employ linearly polarized (LP) antenna arrays. Our measurements reveal that even with perfect beam alignment, UAV orientation can still cause significant signal degradation due to polarization mismatch. To address this challenge, we propose PolarFix, a practical metasurface solution that enables real-time polarization matching without requiring any modifications to existing transceiver hardware. Specifically, we design a linear-to-circular polarization (L2C) metasurface that transforms linearly polarized (LP) waves into circularly polarized signals, allowing LP antennas to maintain consistent signal power despite changes in UAV orientation.

The introduction of Direction Finding (DF) feature has provided commercial Bluetooth Low Energy (BLE) devices with the ability to report raw signal In-phase and Quadrature (IQ) data. This protocol-level support, notably absent in other mainstream wireless technologies like WiFi, highlights the significant potential of BLE for fine-grained sensing applications. Nevertheless, the inherent coverage constraints of BLE and the lack of widespread deployment of DF-capable devices currently restrict the advancement of fine-grained BLE sensing. To address these challenges, we propose CTESense, a novel cross-technology sensing system. CTESense enables ubiquitous WiFi access points to generate BLE-compatible signals through physical-layer waveform emulation, thereby achieving broader coverage and enhanced performance for fine-grained BLE sensing. Specifically, our system enables WiFi devices to broadcast specially designed packets that emulate BLE's DF feature without hardware modifications, thereby activating the sensing capabilities of commercial BLE devices. To demonstrate its practical utility, we integrate our cross-technology sensing method into the respiration sensing scenario and developed a specialized model. We have implemented and evaluated CTESense in real-world environments. Our results show that CTESense expands the effective sensing coverage of BLE by approximately$5 \times$, pushing its capability from a sub-meter scale to a functional room-level, validating the effectiveness of CTESense.

With global population growth, sustainable agriculture requires efficient soil moisture sensing for precise irrigation. While commercial soil moisture sensors are often limited by cost and durability, state-of-the-art RF-based sensing solutions require additional signal transmitter infrastructure, hindering widespread adoption. To fill this gap, we introduce GPSoil—a novel soil moisture sensing system that leverages pervasive Global Navigation Satellite System (GNSS) signals. On the hardware side, we employ two antennas along with a low-cost RF switch, enabling error mitigation for long propagation distances by comparing the signals captured from both antennas. On the software side, we leverage the inherent clock drift errors in commercial GNSS sensors and turn them into tools for fine-grained value extraction, enabling high-resolution sensing from otherwise coarse data. By integrating hardware and software innovations, we successfully achieve practical soil moisture sensing using GNSS signals. Built with off-the-shelf components, GPSoil achieves a soil moisture accuracy of 5.2% at a material cost of only $10.56, making it far more affordable than existing systems. GPSoil can sense moisture up to one meter underground, far surpassing other RF-based sensing systems and meeting the needs of most crops and irrigation systems. This work pioneers the use of GNSS signals in agriculture, offering a scalable, low-cost solution for advancing precision irrigation and promoting sustainable agriculture.

Millimeter-wave (mmWave) has emerged as a promising solution for contact-free sensing due to its high resolution. Although promising, it faces several critical issues, including occlusion from the surrounding environment, unstable orientation-dependent sensing performance, and significant interference when multiple targets are in close proximity. These fundamental issues hinder the widespread adoption of mmWave sensing in the real world. In this paper, we propose SeRadar, the first systematic framework that leverages all useful secondary reflections to significantly enhance reliability and bring mmWave sensing one step closer to real-world adoption. Unlike primary reflections commonly used in wireless sensing, secondary reflections—typically much weaker due to being reflected multiple times—are generally ignored in existing literature. However, we observe that secondary reflections are common in various scenarios and carry valuable information about target movements, which could also contribute to sensing. To effectively utilize secondary reflections for sensing, SeRadar addresses several challenges associated with secondary reflections. Specifically, it boosts weak secondary reflections to improve their sensing capability, identifies useful ones from a large number of secondary reflections captured in the environment, and mitigates primary-secondary interference in multi-target scenarios. We evaluate the performance of SeRadar in various environments, including offices, apartments, and vehicle cabins. Extensive experiments demonstrate SeRadar can enhance accuracy and reliability in diverse sensing scenarios.

Beyond communication, wireless signals have been extensively utilized for localization, tracking, and sensing in recent years. The key information extracted for these purposes includes distance and angle. While distance measurement accuracy is mainly limited by signal bandwidth, angle accuracy depends on the number of antennas and phase noise. Conventional approaches typically improve angle estimation by boosting signal strength and increasing the number of antennas. In this paper, we propose employing a quantum receiver to substantially improve angle estimation performance. Rather than amplifying signal strength, the quantum receiver reduces the inherent hardware noise. Furthermore, we exploit the unique properties of a quantum RF receiver to construct a multi-antenna quantum system. Using only two physical quantum antennas, we generate virtual antennas by leveraging the receiver's broad frequency range, effectively increasing the number of antennas and significantly improving angle measurement performance. Our experimental results demonstrate that, with only two quantum antennas, we achieve angle estimation performance surpassing that of a conventional RF receiver equipped with 40 antennas. Furthermore, quantum antennas are not constrained by the coupling effects that typically limit the spacing between conventional RF antennas, allowing for much closer placement. This represents a significant step toward reducing the size of antenna arrays while preserving localization and tracking performance.

Battery-free sensing has revolutionized IoT applications, but current solutions relying on signal variations between transmitted and backscattered signals remain vulnerable to environmental dynamics and deployment variations. This paper promotes a paradigm shift: inferring targets through antenna impedance variations instead of signal fluctuations, thereby eliminating the impact of unpredictable wireless communication. We demonstrate the effectiveness of this paradigm by reimplementing three existing applications: RIO [1], Keystub [2], and RF-EATS [3]. Compared to original signal-based implementations, our approach shows significant improvements in accuracy and robustness across diverse environments. Furthermore, By integrating antenna engineering with advanced materials science, we also transform antennas into innovative sensors for pressure, temperature, and UV light sensing. This interdisciplinary methodology pushes the boundaries of battery-free sensing, opening new avenues for IoT applications.

Taste perception plays an essential role in promoting human health and maintaining nutritional balance. Current taste perception techniques usually require expensive equipment and delicate storage conditions, which restrict their use primarily to laboratory settings. In this paper, we show that terahertz (THz) signals generate unique fingerprint spectra when interacting with different taste molecules in aqueous solutions. Building on this finding, we propose Molecular-level Taste Sensing (MoleSen), a contact-free gustatory sensing method aimed at achieving wireless human-like perception. Specifically, MoleSen emits terahertz signals towards the aqueous solution, captures the reflected signals, and then determines the type and concentration of the tastes by analyzing the unique fingerprint spectra of the reflected signals influenced by the taste molecules. In MoleSen, we design a bio-inspired deep learning model (DTB, Digital Taste Bud) to identify the subtle taste features diluted by water molecules. Additionally, we incorporate domain adaptive learning to address the issue of feature distribution shifts when multiple tastes are mixed. Through extensive experiments involving over 247,000 samples, we demonstrate that MoleSen can accurately differentiate the five basic tastes—sour, bitter, salty, sweet, and umami—with an accuracy of 98.5% for taste type determination and 96.9% for concentration detection. Moreover, MoleSen outperforms the human's taste sensitivity and achieves a highly accurate perception even for mixed tastes.

Various wireless technologies have been utilized for sensing. Although promising, these wireless sensing technologies have inherent limitations. Prior research mainly focuses on overcoming the limitations of an individual wireless sensing technology, and little attention has been paid to the potential benefits of sensing with more than one wireless technology. In this paper, we introduce the concept of cross-technology sensing for the first time, and propose LoFiSen to enable LoRa-to-WiFi sensing. LoFiSen leverages the strengths of both LoRa and WiFi—combining LoRa's long-range capability with WiFi's pervasiveness. The chirp characteristic of LoRa signal significantly improves the sensing range of WiFi, and the widespread availability of WiFi devices makes LoRa sensing more pervasive. LoFiSen is fully compatible with LoRa and WiFi protocols, and can work on commodity LoRa and WiFi hardware. The key component of our design is enabling the WiFi receiver to capture fine-grained LoRa signal variations for sensing. Real-world experiments demonstrate that LoFiSen improves the WiFi sensing range for respiration monitoring from 8 m to 41 m, and pushes the walking sensing range from 16 m to 73.5 m. Through-wall passive respiration monitoring, previously infeasible with state-of-the-art WiFi sensing, is now possible with LoFiSen.

LoRa-based contact-free wireless sensing has attracted a lot of attention owing to its long sensing range, enabling wide-area sensing for the first time. While promising, existing LoRa sensing assumes there is no communication going on which is usually not true. We observe a severe degradation of sensing performance in real-world settings in the presence of communication. This issue hinders LoRa sensing from being adopted in real life and being integrated into the already established LoRa networking infrastructure. In this paper, we propose LSencom which takes the first step toward making LoRa-based wireless sensing work in the presence of communication. The key design is to employ the reversed chirp, i.e., downchirp, for sensing while keeping the original upchirp for communication. This design smartly leverages the orthogonality between downchirp and upchirp to mitigate the interference between communication and sensing. While the upchirp-downchirp design can remove most of the interference, we further adopt a novel chirp rotation method to deal with the remaining power leakage interference from upchirp to downchirp, enhancing the sensing performance. We implement LSencom on commodity LoRa nodes. Real-world experiments demonstrate that LSencom can reduce the communication-induced interference on sensing by 22.3 dB, and enable LoRa sensing even in the presence of multiple communication links.

LoRa technology holds great promise for wide-area wireless sensing, owing to its long-range connectivity and strong penetration capability. However, existing LoRa sensing systems face a fundamental limitation, i.e., they assume that the gateway only receives signals from the sensing node, without any interference from other communication nodes. This is because transmissions from communication nodes inevitably distort the sensing pattern extracted from the received sensing signal, leading to sensing failure. To address this challenging issue, we propose InterSen, a novel solution specifically designed to address communication interference on LoRa sensing. We conduct an in-depth analysis of how communication interference disrupts LoRa sensing and design innovative signal processing methods to recover the sensing pattern corrupted by interference. We evaluate the performance of InterSen across three real-world sensing applications, i.e., respiration monitoring, walking sensing, and gesture recognition. Comprehensive experiments demonstrate that InterSen achieves accurate sensing even in the presence of multiple communication nodes. This brings LoRa sensing one step closer to practical deployment in real-world scenarios.

Underwater positioning plays a key role in many underwater operations. This paper presents the design, implementation, and evaluation of UWBeacon, a centimeter-level visible light-based underwater positioning system. UWBeacon consists of LED beacons as the light signal transmitter and a camera-based receiver as the target. To address unique challenges in underwater environment such as limited visibility and strong ambient interference, we exploit a novel design that utilizes polarized lights of different colors with different polarization angles for background subtraction. UWBeacon is implemented with commercial-off-the-shelf LEDs and cameras. Comprehensive experiments conducted in various real underwater environments show that UWBeacon can achieve a mean positioning error below 6 cm and an orientation error below 1.5° at a distance of 10 meters.

