Tracking Dynamic Boundaries: Sensors to the Rescue
Speaker: Prof Krithi Ramamritham, IIT Bombay, India
When: 2007-05-22 14:00:00
Venue: 78-420
Host: Prof Xiaofang Zhou
Abstract:Large scale sensor networks are being deployed for realtime monitoring
applications, such as detecting leakage of hazardous material, tracking
forest fires or environmental monitoring. Consider a forest fire monitoring
application that involves knowing the exact region affected by the fire.
Continuous update regarding the spread of the fire, its direction distance
from habitats is required to expedite preventative measures. As a fire
divides the forest field into two regions, one that is affected by the fire
and the one that is not, these regions can be considered to be delineated by
a boundary. Sensor networks are an apt solution to address the problem of
tracking dynamic boundaries. Strategically deployed sensors can operate
unattended (minimizing risk to human life due to the fire) and provide
continuous monitoring for rapid detection and boundary estimation of fires.
In this talk, we examine the problem of tracking dynamic boundaries
occurring in natural phenomena using sensor networks. Two main challenges of
this problem are energy-efficient estimations from noisy observations and
continuous tracking of the boundary. We propose a novel approach which uses
discrete estimations of points on the boundary based on a regression model
and a smoothed-interpolation scheme to estimate the entire boundary with
high confidence. Remotely placed sensor nodes produce noisy measurements of
various points on the boundary using range-sensing technique. A Kalman
Filter is augmented to the basic boundary estimation approach to selectively
refresh the estimated boundary at a point only if it is predicted to move
out of the previous estimated intervals at that point. The combination of
Kalman Filter based temporal estimation with an aperiodically updated
regression-based spatial estimation, allows us to provide a low overhead
solution to track dynamic boundaries. Our experimental results demonstrate
that DBTR is an efficient algorithm for tracking dynamic boundary that does
not require prior knowledge about the dynamics of the same. In addition, the
confidence band obtained from estimates at a selected few locations provides
loss of coverage less than 2%.
Biography:Krithi Ramamritham received the Ph.D. in Computer Science from the
University of Utah. Currently he is a Chair Professor in Computer Science
and Engineering and Dean of Research and Development at IIT Bombay. He is a
leading researcher in timeliness and consistency issues in computer systems,
in particular, databases, real-time systems, and distributed applications.
His recent work addresses these issues in the context of Dynamic Data in
sensor networks, embedded systems, mobile environments, and the Web. During
the last few years, he has also devoted his time in the use of Information
and Communication Technologies for creating tools aimed at socio-economic
development. He is a board member of VLDB Foundation and ACM SIGMOD. He is
on the editorial board of Real-Time Systems (Editor-in-Chief), IEEE
Transactions on Mobile Computing, Distributed and Parallel Databases, World
Wide Web: Internet and Web Information Systems, IEEE Transactions on
Knowledge and Data Engineering, IEEE Internet Computing, IEEE Transactions
on Parallel and Distributed Systems (1997 - 2000), and The VLDB Journal. He
is a Fellow of the Indian National Academy of Engineering, Fellow ACM, and
Fellow IEEE.
Type: DKE
Contact:Prof Xiaofang Zhou, seminar host (zxf@itee.uq.edu.au)
or Guido Governatori (ITEE seminar co-ordinator)
(guido@itee.uq.edu.au)