MobiCom 2015 Papers

We propose a novel mobile system, called EchoTag, that enables phones to tag and remember indoor locations without requiring any additional sensors or pre-installed infrastructure. The main idea behind EchoTag is to actively generate acoustic signatures by transmitting a sound signal with a phone's speakers and sensing its reflections with the phone's microphones. This active sensing provides finer-grained control of the collected signatures than the widely-used passive sensing. For example, because the sensing signal is controlled by EchoTag, it can be intentionally chosen to enrich the sensed signatures and remove noises from useless reflections. Extensive experiments show that EchoTag distinguishes 11 tags at 1cm resolution with 98% accuracy and maintains 90% accuracy even a week after its training. With this accurate location tagging, one can realize many interesting applications, such as automatically turning on the silent mode of a phone when it is placed at a pre-defined location/area near the bed or streaming favorite songs to speakers if it is placed near a home entertainment system. Most participants of our usability study agree on the usefulness of EchoTag's potential applications and the adequacy of its sensing accuracy for supporting these applications.

Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.

This paper describes the design and implementation of AirExpress, a system that enables in-band wireless cut-through transmission. Unlike wired cut-through, wireless cut-through can reduce latency and improve throughput performance of the network at the same time. In AirExpress, all the forwarders along the cut-through path forward the signal they received immediately without decoding. The hierarchical structure of AirExpress enables its interference cancellation ability to handle all kinds of interference among the radios. Novel MAC and routing algorithms based on cut-through transmission are also proposed to support the realization of AirExpress in multi-hop mesh networks.

To reduce page load times and bandwidth usage for mobile web browsing, middleboxes that compress page content are commonly used today. Unfortunately, this can hurt performance in many cases; via an extensive measurement study, we show that using middleboxes to facilitate compression results in up to 28% degradation in page load times when the client enjoys excellent wireless link conditions. We find that benefits from compression are primarily realized under bad network conditions. Guided by our study, we design and implement FlexiWeb, a framework that determines both when to use a middlebox and how to use it, based on the client's network conditions. First, FlexiWeb selectively fetches objects on a web page either directly from the source or via a middlebox, rather than fetching all objects via the middlebox. Second, instead of simply performing lossless compression of all content, FlexiWeb performs network-aware compression of images by selecting from among a range of content transformations. We implement and evaluate a prototype of FlexiWeb using Google's open source Chromium mobile browser and our implementation of a modified version of Google's open source compression proxy. Our extensive experiments show that, across a range of scenarios, FlexiWeb reduces page load times for mobile clients by 35-42% compared to the status quo.

The human eye offers a fascinating window into an individual's health, cognitive attention, and decision making, but we lack the ability to continually measure these parameters in the natural environment. The challenges lie in: a) handling the complexity of continuous high-rate sensing from a camera and processing the image stream to estimate eye parameters, and b) dealing with the wide variability in illumination conditions in the natural environment. This paper explores the power-robustness tradeoffs inherent in the design of a wearable eye tracker, and proposes a novel staged architecture that enables graceful adaptation across the spectrum of real-world illumination. We propose CIDER, a system that operates in a highly optimized low-power mode under indoor settings by using a fast Search-Refine controller to track the eye, but detects when the environment switches to more challenging outdoor sunlight and switches models to operate robustly under this condition. Our design is holistic and tackles a) power consumption in digitizing pixels, estimating pupillary parameters, and illuminating the eye via near-infrared, b) error in estimating pupil center and pupil dilation, and c) model training procedures that involve zero effort from a user. We demonstrate that CIDER can estimate pupil center with error less than two pixels (0.6°), and pupil diameter with error of one pixel (0.22mm). Our end-to-end results show that we can operate at power levels of roughly 7mW at a 4Hz eye tracking rate, or roughly 32mW at rates upwards of 250Hz.

Imagine a user typing on a laptop keyboard while wearing a smart watch. This paper asks whether motion sensors from the watch can leak information about what the user is typing. While its not surprising that some information will be leaked, the question is how much? We find that when motion signal processing is combined with patterns in English language, the leakage is substantial. Reported results show that when a user types a word $W$, it is possible to shortlist a median of 24 words, such that $W$ is in this shortlist. When the word is longer than $6$ characters, the median shortlist drops to $10$. Of course, such leaks happen without requiring any training from the user, and also under the (obvious) condition that the watch is only on the left hand. We believe this is surprising and merits awareness, especially in light of various continuous sensing apps that are emerging in the app market. Moreover, we discover additional "leaks" that can further reduce the shortlist -- we leave these exploitations to future work.

