MobiCom 2024 论文列表

Wireless charging has achieved promising progress in recent years. However, the severe bottlenecks are the small charging range and poor flexibility. This paper presents ChargeX to enable smart and long-range wireless charging for small mobile devices. ChargeX incorporates emerging smart metasurface into the magnetic resonance coupling-based wireless charging to extend the charging range and accommodates the mobility of charging device. Unlike previous endeavors in metasurface-assisted wireless charging that focused on simulation, ChargeX makes efforts across software and hardware to meet three crucial requirements for a practical wireless charging system: (i) realize high-freedom and accurate metasurface control under the premise of low loss; (ii) obtain real-time feedback from the receiver and make effective manipulation for transmitted magnetic flux; and (iii) generate a proper AC signal source at the desired frequency band. We developed a prototype of ChargeX, and evaluated its performance through controlled experiments and real-world phone charging. Extensive experiments demonstrate the great potential of ChargeX for long-range and flexible wireless charging with a compact receiver design.

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.

In this paper, we propose WavePurifier, an audio purification framework to defend against audio adversarial attacks. Audio adversarial attacks craft adversarial examples or perturbations to attack the automated speech recognition (ASR) models. Although existing defense mechanisms can detect such attacks and raise alarms, they fail to recover or maintain benign commands. Consequently, this leads to the denial of users' benign commands. Different than existing defenses, WavePurifier aims to purify adversarial examples, thereby rectifying the user's benign commands. We find that the forward diffusion process of the diffusion model effectively eliminates perturbations, whereas the reverse diffusion process restores benign speech. Based on this, we develop a hierarchical diffusion model to defend against audio adversarial examples. This model is capable of purifying different spectrogram bands to varying degrees. To validate the performance of WavePurifier, we purify the adversarial examples from 3 different adversarial attacks in 140 distinct settings. In total, we collect 78,864 diffused spectrograms and 21,000 purified audios. Then, we evaluate WavePurifier on 2 different ASR models, 4 commercial speech-to-text APIs, 2 real-world attack scenarios, and compare them against 7 existing defense approaches. Our result shows that WavePurifier is a universal framework, demonstrating adaptability across diverse attacks with the same hyperparameters. Notably, WavePurifier outperforms existing methods with the lowest character error rate (CER), word error rate (WER), and a high purification success rate against different attacks.

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate and respiration rate of mobile users with diverse skin tones. MagWear's contributions are twofold. Firstly, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Secondly, leveraging insights derived from this mathematical model, we present a softwarehardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. We have implemented a prototype of MagWear on a two-layer PCB board and followed IRB protocols to conduct system evaluations. Our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate and 1.79% for respiration rate. The head-to-head comparison with Apple Watch 8 further demonstrates MagWear's consistently high performance in different user conditions.

On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-preserving machine learning at the edge. However, the intensive training workload and limited onboard computing resources pose significant challenges to the availability and efficiency of model training. While existing works address these challenges through native resource management optimization, we instead leverage our observation that edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources beyond a single terminal. We propose Asteroid, a distributed edge training system that breaks the resource walls across heterogeneous edge devices for efficient model training acceleration. Asteroid adopts a hybrid pipeline parallelism to orchestrate distributed training, along with a judicious parallelism planning for maximizing throughput under certain resource constraints. Furthermore, a fault-tolerant yet lightweight pipeline replay mechanism is developed to tame the device-level dynamics for training robustness and performance stability. We implement Asteroid on heterogeneous edge devices with both vision and language models, demonstrating up to 12.2× faster training than conventional parallelism methods and 2.1× faster than state-of-the-art hybrid parallelism methods through evaluations. Furthermore, Asteroid can recover training pipeline 14× faster than baseline methods while preserving comparable throughput despite unexpected device exiting and failure.

IMS (IP Multimedia Subsystem) is vital for delivering IP-based multimedia services in mobile networks. Despite constant upgrades by 3GPP over the past two decades to support heterogeneous radio access networks (e.g., 4G LTE, 5G NR, and Wi-Fi) and enhance IMS security, the focus has primarily been on cellular infrastructure. Consequently, IMS security measures on mobile equipment (ME), such as smartphones, lag behind rapid technological advancements. Our study reveals that mandated IMS security measures on ME fail to keep pace, resulting in new vulnerabilities and attack vectors, including denial of service (DoS) across all networks, named SMS source spoofing, and covert communications over Video-over-IMS attacks. All vulnerabilities and proof-of-concept attacks have been experimentally validated in operational 5G/4G networks across various phone models and network operators. Finally, we propose and prototype standard-compliant remedies for these vulnerabilities.

