Liquid leakage detection is critical for product quality and user safety, yet existing methods require line-of-sight (LoS) or direct contact with the liquid, or could even introduce additional health or safety risks. In this paper, we propose SARLiquid, a novel mmWave-based through-package liquid leakage detection method. SARLiquid leverages mmWave signals to see through packaging and identify leakage by reconstructing mmWave images. We employ synthetic aperture radar (SAR) technique to enhance imaging resolution and tame multipath effects. We also develop a dedicated algorithm to calibrate the discontinuous phase in SAR imaging results, and propose a deep learning model for liquid leakage detection and liquid identification. We implement SARLiquid and evaluate it across a wide range of scenarios. Results show that SARLiquid achieves average accuracies above 93% for both liquid leakage detection and liquid identification, 14.70% and 9.98% higher than the baselines, respectively.

As an emerging technology, WiFi sensing has garnered widespread attention in recent years. However, due to limitations in WiFi bandwidth and hardware capacity, multi-target sensing with WiFi remains an unresolved issue. In this paper, we presentSlingShot, a first-of-its-kind approach for multi-target WiFi sensing enabled by metasurface.SlingShotexploits a metasurface's ability of beam scanning to periodically scan among targets in a high frequency, so their sensing signals can be separated in the time domain. However, the WiFi-initiated and the metasurface-initiated signals will interfere with each other, which could significantly degrade the sensing accuracy. Moreover, the WiFi devices and the metasurface operate in a distributed manner, so the inherent clock offset and clock drift among them make it difficult to accurately separate the sensing signals of the targets. To address the above challenges,SlingShotleverages the metasurface's ability of phase shifting to cancel interfering signals and designs a passive clock synchronization scheme to synchronize the metasurface and WiFi devices. We implementSlingShotand evaluate it under various settings. Results show thatSlingShotcan sense up to 8 targets. Respectively compared to the state-of-the-art methods,SlingShothas 6.75 % higher mean accuracy in activity recognition and 58.34 % lower mean error in respiration monitoring.

Parallelizing passive Radio Frequency Identification (RFID) reading is an arguably crucial, yet unsolved challenge in modern IoT applications. Existing approaches remain limited to time-division operations and fail to read multiple tags simultaneously. In this paper, we introduce QuinID, the first frequency-division multiple access (FDMA) RFID system to achieve fully parallel reading. We innovatively exploit the frequency selectivity of the tag antenna rather than a conventional digital FDMA, bypassing the power and circuitry constraint of RFID tags. Specifically, we delicately design the frequency-selective antenna based on surface acoustic wave (SAW) components to achieve extreme narrow-band response, so that QuinID tags (i.e., QuinTags) operate exclusively within their designated frequency bands. By carefully designing the matching network and canceling various interference, a customized QuinReader communicates simultaneously with multiple QuinTags across distinct bands. QuinID maintains high compatibility with commercial RFID systems and presents a tag cost of less than 10 cents. We implement a 5-band QuinID system and evaluate its performance under various settings. The results demonstrate a fivefold increase in read rate, reaching up to 5000 reads per second.

In IoT applications such as environmental monitoring and industrial security surveillance, audio sensors are increasingly used, among which wireless sensors are preferred. In order to achieve a sustained transmission, low-power wireless technology such as backscatter has been widely considered. However, existing backscatter systems encounter difficulties in audio transmissions due to the high power consumption from the complicated digital processing and fast frequency-shifting clocks. In this paper, we propose Satori, the first-of-its-kind in-band analog backscatter system for audio transmission with ultra-low power consumption. Satori eliminates the need for in-place digital processing by directly embedding analog audio voltages into backscattered WiFi symbols through analog modulation. It also avoids the power consumption of the frequency-shifting clock by transmitting the audio within the excitation WiFi signal's band. We implement the Satori prototype and evaluate it under various settings. The results indicate that Satori can transmit audio at a sampling rate of 41.67 kHz and achieve a SNR exceeding 18 dB.

Wi-Fi human sensing has attracted numerous research studies over the past decade. The rapid advancement of machine learning technology further boosts the development of Wi-Fi human sensing. However, current Wi-Fi human sensing suffers from the “data scarcity” problem: all the existing proposals require collecting a large amount of human-based datasets to train the sensing models, which is laborintensive and may raise ethical concerns in certain scenarios. This obstacle seriously restricts the size, quality, and diversity of available datasets, thereby affecting the sensing performance in terms of accuracy and cross-domain applicability. In order to solve this problem, we in this paper propose Metasurface-Assisted Sample Synthesis (MASS), a novel approach to synthesize high-fidelity Wi-Fi sensing samples that effectively capture both the essential features of human motion and environment-specific multipath characteristics without requiring human involvement. The evaluation results show that MASS is effective in boosting machine learning performance, improving classification accuracy by 18%, and enhancing the cross-domain sensing accuracy by 22%. We further analyze inherent synthesis distortions stemming from hardware limitations and introduce a mitigation technique, which significantly enhances data fidelity, achieving 91.8% accuracy even when training exclusively on synthesized samples. These findings underscore the potential of MASS to facilitate the creation of high-quality, diverse datasets with minimal human involvement and associated labor costs.

