MobiCom 2020 论文列表

Energy consumption of Internet-of-Things end devices is a major constraint that limits their long-term and large-scale deployment. Conventionally, the radios and processors used in these end devices are major power consumption that drains at the level of milliwatts (mWs). However, in recent decades, backscatter communication has dramatically reduced the power consumed by the radios in end devices to microwatts (μWs), and thus the processor remains the major bottleneck for energy optimization.

In this paper, we propose iCellSpeed, an on-device solution to increase data access speed by substantiating unrealized performance potentials. We find that performance potentials are missed in today's mobile networks, as the data speed a user device gets is much lower than what the device could get. The issue is rooted in the current cell selection practice, which misses good candidate cells that offer faster access speed, thus under-utilizing the available capabilities in mobile networks. We design iCellSpeed to facilitate network-controlled cell selection with proactive device-side assistance towards more desirable cells. Our evaluation over AT&T and Verizon confirms its effectiveness. iCellSpeed increases data access speed by more than 10 Mbps at 79% of test locations (> 25Mbps at 29% of locations, up to 80.6 Mbps). It doubles access speed at 62.5% of locations with the gain up to 28.4x. Datasets are available at [9].

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect the health and indicate anomalies of the objects. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. By introducing a Multi-Signal Consolidation (MSC) model to describe the properties of the reflected signals, we exploit the inherent consistency among those signals to accurately recover the vibration characteristics. We implement a prototype of mmVib, and the experiments show that this design achieves 8.2% relative amplitude error and 0.5% relative frequency error in median. Typically, the median amplitude error is 3.4um for the 100um-amplitude vibration. Compared to two existing approaches, mmVib reduces the 80th-percentile amplitude error by 62.9% and 68.9% respectively.

We present the first system that can airdrop wireless sensors from small drones and live insects. In addition to the challenges of achieving low-power consumption and long-range communication, airdropping wireless sensors is difficult because it requires the sensor to survive the impact when dropped in mid-air. Our design takes inspiration from nature: small insects like ants can fall from tall buildings and survive because of their tiny mass and size. Inspired by this, we design insect-scale wireless sensors that come fully integrated with an onboard power supply and a lightweight mechanical actuator to detach from the aerial platform. Our system introduces a first-of-its-kind 37 mg mechanical release mechanism to drop the sensor during flight, using only 450 μJ of energy as well as a wireless communication link that can transmit sensor data at 33 kbps up to 1 km. Once deployed, our 98 mg wireless sensor can run for 1.3-2.5 years when transmitting 10-50 packets per hour on a 68 mg battery. We demonstrate attachment to a small 28 mm wide drone and a moth (Manduca sexta) and show that our insect-scale sensors flutter as they fall, suffering no damage on impact onto a tile floor from heights of 22 m.

Respiratory diseases have been a significant public health challenge. Efficient disease evaluation and monitoring call for daily spirometry tests, as an effective way of pulmonary function testing, out of clinic. This requirement, however, is hard to be satisfied due to the large size and high costs of current spirometry equipments. In this paper, we present SpiroSonic, a new system design that uses commodity smartphones to support complete, accurate yet reliable spirometry tests in regular home settings with various environmental and human factors. SpiroSonic measures the humans' chest wall motion via acoustic sensing and interprets such motion into lung function indices, based on the clinically validated correlation between them. We implemented SpiroSonic as a smartphone app, and verified SpiroSonic's monitoring error over healthy humans as <3%. Clinical studies further show that SpiroSonic reaches 5%-10% monitoring error among 83 pediatric patients. Given that the error of in-clinic spirometry is usually around 5%, SpiroSonic can be reliably used for disease tracking and evaluation out of clinic.

Internet of Things (IoT) devices are hindered from communicating with their neighbors by incompatible protocols or electromagnetic interference. Existing solutions adopting physical covert channels have limitations in receiver distinction, additional hardware, conditional placement, or physical contact. Our system, Deaf-Aid, utilizes the stealthy speaker-to-gyroscope channel to build robust protocol-independent communication with automatic receiver identification. Deaf-Aid exploits ultrasonic signals at a frequency corresponding to the target receiver, forcing the gyroscope inside to resonate, so as to convey information. We probe the relationship among axes in a gyroscope to surmount frequency offset ingeniously and support multi-channel communication. Meanwhile, Deaf-Aid identifies the receivers automatically via device fingerprints constituted by the diversity of resonant frequency ranges. Furthermore, we entitle Deaf-Aid the capability of mobile communication which is an essential demand for IoT devices. We address the challenge of accurate signals recovery from motion interference. Extensive evaluations demonstrate that Deaf-Aid yields 47bps with BER lower than 1% under motion interference. To our best knowledge, Deaf-Aid is the first work to enable stealthy mobile IoT communication on the basis of inertial motion sensors.

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

Cellular service carriers often employ reactive strategies to assist customers who experience non-outage related individual service degradation issues (e.g., service performance degradations that do not impact customers at scale and are likely caused by network provisioning issues for individual devices). Customers need to contact customer care to request assistance before these issues are resolved. This paper presents our experience with PACE (ProActive customer CarE), a novel, proactive system that monitors, troubleshoots and resolves individual service issues, without having to rely on customers to first contact customer care for assistance. PACE seeks to improve customer experience and care operation efficiency by automatically detecting individual (non-outage related) service issues, prioritizing repair actions by predicting customers who are likely to contact care to report their issues, and proactively triggering actions to resolve these issues. We develop three machine learning-based prediction models, and implement a fully automated system that integrates these prediction models and takes resolution actions for individual customers. We conduct a large-scale trace-driven evaluation using real-world data collected from a major cellular carrier in the US, and demonstrate that PACE is able to predict customers who are likely to contact care due to non-outage related individual service issues with high accuracy. We further deploy PACE into this cellular carrier network. Our field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues.

With increasing private and sensitive data stored in mobile devices, secure and effective mobile-based user authentication schemes are desired. As the most natural way to contact with mobile devices, finger touches have shown potentials for user authentication. Most existing approaches utilize finger touches as behavioral biometrics for identifying individuals, which are vulnerable to spoofer attacks. To resist attacks for on-touch user authentication on mobile devices, this paper exploits physical characters of touching fingers by investigating active vibration signal transmission through fingers, and we find that physical characters of touching fingers present unique patterns on active vibration signals for different individuals. Based on the observation, we propose a behavior-irrelevant on-touch user authentication system, TouchPass, which leverages active vibration signals on smartphones to extract only physical characters of touching fingers for user identification. TouchPass first extracts features that mix physical characters of touching fingers and behavior biometrics of touching behaviors from vibration signals generated and received by smartphones. Then, we design a Siamese network-based architecture with a specific training sample selection strategy to reconstruct the extracted signal features to behavior-irrelevant features and further build a behavior-irrelevant on-touch user authentication scheme leveraging knowledge distillation. Our extensive experiments validate that TouchPass can accurately authenticate users and defend various attacks.

Millimeter-wave (mmWave) networking represents a core technology to meet the demanding bandwidth requirements of emerging connected vehicles. However, the feasibility of mmWave vehicle-to-everything (V2X) connectivity has long been questioned. One major doubt lies in how the highly directional mmWave links can sustain under high mobility. In this paper, we present the first comprehensive reality check of mmWave V2X networks. We deploy an experimental testbed to mimic a typical mmWave V2X scenario, and customize a COTS mmWave radio to enable microscopic investigation of the channel and the link. We further construct a high-fidelity 3D ray-tracer to reproduce the mmWave characteristics at scale. With this toolset, we study the mmWave V2X coverage, mobility and blockage, codebook/beam management, and spatial multiplexing. Our measurement debunks some common misperceptions of mmWave V2X networks. In particular, due to the constrained roadway network structures, we find the beam management can be handled easily by the often-denounced beam scanning schemes, as long as the codebook is properly designed. Blockage can be almost eliminated through proper basestation deployment and cooperation. Highly effective spatial multiplexing can be realized even without sophisticated MIMO radios. Our work points to possible ways to realize efficient and reliable mmWave networks under high mobility, while maintaining the simplicity of standard network protocols.

The demand for mobile video streaming has experienced tremendous growth over the last decade. However, existing methods of video delivery fall short of delivering high-quality video. Recent advances in neural super-resolution have opened up the possibility of enhancing video quality by leveraging client-side computation. Unfortunately, mobile devices cannot benefit from this because it is too expensive in computation and power-hungry.

Small unmanned autonomous vehicles (UAVs) swarms are becoming ubiquitous in a number of applications (e.g., surveying, monitoring, and situational awareness). Indoor environments may contain metal equipment that temporarily disrupts radio reception. During these momentary interruptions, a small UAV needs to be able to broadcast a 'heartbeat' to indicate that it is not damaged or lost. Considering alternative messaging modalities, we observe that light-based methods require line-of sight, which is not guaranteed when UAVs are moving through a cluttered environment, while a naive sound-based method is easily drowned out by the UAV's own loud motor and propeller noise.

This paper presents WiPose, the first 3D human pose construction framework using commercial WiFi devices. From the pervasive WiFi signals, WiPose can reconstruct 3D skeletons composed of the joints on both limbs and torso of the human body. By overcoming the technical challenges faced by traditional camera-based human perception solutions, such as lighting and occlusion, the proposed WiFi human sensing technique demonstrates the potential to enable a new generation of applications such as health care, assisted living, gaming, and virtual reality. WiPose is based on a novel deep learning model that addresses a series of technical challenges. First, WiPose can encode the prior knowledge of human skeleton into the posture construction process to ensure the estimated joints satisfy the skeletal structure of the human body. Second, to achieve cross environment generalization, WiPose takes as input a 3D velocity profile which can capture the movements of the whole 3D space, and thus separate posture-specific features from the static objects in the ambient environment. Finally, WiPose employs a recurrent neural network (RNN) and a smooth loss to enforce smooth movements of the generated skeletons. Our evaluation results on a real-world WiFi sensing testbed with distributed antennas show that WiPose can localize each joint on the human skeleton with an average error of 2.83cm, achieving a 35% improvement in accuracy over the state-of-the-art posture construction model designed for dedicated radar sensors.