Eggs and chickens serve as crucial animal-source proteins in our diets, making large-scale breeding egg incubation an essential undertaking. However, current solutions, i.e., vision-based and sensor-based methods, are primarily designed for egg fertility detection tasks under single-egg settings, which have not yet satisfied the goal of multi-target and multi-task sensing. In this paper, we propose RF-Egg, the first RF-based fine-grained multi-target and multi-task egg incubation sensing system with respect to sensing fertility, incubation status, and early mortality of chicken embryos. RF-Egg leverages the weak coupling effects of RFID tags when interacting with eggs, which induces different impedance changes of RFID tags with the incubation levels of eggs, thereby resulting in a variation of low-level phase readings of the backscatter signals. Regarding the challenge of multi-target profiling interference, we propose a multipath combating algorithm to extract the target-induced signal component based on the built signal model, and address non-uniformity issues across multiple tags. Moreover, we devise three unique feature maps tailored to each task, and then design an Multi-Task Triplet (MTT) network for multitasking. Our evaluation results based on 189 eggs show that RF-Egg achieves an accuracy of 94.4%, 96.1%, and 90.1% for the aforementioned three tasks when supporting 16 targets. Additionally, our extensive field study in a local egg hatchery suggests that RF-Egg presents the potential to be widely deployed in the modern poultry industry.

Multi-sensor fusion has been widely used by autonomous vehicles (AVs) to integrate the perception results from different sensing modalities including LiDAR, camera and radar. Despite the rapid development of multi-sensor fusion systems in autonomous driving, their vulnerability to malicious attacks have not been well studied. Although some prior works have studied the attacks against the perception systems of AVs, they only consider a single sensing modality or a camera-LiDAR fusion system, which can not attack the sensor fusion system based on LiDAR, camera, and radar. To fill this research gap, in this paper, we present the first study on the vulnerability of multi-sensor fusion systems that employ LiDAR, camera, and radar. Specifically, we propose a novel attack method that can simultaneously attack all three types of sensing modalities using a single type of adversarial object. The adversarial object can be easily fabricated at low cost, and the proposed attack can be easily performed with high stealthiness and flexibility in practice. Extensive experiments based on a real-world AV testbed show that the proposed attack can continuously hide a target vehicle from the perception system of a victim AV using only two small adversarial objects.

This paper investigates how an airborne node can eavesdrop on the underwater acoustic communication between submerged nodes. Conventionally, such eavesdropping has been assumed impossible as acoustic signals do not cross the water-air boundary. Here, we demonstrate that underwater acoustic communications signals can be picked up and (under certain conditions) decoded using an airborne mmWave radar due to the minute vibrations induced by the communication signals on the water surface. We implemented and evaluated a proof-of-concept prototype of our method and tested it in controlled (pool) and uncontrolled environments (lake). Our results demonstrate that an airborne device can identify the modulation and bitrate of acoustic transmissions from an uncooperative underwater transmitter (victim), and even decode the transmitted symbols. Unlike conventional over-the-air communications, our results indicate that the secrecy of underwater links varies depending on the modulation type and provide insights into the underlying reasons behind these differences. We also highlight the theoretical limitations of such a threat model, and how these results may have a significant impact on the stealthiness of underwater communications, with particular concern to submarine warfare, underwater operations (e.g., oil & gas, search & rescue, mining), and conservation of endangered species. Finally, our investigation uncovers countermeasures that can be used to improve or restore the stealthiness of underwater acoustic communications against such threats.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text beamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, the first attack to manipulate traffic in commodity Wi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi-Fi traffic while maintaining a low exposure rate.

Image super-resolution (SR) is widely used on mobile devices to enhance user experience. However, neural networks used for SR are computationally expensive, posing challenges for mobile devices with limited computing power. A viable solution is to use heterogeneous processors on mobile devices, especially the specialized hardware AI accelerators, for SR computations, but the reduced arithmetic precision on AI accelerators can lead to degraded perceptual quality in upscaled images. To address this limitation, in this paper we present SR For Your Eyes (FYE-SR), a novel image SR technique that enhances the perceptual quality of upscaled images when using heterogeneous processors for SR computations. FYE-SR strategically splits the SR model and dispatches different layers to heterogeneous processors, to meet the time constraint of SR computations while minimizing the impact of AI accelerators on image quality. Experiment results show that FYE-SR outperforms the best baselines, improving perceptual image quality by up to 2×, or reducing SR computing latency by up to 5.6× with on-par image quality.

LiDAR technology holds vast potential across various sectors, including robotics, autonomous driving, and urban planning. However, the performance of current LiDAR sensors is hindered by limited field of view (FOV), low resolution, and lack of flexible focusing capability. We introduce αLiDAR, an innovative LiDAR system that employs controllable actuation to provide a panoramic FOV, high resolution, and adaptable scanning focus. The core concept of αLiDAR is to expand the operational freedom of a LiDAR sensor through the incorporation of a controllable, active rotational mechanism. This modification allows the sensor to scan previously inaccessible blind spots and focus on specific areas of interest in an adaptive manner. By modeling uncertainties in LiDAR rotation process and estimating point-wise uncertainty, αLiDAR can correct point cloud distortions resulted from significant rotation. In addition, by optimizing LiDAR's rotation trajectory, αLiDAR can swiftly adapt to dynamic areas of interest. We developed several prototypes of αLiDAR and conducted comprehensive evaluations in various indoor and outdoor real-world scenarios. Our results demonstrate that αLiDAR achieves centimeter-level pose estimation accuracy, with an average latency of only 37 ms. In two typical LiDAR applications, αLiDAR significantly enhances 3D mapping accuracy, coverage, and density by 8.5×, 2×, and 1.6× respectively, compared to conventional LiDAR sensors. Additionally, αLiDAR's adaptive rotation improves the effective sensing distance by 1.8× and increases the number of perceived objects by 1.9×. A video demonstration of αLiDAR's in action in real world is available at https://youtu.be/x4zc_I_xTaw. The code is available at https://github.com/HViktorTsoi/alpha_lidar.

