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 http://robotics.stanford.edu/ba-colloquium/
Abstract
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.