We present the design, implementation, and evaluation of μmedIC, a fully-integrated wireless and batteryless micro-implanted sensor. The sensor powers up by harvesting energy from RF signals and communicates at near-zero power via backscatter. In contrast to prior designs which cannot operate across various in-body environments, our sensor can self-reconfigure to adapt to different tissues and channel conditions. This adaptation is made possible by two key innovations: a reprogrammable antenna that can tune its energy harvesting resonance to surrounding tissues, and a backscatter rate adaptation protocol that closes the feedback loop by tracking circuit-level sensor hints.

Advertisers gather data about users and their mobile devices through ads placed within Android and iOS apps. Most of the time, location, device, and app information are linked to the same device using a unique advertising ID (Ad ID). If the Ad ID is not available, advertisers can still use geo-coordinates or IP address to infer links in data gathered from different ad placements.

Retailers are aiming to enhance customer experience by automating the checkout process. The key impediment here is the effort to manually align the product barcode with the scanner, requiring sequential handling of items without blocking the line-of-sight of the laser beam. While recent systems such as Amazon Go eliminate human involvement using an extensive array of cameras, we propose a privacy-preserving alternative, RFGo, that identifies products using passive RFID tags. Foregoing continuous monitoring of customers throughout the store, RFGo scans the products in a dedicated checkout area that is large enough for customers to simply walk in and stand until the scan is complete (in two seconds). Achieving such low-latency checkout is not possible with traditional RFID readers, which decode tags using one antenna at a time. To overcome this, RFGo includes a custom-built RFID reader that simultaneously decodes a tag's response from multiple carrier-level synchronized antennas enabling a large set of tag observations in a very short time. RFGo then feeds these observations to a neural network that accurately distinguishes the products within the checkout area from those that are outside. We build a prototype of RFGo and evaluate its performance in challenging scenarios. Our experiments show that RFGo is extremely accurate, fast and well-suited for practical deployment in apparel stores.

In this paper, we perform a first comprehensive study of mobile volumetric video streaming. Volumetric videos are truly 3D, allowing six degrees of freedom (6DoF) movement for their viewers during playback. Such flexibility enables numerous applications in entertainment, healthcare, education, etc. However, volumetric video streaming is extremely bandwidth-intensive. We conduct a detailed investigation of each of the following aspects for point cloud streaming (a popular volumetric data format): encoding, decoding, segmentation, viewport movement patterns, and viewport prediction. Motivated by the observations from the above study, we propose ViVo, which is to the best of our knowledge the first practical mobile volumetric video streaming system with three visibility-aware optimizations. ViVo judiciously determines the video content to fetch based on how, what and where a viewer perceives for reducing bandwidth consumption of volumetric video streaming. Our evaluations over real wireless networks (including commercial 5G), mobile devices and users indicate that ViVo can save on average 40% of data usage (up to 80%) with virtually no drop in visual quality.

Voice assistants such as Amazon Echo (Alexa) and Google Home use microphone arrays to estimate the angle of arrival (AoA) of the human voice. This paper focuses on adding user localization as a new capability to voice assistants. For any voice command, we desire Alexa to be able to localize the user inside the home. The core challenge is two-fold: (1) accurately estimating the AoAs of multipath echoes without the knowledge of the source signal, and (2) tracing back these AoAs to reverse triangulate the user's location.

We present Heimdall, a mobile GPU coordination platform for emerging Augmented Reality (AR) applications. Future AR apps impose an explored challenging workload: i) concurrent execution of multiple Deep Neural Networks (DNNs) for physical world and user behavior analysis, and ii) seamless rendering in presence of the DNN execution for immersive user experience. Existing mobile deep learning frameworks, however, fail to support such workload: multi-DNN GPU contention slows down inference latency (e.g., from 59.93 to 1181 ms), and rendering-DNN GPU contention degrades frame rate (e.g., from 30 to ≈12 fps). Multi-tasking for desktop GPUs (e.g., parallelization, preemption) cannot be applied to mobile GPUs as well due to limited architectural support and memory bandwidth. To tackle the challenge, we design a Pseudo-Preemption mechanism which i) breaks down the bulky DNN into smaller units, and ii) prioritizes and flexibly schedules concurrent GPU tasks. We prototyped Heimdall over various mobile GPUs (i.e., recent Adreno series) and multiple AR app scenarios that involve combinations of 8 state-of-the-art DNNs. Our extensive evaluation shows that Heimdall enhances the frame rate from ≈12 to ≈30 fps while reducing the worst-case DNN inference latency by up to ≈15 times compared to the baseline multi-threading approach.