This article presents a scalable algorithm for managing property information about moving objects tracked by a sensor network. Property information is obtained via distributed sensor observations, but will be corrupted when objects mix up with each other. The association between properties and objects then becomes ambiguous. We build a novel representation framework, exploiting an overcomplete Radon basis dictionary to model property uncertainty in such circumstances. By making use of the combinatorial structure of the basis design and sparse representations we can efficiently approximate the underlying probability distribution of the association between target properties and tracks, overcoming the exponential space that would otherwise be required. Based on the proposed theories, we design a fully distributed algorithm on wireless sensor networks. We conduct comparative simulations and the results validate the effectiveness of our approach.

We demonstrate that the network flux over the sensor network provides fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to abstract the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Nonlinear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we show that it is easy to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. Our study indicates that most of existing systems are vulnerable to such attack against the privacy of mobile users. We further propose a set of countermeasures that redistribute and reshape the network traffic to preserve the location privacy of mobile users. With a trace driven simulation, we demonstrate the substantial threats of the attacks and the effectiveness of the proposed countermeasures.

Motivated by the increasing availability of large collections of noisy GPS traces, we present a new data-driven framework for smoothing trajectory data. The framework, which can be viewed of as a generalization of the classical moving average technique, naturally leads to efficient algorithms for various smoothing objectives. We analyze an algorithm based on this framework and provide connections to previous smoothing techniques. We implement a variation of the algorithm to smooth an entire collection of trajectories and show that it performs well on both synthetic data and massive collections of GPS traces.

In this work, we propose to use attributes and parts for recognizing human actions in still images. We define action attributes as the verbs that describe the properties of human actions, while the parts of actions are objects and poselets that are closely related to the actions. We jointly model the attributes and parts by learning a set of sparse bases that are shown to carry much semantic meaning. Then, the attributes and parts of an action image can be reconstructed from sparse coefficients with respect to the learned bases. This dual sparsity provides theoretical guarantee of our bases learning and feature reconstruction approach. On the PASCAL action dataset and a new “Stanford 40 Actions” dataset, we show that our method extracts meaningful high-order interactions between attributes and parts in human actions while achieving state-of-the-art classification performance.

We demonstrate that the network flux over the sensor network provides us fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Non-linear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we are able to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. A particular advantage of this approach is that compared to the vast information we can reveal the required knowledge is extremely cheap. As all fingerprint information comes from the network flux that is public under current wireless communication medium, our study indicates that most of existing systems are vulnerable in protecting the privacy of mobile users.

This poster introduces a new distributed caching algorithm used by the installable Celerra MPFS file system client to allow streaming high throughput High-Definition video streams. Large disk latencies in high throughput streaming result in a high video frame drop rate. Dropped frames rate increases with the video throughput. The algorithm proposed in this paper uses new caching ideas inspired by signal processing techniques to address dropped frames. Our techniques remove the dropped frames effect entirely, while optimizing the aggregate throughput of the storage array.

We have proposed efficient stream scheduling algorithms for multimedia storage servers that are providers of variable bit rate media streams. We have developed three types of stream scheduling algorithms: In-Order Scheduling Algorithm (IOSA), Out-of-Order Scheduling Algorithm (OOSA), and Dynamic Merge Scheduling Algorithm (DMSA). In the IOSA scheme, media blocks must be transmitted according to their natural order. In the OOSA scheme, in-order transmission is not mandated and thus results in an out-of-order transmission. In the DMSA scheme, the requests that are possible to be merged care merged together at first. Then the OOSA scheduling scheme is used for the merged requests. The performance evaluations done through simulations show that the maximum bandwidth requirement, the fetch ahead distance, and the coefficient of variation for the bandwidth requirement are improved by using the IOSA, OOSA, and DMSA algorithms.