The rapid development of wireless sensing technology has opened new possibilities for non-contact health monitoring. Among them, respiration is crucial information for assessing vital signs. However, traditional methods face challenges of signal interference and overlap in multi-person environments. In this work, we propose a novel method for multi-person respiration detection using a digital programmable metasurface (DPM). This method takes advantage of the modulation characteristics of the DPM in the time and space domain. It divides the Channel State Information (CSI) from the Wi-Fi transmitter into multiple sub-signals in the time domain and modulates the radiation directions of the sub-signals for space redistribution. These signals are received by the Wi-Fi receiver and recombined to restore the CSI in each direction, thus enabling the accurate extraction of respiration signals from targets in different directions. Experimental results show that this method can directionally sense the human respiration information in a specific direction under the static working mode. Under dynamic scanning mode, it can effectively separate and detect the respiration information of four people from different directions. This system has great potential for applications in wireless communication, healthcare, and smart home environments.

The emerging integrated sensing and communication (ISAC) technologies show great potential for 5G NR, offering a new wireless-sensing infrastructure paradigm. Users can benefit from pervasive sensing applications in various scenarios without communication penalties. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting the applicability in dealing with diverse sensing tasks in the real world. In this article, we introduce ElaSe , a pioneering sensing technique that enables elastic and prompt scheduling of sensing resources. At the core of ElaSe is the exploration of the user’s state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). ElaSe has been implemented on a CPU-based 5G vRAN and commercial 5G user equipments. We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

mmWave-based activity recognition technology has attracted widespread attention as it provides the ability of device-free, ubiquitous and accurate sensing. Recognition of human activities intrinsically demands to be real-time and continuous, but the state of the arts is still far limited with the capacity in this regard. The main obstacle lies in activity sequence segmentation, i.e., locating the boundaries between consecutive activities in an activity sequence. This is a daunting task, due to the unclear activity boundaries and the variable activity duration. In this paper, we proposeZuMa, the first mmWave-based approach to real-time continuous activity recognition. When resorting to a machine learning model for activity recognition, our insight is that the recognition confidence of the recognition model is highly correlated to the accuracy of activity sequence segmentation, so that the former can be utilized as a feedback metric to finely adjust the segmentation boundaries. Based on this insight,ZuMais a coarse-to-fine grained approach, which includes the fast coarse-grained activity chunk extraction and the find-grained explicit segmentation adjustment and recognition. We have implementedZuMawith the commercial mmWave radar and evaluated its performance under various settings. The results demonstrate thatZuMaachieves an average recognition error of 12.67%, which is 65.08% and 71.87% lower than that of the two baseline methods. The average recognition delay ofZuMais only 1.86 s.

Backscatter is a key technology for battery-free sensing in industrial IoT applications. To fully cover numerous tags in the deployment area, one often needs to deploy multiple readers, each of which communicates with tags within its communication range. However, the actual backscattered signals from a tag are likely to reach a reader outside its communication range and cause interference. Conventional TDMA or CSMA based approaches for interference avoidance separate readers’ media access in time, leading to limited network throughput. In this paper, we propose Trident, a novel backscatter design that enables interference avoidance via frequency-space division. By incorporating a tunable bandpass filter and multiple terminal loads, a Trident tag can detect its channel condition and adaptively adjust the frequency and the power of its backscattered signals. We further propose a frequency assignment algorithm for the readers. With these designs, all the readers in the network can operate concurrently without being interfered. We implement Trident and evaluate its performance under various settings. The results demonstrate that Trident enhances the network throughput by$3.18\times $, compared to the TDMA-based scheme.

WiFi-based device localization is a key technology for smart applications, while most of which rely on LoS signals to work. However, in real-world indoor environments, very few LoS signals are usable for accurate localization. This article presents Bifrost , a novel hardware-software co-design to cope with this practical problem. The core idea of Bifrost is to reinvent WiFi signals to provide sufficient LoS signals. Specifically, we present a low-cost plug-in design of leaky wave antenna (LWA) that can generate orthogonal polarized signals: On the one hand, LWA disperses signals of different frequencies to different angles, thus providing AoA information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. Besides, fine-grained information in CSI is exploited to mitigate multipath and noise. Besides, a dedicated Kalman filter is proposed to facilitate the cooperation of Bifrost and SpotFi, a state-of-the-art approach, to enhance the availability and accuracy of SpotFi. The evaluation results show that the median localization error of Bifrost is 0.81 m, 52.35% less than that of SpotFi. When combined with Bifrost to work in realistic settings, SpotFi can reduce the localization error by 33.54%.

