MobiCom 2023 论文列表

In this paper, we present AccessWear, a system that improves the accessibility of smartphone touchscreen interactions for blind users using smartwatch gestures. Our system design is human-centered, namely, it incorporates the design goals that were learned from a formative user study with 9 blind participants. The formative study showed that blind users liked the idea of using smartwatch gestures as an alternative: 4 participants liked that when using smart-watch gestures, they did not have to bring their expensive phones out in public and 6 participants liked that smart-watch gestures can be performed with one-hand, as the other hand is usually occupied in holding a cane or a guide dog. Even though there are several advantages to smartwatch gestures, our study also shows that gestures performed by blind users have different patterns compared to sighted users, making gesture recognition more challenging. To this end, AccessWear makes two contributions. The first is a gesture recognition system that works specifically for blind users that is lightweight and does not require per-person training. The second is a near-zero-effort gesture replacement system that does not require any changes to the original application. AccessWear uses input virtualization techniques so that a given gesture can replace the touchscreen input seamlessly. We implement AccessWear on an Android smartphone and Android watch. We perform a quantitative and qualitative study with 8 blind participants. Our study shows that AccessWear can recognize gestures with a 92% accuracy and the end-to-end latency when using an alternate gesture was 53 msec on average. The qualitative study shows that when participants perform a task, consisting of a series of gestures, the system is robust, does not have perceived delays, and does not add physical or mental load on the users.

Millimeter-wave backscatter can simultaneously support high-precision sensing and massive communication and represent one prominent technical evolution in next-generation wireless systems. The backscatter tags should ideally work across a wide mmWave spectrum range with consistent signal strength and angular coverage to accommodate highly diverse application scenarios. However, existing tags made of resonant antennas and RFICs only achieve a few GHz of bandwidth and hardly meet these requirements. In this paper, we present UniScatter, a new backscatter tag structure based on metamaterials. The key design of UniScatter is a graphene-based modulator and a lens-based retroreflector, which have consistent electromagnetic responses across an extensive frequency range and wide angular field-of-view. We have developed a robust fabrication process for UniScatter, and tested it on various mmWave sensing and communication devices. Our field tests show that UniScatter can backscatter signals across a wide frequency band from 24 GHz to 77 GHz with consistently high signal strength and wide angular coverage in 3D space.

Spectrum sensing systems provide real-time feedback essential for spectrum sharing. However, the growth of spectrum sharing is limited by the capabilities of these spectrum sensors. Sharing a new frequency band is only possible if sensors can detect activity in that band with sufficient time granularity and signal fidelity to meet spectrum sharing policy requirements. In this work, we introduce Crescendo, a system design that shows we can achieve wideband, real-time, high-fidelity spectrum sensing using sweeping spectrum sensors. We first provide an analysis that demonstrates there are operating points of sweeping sensors that can sense multiple popular protocols. Then we demonstrate these sensors can be built in practice with an adaptive gain super-heterodyne RF frontend with high-fidelity LO generation, and evaluate a prototype built with COTS components. In our benchmarks, Crescendo outperforms prior wideband spectrum sensors, achieving a 30 dB increase in dynamic range and 10 dB increase in SNR.

Facial expression recognition plays a vital role to enable emotional awareness in multimedia Internet of Things applications. Traditional camera or wearable sensor based approaches may compromise user privacy or cause discomfort. Recent device-free approaches open a promising direction by exploring Wi-Fi or ultrasound signals reflected from facial muscle movements, but limitations exist such as poor performance in presence of body motions and not being able to detect multiple targets. To bridge the gap, we propose mmFER, a novel millimeter wave (mmWave) radar based system that extracts facial muscle movements associated with mmWave signals to recognize facial expressions. We propose a novel dual-locating approach based on MIMO that explores spatial information from raw mmWave signals for face localization in space, eliminating ambient noise. In addition, collecting mmWave training data can be very costly in practice, and insufficient training dataset may lead to low accuracy. To overcome, we design a cross-domain transfer pipeline to enable effective and safe model knowledge transformation from image to mmWave. Extensive evaluations demonstrate that mmFER achieves an accuracy of 80.57% on average within a detection range between 0.3m and 2.5m, and it is robust to various real-world settings.

HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique cumulative observations made by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map. By extracting concise bird-eye-view features from infrastructure observations and utilizing vectorized map representations, VI-Map incurs low compute and communication overhead. We conducted end-to-end evaluations of VI-Map on a real-world testbed and a simulator. Experiment results show that VI-Map can construct decentimeter-level (up to 0.3 m) HD maps and achieve real-time (up to a delay of 42 ms) map fusion between driving vehicles and roadside infrastructure. This represents a significant improvement of 2.8× and 3× in map accuracy and coverage compared to the state-of-the-art online HD mapping approaches. A video demo of VI-Map on our real-world testbed is available at https://youtu.be/p2RO65R5Ezg.

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.

