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- W2395804702 abstract "This contribution reviews some recent advances in the field of nearest-neighbor (NN) nonparametric estimation in sensor networks. Upon observing X0, the problem is to estimate the corresponding response variable Y0 by using the knowledge contained in a training set {(Xi, Yi)}i=1, made of n independent copies of (X0, Y0). In the distributed version of the problem, a network made of spatially distributed sensors and a common fusion center (FC) is considered. As X0 is made available at the FC, it is broadcast to all the sensors. Relying upon the locally available pair (Xi, Yi) and upon X0, sensor i sends a message containing Yi to the FC, or stays silent: only the few most informative response variables {Yi} should be sent, but no inter-sensor coordination is allowed. The analysis is asymptotic in the limit of large network size n and we show that, by means of a suitable ordered transmission policy, only a vanishing fraction of NN messages can be selected, yet preserving the consistency of the estimation even under communication constraints." @default.
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- W2395804702 date "2014-11-13" @default.
- W2395804702 modified "2023-09-23" @default.
- W2395804702 title "Nearest-neighbor estimation in sensor networks" @default.
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