RF sensing has attracted a tremendous amount of attention and achieved promising progress in applications such as human gesture recognition and vital sign monitoring. This paper delves into sensing the moisture level of fabrics---an important metric for smart clothing, wound care, and textile manufacturing. We present TMSense, an innovative contact-free fabric moisture measurement system that leverages UWB signals for sensing. We introduce a set of signal processing methods to tackle the challenge of weak fabric reflections that can be easily overwhelmed by noise interference. Additionally, we adopt a model-driven approach to get rid of reliance on extensive datasets. By exploiting the changes in the dielectric properties induced by moisture in textile fabrics, we establish a theoretical model that bridges the characteristics of the RF signal with the moisture content. Based on this model, we successfully eliminate interfering factors such as target-device distance and target attributes through delicate signal processing and parameter calibration. Comprehensive experiments conducted under various conditions, including different materials, sample forms, and parameter settings, demonstrate an impressively low median error of 1.4% on textile moisture measurements, outperforming commodity moisture sensors on the market.

In recent years, wireless signals have been extensively investigated for contactless human respiration monitoring. However, most wireless sensing systems encounter challenges when the target exhibits body movements. In this demo, we present a solution to mitigate the impact of body motion on contactless respiration monitoring. By employing novel signal processing techniques, body motion can be first estimated and subsequently eliminated from the signal reflected signal by the chest. We prototype the proposed system using a MIMO mmWave radar. Evaluations in real-world environments demonstrate the effectiveness of the solution.

Wireless signals have been widely utilized for human sensing. However, wireless sensing systems face a fundamental limitation, i.e., the wireless device must keep static during the sensing process. Also, when sensing fine-grained human motions such as respiration, the human target is required to stay stationary. This is because wireless sensing relies on signal variations for sensing. When device is moving or human body is moving, the signal variation caused by the target area (e.g., chest for respiration sensing) is mixed with the signal variation induced by device or other body parts, failing wireless sensing. In this paper, we propose MSense, a general solution to deal with motion interference from wireless device and/or human body, moving wireless sensing one step forward towards real-life adoption. We establish the sensing model by taking both device motion and interfering body motion into consideration. By extracting the effect of body and device motions through pure signal processing, the motion interference can be removed to achieve accurate target sensing. Comprehensive experiments demonstrate the effectiveness of the proposed scheme. The achieved solution is general and can be applied to different sensing tasks involving both periodic and aperiodic motions.

The elderly over 65 accounts for 80% of COVID deaths in the United States. In response to the pandemic, the federal, state governments, and commercial insurers are promoting video visits, through which the elderly can access specialists at home over the Internet, without the risk of COVID exposure. However, the current video visit practice barely relies on video observation and talking. The specialist could not assess the patient's health conditions by performing auscultations.

While out-vehicle sensing has achieved great success with the development of vehicle radar and Lidar systems, invehicle sensing attracts a lot of attention recently. However, the popular camera-based solutions raise privacy concerns and pose requirement on lighting conditions. Researchers recently utilize wireless signals for sensing. However, besides requiring dedicated hardware, the rich multipath in a small cabin space causes severe interference, degrading the sensing reliability. In this paper, we propose a new sensing modality for in-vehicle sensing, leveraging the leaked EM signals from electric vehicles. The key observation is that the human body can capture the leaked signals, and body motions affect the signal variation patterns. Our solution involves designing conductive cloth tags on the seat to effectively collect body-captured signals and adopting a reference tag to deal with interference. Through extensive experiments conducted over 100 hours, covering a driving distance of 4000 kilometers on various real roads, our system, EVLeSen, can achieve over 90% accuracy in recognizing body motions utilizing just the leaked ambient signals.

Wireless sensing is gaining increasing attention from both academia and industry. Various wireless signals, such as Wi-Fi, UWB, and acoustic signals, have been leveraged for sensing. While promising in many aspects, two critical limitations still exist: a) limited sensing coverage; and b) the requirement for dedicated sensing signals, which may interfere with the original function of the wireless technology. To address these issues, we propose to utilize GPS signals for sensing, as GPS signals are already pervasive and emitted from satellites 24/7 at pre-allocated frequency bands, causing no interference. To make GPS sensing possible, we reconstruct signals with amplitude and phase information which is critical for sensing using the raw measurements reported by commercial GPS receiver module. We also develop sensing models to tailor the unique properties of GPS signals such as extremely long transmission distance. Finally, we introduce the concept of distributed sensing and design signal processing methods to fuse signals from multiple satellites to improve sensing performance. With all these designs, we prototype the first GPS wireless sensing system on commercial GPS receiver modules. Comprehensive experiments demonstrate that the proposed system can realize meaningful sensing applications such as human activity sensing, passive trajectory tracking, and respiration monitoring.

Gas sensing plays a key role in detecting explosive/toxic gases and monitoring environmental pollution. Existing approaches usually require expensive hardware or high maintenance cost, and are thus ill-suited for large-scale long-term deployment. In this paper, we propose Gastag, a gas sensing paradigm based on passive tags. The heart of Gastag design is embedding a small piece of gas-sensitive material to a cheap RFID tag. When gas concentration varies, the conductivity of gas-sensitive materials changes, impacting the impedance of the tag and accordingly the received signal. To increase the sensing sensitivity and gas concentration range capable of sensing, we carefully select multiple materials and synthesize a new material that exhibits high sensitivity and high surface-to-weight ratio. To enable a long working range, we redesigned the tag antenna and carefully determined the location to place the gas-sensitive material in order to achieve impedance matching. Comprehensive experiments demonstrate the effectiveness of the proposed system. Gastag can achieve a median error of 6.7 ppm for CH4 concentration measurements, 12.6 ppm for CO2 concentration measurements, and 3 ppm for CO concentration measurements, outperforming a lot of commodity gas sensors on the market. The working range is successfully increased to 8.5 m, enabling the coverage of many tags with a single reader, laying the foundation for large-scale deployment.

The past decade has witnessed a surge of interest in human respiration sensing with various wireless signals to achieve at-home smart health. While promising in many aspects, two critical limitations still exist: a) dedicated sensing signal transmitters are needed; b) existing sensing schemes affect the original function of the wireless technology such as communication. In this demo, we present the GPSense Respiration system, a new kind of contact-free respiration sensing system that breaks the above limitations. GPSense Respiration operates by receiving and analyzing the GNSS signals reflected by the human body without additional pre-deployed signal transmitters. Also, GPSense Respiration system will not affect wireless communications because GNSS signals operate at different frequencies than communication signals. This demo enables respiration monitoring for different users in real-time without any calibration.

While localization has gained a tremendous amount of attention from both academia and industry, much less attention has been paid to equally important orientation estimation. Traditional orientation estimation systems relying on gyroscopes suffer from cumulative errors. In this paper, we propose UWBOrient, the first fine-grained orientation estimation system utilizing ultra-wideband (UWB) modules embedded in smartphones. The proposed system presents an alternative solution that is more accurate than gyroscope estimates and free of error accumulation. We propose to fuse UWB estimates with gyroscope estimates to address the challenge associated with UWB estimation alone and further improve the estimation accuracy. UWBOrient decreases the estimation error from the state-of-the-art 7.6° to 2.7° while maintaining a low latency (20 ms) and low energy consumption (40 mWh). Comprehensive experiments with both iPhone and Android smartphones demonstrate the effectiveness of the proposed system under various conditions including natural motion, dynamic multipath and NLoS. Two real-world applications, i.e., head orientation tracking and 3D reconstruction are employed to showcase the practicality of UWBOrient.

Real-time indoor crowd counting plays an important role in many applications such as crowd control, resource allocation and advertisement. Current research predominantly relies on camera-based methods. However, computer vision-based solutions raise severe privacy and ethical concerns. In this paper, we propose a privacy-preserving counting solution called BeamCount based on Wi-Fi sensing. Instead of using conventional Wi-Fi Channel State Information (CSI) readings, we utilize Wi-Fi Beamforming Feedback Information (BFI) for crowd counting estimation. Compared to CSI which can only be extracted from few commodity Wi-Fi cards (e.g., Intel 5300), BFI readings can be obtained from a large range of commodity Wi-Fi devices. We establish a mapping relationship between BFI and headcount and extract headcounts from BFI inputs through a carefully designed adversarial network. Owing to the adversarial network's cross-domain capability, the proposed counting system can achieve high accuracy across different environments, demonstrating its generalization capability. To mitigate the effect of BFI compression on sensing performance, we adopt a novel time series prediction model. Extensive real-world experiments validate the effectiveness of BeamCount in various environments, achieving an average counting accuracy of 93.6%.

Acoustic signal has been used to sense important contextual information of targets such as location and movement. This paper explores its potential for measuring the geometric information of moving targets, i.e., shape and size. We propose SONDAR, a novel shape and size measurement system using Inverse Synthetic Aperture Radar (ISAR) imaging on commodity devices. We first design a Doppler-free echo alignment method to accurately align target reflections even if there exists a severe Doppler effect. Then we down-convert the received signals reflected from multiple scatter points on the target and construct a modulated downchirp signal to generate the image. We further develop a lightweight approach to extract the geometric information from a 2-D frequency image. We implement and evaluate a proof-of-concept system on both a Bela platform and a smartphone. Extensive experiments show that we can correctly estimate the target shape and achieve a millimeterlevel size measurement accuracy. Our system can achieve high accuracies even when the target moves along a deviated trajectory, at a relatively high speed, and under obstruction.

Acoustic metasurface was recently proposed to enhance the performance of acoustic communication and sensing. While promising, there are two issues hindering the adoption of acoustic metasurface for real-life usage. The first issue is that configurable metasurface is still expensive and unscalable. The second issue is that it is difficult for an acoustic metasurface to work in a large frequency range. In this paper, we present a wideband and configurable acoustic metasurface for the first time. We show that with a large number of metasurface elements, a cheap and simple two-state element design can achieve performance very close to that achieved by expensive continuous-state elements. We also fine-tune the geometric parameter of the element structure to support similar phase changes across a large frequency range, laying the foundation to enable wideband acoustic metasurface. Extensive experiments show that our system can achieve an average signal strength improvement of 7.5 dB and 10.5 dB in LoS and NLoS scenarios respectively with the help of a metasurface with a size of 17.6 × 17.6 cm. Two representative sensing applications (i.e., respiration sensing and gesture recognition) and one communication case study are employed to show the effectiveness of the metasurface.

