Publication Type:

Journal Article


IEEE Transactions on Parallel and Distributed Systems, IEEE, Volume 22, Number 10, p.1766-1774 (2011)



accuracy, boundary estimation, Distance measurement, distributed applications., dynamic boundary tracking, Estimation, estimation theory, Heuristic algorithms, Kalman filter, Kalman filtering, Kalman filters, Laser Radar, noise, nonparametric statistics, range sensor, Regression analysis, regression-based spatial estimation, sensor network, Sensor networks, Sensors, temporal estimation, Tracking, Wireless sensor networks


We examine the problem of tracking dynamic boundaries occurring in natural phenomena using a network of range sensors. Two main challenges of the boundary tracking problem are accurate boundary estimation from noisy observations and continuous tracking of the boundary. We propose Dynamic Boundary Tracking (DBTR), an algorithm that combines the spatial estimation and temporal estimation techniques. The regression-based spatial estimation technique determines discrete points on the boundary and estimates a confidence band around the entire boundary. In addition, a Kalman Filter-based temporal estimation technique tracks changes in the boundary and aperiodically updates the spatial estimate to meet accuracy requirements. DBTR provides a low energy solution compared to similar periodic update techniques to track boundaries without requiring prior knowledge about the dynamics. Experimental results demonstrate the effectiveness of our algorithm; estimated confidence bands indicate a loss of coverage of less than 2 to 5 percent for a variety of boundaries with different spatial characteristics.

Cite this Research Publication

S. Duttagupta, Ramamritham, K., and Kulkarni, P., “Tracking Dynamic Boundaries Using Sensor Network”, IEEE Transactions on Parallel and Distributed Systems, vol. 22, pp. 1766-1774, 2011.