As new iterations of more powerful and better connected smartphones emerge, their limited battery life remains a leading factor adversely affecting the mobile experience of millions of smartphone users. While it is well-known that many apps can drain battery even while running in background, there has not been any study that quantifies the extent and severity of such background energy drain for users in the wild. To extend battery life, various new features are being incorporated within the phone, one of them being preventing applications from running in background, i.e., when the screen is off, but their impact is largely unknown. This paper makes several contributions. First, we present a large-scale measurement study that performs an in-depth analysis of the activities of various apps running in background on thousands of phones in the wild. Second, we quantify the amount of battery drain by all such background activities and possible energy saving. Third, we develop a metric to measure the usefulness of background activities that is personalized to each user. Finally, we present a system called HUSH (screen-off optimizer) that monitors the metric online and automatically identifies and suppresses background activities during screen-off periods that are not useful to the user experience. In doing so, our proposed HUSH saves screen-off energy of smartphones by 15.7% on average while incurring minimal impact on the user experience with the apps.

This paper introduces EcoDrive, a fuel consumption sensing and control system for modern vehicles, implemented in an embedded platform, to improve fuel efficiency and reduce carbon emissions. EcoDrive senses vehicle dynamics through the standard vehicle On-board diagnostics (OBD) port and models various vehicle forces, i.e., propulsion, drivetrain loss, wind resistance and grade resistance, as functions of instant fuel consumption. By sensing vehicular speed and controlling air/fuel injection rate in real time, EcoDrive can adjust speed carefully to improve fuel efficiency. We have collected more than 10,000 miles of driving traces from 12 different vehicles to build models of vehicle dynamics. Based on the models, a prototype of EcoDrive is implemented in an off-the-shelf embedded platform. The prototype is installed on a regular vehicle and evaluated through test drives of more than 100 miles across both urban and highway environments. In comparison with human drivers, EcoDrive achieves an average of 20% higher fuel efficiency in urban road segments and 30% higher fuel efficiency on highways.

Adaptive HTTP video streaming over LTE has been gaining popularity due to LTE's high capacity. Quality of adaptive streaming depends highly on the accuracy of client's estimation of end-to-end network bandwidth, which is challenging due to LTE link dynamics. In this paper, we present piStream, that allows a client to efficiently monitor the LTE basestation's PHY-layer resource allocation, and then map such information to an estimation of available bandwidth. Given the PHY-informed bandwidth estimation, piStream uses a probabilistic algorithm to balance video quality and the risk of stalling, taking into account the burstiness of LTE downlink traffic loads. We conduct a real-time implementation of piStream on a software-radio tethered to an LTE smartphone. Comparison with state-of-the-art adaptive streaming protocols demonstrates that piStream can effectively utilize the LTE bandwidth, achieving high video quality with minimal stalling rate.

Radio-based passive-object sensing can enable a new form of pervasive user-computer interface. Prior work has employed various wireless signal features to sense objects under a set of predefined, coarse motion patterns. But an operational UI, like a trackpad, often needs to identify fine-grained, arbitrary motion. This paper explores the feasibility of tracking a passive writing object (e.g., pen) at sub-centimeter precision. We approach this goal through a practical design, mTrack, which uses highly-directional 60 GHz millimeter-wave radios as key enabling technology. mTrack runs a discrete beam scanning mechanism to pinpoint the object's initial location, and tracks its trajectory using a signal-phase based model. In addition, mTrack incorporates novel mechanisms to suppress interference from background reflections, taking advantage of the short wavelength of 60 GHz signals. We prototype mTrack and evaluate its performance on a 60 GHz reconfigurable radio platform. Experimental results demonstrate that mTrack can locate/track a pen with 90-percentile error below 8 mm, enabling new applications such as wireless transcription and virtual trackpad.

Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.

Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FollowMe, a "last-mile" navigation system to enable plug-and-play navigation in indoor and semi-outdoor environments. FollowMe exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FollowMe is infrastructure-free, energy-efficient and cost-saving. We implemented FollowMe on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m2. Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.

This paper presents mZig, a novel physical layer design that enables a receiver to simultaneously decode multiple packets from different transmitters in ZigBee. As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks, cyber-physical systems, and smart buildings. Since ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters need to send packets to one receiver. For example, in a smart home, all appliances report data to one control plane via ZigBee. However, concurrent transmissions in convergecast lead to the severe collision problem. The conventional ZigBee avoids collisions using backoff time, which introduces additional time overhead. Advanced methods resolve collisions instead of avoidance, in which the state-of-the-art ZigZag resolves one m-packet collision requiring m retransmissions. We propose mZig to resolve one m-packet collision by this collision itself, so the theoretical throughput is improved m-fold. Leveraging the unique features in ZigBee's physical layer including its chip rate, half-sine pulse shaping and O-QPSK modulation, mZig subtly decomposes multiple packets from one collision in baseband signal processing. The practical factors of noise, multipath, and frequency offset are taken into account in mZig design. We implement mZig on USRPs and establish a seven-node testbed. Experiment results demonstrate that mZig can receive up to four concurrent packets in our testbed. The throughput of mZig is 4.5x of the conventional ZigBee and 3.2x of ZigZag in the convergecast with four or more transmitters.

Many-antenna MU-MIMO faces a critical, previously unaddressed challenge: it lacks a practical control channel. At the heart of this challenge is that the potential range of MU-MIMO beamforming systems scales with up to the square of the number of base-station antennas once they have channel state information (CSI), whereas the range of traditional control channel operations remains constant since they take place before or during CSI acquisition. This range gap between no-CSI and CSI modes presents a critical challenge to the efficiency and feasibility of many-antenna base stations, as their operational range is limited to the no-CSI mode. We present a novel control channel design for many-antenna MU-MIMO, Faros, that allows the number of base-station antennas to scale up to 100s in practice. Faros leverages a combination of open-loop beamforming and coding gains to bridge the range gap between the CSI and no-CSI modes. Not only does Faros provide an elegant and efficient control channel for many-antenna MU-MIMO, but on a more fundamental level it exposes flexible, fine-grained, control over space, time, and code resources, which enables previously impossible optimizations. We implement our design on the Argos many-antenna base station and evaluate its performance in bridging the range gap, synchronization, and paging. With 108 antennas, Faros can provide over 40 dB of gain, which enables it to function reliably at over 250 meters outdoors with less than 100 μW of transmit power per antenna, 10 mW total, at 2.4 GHz.

Web browsing is a key application on mobile devices. However, mobile browsers are largely optimized for performance, imposing a significant burden on power-hungry mobile devices. In this work, we aim to reduce the energy consumed to load web pages on smartphones, preferably without increasing page load time and compromising user experience. To this end, we first study the internals of web page loading on smartphones and identify its energy-inefficient behaviors. Based on our findings, we then derive general design principles for energy-efficient web page loading, and apply these principles to the open-source Chromium browser and implement our techniques on commercial smartphones. Experimental results show that our techniques are able to achieve a 24.4% average system energy saving for Chromium on a latest-generation big.LITTLE smartphone using WiFi (a 22.5% saving when using 3G), while not increasing average page load time. We also show that our proposed techniques can bring a 10.5% system energy saving on average with a small 1.69\% increase in page load time for mobile Firefox web browser. User study results indicate that such a small increase in page load time is hardly perceivable.

Mobile devices are shrinking their form factors for portability, but user-mobile interaction is becoming increasingly challenging. In this paper, we propose a novel system called Okuli to meet this challenge. Okuli is a compact, low-cost system that can augment a mobile device and extend its interaction workspace to any nearby surface area. Okuli piggybacks on visible light communication modules, and uses a low-power LED and two light sensors to locate user's finger within the workspace. It is built on a light propagation/reflection model that achieves around one-centimeter location precision, with zero run-time training overhead. We have prototyped Okuli as an Android peripheral, with a 3D-printed shroud to host the LED and light sensors. Our experiments demonstrate Okuli's accuracy, stability, energy efficiency, as well as its potential in serving virtual keyboard and trackpad applications.

This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.