Accurate measurements of soil macronutrients (i.e., nitrogen, phosphorus, and potassium) and moisture play a key role in smart agriculture. However, existing commodity soil sensors are often expensive and the achieved accuracy is unsatisfactory. To address these issues, we present SoilCares, a low-cost soil sensing system enabling accurate and simultaneous monitoring of the concentration levels of soil moisture and macronutrients. SoilCares overcomes key challenges of accommodating diverse soil types and soil textures by introducing a novel membrane-based scheme. For moisture sensing, SoilCares leverages the multi-modal fusion of RF and NIR signals to significantly increase the sensing accuracy. Through delicate hardware design, we enable negligible-cost sensor data transmission using the existing sensing hardware, building up a complete end-to-end soil sensing system. SoilCares is cost-effective ($63.5), portable (0.5 kg), and low-power (236 μW), making it suitable for insitu deployment. On-site experimental results show that SoilCares achieves high macronutrient sensing accuracy with a low RMSE of 0.138, and extremely low moisture estimation error of 1%, outperforming the state-of-the-art research and expensive commodity moisture sensors on the market.

While a large range of sensing applications such as activity sensing and vital sign monitoring have been realized with WiFi sensing, using commercial WiFi devices to obtain fine-grained size information of objects remains challenging due to the narrow bandwidth of WiFi. Very recent studies attempted to measure object sizes using WiFi signals. However, these systems are still far from practical with a lot of limitations including requiring multiple transceiver pairs and can only measure one-dimensional size, hindering their real-life adoption. Also, these systems rely on Channel State Information (CSI) to work, which is only available on few commercial WiFi cards. In this work, we propose to employ a new channel data, i.e., Beamforming Feedback Information (BFI), widely available on almost all new generation WiFi cards for fine-grained size measurement. Through thoroughly analyzing the mathematical relationship between BFI and CSI, we show how to use BFI to achieve fine-grained size measurement. We propose a novel method to accurately measure the two-dimensional size of an object using a single transceiver pair by identifying the positions of singularities when the object passes through the diffraction zone of the transceiver pair. Experiment results show that Wi2DMeasure can accurately measure the two-dimensional size of objects under various conditions, achieving a small median error of only 3.7 mm.

We propose the design, implementation, and evaluation of an instantaneous angular speed (IAS) measurement system, namely Wi-Rotate, using commercial-off-the-shelf (COTS) WiFi hardware. Wi-Rotate exploits the Channel State Information (CSI) of WiFi signals to extract the physical characteristics of the rotation object to achieve accurate contact-free IAS measurements. Wi-Rotate contains three main components: Wi-Fresnel model, Wi-Phase model, and a combination model. Wi-Fresnel model explores the signal amplitude variation features when the rotating object cuts the Fresnel zone boundary to track target rotation. Wi-Phase model leverages signal phase variation and formalizes the problem of determining IAS as a linear programming problem. The combination model combines the IAS values obtained by Wi-Fresnel and Wi-Phase and utilizes a clustering method to further improve measurement accuracy. Comprehensive experiments are conducted to demonstrate the advantages of Wi-Rotate in terms of accuracy, sensing range, and system latency. Wi-Rotate is able to achieve real-time rotation measurements at an accuracy higher than 99% when the target is within 2 meters. Even when the target is 3 meters away, Wi-Rotate can still achieve an accuracy of 94%, demonstrating the long-range tracking capability which is critical for industrial applications.

As the most natural and convenient way to communicate with people, speech is always preferred in Human-Computer Interactions. However, voice-based interaction still has several limitations. It raises privacy concerns in some circumstances and the accuracy severely degrades in noisy environments. To address these limitations, silent speech recognition (SSR) has been proposed, which leverages the inaudible information (e.g., lip movements and throat vibration) to recognize the speech. In this paper, we present EarSSR, an earphone-based silent speech recognition system to enable interaction with human and device without a need for vocalization. The key insight is that when people are speaking, their ear canals exhibit unique deformation patterns and the corresponding deformation patterns are related to words/letters even without any vocalization. We utilize the built-in microphone and speaker of an earphone to capture the ear canal deformation. Ultrasound signals are emitted and the reflected signals are analyzed to extract the signal features corresponding to speech-induced ear canal deformation for silent speech recognition. We design a two-channel hierarchical convolutional neural network to achieve fine-grained letter/word recognition. Our extensive experiments show that EarSSR can achieve an accuracy of 82% for single alphabetic letter recognition and an accuracy of 93% for word recognition.

Existing WiFi sensing systems transmit dedicated high-rate packets for accurate sensing. These “sensing packets” greatly affect the main data communication function of WiFi and significantly counteract the promised benefit of reusing WiFi communication for sensing. In this work, we propose WiImg2.0, a lightweight system which involves machine learning techniques to enable WiFi sensing under low packet rate, pushing WiFi sensing one step towards real-life adoption. The key idea is to convert the WiFi CSI samples into images and employ the Generative Adversarial Network (GAN) for CSI image inpainting, relaxing the requirement of high sample rate for sensing. We first recover the sensing data from the antenna spatial domain and then from the sample time domain. To avoid the large training overhead of GAN, we design a lightweight GAN that leverages samples of only three rates in a fixed window to recover the CSI traces of arbitrary rates and varying duration. Experiments show that with just 25 packets per second, WiImg2.0 is able to increase the recognition accuracy for hand gesture recognition and daily activity tracking from the state-of-the-art 59.1% and 65.9% to 86.7% and 96.4%, respectively.

Acoustic sensing on smartphones has gained extensive attention from both industry and research communities. Prior studies suffer from one fundamental limit, i.e., audio sampling rates on smartphones are constrained at 48 kHz. In this work, we present PowerPhone, a software reconfiguration to support higher sampling rates on both microphones and speakers of smartphones. We reverse-engineered more than 100 smartphones and found that their sampling rates can be reconfigured to 192 kHz. We conducted benchmark experiments and showcased field studies to demonstrate the unleashed sensing capability using our reconfigured smart-phones. First, we improve the sensing resolution from 7 cm to 1cm and enable multi-finger gesture recognition on smart-phones. Second, we push the sensing granularity of subtle movements to 2 μm and show the feasibility of turning the smartphone into a micrometer-level machine vibration meter. Third, we increase the sensing range to 6 m and showcase room-scale human presence detection using a smartphone. Finally, we demonstrate that PowerPhone can enable new applications that were previously infeasible. Specifically, we can detect the home appliance status by analyzing ultrasonic leakages above 24 kHz from the wireless charger while charging a smartphone. Our open-source artifacts can be found at: https://powerphone.github.io.

In recent years, wireless sensing has attracted lots of research attention with a large range of applications enabled. However, several critical issues still hinder wireless sensing from being adopted in daily use: (a) requiring dedicated devices and (or) dedicated signals; (b) limited sensing coverage; and (c) affecting the original function of the wireless technology (e.g., communication). In this work, we propose a new sensing modality, i.e., leveraging the pervasive powerline leakage for sensing. The key observation is that human body can capture such leaked signals, and the received signals vary with body gestures. We design a cheap ring antenna to collect the powerline leaked signals at human body and establish a body-empowered model to sense body motions. We prototype the proposed system with designs spanning both hardware and software. Comprehensive experiments show that the proposed sensing modality can realize a large range of applications in a different way from existing sensing methods. We showcase the powerful capability of this sensing modality using three typical sensing applications: body gesture recognition, sleep posture sensing, and fall detection.

Ultrasonic microphone jammers protect speech privacy from being eavesdropped by leveraging microphones' non-linearity. However, existing jammers merely introduce independent noises and are vulnerable to capable adversaries who adopt advanced denoising techniques. We propose a novel jammer, namely MicFrozen. It reduces the signal-to-noise ratio (SNR) at the adversary's microphone from two perspectives, i.e., cancelling speech signals and adding noises that are difficult to be removed. It effectively cancels out the protected speech signals at the adversary without compromising the delivery of the signal to the targeted individual. MicFrozen further adds coherent noises that are coupled with the speech signals to resist removal by the adversary. Extensive evaluations show that MicFrozen can cause a low SNR (-13.6 dB) at the adversary and up to 96.9% of speech signals are unrecognized at the adversary even if state-of-the-art denoising techniques are adopted by the adversary. Comprehensive experiments demonstrate the effectiveness of MicFrozen confronted by capable adversaries.

Recent years have witnessed a tremendous amount of interest in wireless sensing, i.e., instead of employing traditional sensors, wireless signal is utilized for sensing purposes. Contact-free wireless sensing has been successfully demonstrated using various RF signals such as WiFi, RFID, LoRa, and mmWave, enabling a large range of applications. However, limited by hardware thermal noise, the granularity of RF sensing is still relatively coarse. In this paper, instead of using the macro signal power/phase for sensing, we propose the first quantum wireless sensing system, which uses the micro energy level of atoms for sensing, improving the sensing granularity by an order of magnitude. The proposed quantum wireless sensing system is capable of utilizing a wide spectrum of frequencies (e.g., 2.4 GHz, 5 GHz and 28 GHz) for sensing. We demonstrate the superior performance of quantum wireless sensing with two widely-used signals, i.e., WiFi and 28 GHz millimeter wave. We show that quantum wireless sensing can push the sensing granularity of WiFi from millimeter level to sub-millimeter level and push the sensing granularity of millimeter wave to micrometer level.

Radio-Frequency (RF) backscatter has emerged as a low-power communication technique. Backscatter systems either rely on active signal generators (spectrum efficient, but dedicated infrastructure) or existing ambient wireless transmissions (existing infrastructure, but spectrum inefficient). In this paper, we aim to make RF backscatter spectrum efficient and at the same time work with existing infrastructure. We propose to leverage the deployment of LiFi networks built upon LED bulbs for pervasive RF backscatter. We experimentally demonstrate that LiFi, which passively leaks RF signals, can be exploited as a radio carrier generator for low-power RF backscatter. We further design LeakageScatter, the first backscatter system operating in the ISM band and exploiting LiFi-leaked RF signals, without the need to actively generate the carrier wave. We customize the design of the loop at the LiFi transmitter, as well as the coil antennas at the tag and RF backscatter receiver, to optimize the system performance. We propose to opportunistically enable the oscillator of the backscatter tag in the software that could reduce the energy consumption on backscattering by up to 75%. Experimental results show that LeakageScatter achieves a backscattering distance up to 10 m and 18 m in indoor and outdoor scenarios, respectively, without using a dedicated RF carrier generator.

