Optimizing Sensor Networks in the Energy-Latency-Density Design Space
IEEE Transactions on Mobile Computing
Lightweight sensing and communication protocols for target enumeration and aggregation
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Poster abstract: cooperative tracking with binary-detection sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Power conservation and quality of surveillance in target tracking sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
A MAC Protocol to Reduce Sensor Network Energy Consumption Using a Wakeup Radio
IEEE Transactions on Mobile Computing
Coordinated sensor deployment for improving secure communications and sensing coverage
Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks
Contour estimation using collaborating mobile sensors
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
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We study the problem of target tracking and boundary detection of a substance diffusing from a mobile source using a wireless sensor network. We present a Prediction-based Mobility Adaptive Tracking (P-MAT algorithm to study the tradeoff among energy, accuracy of tracking, coverage and boundary estimation. P-MAT minimises overall energy consumption by incorporating adaptivity in two forms: (1) the size of the active region and (2) modulation of the sampling rate. It uses adaptive Kalman filtering to predict the target's future location and velocity. The predicted target location determines a set of sensors surrounding that location to be activated known as the active region. Sensors in the active region are responsible for target tracking and boundary detection. In this article, we include dynamic boundary estimation. Boundary estimation in many situations can be performed efficiently using a subset of nodes within the vicinity of the phenomenon. This subset of nodes in our algorithm is the set of nodes in the active region. As the substance spreads, sensors in the active region determine if additional sensors outside of the active region are needed to enclose the boundary. Results from simulation experiments show that P-MAT can perform both tracking and boundary.