Wave-based human tracking is a key enabling technology for smart applications. Most of the existing works on this topic employ the conventional approach of device-free object localization, which treat any person as a general moving target rather than distinguish different persons. As a result, the existing approaches have poor performance in the scenarios of multi-person tracking, especially when there are crossovers among different persons’ trajectories. This paper presents mMTAI, a novel approach for multi-person tracking with a mmWave radar. By exploiting mmWave sensing to capture a human’s biometric features, MMTAI augments mmWave radar based human tracking with the ability of identifying different persons. Specifically, MMTAI is able to sense persons’ scalp responses to the signals and their head-shoulder distances, which are then continuously mapped to their trajectories using a bipartite matching algorithm. We implement MMTAI with a commercial mmWave radar and evaluate its performance under various settings. The results show that in the multi-person tracking scenarios, mmTAI has a median tracking error of 12.33 cm, which is $35.88 \%$ lower than that of the state-of-the-art approach.

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method.

Backscatter is an enabling technology for battery-free sensing in industrial IoT applications. For the purpose of full coverage of numerous tags in the deployment area, one often needs to deploy multiple readers, each of which is to communicate with tags within its communication range. But the actual backscattered signals from a tag are likely to reach a reader outside its communication range, causing undesired interference. Conventional approaches for interference avoidance, either TDMA or CSMA based, separate the readers’ media accesses in the time dimension and suffer from limited network throughput. In this paper, we propose Trident, a novel backscatter tag design that enables interference avoidance with frequency-space division. By incorporating a tunable bandpass filter and multiple terminal loads, a Trident tag is able to detect its channel condition and adaptively adjust the frequency band and the power of its backscattered signals, so that all the readers in the network can operate concurrently without being interfered. We implement Trident and evaluate its performance under various settings. The results demonstrate that Trident enhances the network throughput by 3.18×, compared to the TDMA based scheme.

Integrated Sensing and Communication (ISAC) has been witnessed to be a new paradigm of wireless sensing in 5G networks. Users can benefit from pervasive sensing applications in various scenarios with no communication penalty. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting their applicability in dealing with diverse sensing tasks in the real world. In this paper, we introduce ElaSe, a pioneering sensing technique that enables real-time elastic scheduling of sensing resources. At the core of ElaSa is the exploration of the user's state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

We presentMicNest: an acoustic localization system enabling precise drone landing. InMicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging:i)because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low;ii)the pulses experience non-linear Doppler distortion due to the physical drone dynamics;iii)as location information is used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our experiments show thatMicNestcan localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. For navigating drone landing,MicNestcan achieve a success rate of 94%. The average landing error (distance between landing point and target point) is only 4.3 cm.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does not require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low signal-to-noise ratio and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering and then identify it with a Long Short-Term Memory–based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80 m.

This paper proposes Meta-Speaker, an innovative speaker capable of projecting audible sources into the air with a high level of manipulability. Unlike traditional speakers that emit sound waves in all directions, Meta-Speaker can manipulate the granularity of the audible region, down to a single point, and can manipulate the location of the source. Additionally, the source projected by Meta-Speaker is a physical presence in space, allowing both humans and machines to perceive it with spatial awareness. Meta-Speaker achieves this by leveraging the fact that air is a nonlinear medium, which enables the reproduction of audible sources from ultrasounds. Meta-Speaker comprises two distributed ultrasonic arrays, each transmitting a narrow ultrasonic beam. The audible source can be reproduced at the intersection of the beams. We present a comprehensive profiling of Meta-Speaker to validate the high manipulability it offers. We prototype Meta-Speaker and demonstrate its potential through three applications: anchor-free localization with a median error of 0.13 m, location-aware communication with a throughput of 1.28 Kbps, and acoustic augmented reality where users can perceive source direction with a mean error of 9.8 degrees.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn’t require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV’s position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.

WiFi-based device localization is a key enabling technology for smart applications, which has attracted numerous research studies in the past decade. Most of the existing approaches rely on Line-of-Sight (LoS) signals to work, while a critical problem is often neglected: In the real-world indoor environments, WiFi signals are everywhere, but very few of them are usable for accurate localization. As a result, the localization accuracy in practice is far from being satisfactory. This paper presents Bifrost, a novel hardwaresoftware co-design for accurate indoor localization. The core idea of Bifrost is to reinvent WiFi signals, so as to provide sufficient LoS signals for localization. This is realized by exploiting the dispersion effect of signals emitted by the leaky wave antenna (LWA). We present a low-cost plug-in design of LWA that can generate orthogonal polarized signals: On one hand, LWA disperses signals of different frequencies to different angles, thus providing Angle-of-Arrival (AoA) information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. In the software layer, fine-grained information in Channel State Information (CSI) is exploited to cope with multipath and noise. We implement Bifrost and evaluate its performance under various settings. The results show that the median localization error of Bifrost is 0.81m, which is 52.35% less than that of SpotFi, a state-of-the-art approach. SpotFi, when combined with Bifrost to work in the realistic settings, can reduce the localization error by 33.54%.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR). We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays.

We present MicNest: an acoustic localization system enabling precise landing of aerial drones. Drone landing is a crucial step in a drone's operation, especially as high-bandwidth wireless networks, such as 5G, enable beyond-line-of-sight operation in a shared airspace and applications such as instant asset delivery with drones gain traction. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics while airborne; iii) as location information is to be used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our real-world experiments show that MicNest is able to localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency.