Environmental fingerprinting has been proposed as a key enabler to immersive, highly contextualized mobile computing applications, especially augmented reality. While fingerprints can be constructed in many domains (e.g., wireless RF, magnetic field, and motion patterns), visual fingerprinting is especially appealing due to the inherent heterogeneity in many indoor spaces. This visual diversity, however, is also its Achilles' heel -- matching a unique visual signature against a database of millions requires either impractical computation for a mobile device, or to upload large quantities of visual data for cloud offload. Further, most visual "features" tend to be low entropy -- e.g., homogeneous repetitions of floor and ceiling tiles. Our system VisualPrint, proposes a means to offload only the most distinctive visual data, that is, only those visual signatures which stand a good chance to yield a unique match. VisualPrint enables cloud-offloaded visual fingerprinting with efficacy comparable to using whole images, but with an order reduction in network transfer.

Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

Many emerging mobile computing applications are continuous vision based. The primary challenge these applications face is computation partitioning between the phone and cloud. The indoor location information is one metadata that can help these applications in making this decision. In this extended-abstract, we propose a vision based scheme to uniquely fingerprint an environment which can in turn be used to identify user's location from the uploaded visual features. Our approach takes into account that the opportunity to identify location is fleeting and the phones are resource constrained -- therefore minimal yet sufficient computation needs to be performed to make the offloading decision. Our work aims to achieve near real-time performance while scaling to buildings of arbitrary sizes. The current work is in preliminary stages but holds promise for the future -- may apply to many applications in this area.

The idea of augmented reality - the ability to look at a physical object through a camera and view annotations about the object - is certainly not new. Yet, this apparently feasible vision has not yet materialized into a precise, fast, and comprehensively usable system. This paper asks: What does it take to enable augmented reality (AR) on smartphones today? To build a ready-to-use mobile AR system, we adopt a top-down approach cutting across smartphone sensing, computer vision, cloud offloading, and linear optimization. Our core contribution is in a novel location-free geometric representation of the environment - from smartphone sensors - and using this geometry to prune down the visual search space. Metrics of success include both accuracy and latency of object identification, coupled with the ease of use and scalability in uncontrolled environments. Our converged system, OverLay, is currently deployed in the engineering building and open for use to regular public; ongoing work is focussed on campus-wide deployment to serve as a "historical tour guide" of UIUC. Performance results and user responses thus far have been promising, to say the least.

Today's smartphones provide a variety of sensors, enabling high-resolution measurements of user behavior. We envision that many services can benefit from short-term predictions of complex human behavioral patterns. While enablement of behavior awareness through sensing is a broad research theme, one possibility is in predicting how quickly a person will move through a space. Such a prediction service could have numerous applications. For one example, we imagine shop owners predicting how long a particular customer is likely to browse merchandise, and issue targeted mobile coupons accordingly - customers in a hurry can be encouraged to stay and consider discounts. Within a space of moderate size, WiFi access points are uniquely positioned to track a statistical framework for user length of stay, passively recording metrics such as WiFI signal strength (RSSI) and potentially receiving client-uploaded sensor data. In this work, we attempt to quantity this opportunity, and show that human dwell time can be predicted with reasonable accuracy, even when restricted to passively observed WiFi RSSI.

Crowdsourced video often provides engaging and diverse perspectives not captured by professional videographers. Broad appeal of user-uploaded video has been widely confirmed: freely distributed on YouTube, by subscription on Vimeo, and to peers on Facebook/Google+. Unfortunately, user-generated multimedia can be difficult to organize; these services depend on manual "tagging" or machine-mineable viewer comments. While manual indexing can be effective for popular, well-established videos, newer content may be poorly searchable; live video need not apply. We envisage video-sharing services for live user video streams, indexed automatically and in realtime, especially by shared content. We propose FOCUS, for Hadoop-on-cloud video-analytics. FOCUS uniquely leverages visual, 3D model reconstruction and multimodal sensing to decipher and continuously track a video's line-of-sight. Through spatial reasoning on the relative geometry of multiple video streams, FOCUS recognizes shared content even when viewed from diverse angles and distances. In a 70-volunteer user study, FOCUS' clustering correctness is roughly comparable to humans.

