Paper Info
Title
Optimal sensor selection in binary heterogeneous sensor networks
Abstract
We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric KuUback-Leibler divergence, or J-divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is "winner takes all" like. The sensor class with the best performance/cost ratio is selected.
Year
DOI
Venue
2009
10.1109/TSP.2009.2012902
IEEE Transactions on Signal Processing
Keywords
Field
DocType
maximization problem,different discrimination performance,binary sensor,sensor selection,conditional distribution,optimal hypothesis test,sensor class,heterogeneous sensor network,optimal proportion,binary heterogeneous sensor network,best performance,optimal sensor selection,wireless sensor networks,sensor networks,hypothesis test,testing,linearity,sensor network,sensor fusion,kullback leibler divergence,cost function,helium
Mathematical optimization,Conditional probability distribution,Sensor array,Sensor fusion,Fusion center,Winner-take-all,Wireless sensor network,Maximization,Mathematics,Statistical hypothesis testing
Journal
Volume
Issue
ISSN
57
4
1053-587X
Citations 
PageRank 
References 
10
0.63
25
Authors
3
Name
Order
Citations
PageRank
Marcelino Lázaro17811.34
Matilde Sánchez Fernández28312.25
Antonio Artés-Rodríguez320634.76