The unprecedented advance of wireless human sensing is enabled by the proliferation of the deep learning techniques, which, however, rely heavily on the completeness and representativeness of the data patterns contained in the training set. Thus, deep learning based wireless human perception models usually fail when the human subject is conducting activities that are unseen during the model training. To address this problem, we propose a novel wireless signal augmentation framework, named mmGPE, for Generalized mmWave-based Pose Estimation. In mmGPE, we adopt a physical simulator to generate mmWave FMCW signals. However, due to the imperfect simulation of the physical world, there is a big gap between the signals generated by the physical simulator and the real-world signals collected by the mmWave radar. To tackle this challenge, we propose to integrate the physical signal simulation with deep learning techniques. Specifically, we develop a deep learning-based signal refiner in mmGPE that is capable of bridging the gap and generating realistic signal data. Through extensive evaluations on a COTS mmWave testbed, our mmGPE system demonstrates high accuracy in generating human meshes for unseen activities.

Carrier aggregation (CA) is an important component technology in 5G and beyond. It aggregates multiple spectrum fragments to serve a mobile device. However, the current CA suffers under both high mobility and increased spectrum space. The limitations are rooted in its sequential, cell-by-cell operations. In this work, we propose CA++, which departs from the current paradigm and explores a group-based design scheme. We thus propose new algorithms that enable concurrent channel inference by measuring one or few cells but inferring all, while minimizing measurement cost via set cover approximations. Our evaluations have confirmed the effectiveness of CA++. Our solution can also be adapted to fit in the current 5G OFDM PHY and the 3GPP framework.

Wireless channels are vulnerable to eavesdroppers due to their broadcast nature. One approach to thwart an eavesdropper (Eve) is to decrease her SNR, e.g., by reducing the signal in her direction. Unfortunately, such methods are vulnerable to (1) a highly directional Eve that can increase her received signal strength and (2) Eve that is close to the receiver, Bob, or close to the transmitter, Alice. In this paper, we design and experimentally evaluate Multipath Multicarrier Misinformation to Adversaries (M3A), a system for Alice to send data to Bob while simultaneously sending misinformation to Eve. Our approach does not require knowledge of Eve's channel or location and, with multipath channels, randomly transforms Eve's symbols even if Eve is located one wavelength-scale distance from Bob (approximately 10 cm) or if Eve is located between Alice and Bob in their direct path (Eve is approximately 1/3 closer to Alice). In particular, our approach is to move each of Eve's received symbols (over time and across subcarriers), to an independently random transformation as compared to Bob, without Alice or Bob knowing Eve's location or channel. We realize this by modulating Alice's per-subcarrier beamforming weights with an i.i.d. random binary sequence, as if Alice had a separate antenna array for each subcarrier, and could randomly turn antennas in each array on and off. We implement M3A on a real-time Massive MIMO testbed and show that M3A can increase Eve's bit error rate more than two hundredfold compared to beamforming, even if she is positioned approximately a wavelength away, whether above, below, or beside Bob. Finally, to ensure reliability at Bob, we show that with M3A, Bob's bit error rate is approximately an order of magnitude lower than achieved with prior work.

IoT uplink performance is crucial for a wide variety of IoT applications such as health sensing and industrial control, which demand reliable delivery of sensor data to the cloud. However, due to the limited transmission power budget imposed on many power-constrained IoT devices, IoT uplinks are highly susceptible to cross-technology interference (CTI) caused by coexisting networks. Previous approaches to mitigating CTI have relied on MAC/PHY designs. They suffer from poor performance and limited generality in the presence of wideband CTI sources such as Wi-Fi and RF jammer, which transmit aggressively on large spectrum chunks using diverse radio technologies.

LoRaWAN suffers dramatic performance degradation over a long communication range due to signal attenuation and blockages. To ensure reliable data transfer, LoRaWAN adopts retransmission mechanism where an unacknowledged packet is retransmitted multiple times in the hope of successfully delivering the packet at least once over harsh wireless channels. This retransmission mechanism is ill-suited for LoRa: 1) unsuccessful retransmissions lead to high power consumption for battery-powered LoRa nodes, and 2) a retransmission at another time does not necessarily improve the signal strength over harsh wireless channels.

Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs - radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation1 shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.

Visual codes such as QR codes provide a low-cost and convenient communication channel between physical objects and mobile devices, but typically operate when the code and the device are in close physical proximity. We propose a system, called QfaR, which enables mobile devices to scan visual codes across long distances even where the image resolution of the visual codes is extremely low. QfaR is based on location-guided code scanning, where we utilize a crowd-sourced database of physical locations of codes. Our key observation is that if the approximate location of the codes and the user is known, the space of possible codes can be dramatically pruned down. Then, even if every "single bit" from the low-resolution code cannot be recovered, QfaR can still identify the visual code from the pruned list with high probability. By applying computer vision techniques, QfaR is also robust against challenging imaging conditions, such as tilt, motion blur, etc. Experimental results with common iOS and Android devices show that QfaR can significantly enhance distances at which codes can be scanned, e.g., 3.6cm-sized codes can be scanned at a distance of 7.5 meters, and 0.5m-sized codes at about 100 meters. QfaR has many potential applications, and beyond our diverse experiments, we also conduct a simple case study on its use for efficiently scanning QR code-based badges to estimate event attendance.

