On active contour models and balloons
CVGIP: Image Understanding
Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in 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
Contour maps: monitoring and diagnosis in sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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
Tracking Dynamics Using Sensor Networks: Some Recurring Themes
ICDCN '09 Proceedings of the 10th International Conference on Distributed Computing and Networking
Experimental validation of cooperative environmental boundary tracking with on-board sensors
ACC'09 Proceedings of the 2009 conference on American Control Conference
In-network data acquisition and replication in mobile sensor networks
Distributed and Parallel Databases
Accuracy-aware aquatic diffusion process profiling using robotic sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Pervasive and Mobile Computing
Method for tracking of environmental level sets by a unicycle-like vehicle
Automatica (Journal of IFAC)
Active learning for level set estimation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
This paper focuses on the use of mobile sensors to estimate contours in a field. In particular, we focus onstrategies to estimate the contour with minimum latency and maximum precision. We propose a novel algorithm, ACE (Adaptive Contour Estimation), that (a) estimates and exploits information regarding the gradients in the field to move towards the contour and (b) uses a spread component to surround the contour in order to optimize latency. While it is possible for sensors to spread as they approach the contour, it is crucial to judiciously determine when and how much to spread. Spreading too early or too much may result in increasing the latency or affecting the precision. ACE dynamically makes this decision using local sensor measurements, history of measurements as well as collaboration between sensors while adapting to different types of deployment, distance from the contour and shapes of the contour. We demonstrate that ACE, in the absence of energy constraints precisely determines the contour with a lower latency than when only gradients are used for movement or when the sensors spread out right from the start of estimation. Additionally, we show that ACE significantly improves precision of contour estimation in the presence of energy constraints. We also demonstrate a proof of concept implementation on a mobile robot testbed.