Wireless sensing is capable of capturing rich information of human target without requiring sensors attached to the target. Although promising, two critical issues still exist, i.e., (i) limited sensing range, and (ii) severe interference in real-world settings. Recently, LoRa is employed to improve the sensing range. Although LoRa sensing is able to achieve a longer sensing range than WiFi and acoustic sensing, it is still limited to tens of meters. In this paper, we propose ChirpSen, which fully exploits the property of chirp signal to increase the sensing range. ChirpSen adopts a chirp concentration scheme to concentrate the power of all signal samples in a LoRa chirp at one timestamp, improving the signal power and accordingly boosting the sensing range. With a longer sensing range, the interference issue also becomes more severe. We propose a novel scheme to flexibly control the sensing coverage by tuning the LoRa chirp length in software. Real-world experiments show that ChirpSen is able to increase the detection range of a small size drone (12 cm × 10 cm × 8 cm) from 18 m to 160 m. ChirpSen is capable of monitoring a human's respiration rate at 138 m and tracking a human target's walking trajectory 210 m away.

While radio communication still dominates in 5G, light and radios are expected to complement each other in the coming 6G networks. Visible Light Communication (VLC) is therefore attracting a tremendous amount of attention from both academia and industry. Recent studies showed that the front camera of pervasive smartphones is an ideal candidate to serve as the VLC receiver. While promising, we observe a recent trend with smartphones that can greatly hinder the adoption of smartphones for VLC, i.e., smartphones are moving towards full-screen for the best user experience. This trend forces front cameras to be placed under the devices' screen---leading to the so-called Under-Screen Camera (USC)---but we observe a severe performance degradation in VLC with USC: the transmission range is reduced from a few meters to merely 0.04 m, and the throughput is decreased by more than 90%. To address this issue, we leverage the unique spatiotemporal characteristics of the rolling shutter effect on USC to design a pixel-sweeping algorithm to identify the sampling points with minimal interference from the translucent screen. We further propose a novel slope-boosting demodulation method to deal with color shift brought by the leakage interference. We build a proof-of-concept prototype using two commercial smart-phones. Experiment results show that our proposed design reduces the BER by two orders of magnitude on average and improves the data rate by 59×: from 914 b/s to 54.43 kb/s. The transmission range is extended by roughly 100×: from 0.04 m to 4.2 m.

Human activity recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting radio-frequency (RF) signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer human activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. To overcome these weaknesses, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet substantially outperforms the state-of-the-art algorithms and systems.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents Widesee to realize wide-area sensing with only one transceiver pair. Widesee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone’s mobility to broaden the sensing area. Widesee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by device mobility and LoRa’s high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers for sensing. We have developed a working prototype of Widesee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated Widesee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of Widesee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.

Authentication mechanisms deployed on access control systems undertake the responsibility of judging user identity to prevent unauthorized individuals from illegally approaching. In this article, we propose LIPAuth leveraging hand-dependent L ight I ntensity P attern to Auth enticate users. To be specific, lights released by a screen, are blocked and reflected by one hand above it; in this propagation process, hands exhibit user-specific ability in driving light absorption and attenuation due to owning unique structures, thereby outputting discriminative intensity patterns representing user identity. To implement LIPAuth , we first study the impact of screen contents on light intensity patterns, also explore the possibility of embedding hand structure biometrics into these patterns. We then design a customized dynamic stimulus-response mechanism for LIPAuth and make it resilient to the risks of potential registration profile leakage. Subsequently, we construct a joint pipeline consisting of signal processing and a learning-based generative adversarial network to overcome interference from variable user behaviors. More importantly, LIPAuth just utilizes common sensors to capture light signals, hence achieving low cost. We finally conduct extensive experiments in three scenarios to evaluate the authentication performance of LIPAuth prototype.

RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present Tamera , a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of Tamera in a 5 m × 6 m laboratory. Tamera achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them. Tamera successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%. Tamera successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.

Acoustic sensing is a new sensing modality that senses the contexts of human targets and our surroundings using acoustic signals. It becomes a hot topic in both academia and industry owing to its finer sensing granularity and the wide availability of microphone and speaker on commodity devices. While prior studies focused on addressing well-known challenges such as increasing the limited sensing range and enabling multi-target sensing, we propose a novel scheme to leverage the non-linearity distortion of microphones to further boost the sensing granularity. Specifically, we observe the existence of the non-linear signal generated by the direct path signal and target reflection signal. We mathematically show that the non-linear chirp signal amplifies the phase variations and this property can be utilized to improve the granularity of acoustic sensing. Experiment results show that, by properly leveraging the hardware non-linearity, the amplitude estimation error for sub-millimeter-level vibration can be reduced from 0.137 mm to 0.029 mm.

While a tremendous amount of effort has been devoted to localization, the orientation of a device, especially in 3D space, is seldom explored. Although many sensor-based methods utilizing gyro-scope, accelerometer, and magnetometer have been proposed to measure 3D orientation, these methods generally suffer from high cumulative errors and performance degradation when the device is moving. In this paper, we present MOM, the first microphone-based system that estimates the 3D orientation of a device. The key idea of MOM is to employ free sound sources in our surrounding environment as anchors. The prior knowledge of these sound sources, including the signal waveform and the locations of the sound sources, is not required to be known. In particular, we propose an angle-of-arrival (AoA) extraction algorithm that compares fine-grained time delays over microphones at a low computational cost. We implement our system on three platforms including a 6-microphone array Seeed Studio ReSpeaker, a commodity earphone Sennheiser AMBEO smart headset and a commodity smartphone Google Pixel 4. Extensive experiments show that MOM can achieve significantly higher accuracy compared with status quo approaches and is robust against cumulative errors. We apply MOM to two real-life applications, i.e., head tracking and 3D reconstruction, to demonstrate the applicability and generality of MOM in practice.

Acoustic sensing shows great potential to transform billions of consumer-grade electronic devices that people interact with on a daily basis into ubiquitous sensing platforms. In this paper, we share our experience and findings during the process of developing and deploying acoustic sensing systems for real-world usage. We identify multiple practical problems that were not paid attention to in the research community, and propose the corresponding solutions. The challenges include: (i) there exists annoying audible sound leakage caused by acoustic sensing; (ii) acoustic sensing actually affects music play and voice call; (iii) acoustic sensing consumes a significant amount of power, degrading the battery life; (iv) real-world device mobility can fail acoustic sensing. We hope the shared experience can benefit not only the future development of sensing algorithms but also the hardware design, pushing acoustic sensing one step further towards real-life adoption.

As the most important organs of sense, human eyes perceive 80% information from our surroundings. Eyeball movement is closely related to our brain health condition. Eyeball movement and eye blink are also widely used as an efficient human-computer interaction scheme for paralyzed individuals to communicate with others. Traditional methods mainly use intrusive EOG sensors or cameras to capture eye activity information. In this work, we propose a system named SmartLens to achieve eye activity sensing using zero-power contact lens. To make it happen, we develop dedicated antenna design which can be fitted in an extremely small space and still work efficiently to reach a working distance more than 1 m. To accurately track eye movements in the presence of strong self-interference, we employ another tag to track the user's head movement and cancel it out to support sensing a walking or moving user. Comprehensive experiments demonstrate the effectiveness of the proposed system. At a distance of 1.4 m, the proposed system can achieve an average accuracy of detecting the basic eye movement and blink at 89.63% and 82%, respectively.

Besides the communication function, various RF signals such as WiFi and RFID have been actively exploited for sensing purposes recently. However, a missing component of existing RF sensing is sensing under device motions. This paper takes the first step to involve device mobility into the ecosystem of RF sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chips in recent years, we propose to integrate the accuracy of UWB sensing with device mobility to support truly ubiquitous RF sensing. This is a challenging task because the motion artifacts from RF devices can easily overwhelm the target motion, such as subtle chest displacement for respiration sensing. In this demo, we propose Mobi2Sense to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using a commodity UWB module. Comprehensive evaluation demonstrates that Mobi2Sense is able to "hear" music and "see" human respiration at high accuracy in the presence of device motions.

Among various wireless sensing modalities, Ultra-Wideband (UWB) exhibits unique advantages such as fine granularity owing to its super large bandwidth (500 MHz - 2 GHz). Though promising, UWB sensing was only demonstrated on dedicated hardware including DW1000 and XETHRU X4 which are not available in existing consumer-level devices. In the last few years, we observed an interesting trend of UWB module being embedded into consumer-level devices such as smartphones and smart watches. However, leveraging UWB module inside consumer-level devices for sensing poses new challenges. One key challenge is that while dedicated UWB hardware can present us with raw physical-layer signal amplitude and phase, only upper-layer distance and angle information can be extracted from consumer-level devices. In this demo, we address the challenges and present the first UWB sensing system hosted on iPhone and Apple Watch without any dedicated hardware components. We show that with just the upper-layer UWB data reported from smartphones, exciting sensing applications such as fine-grained 3D handwriting and multi-target tracking can be realized, pushing RF sensing one step forward towards real-life adoption.

While GPS-based outdoor localization has become a norm, very few indoor localization systems have been deployed and used. In this paper, we share our 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system. We address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild. The system is currently used in 1469 shopping malls, 393 office buildings and 35 hospitals across 35 cities to provide location service to millions of users on a daily basis. We hope the shared experience can benefit the design of real-world indoor localization systems and the practical problems identified can change the focus of indoor localization research. We released our dataset that contains fingerprints collected from 1469 shopping malls and one office building.

Besides the conventional communication function, wireless signals are actively exploited for sensing purposes recently. However, a missing component of existing wireless sensing is sensing under device motions. This is challenging because device motions can easily overwhelm target motions such as chest displacement used for respiration sensing. This paper takes a first step in the direction of involving device mobility into the ecosystem of wireless sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chip in recent years, we propose to integrate the accuracy of UWB sensing with mobility to support truly ubiquitous wireless sensing. We propose Mobi2Sense, a system design to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using commodity UWB hardware. Real-world applications demonstrate that even in the presence of device motions, fine-grained Mobi2Sense is able to capture subtle target motions to "hear" music, "see" human respiration, and "recognize" multi-target gestures at a high accuracy.

WiFi has achieved great success in data communication in the past two decades and WiFi signals are recently further exploited for sensing purposes. Promising progress has been achieved and diverse WiFi sensing applications have been enabled. However, one critical issue which was not paid much attention to and we believe would greatly hinder the real-life adoption of WiFi sensing is that it actually affects WiFi communication. The fundamental reason is that WiFi sensing requires high-frequency signal samples and WiFi data packets can not meet this requirement. Therefore, existing WiFi sensing systems transmit dedicated high-frequency packets (200-2000 packets per second) for sensing and these “sensing packets” greatly affect the main data communication function of WiFi. In this work, we propose WiImg, a lightweight system which involves machine learning techniques to enable WiFi sensing under low packet rate, pushing WiFi sensing one step towards real-life adoption. The key idea is to convert the CSI samples into images and improve the Generative Adversarial Network (GAN) for CSI image inpainting, relaxing the requirement of high sample rate in sensing. To avoid the large training overhead of GAN, we design a lightweight GAN that leverages samples of only three rates to recover the CSI traces of any arbitrary rates. Experiments show that with just 25 packets per second, WiImg is able to increase the recognition accuracy for hand gesture recognition and daily activity tracking from the state-of-the-art 59.1% and 65.9% to 86.7% and 96.4%, respectively.

