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Broad Area Colloquium for Artificial Intelligence,
Geometry, Graphics, Robotics and Vision

Collaborative signal and information processing in microsensor networks

Feng Zhao
Xerox Palo Alto Research Center

Monday, December 3, 2001, 4:15PM
Gates B01


Collaborative signal and information processing (CSIP) in distributed sensor networks is an emerging research area, drawing upon traditionally disparate disciplines such as lower-power communication and computation, space-time signal processing, distributed and fault tolerant algorithms, adaptive systems, and sensor fusion and decision theory.

Recent advances in wireless networking, microfabrication (e.g. MEMS), and embedded processing have enabled a new generation of sensor networks for a wide range of tracking and identification problems in both civilian and military applications, including human-aware environments, intelligent transportation grids, factory condition-based monitoring and maintenance, and battlefield situational awareness. However, unlike centralized sensor platforms, distributed sensor nets are characterized by limited battery power, frequent node attrition, and variable data and communication quality. To scale up to more realistic tracking and classification applications involving tens of thousands of sensors, heterogeneous sensing modalities, multiple targets, and non-uniform spatio-temporal scales, these systems have to rely primarily on collaboration among distributed sensors to significantly improve tracking accuracy and reduce detection latency.

The Xerox PARC Collaborative Sensing Project has taken a systemic approach to address key CSIP issues of representing, processing, storing, and querying spatially distributed, multi-modal information from a sensor field. To extract reliable and timely information from a sensor field, CSIP must suport cross-node data aggregation, asynchronous execution, and progressive accuracy. We have developed information driven sensor querying (IDSQ) as a mechanism for mediating between data and queries in the network. IDSQ dynamically tasks sensor nodes to maximize information gain while minimizing latency and bandwidth consumption, thereby realizing energy-aware and low-latency computation and communication. In this talk, I will describe the theory, algorithms, as well as experimental results from a recent DARPA field test and from a testbed of electro-mechanical machine diagnosis.

About the Speaker

Feng Zhao is a Principal Scientist in the Systems and Practices Laboratory at Xerox PARC. Dr. Zhao leads the Collaborative Sensing and Smart Matter Diagnostics Projects that investigate how MEMS sensor and networking technology can change the way we build and interact with physical devices and environments. His research interest includes distributed sensor data processing, diagnostics, qualitative reasoning, and control of dynamical systems.

Dr. Zhao received his PhD in Electrical Engineering and Computer Science from MIT in 1992, where he developed one of the first algorithms for fast N-body computation in three spatial dimensions and for phase-space nonlinear control synthesis. From 1992 to 1999, he was Assistant and Associate Professor of Computer and Information Science at Ohio State University. His INSIGHT Group developed the SAL software tool for rapid prototyping of spatio-temporal data analysis applications; the tool is being used by a number of other research groups. Currently, he is also a Consulting Associate Professor of Computer Science at Stanford.

Dr. Zhao was National Science Foundation and Office of Naval Research Young Investigators, and an Alfred P. Sloan Research Fellow in Computer Science. He has authored or co-authored over 60 peer-reviewed technical papers in the areas of sensor networks, artificial intelligence, nonlinear control, and programming tools.


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