MobiCom 2019 Papers

In this paper, we for the first time demonstrate how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our approach, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle asychnronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-16Mbps aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-5x capacity and 48x concurrency compared with the existing design respectively. We also design an OFDMA tag IC, and the simulation and numerical analysis results show that the tag's power consumption is in tens of μW.

Wireless sensing has demonstrated the potential of using Wi-Fi signals to track people and objects, even behind walls.Yet, prior work in this space aims to merely detect the presence of objects around corners, rather than their type. In this paper, we explore the feasibility of the following re-search question: ?Can commodity Wi-Fi radios detect both the location and type of moving objects around them?". We present IntuWition, a complementary sensing system that can sense the location and type of material of objects in the environment, including those out of line-of-sight. It achieves this by sensing wireless signals reflected off surrounding objects using commodity Wi-Fi radios, whose signals penetrate walls and occlusions. At the core of IntuWition is the idea that different materials reflect and scatter polarized waves in different ways. We build upon ideas from RADAR Polarimetry to detect the material of objects across spatial locations, despite mobility of the sensing device and the hardware non-idealities of commodity Wi-Fi radios. A detailed feasibility study reveals an average accuracy of 95% in line-of-sight and 92% in non-line-of-sight in classifying five types of materials:copper, aluminum, plywood, birch, and human. Finally, we present a proof-of-concept application of our system on an autonomous UAV that uses its onboard Wi-Fi radios to sense whether an occlusion is a person versus another UAV.

Low-power wide-area networks (LPWANs) are a compelling answer to the networking challenges faced by many Internet of Things devices. Their combination of low power, long range, and deployment ease has motivated a flurry of research, including exciting results on backscatter and interference cancellation that further lower power budgets and increase capacity. But despite the interest, we argue that unlicensed LPWAN technologies can only serve a narrow class of Internet of Things applications due to two principal challenges: capacity and coexistence. We propose a metric, bit flux, to describe networks and applications in terms of throughput over a coverage area. Using bit flux, we find that the combination of low bit rate and long range restricts the use case of LPWANs to sparse sensing applications. Furthermore, this lack of capacity leads networks to use as much available bandwidth as possible, and a lack of coexistence mechanisms causes poor performance in the presence of multiple, independently-administered networks. We discuss a variety of techniques and approaches that could be used to address these two challenges and enable LPWANs to achieve the promise of ubiquitous connectivity.

Despite widespread popularity, today's microwave ovens are limited in their cooking capabilities, given that they heat food blindly, resulting in a non-uniform and unpredictable heating distribution. We present SDC (software-defined cooking), a low-cost closed-loop microwave oven system that aims to heat the food in a software-defined thermal trajectory. SDC achieves this through a novel high-resolution heat sensing and actuation system that uses microwave-safe components to augment existing microwaves. SDC first senses thermal gradient by using arrays of neon lamps that are charged by the Electromagnetic (EM) field a microwave produces. SDC then modifies the EM-field strength to desired levels by accurately moving food on a programmable turntable towards sensed hot and cold spots. To create a more skewed arbitrary thermal pattern, SDC further introduces two types of programmable accessories: microwave shield and susceptor. We design and implement one experimental test-bed by modifying a commercial off-the-shelf microwave oven. Our evaluation shows that SDC can programmatically create temperature deltas at a resolution of 21 degrees with a spatial resolution of 3 cm without accessories and 183 degrees with the help of accessories. We further demonstrate how a SDC-enabled microwave can be enlisted to perform unexpected cooking tasks: cooking meat and fat in bacon discriminatively and heating milk uniformly.

Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet? an open-hardware, open-software wrist-worn computing device? and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.

