In this work, we offer a novel system, MicroStation (μStation) that allows ubiquitous data broadcasting applications using the FM radio on mobile devices such as smartphones. μStation includes two key modules to enable data broadcasting based on existing mobile FM radio hardware. Channel Selector assigns different FM channels to neighboring μStation broadcasters to avoid collision and guides μStation listeners to find their broadcasting of interest. Data Codec realizes bit-level communication between mobile devices through existing FM radio hardware. We describe an implementation of μStation on the Nokia N900 smartphone, and provide low-level APIs and services to support application development. We also demonstrate two representative applications: Facebook-FM and Sync-Flash. These applications demonstrate the capability of μStation to readily enable a new class of ubiquitous data broadcasting applications on mobile devices.

We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.

The explosion in content availability, coupled with the diversity in consumption devices, has fragmented the market. Users need new mechanisms for content discovery and navigation, while providers and advertisers need new ways to analyze and monetize usage. In this paper, we advocate 'social bookmarking' as a solution, given its proven utility in enabling affinity-based discovery online. But, adapting such web-centric ideas to rich media consumption raises technical and business challenges. With TV Clips, we introduce a system that tackles these concerns for broadcast television with extensibility to 3-screen usage. We describe the initial architecture and prototype, and discuss its evolution to support product-centric requirements like scalability and rights management. A secondary goal is to share our experiences in developing this prototype for a commercial environment (i.e., TV) and motivate further research in adapting 'social search' strategies to such media-rich domains.

In this paper, we consider privacy challenges in event-driven pervasive spaces where multimedia streams captured by sensors embedded in the infrastructure are used to detect a variety of application-specific media events. In particular, we develop techniques to detect events without disclosing any identifying information unless necessary. We characterize the nature of inference channels that arise and model privacy preserving event detection as an optimization problem that attempts to balance disclosure with performance. We design and test efficient communication protocols that realize this tradeoff.

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.

Around the world as both crime and technology become more prevalent, officials find themselves relying more and more on video surveillance as a cure-all in the name of public safety. Used properly, video cameras help expose wrongdoing but typically come at the cost of privacy to those not involved in any maleficent activity. What if we could design intelligent systems that are more selective in what video they capture, and focus on anomalous events while protecting the privacy of authorized personnel? This paper proposes a novel way of combining sensor technology with traditional video surveillance in building a privacy protecting framework that exploits the strengths of these modalities and complements their individual limitations. Our fully functional system utilizes off the shelf sensor hardware (i.e. RFID, motion detection) for localization, and combines this with a XML-based policy framework for access control to determine violations within the space. This information is fused with video surveillance streams in order to make decisions about how to display the individuals being surveilled. To achieve this, we have implemented several video masking techniques that correspond to varying user privacy levels. These results were achievable in real-time at acceptable frame rates, while meeting our requirements for privacy preservation.

No abstract available.

With the evolution of the global information infrastructure, service providers will need to provide effective and adaptive resource management mechanisms that can serve more concurrent clients and deal with applications that exhibit quality-of-service (QoS) requirements. Flexible, scalable and customizable middleware can be used as an enabling technology for next-generation systems that adhere to the QoS requirements of applications that execute in highly dynamic distributed environments. To enable application-aware resource management, we are developing a customizable and composable middleware framework called CompOSE|Q (Composable Open Software Environment with QoS), based on a reflective meta-model. In this paper, we describe the architecture and runtime environment for CompOSE|Q and briefly assess the performance overhead of the additional flexibility. We also illustrate how flexible communication mechanisms can be supported efficiently in the CompOSE|Q framework.