Visible Light Communication (VLC) is widely considered a promising technology for the coming 6G networks. Recent studies show that a VLC transmitter not only emits visible light signals but also leaks RF signals during the transmission. In this work, we devote effort to harvesting the free leaked RF energy from VLC transmissions. We observe that the surrounding objects could help a coil antenna harvest significantly more RF energy. Based on this observation, we propose our system Bracelet+, which involves the human body in the harvesting system to increase the harvested power. After careful analysis of the influence of the human body on the harvested power, we prototype the coil antenna as a bracelet that achieves both high harvested power and convenience for wearing. The average power of the RF energy harvested by our design is 10× larger than that of the conventional coil antenna, without causing any interference to the communication of VLC systems. The harvested power can reach up to micro-watts in our tested scenarios. Such a micro-watt level of harvested energy has the potential to power up ultra-low-power sensors such as temperature sensors and glucose sensors.

Soil moisture sensing is a basic function required by applications like precision irrigation. Recently, RF based soil moisture sensing solutions [10, 43] have been proposed, which, however, can hardly support large scale deployment in challenging outdoor environments, since they must have dedicated signal emitters and also require power supply for either the signal emitters (WiFi or RFID reader) or both the transceivers (WiFi AP and client). LTE signal provides a unique opportunity for soil moisture sensing as the ubiquitously deployed base stations are naturally always-on signal emitters, eliminating the need for deploying extra hardware. In this paper, we implement a low-cost LTE based soil moisture sensor using commercial off-the-shelf hardware. We also realize duty-cycled soil sensing by automatically self-calibrating the phase offset after powering on the devices, significantly reducing the overall power consumption of the sensor. Extensive experiments show that our low-cost sensor ($55) achieves a high accuracy (3.15%) which is comparable to high-end soil moisture sensors ($850), wide coverage (2.4 km from the base station) and low power consumption (lasting 16 months using batteries).

Acoustic signal has been recently adopted for contact-free hand gesture recognition due to its fine-grained sensing granularity and wide availability of microphone and speaker in consumer-grade electronic devices such as smartphones. However, a very limited sensing range constrains acoustic sensing to application scenarios where users interact with devices in close proximity. In this paper, we improve the range of acoustic sensing and demonstrate the feasibility of enabling room-scale hand gesture recognition using commodity smart speakers. We develop a series of novel signal processing techniques and implement our system on two commodity smart speaker prototypes with different numbers of microphones. Extensive evaluations are performed in three different environments with 1440 gestures collected from 16 participants. Experiment results show that our system can significantly increase the sensing range from 1 m to 4--5 m. In the challenging scenario where the user is 4 m away from the smart speaker and there is strong interference, the achieved gesture recognition accuracy is still higher than 90%.

Wireless sensing is an emerging technology that can obtain rich context information of human targets in a contact-free manner. Though promising, a missing component of current wireless sensing is sensing under device motions. In this work, we propose to integrate wireless sensing with the mobility of a robot. This is non-trivial because we find that device motions can severely degrade the sensing performance and even completely fail existing wireless sensing systems. In this paper, we propose novel signal processing schemes to address the impact of device motions to enable sensing with device mobility. For the first time, we integrate the robot's mobility with LoRa sensing to enlarge the sensing coverage. Comprehensive experiments demonstrate the effectiveness of the proposed system. We employ two representative sensing applications, i.e., fine-grained respiration monitoring and coarse-grained human walking sensing, to showcase the performance of our system. The proposed system is able to achieve accurate sensing in the presence of device motions, moving wireless sensing one step forward towards truly ubiquitous sensing for real-life adoption.

Respiration rate is an essential vital indicator for health monitoring. While traditional sensor-based methods support acceptable sensing performance, the recent advance in wireless sensing could enable sensor-free and contact-free respiration sensing, which is particularly important during the practice of social distancing against a pandemic like COVID-19. Among a variety of wireless technologies employed for respiration sensing, Wi-Fi-based solutions are most popular due to the pervasive development of infrastructure. However, the existing Wi-Fi-based approaches need to retrieve Wi-Fi readings from access points, which are not often accessible for the end users. In this article, we propose a novel system, MoBreath, in which we utilize the Wi-Fi channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time. We introduce and address unique technical challenges, such as selecting the optimum CSI subcarriers from many noisy candidates and providing smartphone placement strategies for both single and multiple human target scenarios based on the Fresnel zone model to support highly accurate respiration sensing. Our evaluation of MoBreath using commodity smartphones in different environments shows that it can accurately estimate the respiration rate at a low error rate of 0.34 breaths per minute and support the sensing range of up to 3–4 m. Even for challenging scenarios such as the target is covered by a quilt and multiple targets are in the sensing area, MoBreath can still support highly accurate results.

The global IoT market is experiencing a fast growth with a massive number of IoT/wearable devices deployed around us and even on our bodies. This trend incorporates more users to upload data frequently and timely to the APs. Previous work mainly focus on improving the up-link throughput. However, incorporating more users to transmit concurrently is actually more important than improving the throughout for each individual user, as the IoT devices may not require very high transmission rates but the number of devices is usually large. In the current state-of-the-arts (up-link MU-MIMO), the number of transmissions is either confined to no more than the number of antennas (node-degree-of-freedom, node-DoF) at an AP or clock synchronized with cables between APs to support more concurrent transmissions. However, synchronized APs still incur a very high collaboration overhead, prohibiting its real-life adoption. We thus propose novel schemes to remove the cable-synchronization constraint while still being able to support more concurrent users than the node-DoF limit, and at the same time minimize the collaboration overhead. In this paper, we design, implement, and experimentally evaluate OpenCarrier, the first distributed system to break the user limitation for up-link MU-MIMO networks with coordinated APs. Our experiments demonstrate that OpenCarrier is able to support up to five up-link high-throughput transmissions for MU-MIMO network with 2-antenna APs.

Continuous user authentication is of great importance to maintain security for a mobile system and protect user's privacy throughout a login session. In this paper, we propose HandPass, a continuous user authentication system that employs the vibration responses of concealed hand biometrics, which are passively activated by the natural user-device interactions on the touchscreen. Hand vibration responses are instantly triggered and embodied in the mechanical vibration of the force-bearing body (i.e., the mobile device and the holding hand). Therefore, a built-in accelerometer can effectively capture the intrinsic features of hand vibration responses. The hand vibration response is determined by the trigger force and the complex hand structure, which is unique to each user and is difficult (if not impossible) to counterfeit. HandPass is a passive hand vibration response-based continuous user authentication system hosted on smartphones, with advantages of non-intrusiveness, high efficiency, and user-friendliness. We prototyped HandPass on Android smartphones and comprehensively evaluated its performance by recruiting 43 volunteers. Experiment results show that HandPass can achieve 97.3 % overall authentication accuracy and only 1.8 % false acceptance rate in diverse scenarios.

Visible Light Communication (VLC) is considered a new paradigm for next-generation wireless communication. Recently, studies show that during the process of VLC transmission, besides the visible light signals, the transmitter also leaks out RF signals through a side channel. What is interesting is that the data transmitted in the VLC channel can be inferred from the leaked RF signals. Fundamentally, it means the leaked RF signals carry a copy of the same data in the VLC channel. In this work, we show for the first time that besides inferring the original VLC data, the leaked side channel can be smartly leveraged to carry new data, significantly increasing the data rate of current VLC systems. To realize this objective, we propose a system named RadioInLight, with designs spanning across hardware and software. Without any dedicated active RF transmission front-end which consumes power and hardware resources, RadioInLight is able to double the data rate of the VLC system by purely manipulating the free passively leaked RF signals without affecting the data rate of the original VLC transmissions.

Headphones continue to become more intelligent as new functions (e.g., touch-based gesture control) appear. These functions usually rely on auxiliary sensors (e.g., accelerometer and gyroscope) that are available in smart headphones. However, for those headphones that do not have such sensors, supporting these functions becomes a daunting task. This paper presents HeadFi, a new design paradigm for bringing intelligence to headphones. Instead of adding auxiliary sensors into headphones, HeadFi turns the pair of drivers that are readily available inside all headphones into a versatile sensor to enable new applications spanning across mobile health, user-interface, and context-awareness. HeadFi works as a plug-in peripheral connecting the headphones and the pairing device (e.g., a smartphone). The simplicity (can be as simple as only two resistors) and small form factor of this design lend itself to be embedded into the pairing device as an integrated circuit. We envision HeadFi can serve as a vital supplementary solution to existing smart headphone design by directly transforming large amounts of existing "dumb" headphones into intelligent ones. We prototype HeadFi on PCB and conduct extensive experiments with 53 volunteers using 54 pairs of non-smart headphones under the institutional review board (IRB) protocols. The results show that HeadFi can achieve 97.2%--99.5% accuracy on user identification, 96.8%--99.2% accuracy on heart rate monitoring, and 97.7%--99.3% accuracy on gesture recognition.

This paper presents the design, implementation, and evaluation of Shrimp, an underwater visible light communication (VLC) system. To address the unique issues in underwater environment such as water flow and scattered sunlight interference, we exploit the circularly polarized light (CPL) and double links for underwater VLC transmission. A coding scheme tailored for underwater communication based on double CPL design is developed. We prototype Shrimp on commercial-off-the-shelf (COTS) LEDs with fabricated printed circuit boards (PCBs). Extensive experiments conducted in an indoor water pool, a lake, and the sea demonstrate that Shrimp can combat against environmental interference and achieve robust communication in underwater environments. The communication distance can be up to 3 m in sea/lake water using a 3 W commodity LED, outperforming the VLC schemes designed for in-air communication.

Besides the communication function, wireless signals are recently exploited for sensing purposes, enabling diverse applications. However, designing a wireless sensing system that provides truly pervasive coverage at city or even national scale and at the same time does not affect ongoing data communication is still challenging. In this work, we propose to involve the pervasive LTE signals into the ecosystem of wireless sensing. Although LTE sensing solves the coverage issue and does not compromise the communication function, it brings unique challenges. Due to the long distance between LTE base stations and terminals, the LTE signal interacts with diverse objects during the propagation process which causes severe interference in sensing. We enable LTE sensing by designing delicate signal processing schemes to combat against the severe interference. We demonstrate the advantages of LTE sensing using two typical applications, indoor respiration sensing and outdoor traffic monitoring. Extensive experiments show that the proposed system can achieve highly accurate respiration sensing with the blind spot and orientation-sensitive issues greatly mitigated. For traffic monitoring, the error of car speed estimation is lower than 2 mph, as good as commercial devices on the market.