LTE test cases are standardized by 3GPP. They must be executed on every LTE-capable device model before commercial release. In this work, we examine the LTE testing practices in terms of completeness and efficiency. We discover that the standardized tests are incomplete in that a number of test cases related to multiple protocol interactions are missing. Our analysis also shows that, the isolated treatment of test cases, but not from the system perspective, incurs repetitive executions of test operations, thus resulting in testing inefficiencies. We thus make a case for a paradigm shift from ad hoc testing to a methodical approach to LTE testing. We follow a few guidelines from the LTE standards and propose an algorithmic approach to systematic testing. In the process, we address various challenges, provide complete list of test cases, and present the related algorithms. Our evaluation shows that, by eliminating repetitive operations, our new scheme reduces up to 70% of LTE testing steps. We also find 87 new, yet valid test cases that are not defined by the LTE standards.

With the rise of ever-more sophisticated wearables and sensing technologies, mobile health continues to be an active area of research. However, from a clinical researcher point of view, testing novel use of the mobile health innovations remains a major hurdle, as composing a clinical trial using a combination of technologies still remains in the realm of computer scientists. We take a software-inspired viewpoint of clinical trial designs to design, develop and validate HealthSense to enable expressibility of complex ideas, composability with diverse devices and services while maximally maintaining simplicity for a clinical research user. A key innovation in HealthSense is the concept of a study state manager (SSM) that modifies parameters of the study over time as data accumulates and can trigger external events that affect the participant; this design allows us to implement nearly arbitrary clinical trial designs. The SSM can funnel data streams to custom or third-party cloud processing pipelines and the result can be used to give interventions and modify parameters of the study. HealthSense supports both Android and iOS platforms and is secure, scalable and fully operational. We outline three trials (two with clinical populations) to highlight simplicity, composability, and expressibility of HealthSense.

Deep-learning (DL) is receiving huge attention as enabling techniques for emerging mobile and IoT applications. It is a common practice to conduct DNN model-based inference using cloud services due to their high computation and memory cost. However, such a cloud-offloaded inference raises serious privacy concerns. Malicious external attackers or untrustworthy internal administrators of clouds may leak highly sensitive and private data such as image, voice and textual data. In this paper, we propose Occlumency, a novel cloud-driven solution designed to protect user privacy without compromising the benefit of using powerful cloud resources. Occlumency leverages secure SGX enclave to preserve the confidentiality and the integrity of user data throughout the entire DL inference process. DL inference in SGX enclave, however, impose a severe performance degradation due to limited physical memory space and inefficient page swapping. We designed a suite of novel techniques to accelerate DL inference inside the enclave with a limited memory size and implemented Occlumency based on Caffe. Our experiment with various DNN models shows that Occlumency improves inference speed by 3.6x compared to the baseline DL inference in SGX and achieves a secure DL inference within 72% of latency overhead compared to inference in the native environment.

In this paper, we propose XModal-ID, a novel WiFi-video cross-modal gait-based person identification system. Given the WiFi signal measured when an unknown person walks in an unknown area and a video footage of a walking person in another area, XModal-ID can determine whether it is the same person in both cases or not. XModal-ID only uses the Channel State Information (CSI) magnitude measurements of a pair of off-the-shelf WiFi transceivers. It does not need any prior wireless or video measurement of the person to be identified. Similarly, it does not need any knowledge of the operation area or person's track. Finally, it can identify people through walls. XModal-ID utilizes the video footage to simulate the WiFi signal that would be generated if the person in the video walked near a pair of WiFi transceivers. It then uses a new processing approach to robustly extract key gait features from both the real WiFi signal and the video-based simulated one, and compares them to determine if the person in the WiFi area is the same person in the video. We extensively evaluate XModal-ID by building a large test set with $8$ subjects, $2$ video areas, and $5$ WiFi areas, including 3 through-wall areas as well as complex walking paths, all of which are not seen during the training phase. Overall, we have a total of 2,256 WiFi-video test pairs. XModal-ID then achieves an $85%$ accuracy in predicting whether a pair of WiFi and video samples belong to the same person or not. Furthermore, in a ranking scenario where XModal-ID compares a WiFi sample to $8$ candidate video samples, it obtains top-1, top-2, and top-3 accuracies of $75%$, $90%$, and $97%$. These results show that XModal-ID can robustly identify new people walking in new environments, in various practical scenarios.

