MobiCom 2020 论文列表

Millimeter-wave (mmWave) networks are conventionally considered to bear a fundamental coverage limitation, due to the directional beams and limited field-of-view (FoV) of the phased array antennas. In this paper, we explore an array of phased arrays (APA) architecture, which aggregates co-located phased arrays with complementary FoVs to approximate WiFi-like omni-directional coverage. We found that straightforwardly activating all the arrays may even hamper network performance. To fully exploit the APA's potential, we propose X-Array, which jointly selects the arrays and beams, and applies a dynamic co-phasing mechanism to ensure different arrays' signals enhance each other. X-Array also incorporates a link recovery mechanism to identify alternative arrays/beams that can efficiently recover the link from outage. We have implemented X-Array on a commodity 802.11ad APA radio. Our experiments demonstrate that X-Array can approach omni-directional coverage and maintain high performance in spite of link dynamics.

Drug effectiveness management is a complicated and challenging task in chronic diseases, like Parkinson's Disease (PD). Drug effectiveness control is not only linked to personal out-of-pocket cost but also affecting the quality of life among patients with chronic symptoms. In the current practice, although that health and medical professionals still play a key role in the personalized treatment plan, the critical decision on drug selection falls upon the individual report when patients call in or visit the clinics. Unfortunately, most of the patients with chronic diseases either fail to report their day-to-day symptoms or have a limited access to medical resources due to economic constraints. In this paper, we present PDLens, a first smartphone-based system to detect drug effectiveness among Parkinson's in daily life. Specifically, PDLens can extract digital behavioral markers related to PD drug responses from everyday activities, including phone calls, standing, and walking. PDLens models the PD symptom severity on drug treatment and detects the change of severity scores before and after drug intake. A ranking-based multi-view deep neural network is developed to decide the drug effectiveness upon the symptom severity changes. To validate the performance of PDLens, we conduct a pilot study with 81 PD patients and monitor their smartphone activities and severity changes over 33693 drug intake events across six (6) months. Compared with the standard clinical drug effectiveness test developed by Motor Disorder Society, results reveal that PDLens is a promising tool to facilitate drug effectiveness detection among PD patients in their daily lives.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. This paper fills the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 288 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. The key design principle behind M3 is to hijack a low-cost commodity 802.11ad radio, separate the control path and data path inside, regenerate the phased array control signals, and recreate the data signals using a programmable baseband. Extensive experiments have demonstrated the effectiveness of the M3 design, and its usefulness for research in mmWave massive MIMO communication and sensing.

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an "offline learning" paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing OnRL, an online RL framework for real-time mobile video telephony. OnRL puts many individual RL agents directly into the video telephony system, which make video bitrate decisions in real-time and evolve their models over time. OnRL then aggregates these agents to form a high-level RL model that can help each individual to react to unseen network conditions. Moreover, OnRL incorporates novel mechanisms to handle the adverse impacts of inherent video traffic dynamics, and to eliminate risks of quality degradation caused by the RL model's exploration attempts. We implement OnRL on a mainstream operational video telephony system, Alibaba Taobao-live. In a month-long evaluation with 543 hours of video sessions from 151 real-world mobile users, OnRL outperforms the prior algorithms significantly, reducing video stalling rate by 14.22% while maintaining similar video quality.

This paper finds that actions of the teeth, namely tapping and sliding, produce vibrations in the jaw and skull. These vibrations are strong enough to propagate to the edge of the face and produce vibratory signals at an earphone. By re-tasking the earphone speaker as an input transducer - a software modification in the sound card - we are able to sense teeth-related gestures across various models of ear/headphones. In fact, by analyzing the signals at the two earphones, we show the feasibility of also localizing teeth gestures, resulting in a human-to-machine interface. Challenges range from coping with weak signals, distortions due to different teeth compositions, lack of timing resolution, spectral dispersion, etc. We address these problems with a sequence of sensing techniques, resulting in the ability to detect 6 distinct gestures in real-time. Results from 18 volunteers exhibit robustness, even though our system - EarSense - does not depend on per-user training. Importantly, EarSense also remains robust in the presence of concurrent user activities, like walking, nodding, cooking and cycling. Our ongoing work is focused on detecting teeth gestures even while music is being played in the earphone; once that problem is solved, we believe EarSense could be even more compelling.

Inexpensive and highly capable unmanned aerial vehicles (aka drones) have enabled people to contribute high-quality videos at a global scale. However, a key challenge exists for accepting videos from untrusted sources: establishing when a particular video was taken. Once a video has been received or posted publicly, it is evident that the video was created before that time, but there are no current methods for establishing how long it was made before that time.

Almost every Android user has unsatisfying experiences regarding responsiveness, in particular Application Not Responding (ANR) and System Not Responding (SNR) that directly disrupt user experience. Unfortunately, the community have limited understanding of the prevalence, characteristics, and root causes of unresponsiveness. In this paper, we make an in-depth study of ANR and SNR at scale based on fine-grained system-level traces crowdsourced from 30,000 Android systems. We find that ANR and SNR occur prevalently on all the studied 15 hardware models, and better hardware does not seem to relieve the problem. Moreover, as Android evolves from version 7.0 to 9.0, there are fewer ANR events but more SNR events. Most importantly, we uncover multifold root causes of ANR and SNR and pinpoint the largest inefficiency which roots in Android's flawed implementation of Write Amplification Mitigation (WAM). We design a practical approach to eliminating this largest root cause; after large-scale deployment, it reduces almost all (>99%) ANR and SNR caused by WAM while only decreasing 3% of the data write speed. In addition, we document important lessons we have learned from this study, and have also released our measurement code/data to the research community.

Electric toothbrushes are widely used for home oral care, but many users do not achieve desired hygiene results due to insufficient brushing coverage or incorrect brushing techniques. Existing electric toothbrushing monitoring systems fail to detect these issues because they cannot achieve fine-grained position tracking. In this paper, we present a novel electric toothbrushing monitoring system called MET that tracks brushing coverage for all the 15 surfaces of teeth and detects different types of incorrect brushing techniques. This design is inspired by our observation that the motor inside an electric toothbrush generates a unique magnetic field, which can serve as a reliable signal for position and orientation tracking. MET is the first system that tracks both the position and orientation of an unmodified electric motor using magnetic inductive sensing. Experiments with fourteen users show that the average toothbrushing surface recognition accuracy of MET is 85.3%. Moreover, MET is robust to user location changes and posture variations and does not require any training from the users. Experimental results also demonstrate our significant advantages over existing commercial systems.

Wireless communication over long distances has become the bottleneck for battery-powered, large-scale deployments. Currently used low-power protocols such as Zigbee and Bluetooth Low Energy have limited communication range, whereas long-range communication strategies used in cellular and satellite networks are heavy on energy consumption. Methods that use narrow-band communication such as LoRa, SigFox, and NB-IoT have low spectral efficiency, leading to scalability issues. The goal of this work is to develop a communication framework that can satisfy the following requirements: (1) Increased battery life, (2) Longer communication range, (3) Scalability in a wireless network. In this work, we propose, design, and prototype WiChronos, a communication paradigm that encodes information in the time interval between two narrowband symbols in order to drastically reduce the energy consumption in a wide area network with a large number of senders. We leverage the low data-rate and relaxed latency requirements of such applications to achieve the desired features identified above. Based on our prototype using off-the-shelf components, WiChronos achieves an impressive 60% improvement in battery life compared to state-of-the-art LPWAN technologies at distances of over 800 meters. We also show that more than 1000 WiChronos senders can co-exist with less than 5% probability of collisions under low traffic conditions.

This paper focuses on COSMOS - Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology "sweet spot" of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed's architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS' computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS' deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool.

