Justin Manweiler

Demo: avoiding the rush hours, wifi energy management via traffic isolation

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 clients 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.

Switchboard: a matchmaking system for multiplayer mobile games

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.

Demo: satellites in our pockets: an object positioning system using smartphones

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 objects GPS position. We have developed OPS on Android NexusS phones and experimented with localizing 50 objects in the Duke University campus. We believe that OPS shows promising results, enabling a variety of applications. Our ongoing work is focused on coping with large GPS errors, which proves to be the prime limitation of the current prototype.

Predicting length of stay at WiFi hotspots

Todays 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.

FOCUS: clustering crowdsourced videos by line-of-sight

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 videos 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. In the demonstration, we will show 325 video clips taken at Duke University Wallace Wade Stadium being processed in real-time via FOCUS pipeline. The recorded video clips contain one of three spots in the stadium: East Stand, Scoreboard, and West Stand. The demo shall be shown in the form of a web interface, first showing randomly clustered video clips. Later, on a button click, FOCUS shall process displayed videos in real-time, outputting clusters of videos, containing the common shared subject in each of them. For each successfully processed video clip in a cluster, we will further show similar clips from near, medium, and wide angle. To display performance and accuracy of FOCUS in indoor environments, a similar demonstration will be shown for an office space. FOCUS shall run on multi-node Hadoop cluster built on top of IBM SmartCloud platform.

Order matters: transmission reordering in wireless networks

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.

Avoiding the Rush Hours: WiFi Energy Management via Traffic Isolation

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 clients 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.

RxIP: Monitoring the health of home wireless networks

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.

Tracking drone orientation with multiple GPS receivers

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 drones 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.

Sensor assisted wireless communication

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 todays communication systems. Since such a framework is located at the boundaries of mobile sensing and wireless communication, we call it sensor assisted wireless communication.