Due to extremely low power consumption, backscatter has become the transmission mechanism of choice for battery-free devices that operate on harvested energy. However, a limitation of recent backscatter systems is that the communication range scales with the strength of the ambient carrier signal~(ACS). This means that to achieve a long-range, a backscatter tag needs to reflect a strong ACS, which in practice means that it needs to be close to an ACS emitter. We present TunnelScatter, a mechanism that overcomes this limitation. TunnelScatter uses a tunnel diode-based radio frequency oscillator to enable transmissions when there is no ACS, and the same oscillator as a reflection amplifier to support backscatter transmissions when the ACS is weak. Our results show that even without an ACS, TunnelScatter is able to transmit through several walls covering a distance of 18m while consuming a peak biasing power of \SI57 \micro\watt. Based on TunnelScatter, we design battery-free sensor tags, called TunnelTags, that can sense physical phenomena and transmit them using the TunnelScatter mechanism.

Client devices must cooperate to share limited resources in wireless local area networks (WLANs). Standards exist for access points (APs), to use a more holistic view of the RF environment, to share information with clients (802.11v/k), allowing clients to make better performance-impacting decisions, e.g. choosing to roam between APs. Standards intended to lower the cost of these expensive control-plane operations (802.11r) also exist. In this paper, we use large scale measurements to show the limited adoption of existing client-based techniques. We propose ClientMarshal for enterprise WLANs, which provides network infrastructure more control over these decisions without modification to client device software. ClientMarshal divides the enterprise network into logical groups called cells, each consisting of a small number of neighboring APs operating on different channels. All APs belonging to the same cell broadcast the same wireless identity (BSSID) to client devices, and appear as a single virtual AP. The key enabler behind ClientMarshal is FreeSteer (FST), a novel protocol we develop that allows an AP to dynamically steer an associated client to another AP in the same cell based on various network measurements in order to improve overall network performance. This design also improves the roaming experience by bypassing traditional control-plane operations, avoiding significant overhead as well as client implementation defects. Using systematic evaluation and implementation on commodity APs, we show the efficacy and control of FST protocol in client steering. Compared to state-of-the-art approaches, FST can steer clients almost 2.5X faster, with 52% higher success rate, and reduce packet loss by more than 68%.

The popularity of 4K videos has grown significantly in the past few years. Yet coding and streaming live 4K videos incurs prohibitive cost to the network and end system. Motivated by this observation, we explore the feasibility of supporting live 4K video streaming over wireless networks using commodity devices. Given the high data rate requirement of 4K videos, 60 GHz is appealing, but its large and unpredictable throughput fluctuation makes it hard to provide desirable user experience. In particular, to support live 4K video streaming, we should (i) adapt to highly variable and unpredictable wireless throughput, (ii) support efficient 4K video coding on commodity devices. To this end, we propose a novel system, Jigsaw. It consists of (i) easy-to-compute layered video coding to seamlessly adapt to unpredictable wireless link fluctuations, (ii) efficient GPU implementation of video coding on commodity devices, and (iii) effectively leveraging both WiFi and WiGig through delayed video adaptation and smart scheduling. Using real experiments and emulation, we demonstrate the feasibility and effectiveness of our system. Our results show that it improves PSNR by 6-15dB and improves SSIM by 0.011-0.217 over state-of-the-art approaches. Moreover, even when throughput fluctuates widely between 0.2Gbps-2Gbps, it can achieve an average PSNR of 33dB.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.