The growing diversification of mobile services imposes requirements on network performance that are ever more stringent and heterogeneous. Network slicing aligns mobile network operation to this context, by enabling operators to isolate and customize network resources on a per-service basis. A key input for provisioning resources to slices is real-time information about the traffic demands generated by individual services. Acquiring such knowledge is however challenging, as legacy approaches based on in-depth inspection of traffic streams have high computational costs, which inflate with the widening adoption of encryption over data and control traffic. In this paper, we present a new approach to service-level demand estimation for slicing, which hinges on decomposition, i.e., the inference of per-service demands from traffic aggregates. By operating on total traffic volumes only, our approach overcomes the complexity and limitations of legacy traffic classification techniques, and provides a suitable input to recent 'Network Slice as a Service' (NSaaS) models. We implement decomposition through Microscope, a novel framework that uses deep learning to infer individual service demands from complex spatiotemporal features hidden in traffic aggregates. Microscope (i) transforms traffic data collected in irregular radio access deployments in a format suitable for convolutional learning, and (ii) can accommodate a variety of neural network architectures, including original 3D Deformable Convolutional Neural Networks (3D-DefCNNs) that we explicitly design for decomposition. Experiments with measurement data collected in an operational network demonstrate that Microscope accurately estimates per-service traffic demands with relative errors below 1.2%. Further, tests in practical NSaaS management use cases show that resource allocations informed by decomposition yield affordable costs for the mobile network operator.

Global Positioning System is a widely adopted localization technique. With the increasing demand for small satellites, the need for a low-power GPS for satellites is also increasing. To enable many state-of-the-art applications, the exact position of the satellites is necessary. However, building low-power GPS receivers which operate in low earth orbit pose significant challenges. This is mainly due to the high speed (~7.8 km/s) of small satellites. While duty-cycling the receiver is a possible solution, the high relative Doppler shift between the GPS satellites and the small satellite contributes to the increase in Time To First Fix (TTFF), thus increasing the energy consumption. Further, if the GPS receiver is tumbling along with the small satellite on which it is mounted, longer TTFF may lead to no GPS fix due to disorientation of the receiver antenna. In this paper, we elucidate the design of a low-cost, low-power GPS receiver for small satellite applications. We also propose an energy optimization algorithm called F3to improve the TTFF which is the main contributor to the energy consumption during cold start. With simulations and in-orbit evaluation from a launched nanosatellite with our μGPS and high-end GPS simulators, we show that up to 96.16% of energy savings (consuming only ~ 1/25th energy compared to the state of the art) can be achieved using our algorithm without compromising much (~10 m) on the navigation accuracy. The TTFF achieved is at most 33 s.

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa's lack of carrier sense capability. From our extensive measurements, the Channel Activity Detection (CAD) feature that is recently introduced to LoRa for energy-efficiently detecting preamble chirps, can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access (CSMA) protocol that we call LMAC for LoRa networks. This paper presents the designs of three advancing versions of LMAC that respectively implements CSMA, balances the communication loads among the channels defined by frequencies and spreading factors based on the end nodes' local information and then additionally the gateway's global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should the LoRaWAN's ALOHA be replaced with LMAC, network performance boosts can be realized.

We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based decoder design for Low Density Parity Check (LDPC) error control codes, which have found many useful applications in Wi-Fi, satellite communications, mobile cellular systems, and data storage systems. QBP reduces the LDPC decoding to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. QBP's embedding design can support LDPC codes of block length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits. We evaluate performance on real quantum annealer hardware, performing sensitivity analyses on a variety of parameter settings. Our design achieves a bit error rate of 10--8 in 20 μs and a 1,500 byte frame error rate of 10--6 in 50 μs at SNR 9 dB over a Gaussian noise wireless channel. Further experiments measure performance over real-world wireless channels, requiring 30 μs to achieve a 1,500 byte 99.99% frame delivery rate at SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of 10--8 and a frame error rate of 10--6 at an SNR 2.5--3.5 dB lower. In terms of limitations, QBP currently cannot realize practical protocol-sized (e.g., Wi-Fi, WiMax) LDPC codes on current QA processors. Our further studies in this work present future cost, throughput, and QA hardware trend considerations.

Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, energy efficiency, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of carefully deployed PIR sensors. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these data-driven approaches require abundant training data and suffer from environmental change. In this paper, we propose PIRATES, a PIR-based device-free localization system based on the raw data of PIR sensors. The key of PIRATES is to extract a new type of location information called azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrate that PIRATES can achieve higher localization accuracy than the state-of-the-art approaches. In addition, the information of the azimuth change can be easily incorporated with other information of PIR signals (e.g. amplitude) to improve the localization accuracy.

Backscatter enables transmissions at orders of magnitude lower energy consumption when compared to conventional radio transmitters. Backscatter tags achieve this by the reflection or absorption of carrier signal generated from emitter devices. However, backscatter systems are limited by these emitter devices, as they are significantly energy-expensive when compared to the tags. While backscatter tags can operate without requiring batteries, relying on the minuscule amounts of energy harvested from the ambient environment. However, the emitter devices, are commonly tethered to an external power supply or operate on large batteries.

In the last decade, the bandwidth expansion and MIMO spatial multiplexing have promised to increase data throughput by orders of magnitude. However, we are yet to enjoy such improvement in real-world environments, as they lack rich scattering and preclude effective MIMO spatial multiplexing. In this paper, we present ScatterMIMO, which uses smart surface to increase the scattering in the environment, to provide MIMO spatial multiplexing gain. Specifically, smart surface pairs up with a wireless transmitter device say an active AP and re-radiates the same amount of power as any active access point (AP), thereby creating virtual passive APs. ScatterMIMO avoids the synchronization, interference, and power requirements of conventional distributed MIMO systems by leveraging virtual passive APs, allowing its smart surface to provide spatial multiplexing gain, which can be deployed at a very low cost. We show that with optimal placement, these virtual APs can provide signals to their clients with power comparable to real active APs, and can increase the coverage of an AP. Furthermore, we design algorithms to optimize ScatterMIMO's smart surface for each client with minimal measurement overhead and to overcome random per-packet phase offsets during the measurement. Our evaluations show that with commercial off-the-shelf MIMO WiFi (11ac) AP and unmodified clients, ScatterMIMO provides a median throughput improvement of 2 X over the active AP alone.

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

With the explosive increase in wireless devices, physical-layer signal analysis has become critically beneficial across distinctive domains including interference minimization in network planning, security and privacy (e.g., drone and spycam detection), and mobile health with remote sensing. While SDR is known to be highly effective in realizing such services, they are rarely deployed or used by the end-users due to the costly hardware ~1K USD (e.g., USRP). Low-cost SDRs (e.g., RTL-SDR) are available, but their bandwidth is limited to 2-3 MHz and operation range falls well below 2.4 GHz - the unlicensed band holding majority of the wireless devices. This paper presents SDR-Lite, the first zero-cost, software-only software defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal. With the full compatibility to pervasively-deployed WiFi infrastructure (without any change to the hardware and firmware), SDR-Lite aims to spread the blessing of SDR receiver functionalities to billions of WiFi users and households to enhance our everyday lives. The key idea of SDR-Lite is to trick WiFi to begin packet reception (i.e., the decoding process) when the packet is absent, so that it accepts ambient signals in the air and outputs corresponding bits. The bits are then reconstructed to the original physical-layer waveform, on which diverse SDR applications are performed. Our comprehensive evaluation shows that the reconstructed signal closely reassembles the original ambient signal (>85% correlation). We extensively demonstrate SDR-Lite effectiveness across seven distinctive SDR receiver applications under three representative categories: (i) RF fingerprinting, (ii) spectrum monitoring, and (iii) (ZigBee) decoding. For instance, in security applications of drone and rogue WiFi AP detection, SDR-Lite achieves 99% and 97% accuracy, which is comparable to USRP.