Intelligent wireless sensor networks using fuzzyart neural-networks
Date Issued
2007-07-01
Author(s)
Abstract
An adaptation of one popular model of neuralnetworks algorithm (ART model) in the field of
wireless sensor networks is demonstrated in this paper.
The important advantages of the ART class algorithms
such as simple parallel distributed computation,
distributed storage, data robustness and autoclassification of sensor readings are confirmed within
the proposed architecture consisting of one
clusterhead which collects only classified input data
from the other units.
This architecture provides a high dimensionality
reduction and additional communication savings, since
only identification numbers of the classified input data
are passed to the clusterhead instead of the whole
input samples.
We have adapted and implemented the FuzzyART
neural-network algorithm and used it for initial
clustering of the sensor data as a sort of pattern
recognition. This adaptation was made specifically for
MicaZ sensor motes by solving mainly problems
concerning the small memory capacity ofthe motes. At
the final clusterhead - server, the data are stored in a
database and the results of the data processing are
continuously presented in a classification graph.
wireless sensor networks is demonstrated in this paper.
The important advantages of the ART class algorithms
such as simple parallel distributed computation,
distributed storage, data robustness and autoclassification of sensor readings are confirmed within
the proposed architecture consisting of one
clusterhead which collects only classified input data
from the other units.
This architecture provides a high dimensionality
reduction and additional communication savings, since
only identification numbers of the classified input data
are passed to the clusterhead instead of the whole
input samples.
We have adapted and implemented the FuzzyART
neural-network algorithm and used it for initial
clustering of the sensor data as a sort of pattern
recognition. This adaptation was made specifically for
MicaZ sensor motes by solving mainly problems
concerning the small memory capacity ofthe motes. At
the final clusterhead - server, the data are stored in a
database and the results of the data processing are
continuously presented in a classification graph.
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