Name
Abstract | ||
|---|---|---|
Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, in-network data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the application of value fusion (for clusters of nodes with correlated measurements) and decision fusion (combining the local decisions of the clusters) is a common strategy. In this work, we propose an algorithm for properly selecting the groups of nodes with correlated measurements. Experiments show that our algorithm is 30% better than a solution that considers only the spatial coherence regions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IJCNN.2012.6252430 | Neural Networks |
Keywords | Field | DocType |
image classification,sensor fusion,target tracking,wireless sensor networks,data correlation,in-network data fusion,sensor nodes,similarity clustering,spatial coherence regions,target classification,target tracking,wireless sensor networks | Data mining,Computer science,Soft sensor,Visual sensor network,Artificial intelligence,Cluster analysis,k-means clustering,Key distribution in wireless sensor networks,Pattern recognition,Sensor fusion,Mobile wireless sensor network,Wireless sensor network,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 E-ISBN : 978-1-4673-1489-3 | 978-1-4673-1489-3 | 8 |
PageRank | References | Authors |
1.01 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afonso D. Ribas | 1 | 8 | 1.01 |
| Juan Gabriel Colonna | 2 | 38 | 5.19 |
| Carlos Mauricio S. Figueiredo | 3 | 9 | 1.38 |
| Eduardo Freire Nakamura | 4 | 320 | 31.97 |