Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed state representation for tracking problems in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
EESR '05 Proceedings of the 2005 workshop on End-to-end, sense-and-respond systems, applications and services
Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in sensor networks
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Tracking Dynamics Using Sensor Networks: Some Recurring Themes
ICDCN '09 Proceedings of the 10th International Conference on Distributed Computing and Networking
Pervasive and Mobile Computing
On detection and tracking of variant phenomena clouds
ACM Transactions on Sensor Networks (TOSN)
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We examine the problem of tracking dynamic boundaries occurring in natural phenomena using range sensors. 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 a regression-based spatial estimation technique to determine discrete points on the boundary and estimates a confidence band around the entire boundary. In addition, a Kalman Filter-based temporal estimation technique is used to selectively refresh the estimated boundary to meet the accuracy requirements. Our algorithm for dynamic boundary tracking (DBTR) combines temporal estimation with an aperiodically updated spatial estimation and provides a low overhead solution to track boundaries without requiring prior knowledge about the dynamics of the boundary. Experimental results demonstrate the effectiveness of our algorithm and estimated confidence bands achieve loss of coverage of less than 2 - 5% for a variety of boundaries with different spatial characteristics.