We examine the problem of tracking dynamic boundaries occurring in natural phenomena using sensor networks. Remotely
placed sensor nodes produce noisy measurements of various points on the boundary using range-sensing. Two main challenges
of the boundary tracking problem are energy-efficient boundary estimations from noisy observations and continuous tracking of
the boundary. We propose a novel approach which uses discrete estimations of points on the boundary using a regression-based
spatial estimation technique and a smoothing interpolation scheme to estimate a confidence band around the entire boundary. In
addition, a Kalman Filter-based temporal estimation is used to help selectively refresh the estimated boundary at a point only
if the boundary is predicted to move out of the previous estimated intervals at that point. An algorithm for dynamic boundary
tracking (DBTR), the combination of temporal estimation with an aperiodically updated spatial estimation, allows us to provide
a low overhead solution to track dynamic boundaries that does not require prior knowledge about the nature of the dynamics.
Experimental results demonstrate the effectiveness of our algorithm and estimated confidence bands achieve loss of coverage of
less than 2% for smooth boundaries.
S. Duttagupta, Ramamritham, K., and Kulkarni, P., “Tracking Dynamic Boundary Fronts Using Range Sensors”, The fifth European Conference on Wireless Sensor Networks. Springer Berlin/Heidelberg, Bologna, Italy, pp. 125-140, 2008.