We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods.

With 30 billion RFID tags sold worldwide in 2021, a common basic functionality needed by RFID-enabled applications is cardinality estimation --- to quickly estimate the number of distinct tags in an RFID system. Although many advanced solutions have been proposed over the past decade, they suffer from one major limitation in practical use: they need to either modify the existing RFID standard or obtain MAC-layer information, both of which however cannot be supported by commercial off-the-shelf (COTS) devices. In this paper, we revisit the counting problem and propose a novel counting scheme called average time duration based counter (ATD) that quickly estimates the number of distinct tags in a standards-compliant manner. Compared with existing work, the competitive advantage of ATD is that it can be directly deployed on a COTS RFID system, with no need for any hardware modifications. In ATD, we found a new and measurable indicator --- the time duration between two adjacent singleton slots, which depends on the number of tags. Following this observation, we derive the theoretical relationship between the time indicator and the number of tags and then give the proof of the estimation as well as its parameter settings. Additionally, we propose a flag-flipping solution to address the overlapping problem in the multi-reader case. We implement ATD in a COTS RFID system with 1000 tags. Experimental results show that ATD is 4.2× faster than the baseline of tag inventory; the performance gain will be further increased in a larger RFID system.

Implantable and edible medical devices promise to provide continuous, directed, and comfortable healthcare treatments. Communicating with such devices and localizing them is a fundamental, but challenging, mobile networking problem. Recent work has focused on leveraging near field magnetism-based systems to avoid the challenges of attenuation, refraction, and reflection experienced by radio waves. However, these systems suffer from limited range, and require fingerprinting-based localization techniques. We present InnerCompass, a magnetic backscatter system for in-body communication and localization. InnerCompass relies on new magnetism-native design insights that enhance the range of these devices. We design the first analytical model for magnetic-field-based localization, that generalizes across different scenarios. We've implemented InnerCompass and evaluated it in porcine tissue. Our results show that Inner-Compass can communicate at 5 Kbps at a distance of 25 cm, and localize with an accuracy of 5 mm.

Virtualized radio access networks (vRANs), which allow running RAN processing on commodity servers instead of proprietary hardware, are gaining adoption in cellular networks. Two properties of the vRAN's "Distributed Unit (DU)" that implements the lower RAN layers---its real-time deadlines and its black-box nature---make it challenging to provide resilience features such as upgrades and failover without long service disruptions. These properties preclude the use of existing resilience techniques like virtual machine migration or state replication that are used for typical workloads. This paper presents Atlas, the first system that provides resilience for the DU. The central insight in Atlas is to repurpose existing cellular mechanisms for wireless resilience, namely handovers and cell reselection, to provide software resilience for the DU. For planned resilience events like upgrades, we design a novel technique that simultaneously serves cells from both the old and new DUs via the same radio, and uses handovers between these cells to migrate user devices. For unplanned failures, we identify deficiencies in existing RAN protocols that disrupt cell reselection after DU failure, and show how we can eliminate these disruptions using a middlebox between the DU and higher layers. Our evaluation with a state-of-the-art 5G vRAN testbed shows that Atlas achieves minimal disruption to cellular connectivity during resilience events, while incurring low overhead.

Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable to generate tailored model architectures for different conditions. However, conventional pre-deployment model generation approaches are not satisfactory due to the difficulty of handling the diversity of edge environments and the demand for edge information. In this paper, we propose to adapt the model architecture after deployment in the target environment, where the model quality can be precisely measured and private edge data can be retained. To achieve efficient and effective edge model generation, we introduce a pretraining-assisted on-cloud model elastification method and an edge-friendly on-device architecture search method. Model elastification generates a high-quality search space of model architectures with the guidance of a developer-specified oracle model. Each subnet in the space is a valid model with different environment affinity, and each device efficiently finds and maintains the most suitable subnet based on a series of edge-tailored optimizations. Extensive experiments on various edge devices demonstrate that our approach is able to achieve significantly better accuracy-latency tradeoffs (e.g. 46.74% higher on average accuracy with a 60% latency budget) than strong baselines with minimal overhead (13 GPU hours in the cloud and 2 minutes on the edge server).

This paper presents Audioplethysmography (APG), a novel cardiac monitoring modality for active noise cancellation (ANC) headphones. APG sends a low intensity ultrasound probing signal using an ANC headphone's speakers and receives the echoes via the on-board feedback microphones. We observed that, as the volume of ear canals slightly changes with blood vessel deformations, the heartbeats will modulate these ultrasound echoes. We built mathematical models to analyze the underlying physics and propose a multi-tone APG signal processing pipeline to derive the heart rate and heart rate variability in both constrained and unconstrained settings. APG enables robust monitoring of cardiac activities using mass-market ANC headphones in the presence of music playback and body motion such as running.