On active contour models and balloons
CVGIP: Image Understanding
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Ef.cient Continuous Mapping in Sensor Networks Using Isolines
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Geographic routing made practical
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Iso-Map: Energy-Efficient Contour Mapping in Wireless Sensor Networks
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Preliminaries for Topological Change Detection Using Sensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Multi-dimensional phenomenon-aware stream query processing
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient data collection and event boundary detection in wireless sensor networks using tiny models
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Efficient tracking of 2D objects with spatiotemporal properties in wireless sensor networks
Distributed and Parallel Databases
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Geosensor networks are deployed to detect, monitor and track continuous environmental phenomena such as toxic clouds or dense areas of air pollution in an urban environment. In this paper, we abstract such continuous phenomena as 2D objects and only consider their boundary using wireless sensor networks to monitor them over time. In order to maximize energy-efficient monitoring of the phenomena, we present an in-network algorithm based on the concept of deformable curves to incrementally track spatiotemporal changes of the object. We show that the in-network incremental boundary tracking approach based on deformable curves collects sufficient information efficiently to track the overall spatiotemporal properties about a 2D object. By simulations, we demonstrate the energy-efficiency of our approach.