Computational geometry: an introduction
Computational geometry: an introduction
Qualitative Representation of Change
COSIT '97 Proceedings of the International Conference on Spatial Information Theory: A Theoretical Basis for GIS
Monitoring dynamic spatial fields using responsive geosensor networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Sweeps over wireless sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Transforming Agriculture through Pervasive Wireless Sensor Networks
IEEE Pervasive Computing
Gradient Boundary Detection for Time Series Snapshot Construction in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Effect of Neighborhood on In-Network Processing in Sensor Networks
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Detecting basic topological changes in sensor networks by local aggregation
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Event-based topology for dynamic planar areal objects
International Journal of Geographical Information Science
Modeling geospatial events and impacts through qualitative change
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
A unified framework for decentralized reasoning about gradual changes in topological relations
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
| Hi-index | 0.00 |
Wireless sensor networks are deployed to monitor dynamic geographic phenomena, or objects, over space and time. This paper presents a new spatiotemporal data model for dynamic areal objects in sensor networks. Our model supports for the first time the analysis of change in sequences of snapshots that are captured by different granularity of observations, and our model allows both incremental and nonincremental changes. This paper focuses on detecting qualitative spatial changes, such as merge and split of areal objects. A decentralized algorithm is developed, such that spatial changes can be efficiently detected by in-network aggregation of decentralized datasets.