The papers in this special section focus on advanced complex data analytics for smart city industrial environments. With the continuous increase in size of populations living in cities, those residents face increasing environmental pressures and infrastructure needs. To deliver a better quality of life for these residents to address these demands at a sustainable cost, smart technologies can help cities meet these challenges, and have been become the next wave of public investment. It all starts with data to be generated by the residents living in the cities. This special section will focus on the ever-increasing challenges of big complex data like social network data, traffic network data, and IoT network data altogether in the industrial environment of smart city.

Owing to the ubiquitous penetration of Wi-Fi in our daily lives, Wi-Fi indoor localization has attracted intensive attentions in the last decade or so. Despite some significant progresses, the high accuracy of existing systems is still achieved at the cost of dense access point (AP) deployment. The more practical single AP localization is largely left as an open problem because the hardware-induced time delay “contaminates” the measurement of signal propagation time in the air. In this article, we design and implement M3to tackle this challenge with commodity Wi-Fi cards. M3exploits a multipath-assisted approach that turns the harmful multipath from foe to friend to enable single AP localization: a device can be pinpointed through the combination of azimuths and the relative time of flight (ToF) of Line-of-Sight (LoS) signal and reflection signals, eliminating the need for multiple APs along with their absolute ToF measurements. M3further utilizes frequency hopping to combine multiple channels to form a virtually wider-spectrum channel for higher ToF resolution. As a prominent feature of M3, the channels do not need to be adjacent. Comprehensive experiments demonstrate that M3outperforms the state-of-the-art systems and achieves a median localization accuracy of 71 cm in three environments with a single AP.

Material identification and target imaging play an important role in many applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commodity Radio-Frequency IDentification (RFID) devices. The key intuition is that different materials and/or target sizes cause different amounts of phase and RSS (Received Signal Strength) changes, when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system, including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94 percent material identification accuracies for 10 liquids and differentiates even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

In theory, the capacity of a wireless network grows linearly with the number of users and antennas equipped at the communication devices, and hence large-scale MU-MIMO can scale up the network throughput. However, three main challenges are impeding the implementation of this promising technology in the state-of-the-art WLANs. Firstly, the current large-scale MU-MIMO technology demands a large number of high-priced RF chains. Secondly, the wireless access points (APs) are overwhelmed by channel state information (CSI) feedback for nulling multi-user and -antenna interference. Thirdly, the lack of scalable user selection scheme limits the capability of APs to serve a large user population. To address these problems, we design BUSH, a large-scale MU-MIMO prototype that performs scalable beam user selection with hybrid beamforming for phased-array antennas in legacy WLANs. We design a low complexity algorithm that assigns each pair of RF chain and analog beam to the users to effectively reduce channel correlation and cross-talk interference without instantaneous CSI feedbacks. As a prerequisite of user selection, BUSH presents a low-overhead probing scheme in multi-carrier WLANs and designs a highly accurate blind Power Azimuth Spectrum (PAS) estimation algorithm using a single RF chain. For reducing the number of RF-chains used, the phased-array antennas use analog beamforming to steer beams toward each selected downlink user, and multiple RF chains use beamforming to further mitigate the interference among users. We implement BUSH on a software-defined radio platform and evaluate its performance in more than 30 indoor scenarios. The experimental results reveal that for throughput, BUSH outperforms the legacy 802.11ac by 2.08×, and an alternative benchmark system by 1.22× on average.

Handwriting recognition system provides people a convenient and alternative way for writing in the air with fingers rather than typing keyboards. For people with blurred vision and patients with generalized hand neurological disease, writing in the air is particularly attracting due to the small input screen of smartphones and smartwatches. Existing recognition systems still face drawbacks such as requiring to wear dedicated devices, relatively low accuracy and infeasible for cross domain identification, which greatly limit the usability of these systems. To address these issues, we propose WiWrite, an accurate device-free handwriting recognition system which allows writing in the air without a need of attaching any device to the user. Specifically, we use Commercial Off-The-Shelf (COTS) WiFi hardware to achieve fine-grained finger tracking. We develop a CSI division scheme to process the noisy raw WiFi channel state information (CSI), which stabilizes the CSI phase and reduces the noise of the CSI amplitude. To automatically retain low noise data for identification, we propose a self-paced dense convolutional network (SPDCN), which consists of the self-paced loss function based on a modified convolutional neural network, together with a dense convolutional network. Comprehensive experiments are conducted to show the merits of WiWrite, revealing that, the recognition accuracies for the same-size input and different-size input are 93.6% and 89.0%, respectively. Moreover, WiWrite can achieve a one-fit-for-all recognition regardless of environment diversities.

RFID technology has recently been exploited for not only identification but also for sensing including trajectory tracking and gesture recognition. While contact-based (an RFID tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and the situation gets even worse when the target is non-static, restricting its applicability in real world deployment. In this work, we present TagRay, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enabling mobile object tracking and even material identification. We design and implement our prototype on commodity RFID device. Comprehensive experiments show that TagRay achieves a high accuracy of 1.3 cm which is a 200% improvement over the-state-of-arts for trajectory tracking. For commonly seen four material types, the material identification accuracy is higher than 95% even with interference from people moving around.

Mechanical vibration sensing/monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing solutions usually involve directly attaching sensors to the target objects, which is invasive and may affect the operations of delicate devices. Non-invasive approaches such as video and laser methods have drawbacks in that, the former incurs poor performance in low light conditions, while the latter has difficulties to monitor multiple objects simultaneously.In this work, we design RF-Ear, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID hardware. RF-Ear could accurately monitor the mechanical vibrations of multiple (up to 8) devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear can measure the vibration frequency up to 400Hz with a mean error rate of 0.2%. Our evaluation results show that RF-Ear can effectively detect 0.2cm screw loose with 90% accuracy. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. We also show that RF-ear can monitor not just the vibrations but also a large range of mechanical motions. Comprehensive experiments conducted in a real power plant demonstrate the effectiveness of our system with outstanding performance.

Visible light communication (VLC) is gaining a significant amount of interest as a new paradigm to meet rapidly increasing demands on wireless capacity required by a digitalized world. VLC is considered as a secure wireless communication scheme because VLC signals can be easily constrained within physical boundaries. In this paper, for the first time, we show that VLC is not as secure as people thought: VLC can be sniffed through walls! The key principle behind this is that in VLC transmissions, a VLC transmitter not only emits visible light signals but also leaks out 'side channel RF signals'. The leaked RF signals can be sniffed by a receiver to decode the VLC transmissions even the receiver is blocked (e.g., by walls) from the VLC transmitter. In this work, we establish a theoretical model to quantify the amplitude of the leaked RF signal and verify the model with comprehensive experiments. We design and implement a VLC sniffing system including receiver coil design, signal processing and frame decoding, spanning across hardware and software. Field studies show that with a cheap receiver design, our system can simultaneously sniff transmissions from multiple VLC transmitters 6.4 meters away with a 14 cm concrete wall in between, where the distance exceeds the communication range of most state-of-the-art VLC systems. By simply twining a wired earphone on the arm, we can sniff the VLC transmission 1.9 meters away.

This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming power, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We proto-type In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37 mW average charging power inside a 10 cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4X-18.1X power gain when the implant is stationary, and 5.3X-7.4X power gain when the implant is in motion.

Contactless acoustic motion tracking enables new opportunities to interact with smart devices, such as smartphones and voice-controlled smart assistants. The speakers and microphones integrated in these devices provide unique opportunities to simultaneously track multiple targets in a fine-grained manner. To this end, we propose a system, namely FM-Track, that enables contactless multi-target tracking using acoustic signals. We first introduce a signal model to characterize the location and motion status of targets by fusing the information from multiple dimensions (i.e., range, velocity, and angle of targets). Then we develop a series of techniques to separate signals reflected from multiple targets and accurately track each individual target. We implement and evaluate FM-Track on both research-purpose hardware platform (i.e., Bela) and commercial devices (i.e., smartphones and smart speakers). Extensive experiments show that FM-Track can successfully differentiate two targets with a spacing as small as 1 cm, and achieve a median tracking accuracy of 0.86 cm and 0.11 cm for absolute range and displacement estimates respectively. For multi-target tracking, FM-Track can accurately track four targets and the tracking range can be up to 3 m.

Acoustic motion tracking is an exciting new research area with promising progress in the last few years. Due to the inherent low propagation speed in the air, acoustic signals have the unique advantage of fine sensing granularity compared to RF signals. Speakers and microphones nowadays are pervasively available in devices surrounding us, such as smartphones and voice-controlled smart speakers. Though promising, one fundamental issue hindering the adoption of acoustic-based motion tracking is that the positions of microphones and speakers inside a device are fixed, which greatly limits the flexibility of acoustic motion tracking. In this work, we propose a new modality of acoustic motion tracking using earphones. Earphone-based tracking mitigates the constraints associated with traditional smartphone-based tracking. With novel designs and comprehensive experiments, we show earphone-based motion tracking can achieve a great flexibility and a high accuracy at the same time. We believe this is an important step towards "earable" sensing.

Visible Light Communication (VLC) is a promising technology for future wireless communications. By modulating the visible light---that has about 10,000x larger frequency band than that of radios---to transmit data, VLC has the potential to provide ultra-high-speed wireless connectivities. However, it also has limitations such as i) surrounding objects can easily block VLC links, and ii) intense ambient light can saturate the photodiodes of VLC receivers.

Wireless sensing has become a hot research topic recently, enabling a large range of applications. However, due to the intrinsic nature of employing weak target-reflection signal for sensing, the sensing range is limited. Another issue is the strong interference from surroundings and therefore a lot of wireless sensing systems assume there is no interferer in the environment. One recent work explored the possibility of employing LoRa signal for long range sensing which is a favorable step in addressing the first issue. However, the interference issue becomes even more severe with LoRa due to its larger sensing range. In this paper, we propose Sen-fence - a LoRa-based sensing system - to significantly increase the sensing range and at the same time mitigate the interference. With careful signal processing, Sen-fence is able to maximize the movement-induced signal variation in software to increase the sensing range. To address the interference issue, we propose the concept of "virtual fence" to constrain sensing only within the area of interest. The location and size of virtual fence can be flexibly controlled in software to meet the requirements of different applications. Sen-fence is able to (i) achieve a 50 m sensing range for fine-grained human respiration, which is twice the state-of-the-art; and (ii) efficiently mitigate the interference to make LoRa sensing work in practice.

Monitoring the vibration characteristics of a machine or structure provides valuable information of its health condition and this information can be used to detect problems in their incipient stage. Recently, researchers employ RFID signals for vibration sensing. However, they mainly focus on vibration frequency estimation and still face difficulties in accurately sensing the other important characteristic of vibration which is vibration amplitude in the scale of sub-millimeter. In this paper, we introduce TagSMM, a contactless RFID-based vibration sensing system which can measure vibration amplitude in sub-millimeter resolution. TagSMM employs the signal propagation theory to deeply understand how the signal phase varies with vibration and proposes a coupling-based method to amplify the vibration-induced phase change to achieve sub-millimeter level amplitude sensing for the first time. We design and implement TagSMM with commodity RFID hardware. Our experiments show that TagSMM can detect a 0.5 mm vibration, 10 times better than the state-of-the-arts. Our field studies show TagSMM can sense a drone's abnormal vibration and can also effectively detect a small 0.2 cm screw loose in a motor at a 100% accuracy.

Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts assume the intensity of LED light is constant. This hypothesis is not true when Smart Lighting is introduced to VLC, which requires LEDs to adapt their brightness based on the intensity of natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A novel Adaptive Multiple Pulse Position Modulation (AMPPM) scheme is proposed to support fine-grained dimming levels to avoid flickering while maximizing the throughput under each dimming level. SmartVLC is implemented on off-the-shelf commodity hardware. Several real-life challenges in both hardware and software are addressed to make it a robust real-time system. Comprehensive experiments are carried out to evaluate the system performance under multifaceted scenarios. Experimental results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40 and 12 percent on average, respectively, without bringing any flickering to users.

Target material identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in material identification. This paper introduces WiMi, a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. We also design a new material feature which is only related to the material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained material identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.

The ever-increasing number of IoT devices in our surrounding environment bring us tremendous amount of opportunities but also challenges including limited battery life, low computational capability and scalability of multiple access. Recent advances in backscatter communication have enabled ubiquitous IoT devices to communicate in a cost-and power-efficient way. However, most of the proposed backscatter solutions nowadays focus on the single tag paradigm, i.e., multiple tags do not transmit simultaneously and thus the solutions have difficulties to scale with a large number of tags. This work presents CBMA, a backscatter system that enables multiple concurrent backscatter tags to communicate reliably and efficiently. For the first time, we demonstrate that multiple tags can backscatter concurrently and efficiently with novel impedance-based power control at the tag, and can be successfully decoded with commodity WiFi devices without affecting the existing WiFi communication. We present the design details of CBMA and build a prototype with off-the-shelf WiFi devices and FPGA. The CBMA system achieves a 10-tag bit rate of 8Mbps while supporting a communication distance up to 10m. Compared to single-tag solutions, CBMA improves the backscatter throughput by more than 10× even in challenging indoor scenarios with rich multipath and interference.

Wi-Fi localization and tracking face accuracy limitations dictated by antenna count (for angle-of-arrival methods) and frequency bandwidth (for time-of-arrival methods). This paper presents mD-Track, a device-free Wi-Fi tracking system capable of jointly fusing information from as many dimensions as possible to overcome the resolution limit of each individual dimension. Through a novel path separation algorithm, mD-Track can resolve multipath at a much finer-grained resolution, isolating signals reflected off targets of interest. mD-Track can localize human passively at a high accuracy with just a single Wi-Fi transceiver pair. mD-Track also introduces novel methods to greatly streamline its estimation algorithms, achieving real-time operation. We implement mD-Track on both WARP and cheap off-the-shelf commodity Wi-Fi hardware, and evaluate its performance in different indoor environments.

Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~23 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what can be instantaneously harvested. We provide experimental validation, using both careful measurement studies as well as a controlled study with human participants, to show the viability of a custom-built WiWear-based wearable device, at least in office environments.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. WideSee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by the device mobility and LoRa's high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated WideSee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of WideSee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation in this field is the limited sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios like rescue and terrorist search. We also evaluated WideSee with field study in a high-rise building, which demonstrates the great potential of WideSee for supporting wide-area contactless sensing applications with a single LoRa transceiver pair hosted on a drone.

Material sensing is an essential ingredient for many IoT applications. While hyperspectral camera, infrared, X-Ray, and Radar provide potential solutions for material identification, high cost is the major concern limiting their applications. In this paper, we explore the capability of employing RF signals for fine-grained material sensing with commodity RFID device. The key reason for our system to work is that the tag antenna's impedance is changed when it is close or attached to a target. The amount of impedance change is dependent on the target's material type, thus enabling us to utilize the impedance-related phase change available at commodity RFID devices for material sensing. Several key challenges are addressed before we turn the idea into a functional system: (i) the random tag-reader distance causes an additional unknown phase change on top of the phase change caused by the target material; (ii) the tag rotations cause phase shifts and (iii) for conductive liquid, there exists liquid reflection which interferes with the impedance-caused phase change. We address these challenges with novel solutions. Comprehensive experiments show high identification accuracies even for very similar materials such as Pepsi and Coke.

Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with motor neurone disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most of the patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus, we propose to develop a Morse code-based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the channel state information (CSI) between two antennas to enhance the signal to noise ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity.

With a big success in data communication, wireless signals are now exploited for fine-grained contactless activity sensing including human respiration monitoring, finger gesture recognition, subtle chin movement tracking when speaking, etc. Different from coarsegrained body and limb movements, these fine-grained movements are in the scale of millimetres and are thus difficult to be sensed. While good sensing performance can be achieved at one location, the performance degrades dramatically at a very nearby location. In this paper, by revealing the effect of static multipaths in sensing, we propose a novel method to add man-made "virtual" multipath to significantly improve the sensing performance. With carefully designed "virtual" multipath, we are able to boost the sensing performance at each location purely in software without any extra hardware.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.

Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the “clean” subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.

Owing to great potential in smart home and human-computer interactive applications, WiFi indoor localization has attracted extensive attentions in the past several years. The state-of-the-art systems have successfully achieved decimeter-level accuracies. However, the high accuracy is acquired at the cost of dense access point (AP) deployment, employing large size of frequency bandwidths or special-purpose radar signals which are not compatible with existing WiFi protocol, limiting their practical deployments. This paper presents the design and implementation of AWL, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping. The key enabler of the system is we novelly employ channel hopping to create virtual antennas, without the need of adding more antennas or physically move the antennas' positions for a larger antenna array. We successfully utilize the widely known "bad" spatial aliasing to improve the AoA estimation accuracy. A novel multipath suppression scheme is also proposed to combat the severe multipath issue indoors. We build a prototype of AWL on WARP software-defined radio platform. Comprehensive experiments manifest that AWL achieves a median localization accuracy of 38 cm in a rich multipath indoor environment with only a single AP equipped with 6 antennas. In a small scale area, AWL is able to accurately track a moving device's trajectory, enabling applications such as writing/drawing in the air.

Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts in this area assume the intensity of the light emitted from LEDs is constant. This is not true any more when Smart Lighting is introduced to VLC in recent years, which requires the LEDs to adapt their brightness according to the intensity of the natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of the data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A new adaptive multiple pulse position modulation scheme is proposed to support fine-grained dimming levels to avoid flickering and at the same time, maximize the throughput under each dimming level. SmartVLC is implemented on low-cost commodity hardware and several real-life challenges in both hardware and software are addressed to make SmartVLC a robust realtime system. Comprehensive experiments are carried out to evaluate the performance of SmartVLC under multifaceted scenarios. The results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40% and 12% on average, respectively, without bringing any flickering to users.

While most existing indoor localization techniques are device-based, many emerging applications such as intruder detection and elderly monitoring drive the needs of device-free localization, in which the target can be localized without any device attached. Among the diverse techniques, received signal strength (RSS) fingerprint-based methods are popular because of the wide availability of RSS readings in most commodity hardware. However, current fingerprint-based systems suffer from high human labor cost to update the fingerprint database and low accuracy due to the large degree of RSS variations. In this paper, we propose a fingerprint-based device-free localization system named iUpdater to significantly reduce the labor cost and increase the accuracy. We present a novel self-augmented regularized singular value decomposition (RSVD) method integrating the sparse attribute with unique properties of the fingerprint database. iUpdater is able to accurately update the whole database with RSS measurements at a small number of reference locations, thus reducing the human labor cost. Furthermore, iUpdater observes that although the RSS readings vary a lot, the RSS differences between both the neighboring locations and adjacent wireless links are relatively stable. This unique observation is applied to overcome the short-term RSS variations to improve the localization accuracy. Extensive experiments in three different environments over 3 months demonstrate the effectiveness and robustness of iUpdater.

Large-scale MU-MIMO is a promising technology to scale network capacity and the capacity gain grows linearly with the numbers of antennas and users in theory. However, its practical deployment faces three critical challenges in the state-of-the-art WLANs: i) the demand of a large number of expensive RF chains; ii) the linear growth of feedback overheads with the number of antennas; iii) the lack of scalable user selection scheme for a large user population. In this paper, we design BUSH, a large-scale MU-MIMO prototype that performs scalable beam user selection with hybrid beamforming for phased-array antennas in legacy WLANs. The architecture of BUSH consists of three components. Firstly, a low complexity algorithm assigns each pair of RF chain and analog beam to the users to effectively reduce channel correlation and cross-talk interference without instantaneous CSI feedbacks. Secondly, as a prerequisite of user selection, BUSH presents a low-overhead probing scheme in multi-carrier WLANs, and designs a highly accurate blind Power Azimuth Spectrum (PAS) estimation algorithm using a single RF chain. Thirdly, the phased-array antennas use analog beamforming to steer spatial beams toward each selected downlink user, and the finite number of RF chains use beamforming to further mitigate the interference among users. We implement BUSH on the WARPv3 boards and evaluate its performance in more than 30 indoor scenarios. The experimental results show that in terms of total throughput BUSH outperforms the legacy 802.11ac by 2.08×, and an alternative benchmark system by 1.22× on average.

Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

Multiview video is essential for various mobile three-dimensional (3D) and immersive applications that can capture scenes from multiple angles for better user experience. However, robust transmission of multiview video is very challenging in wireless networks due to high bandwidth requirement and time-varying channel quality. Though the up-to-date 802.11 system enables spatial multiplexing MIMO to enhance transmission capacity, it is still agnostic to 3D source coding structure in the transmission. In this paper, we study the optimal resource allocation problem in MIMO systems that deliver 3D content with multiview video coding. The basic idea is to exploit the channel diversity of multiple antennas and the source coding characteristics so as to achieve unequal error protection against channel errors. To achieve this goal, we develop a nonlinear mixed integer programming framework to perform antenna selection and power allocation, and propose low-complexity algorithms to assign these resources. We implement a proof-of-concept system, namely MuVi, on the software-defined-radio platform, WARP, to evaluate the proposed algorithms. MuVi is the practical system to tackle 3D multiview streaming in the latest Wi-Fi networks such as IEEE 802.11ac under realistic channel conditions. Extensive experimental results demonstrate that the peak signal-to-noise-ratio of MuVi significantly outperforms that of the conventional power allocation scheme in a variety of indoor environments.