The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource controller for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and signal quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map (encoded) contexts into resource control decisions. We have implemented vrAIn using an open-source LTE stack over different platforms. Our results show that vrAIn successfully derives appropriate compute and radio control actions irrespective of the platform and context: (i) it provides savings in computational capacity of up to 30% over CPU-unaware methods; (ii) it improves the probability of meeting QoS targets by 25% over static allocation policies using similar CPU resources in average; (iii) upon CPU capacity shortage, it improves throughput performance by 25% over state-of-the-art schemes; and (iv) it performs close to optimal policies resulting from an offline oracle. To the best of our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs, and the first approach to a model-free solution that does not need to assume any particular vRAN platform or system conditions.

Web browsing, previously optimized for the desktop environment, is being fine-tuned for energy-efficient use on mobile devices. Although active attempts have been made to reduce energy consumption, the advent of energy-aware scheduling (EAS) integrated in the recent devices suggests the possibility of a new approach for optimizing energy use by browsers. Our preliminary analysis showed that the existing EAS-enabled system is overly optimized for performance, leading to energy inefficiencies while a web browser is running. In this paper, we analyze the characteristics of web browsers, and investigate the cause of energy inefficiency in EAS-enabled mobile devices. We then propose a system, called WebTune, to improve the energy efficiency of mobile browsers. Exploiting the reinforcement learning technique, WebTune learns the optimal execution speed of the web browser's processes, and adjusts the speed at runtime, thus saving energy and ensuring the quality of service (QoS). WebTune is implemented on the latest Android-based smartphones, and evaluated with Alexa's top 200 websites. The experimental results show that WebTune reduced the device-level energy consumption of the Google Pixel 2 XL and Samsung Galaxy S9 Plus smartphones by 18.7-22.0% and 13.7-16.1%, respectively, without degrading the QoS.

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantly, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. We compared VeMo with state-of-the-art benchmark mobility models, and the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we addressed this issue by developing a novel QR code (called mQRCode), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moire pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQRCode was > 98.6% within 10.2 frames via a multi-frame decryption method. The decryption rate for cameras positioned 20° off axis or > 10cm away from the designated location dropped to 0%, indicating that mQRCode is robust against attacks.

Recent studies show that the voice control system (VCS) is subject to the inaudible voice command attack, which can not be heard by human ears but can be recorded by the microphone. An adversary could leverage the attack to disable the VCS user's home security system, leak the victim's privacy or download malware stealthily. Efforts have been dedicated to developing forensics based defense mechanisms, which target at detecting traces of the attack signal; however, we find that existing approaches of the kind still leave loopholes. Moreover, a complete defense mechanism should be able to not only detect the attack but also cancel out the attack signal, and meanwhile ensure the legitimate voice commands unaffected, which however is still unavailable to the best of our knowledge. This paper is an attempt to fill the gap. We first systematically analyze existing forensics based defense mechanisms and reveal the root cause of their loopholes. Then we present an active inaudible-voice-command cancellation (AIC) design, which can reliably detect and capture the attack signal facilitated by our custom-designed "guard'' signal transmitter. AIC can create a special spectrum in the passband of the VCS microphone, based on which we are able to neutralize the attack signal in software means. We implement a prototype of our defense system and conduct comprehensive experiments to validate our design.

Sign language is a natural and fully-formed communication method for deaf or hearing-impaired people. Unfortunately, most of the state-of-the-art sign recognition technologies are limited by either high energy consumption or expensive device costs and have a difficult time providing a real-time service in a daily-life environment. Inspired by previous works on motion detection with wearable devices, we propose Sign Speaker - a real-time, robust, and user-friendly American sign language recognition (ASLR) system with affordable and portable commodity mobile devices. SignSpeaker is deployed on a smartwatch along with a smartphone; the smartwatch collects the sign signals and the smartphone outputs translation through an inbuilt loudspeaker. We implement a prototype system and run a series of experiments that demonstrate the promising performance of our system. For example, the average translation time is approximately $1.1$ seconds for a sentence with eleven words. The average detection ratio and reliability of sign recognition are 99.2% and 99.5%, respectively. The average word error rate of continuous sentence recognition is 1.04% on average.