We present a demonstration of FOCUS [1], a system to appear in the SenSys 2013 main conference. FOCUS is a video-clustering service for live user video streams, indexed automatically and in realtime by shared content. FOCUS uniquely leverages visual, 3D model reconstruction and multimodal sensing to decipher and continuously track a video's line-of-sight. Through spatial reasoning on the relative geometry of multiple video streams, FOCUS recognizes shared content even when viewed from diverse angles and distances. We believe FOCUS can enable a new family of applications, such as instant replay, augmented reality, citizen journalism, security breach detection, and disaster assessment.

Deploying home access points (AP) is hard. Untrained users typically purchase, install, and configure a home AP with very little awareness of wireless signal coverage and complex interference conditions. We envision a future of autonomous wireless network management that uses the Internet as an enabling technology. By leveraging a P2P architecture over wired Internet connections, nearby APs can coordinate to manage their shared wireless spectrum, especially in the face of network-crippling faults. As a specific instance of this architecture, we build RxIP, a network diagnostic and recovery tool, initially targeted towards hidden terminal mitigation. Our stable, in-kernel implementation demonstrates that APs in real home settings can detect hidden interferers, and agree on a mutually beneficial channel access strategy. Consistent throughput and fairness gains with TCP traffic and in-home micro-mobility confirm the viability of the system. We believe that using RxIP to address other network deficiencies opens a rich area for further research, helping to ensure that smarter homes of the future embed smarter networks. In the near term, with the wireless and entertainment industries poised for home-centric wireless gadgets, RxIP-type home management systems will become increasingly relevant.

This paper attempts to solve the following problem: can a distant object be localized by looking at it through a smartphone. As an example use-case, while driving on a highway entering New York, we want to look at one of the skyscrapers through the smartphone camera, and compute its GPS location. While the problem would have been far more difficult five years back, the growing number of sensors on smartphones, combined with advances in computer vision, have opened up important opportunities. We harness these opportunities through a system called Object Positioning System (OPS) that achieves reasonable localization accuracy. Our core technique uses computer vision to create an approximate 3D structure of the object and camera, and applies mobile phone sensors to scale and rotate the structure to its absolute configuration. Then, by solving (nonlinear) optimizations on the residual (scaling and rotation) error, we ultimately estimate the object's GPS position.

We attempt to localize a distant object by looking at it through a smartphone. As an example use-case, while driving on a highway entering New York, we want to look at one of the skyscrapers through the smartphone camera, and compute its GPS location. While the problem would have been far more difficult five years back, the growing number of sensors on smartphones, combined with advances in computer vision, have opened up important opportunities. We harness these opportunities through a system called Object Positioning System (OPS) [1] that achieves reasonable localization accuracy. Our core technique uses computer vision to create an approximate 3D structure of the object and camera, and applies mobile phone sensors to scale and rotate the structure to its absolute configuration. Then, by solving (nonlinear) optimizations on the residual (scaling and rotation) error, we ultimately estimate the object's GPS position.

WiFi continues to be a prime source of energy consumption in mobile devices. This paper observes that, despite a rich body of research in WiFi energy management, there is room for improvement. Our key finding is that WiFi energy optimizations have conventionally been designed with a single AP in mind. However, network contention among different APs can dramatically increase a client's energy consumption. Each client may have to keep awake for long durations before its own AP gets a chance to send it packets to it. As AP density increases, the waiting time inflates, resulting in a proportional decrease in battery life. We design SleepWell, a system that achieves energy efficiency by evading network contention. The APs regulate the sleeping window of their clients in a way that different APs are active/inactive during nonoverlapping time windows. The solution is analogous to the common wisdom of going late to office and coming back late, thereby avoiding the rush hours. We implement SleepWell on a testbed of eight Laptops and nine Android phones, and evaluate it over a wide variety of scenarios and traffic patterns. Results show a median gain of up to 2x when WiFi links are strong; when links are weak and the network density is high, the gains can be even more.