Device-free localization, which does not require any device attached to the target, is playing a critical role in many applications, such as intrusion detection, elderly monitoring and so on. This paper introduces D-Watch, a device-free system built on the top of low cost commodity-off-the-shelf RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the “bad” multipaths to provide a decimeter-level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be accurately detected by the proposed novel P-MUSIC algorithm. The proposed wireless phase calibration scheme does not interrupt the ongoing data communication and thus reduces the deployment burden. We implement and evaluate D-Watch with extensive experiments in three different environments. D-Watch achieves a median accuracy of 16.5 cm for library, 25.5 cm for laboratory, and 31.2 cm for hall environment, outperforming the state-of-the-art systems. In a table area of 2 $\text{m}\times 2$ m, D-Watch can track a user's fist at a median accuracy of 5.8 cm. D-Watch is also capable of localizing multiple targets which is well known to be challenging in passive localization.

Device-free localization, which does not require any device attached to the target is playing a critical role in many applications such as intrusion detection, elderly monitoring, etc. This paper introduces D-Watch, a device-free system built on top of low cost commodity-off-the-shelf (COTS) RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the "bad" multipaths to provide a decimeter level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival (AoA) information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be accurately captured by the proposed novel P-MUSIC algorithm. The wireless phase calibration scheme proposed does not interrupt the ongoing communication. Real-world experiments demonstrate the effectiveness of D-Watch. In a rich-multipath library environment, D-Watch can localize a human target at a median accuracy of 16.5 cm. In a table area of 2 m×2 m, D-Watch can track a user's fist at a median accuracy of 5.8 cm. D-Watch is capable of localizing multiple targets which is well known to be challenging in passive localization

Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free localization systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which is implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects (furniture, walls, etc.) to identify the human target's angle for localization. MaTrack does not require any offline training compared to existing signature-based systems and is insensitive to changes in environment. With just two receivers, MaTrack is able to achieve a median localization accuracy below 0.6 m when the human is walking, outperforming the state-of-the-art schemes.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization.

Many emerging applications drive the needs of device-free localization (DfL), in which the target can be localized without any device attached. Because of the ubiquitousness of WiFi infrastructures nowadays, the widely available Received Signal Strength (RSS) information at the WiFi Access points are commonly employed for localization purposes. However, current RSS based DfL systems have one main drawback hindering their real-life applications. That is, the RSS measurements (fingerprints) vary slowly in time even without any change in the environment and frequent updates of RSS at each location lead to a high human labor cost. In this paper, we propose an RSS based low cost DfL system named TafLoc which is able to accurately localize the target over a long time scale. To reduce the amount of human labor cost in updating the RSS fingerprints, TafLoc represents the RSS fingerprints as a matrix which has several unique properties. Based on these properties, we propose a novel fingerprint matrix reconstruction scheme to update the whole fingerprint database with just a few RSS measurements, thus the labor cost is greatly reduced. Extensive experiments illustrate the effectiveness of TafLoc, outperforming the state-of-the-art RSS based DfL systems.

Short-term traffic flow forecasting has been a crucial component in the area of intelligent transportation systems (ITS), which plays a significant role in operating traffic management systems and dynamic traffic assignment effectively as well as proactively. In this paper, a novel short-term traffic flow prediction method called Ensemble Real-time Sequential Extreme Learning Machine (ERS-ELM) with simplified single layer feed- forward networks (SLFN) structure under freeway peak traffic condition and non-stationary condition is proposed. By quickly training historical data and incrementally updating model with new arrived data, ERE-ELM has the characteristics of less training time consumption and high prediction accuracy. Ensemble mechanism is also used to improve stability and robustness. Experiment results show that average mean absolute percentage error (MAPE), test root mean square error (RMSE) as well as training time consumption of proposed method is superior to classical Wave-NN, MLP-NN and ELM methods.

Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.

Next generation WLANs (802.11ac) are undergoing a major shift in their communication paradigm with the introduction of multi-user MIMO (MU-MIMO), transitioning from single-user to multi-user communications. We argue that the conventional AP deployment model of co-located antennas as well as their PHY and MAC mechanisms are not designed to realize the complete potential of MU-MIMO. We propose to leverage distributed antenna systems (DAS) to empower next generation 802.11ac networks. We highlight the multitude of benefits that DAS brings to MU-MIMO and 802.11ac in general. However, several challenges arise in the process of realizing these benefits in practice, where avoiding client modifications and making only minimal software modifications to APs is important to enable rapid adoption. Towards addressing these challenges, we present the design and implementation of MIDAS, the Multiple-Input Distributed Antenna System. MIDAS couples a DAS deployment of AP antennas with a suite of novel yet standards-compatible mechanisms at the PHY and MAC layers that best leverage the DAS deployment to maximize 802.11ac performance. Our WARP-based experimental evaluation demonstrates MIDAS's ability to significantly boost the performance of current 802.11ac design, demonstrating throughput gains over 802.11ac MU-MIMO for 100-200%, while remaining amenable to commercial adoption.

Signal processing on antenna arrays has received much recent attention in the mobile and wireless networking research communities, with array signal processing approaches addressing the problems of human movement detection, indoor mobile device localization, and wireless network security. However, there are two important challenges inherent in the design of these systems that must be overcome if they are to be of practical use on commodity hardware. First, phase differences between the radio oscillators behind each antenna can make readings unusable, and so must be corrected in order for most techniques to yield high-fidelity results. Second, while the number of antennas on commodity access points is usually limited, most array processing increases in fidelity with more antennas. These issues work in synergistic opposition to array processing: without phase offset correction, no phase-difference array processing is possible, and with fewer antennas, automatic correction of these phase offsets becomes even more challenging. We present Phaser, a system that solves these intertwined problems to make phased array signal processing truly practical on the many WiFi access points deployed in the real world. Our experimental results on three- and five-antenna 802.11-based hardware show that 802.11 NICs can be calibrated and synchronized to a 20° median phase error, enabling inexpensive deployment of numerous phase-difference based spectral analysis techniques previously only available on costly, special-purpose hardware.

Despite the important role that WiFi networks play in home and enterprise networks they are relatively weak from a security standpoint. With easily available directional antennas, attackers can be physically located off-site, yet compromise WiFi security protocols such as WEP, WPA, and even to some extent WPA2 through a range of exploits specific to those protocols, or simply by running dictionary and human-factors attacks on users' poorly-chosen passwords. This presents a security risk to the entire home or enterprise network. To mitigate this ongoing problem, we propose SecureArray, a system designed to operate alongside existing wireless security protocols, adding defense in depth against active attacks. SecureArray's novel signal processing techniques leverage multi-antenna access point (AP) to profile the directions at which a client's signals arrive, using this angle-of-arrival (AoA) information to construct highly sensitive signatures that with very high probability uniquely identify each client. Upon overhearing a suspicious transmission, the client and AP initiate an AoA signature-based challenge-response protocol to confirm and mitigate the threat. We also discuss how SecureArray can mitigate direct denial-of-service attacks on the latest 802.11 wireless security protocol. We have implemented SecureArray with an eight-antenna WARP hardware radio acting as the AP. Our experimental results show that in a busy office environment, SecureArray is orders of magnitude more accurate than current techniques, mitigating 100% of WiFi spoofing attack attempts while at the same time triggering false alarms on just 0.6% of legitimate traffic. Detection rate remains high when the attacker is located only five centimeters away from the legitimate client, for AP with fewer numbers of antennas and when client is mobile.

Wireless multicast applications, such as MobiTV, web telecast, and multimedia classrooms, are gaining rapid popularity. The broadcast nature of the wireless channel is amenable to such multicasts because a single packet transmission can be received by all clients. Unfortunately, the rate of this transmission is bottlenecked by data rate of the weakest client, degrading system performance. Attempts to increase the data rate results in lower reliability and higher unfairness. This paper presents PeerCast, a wireless multicast protocol that engages clients in cooperative relaying. The main idea is simple. Instead of multicasting at the bottleneck rate, the access point transmits at a high rate and suitably chooses a few stronger clients to relay the packet to the weaker ones. Multiple transmissions of the same packet, each at higher rate, can achieve better throughput than one transmission at the low, bottleneck rate. We propose a new simultaneous reply-back scheme for clients and detect the power level to estimate the AP's transmission strategy. PeerCast translates these ideas into a functional system using off-the-shelf hardware. Performance evaluation on a 9 node testbed demonstrates consistent throughput and reliability improvements over 802.11. Simulations in QualNet indicate similar trends in large-scale networks.

Wireless networks play an important role in our everyday lives, at the workplace and at home. However, they are also relatively vulnerable: physically located off site, attackers can circumvent wireless security protocols such as WEP, WPA, and even to some extent WPA2, presenting a security risk to the entire network. To address this problem, we propose SecureAngle, a system designed to operate alongside existing wireless security protocols, adding defense in depth. SecureAngle leverages multi-antenna APs to profile the directions at which a client's signal arrives, using this angle-of-arrival (AoA) information to construct signatures that uniquely identify each client. We identify SecureAngle's role of providing a fine-grained location service in a multi-path indoor environment. With this location information, we investigate how an AP might create a "virtual fence" that drops frames received from clients physically located outside a building or office. With SecureAngle signatures, we also identify how an AP can prevent malicious parties from spoofing the link-layer address of legitimate clients. We discuss how SecureAngle might aid whitespace radios in yielding to incumbent transmitters, as well as its role in directional downlink transmissions with uplink AoA information.

Wireless local area networks play an important role in our everyday lives, at the workplace and at home. However, wireless networks are also relatively vulnerable: physically located off-premises, attackers can circumvent wireless security protocols such as WEP, WPA, and even to some extent WPA2, presenting a security risk to the entire network. To address this problem, we propose SecureAngle, a system designed to operate alongside existing wireless security protocols, adding defense in depth. SecureAngle employs multiantenna APs to profile the directions at which a client's signal arrives, using this angle-of-arrival information to construct unique signatures that identify each client. With these signatures, we are currently investigating how a SecureAngle enabled AP can enable a "virtual fence" that drops frames injected into the network from a client physically located outside a building, and how a SecureAngle-enabled AP can prevent malicious parties from spoofing the link-layer address of legitimate clients.

Wireless link layer multicast is an important service primitive for emerging applications, such as live video, streaming audio, and other content telecasts. The broadcast nature of the wireless channel is amenable to multicast because a single packet transmission may be received by all clients in the multicast group. However, in view of diverse channel conditions at different clients, the rate of such a transmission is bottlenecked by the rate of the weakest client. Multicast throughput degrades severely. Attempts to increase the data rate result in lower reliability and higher unfairness. This paper utilizes smart beamforming antennas to improve multicast performance in wireless LANs. The main idea is to satisfy the stronger clients with a high-rate omnidirectional transmission, followed by high-rate directional transmission(s) to cover the weaker ones. By selecting an optimal transmission strategy (using dynamic programming), we show that the multicast throughput can be maximized while achieving a desired delivery ratio at all the clients. We use testbed measurements to verify our main assumptions. We simulate our protocol in Qualnet, and observe consistent performance improvements over a range of client topologies and time-varying channel conditions.