Quinn Jacobson

Virtual trip lines for distributed privacy-preserving traffic monitoring

Automotive traffic monitoring using probe vehicles with Global Positioning System receivers promises significant improvements in cost, coverage, and accuracy. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we propose a system based on virtual trip lines and an associated cloaking technique. Virtual trip lines are geographic markers that indicate where vehicles should provide location updates. These markers can be placed to avoid particularly privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus they facilitate the design of a distributed architecture, where no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GPS smartphone clients and conducted a controlled experiment with 20 phone-equipped drivers circling a highway segment. Results show that even with this low number of probe vehicles, travel time estimates can be provided with less than 15% error, and applying the cloaking techniques reduces travel time estimation accuracy by less than 5% compared to a standard periodic sampling approach.

ERSA: error resilient system architecture for probabilistic applications

There is a growing concern about the increasing vulnerability of future computing systems to errors in the underlying hardware. Traditional redundancy techniques are expensive for designing energy-efficient systems that are resilient to high error rates. We present Error Resilient System Architecture (ERSA), a low-cost robust system architecture for emerging killer probabilistic applications such as Recognition, Mining and Synthesis (RMS) applications. While resilience of such applications to errors in low-order bits of data is well-known, execution of such applications on error-prone hardware significantly degrades output quality (due to high-order bit errors and crashes). ERSA achieves high error resilience to high-order bit errors and control errors (in addition to low-order bit errors) using a judicious combination of 3 key ideas: (1) asymmetric reliability in many-core architectures, (2) error-resilient algorithms at the core of probabilistic applications, and (3) intelligent software optimizations. Error injection experiments on a multi-core ERSA hardware prototype demonstrate that, even at very high error rates of 20,000 errors/second/core or 2×10−4 error/cycle/core (with errors injected in architecturally-visible registers), ERSA maintains 90% or better accuracy of output results, together with minimal impact on execution time, for probabilistic applications such as K-Means clustering, LDPC decoding and Bayesian networks. Moreover, we demonstrate the effectiveness of ERSA in tolerating high rates of static memory errors that are characteristic of emerging challenges such as Vccmin problems and erratic bit errors. Using the concept of configurable reliability, ERSA platforms may also be adapted for general-purpose applications that are less resilient to errors (but at higher costs).

Enhancing Privacy and Accuracy in Probe Vehicle-Based Traffic Monitoring via Virtual Trip Lines

Traffic monitoring using probe vehicles with GPS receivers promises significant improvements in cost, coverage, and accuracy over dedicated infrastructure systems. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we describe a system based on virtual trip lines and an associated cloaking technique, followed by another system design in which we relax the privacy requirements to maximize the accuracy of real-time traffic estimation. We introduce virtual trip lines which are geographic markers that indicate where vehicles should provide speed updates. These markers are placed to avoid specific privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus, they facilitate the design of a distributed architecture, in which no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GPS smartphone clients and conducted a controlled experiment with 100 phone-equipped drivers circling a highway segment, which was later extended into a year-long public deployment.

Lagrangian sensing: traffic estimation with mobile devices

An inverse modeling algorithm is developed to reconstruct the state of traffic (velocity field) on highways from GPS measurements gathered from mobile phones traveling on-board vehicles. The algorithm is based on ensemble Kalman filtering (EnKF), to overcome the nonlinearity and non-differentiability of a distributed highway traffic model for velocity. The algorithm is implemented in an architecture which includes GPS enabled phones and a privacy aware data collection infrastructure based on the novel concept of Virtual Trip Lines (a technology developed by Nokia). The data collection infrastructure is connected to a traffic estimation server running the EnKF algorithm online, and the estimation results are broadcast in real time back to mobile phones and to the internet. Results from the algorithm are presented using data collected during the February 8, 2008 Mobile Century experiment, in which a shock wave from a five-car accident is captured. A prototype estimation algorithm and system were run during the experiment, and highlight that measurements from as few as 2% to 5% of the commuting public are sufficient to accurately reconstruct the highway traffic state.

MARS: a muscle activity recognition system enabling self-configuring musculoskeletal sensor networks

Poor posture and incorrect muscle usage are a leading cause of many injuries in sports and fitness. For this reason, non-invasive, fine-grained sensing and monitoring of human motion and muscles is important for mitigating injury and improving fitness efficacy. Current sensing systems either depend on invasive techniques or unscalable approaches whose accuracy is highly dependent on body sensor placement. As a result these systems are not suitable for use in active sports or fitness training where sensing needs to be scalable, accurate and un-inhibitive to the activity being performed. We present MARS, a system that detects both body motion and individual muscle group activity during physical human activity by only using unobtrusive, non-invasive inertial sensors. MARS not only accurately senses and recreates human motion down to the muscles, but also allows for fast personalized system setup by determining the individual identities of the instrumented muscles, obtained with minimal system training. In a real world human study conducted to evaluate MARS, the system achieves greater than 95% accuracy in identifying muscle groups.