Distributed data processing in wireless sensor networks based on artificial neural-networks algorithms
Date Issued
2005-06-27
Author(s)
Abstract
Most of the current in-network data processing
algorithms are modified regression techniques like
multidimensional data series analysis. In our opinion,
several algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless
sensor network platforms and will meet the requirements
for sensor networks like: simple parallel-distributed
computation, distributed storage, data robustness and
auto-classification of sensor readings. Lower
communication costs and energy savings can be obtained
as a consequence of the dimensionality reduction achieved
by the neural-networks clustering algorithms,
In this paper we will present three possible
implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning
methods for categorization of the sensory inputs. They are
tested on a data obtained from a set of several motes,
equipped with several sensors each. Results from
simulations of deliberately made faulty sensors show the
data robustness of these architectures.
algorithms are modified regression techniques like
multidimensional data series analysis. In our opinion,
several algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless
sensor network platforms and will meet the requirements
for sensor networks like: simple parallel-distributed
computation, distributed storage, data robustness and
auto-classification of sensor readings. Lower
communication costs and energy savings can be obtained
as a consequence of the dimensionality reduction achieved
by the neural-networks clustering algorithms,
In this paper we will present three possible
implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning
methods for categorization of the sensory inputs. They are
tested on a data obtained from a set of several motes,
equipped with several sensors each. Results from
simulations of deliberately made faulty sensors show the
data robustness of these architectures.
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