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.

In this paper, we present GazeTrak, the first acoustic-based eye tracking system on glasses. Our system only needs one speaker and four microphones attached to each side of the glasses. These acoustic sensors capture the formations of the eyeballs and the surrounding areas by emitting encoded inaudible sound towards eyeballs and receiving the reflected signals. These reflected signals are further processed to calculate the echo profiles, which are fed to a customized deep learning pipeline to continuously infer the gaze position. In a user study with 20 participants, GazeTrak achieves an accuracy of 3.6° within the same remounting session and 4.9° across different sessions with a refreshing rate of 83.3 Hz and a power signature of 287.9 mW. Furthermore, we report the performance of our gaze tracking system fully implemented on an MCU with a low-power CNN accelerator (MAX78002). In this configuration, the system runs at up to 83.3 Hz and has a total power signature of 95.4 mW with a 30 Hz FPS.

Rural Internet of Things (IoT) systems connect sensors and actuators in remote areas, serving crucial roles in agriculture and environmental monitoring. Given the absence of networking infrastructure for backhaul in these regions, satellite IoT techniques offer a cost-effective solution for connectivity. However, current satellite IoT architectures often struggle to deliver high performance due to temporal and spatial link challenges. This paper presents SateRIoT, a new network architecture with temporal link estimation and spatial link sharing that fully exploits the capability of space low-cost low-earth-orbit (LEO) IoT satellites and ground low-power wide area (LPWA) IoT techniques in rural areas. First, we introduce a bursty link model that predicts the number of transmittable packets within a transmission window, reducing energy waste from failed uplink transmissions. Moreover, we enhance the model by selecting informative features and optimizing the window length. Additionally, we develop a multi-hop flooding protocol that enables gateways to buffer and share data packets across the network while incorporating a priority data queue to avoid duplicate transmissions. We implement SateRIoT with commercial-off-the-shelf (COTS) IoT satellite and LoRa radios, then evaluate its performance based on real deployment and real-world collected traces. The results show that SateRIoT can consume 3.3X less energy consumption for an individual gateway. Moreover, SateRIoT offers up to a 5.6X reduction in latency for a single packet and a 1.9X enhancement in throughput.

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point.

Human external biofluid (e.g., sweat, urine) contains vast health data that is readily harvestable. Currently, wearable sweat sensors require an electrochemical-based approach that is used in single use, creating environmental pollution as people track their exercise in the wild. Moreover, such solution relies on a battery-powered design, which brings battery health and thermal related issues. We present MetaBioLiq, a 3D printed wireless-readable sweat sensing system that offers continuous monitoring, featuring completely passive, environmentally friendly, and easily accessible. MetaBioLiq is developed upon sweat liquid's resonance upon high frequency RF interaction, with different sweat content driving RF resonance characteristics. To activate such resonance, we design 3D PolyLactic Acid (PLA) structures that capture e-field energy from the air, and tunneling it to the sweat. Once the resonance effect occurs, we analyze return signal from a wireless RF receiver to decouple the sweat's resonance. Lastly, we evaluate MetaBioLiq's performance with 24 artificial sweat samples containing different levels of glucose, electrolytes, and fat. MetaBioLiq proves its effectiveness with 95% liquid level detection performance, and 96% sweat liquid identification performance. We further investigate MetaBioLiq's robustness and reliability, as well as limitations. Overall, MetaBioLiq shows promising results to expand the realm of mobile continuous sensing to microscopic realm untangible in the past.

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.

Due to the poor indoor coverage and positioning accuracy, existing indoor GPS positioning systems leverages additional RF infrastructure as relay with known position. However, in practice, learning the relay position requires establishing an additional connection between user and relays, which is user unfriendly and even infeasible. In this paper, we propose GPSWindow, a plug-and-play indoor GPS positioning system without the prior knowledge of the relay position. By attaching optically transparent metasurfaces to windows, GPSWindow focuses the incident signal towards determined direction and provide an indoor continuous GPS signal coverage. We exploit the difference between consecutive satellite measurements and the Doppler shift measurements to recover the satellite-to-user true distance from the measured satellite-metasurface-user distance, and then locate the user using the traditional trilateration positioning method, eliminating the requirement of relay position. We also design an error correction method that leverages IMU on smartphone and the Doppler shift information to enhance GPSWindow in mobile scenarios. Extensive real-world experiments demonstrate that GPSWindow can provide continuous position service and achieve median positioning accuracy of 3.6m in indoor environments.