Modern wireless interfaces support a physical-layer capability called Message in Message (MIM). Briefly, MIM allows a receiver to disengage from an ongoing reception and engage onto a stronger incoming signal. Links that otherwise conflict with each other can be made concurrent with MIM. However, the concurrency is not immediate and can be achieved only if conflicting links begin transmission in a specific order. The importance of link order is new in wireless research, motivating MIM-aware revisions to link-scheduling protocols. This paper identifies the opportunity in MIM-aware reordering, characterizes the optimal improvement in throughput, and designs a link-layer protocol for enterprise wireless LANs to achieve it. Testbed and simulation results confirm the performance gains of the proposed system.

Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.

WiFi continues to be a prime source of energy consumption in mobile devices. This paper observes that, despite a rich body of research in WiFi energy management, there is room for improvement. Our key finding is that WiFi energy optimizations have conventionally been designed with a single AP in mind. However, network contention among different APs can dramatically increase a client's energy consumption. Each client may have to keep awake for long durations before its own AP gets a chance to send packets to it. As the AP density increases in the vicinity, the waiting time inflates, resulting in a proportional decrease in battery life.We design SleepWell, a system that achieves energy efficiency by evading network contention. The APs regulate the sleeping window of their clients in a way that different APs are active/inactive during non-overlapping time windows. The solution is analogous to the common wisdom of going late to office and coming back late, thereby avoiding the rush hours. We implement SleepWell on a testbed of 8 Laptops and 9 Android phones, and evaluate it over a wide variety of scenarios and traffic patterns (YouTube, Pandora, FTP, Internet radio, and mixed). Results show a median gain of up to 2x when WiFi links are strong; when links are weak and the network density is high, the gains can be even more. We believe SleepWell is a desirable upgrade to WiFi systems, especially in light of increasing WiFi density.

WiFi continues to be a prime source of energy consumption in mobile devices. We observe that, despite a rich body of research in WiFi energy management, there is room for improvement. Our key finding is that WiFi energy optimizations have conventionally been designed with a single AP in mind. However, network contention among different APs can dramatically increase a client's energy consumption. Each client may have to keep awake for long durations before its own AP gets a chance to send packets to it. As the AP density increases in the vicinity, the waiting time inflates, resulting in a proportional decrease in battery life.

The nature of human mobility demands that mobile devices become agile to diverse operating environments. Coping with such diversity requires the device to assess its environment, and trigger appropriate responses to each of them. While existing communication subsystems rely on in-band wireless signals for context-assessment and response, we explore a lateral approach of using out-of-band sensor information. We propose a relatively novel framework that synthesizes in-band and out-of-band information, facilitating more informed communication decisions. We believe that further research in this direction could enable a new kind of device agility, deficient in today's communication systems. Since such a framework is located at the boundaries of mobile sensing and wireless communication, we call it sensor assisted wireless communication.

Conventional mobile social services such as Loopt and Google Latitude rely on two classes of trusted relationships: participants trust a centralized server to manage their location information and trust between users is based on existing social relationships. Unfortunately, these assumptions are not secure or general enough for many mobile social scenarios: centralized servers cannot always be relied upon to preserve data confidentiality, and users may want to use mobile social services to establish new relationships. To address these shortcomings, this paper describes SMILE, a privacy-preserving service in which the service provider is untrusted and users are not assumed to have pre-established social relationships with each other. At a high-level, SMILE uses short-range wireless communication and standard cryptographic primitives to mimic the behavior of users in existing missed-connections services such as Craigslist: trust is founded solely on anonymous users' ability to prove to each other that they shared an encounter in the past. We have evaluated SMILE using protocol analysis, an informal study of Craigslist usage, and experiments with a prototype implementation and found it to be both privacy-preserving and feasible.

Modern wireless interfaces support a physical layer capability called Message in Message (MIM). Briefly, MIM allows a receiver to disengage from an ongoing reception, and engage onto a stronger incoming signal. Links that otherwise conflict with each other, can be made concurrent with MIM. However, the concurrency is not immediate, and can be achieved only if conflicting links begin transmission in a specific order. The importance of link order is new in wireless research, motivating MIM-aware revisions to link scheduling protocols. This paper identifies the opportunity in MIM-aware reordering, characterizes the optimal improvement in throughput, and designs a link layer protocol to achieve it. Testbed results confirm the performance gains of the proposed system.