Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms
Date Issued
2005-04-04
Author(s)
Abstract
Some of the algorithms developed within the
artificial neural-networks 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
and data robustness. As a result of the dimensionality
reduction obtained simply from the outputs of the
neural-networks clustering algorithms, lower
communication costs and energy savings can also be
obtained.
In this paper we will present two 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 purposefully faulty sensors show
the data robustness of these architectures. The
proposed neural-networks classifiers have distributed
short and long-term memory of the sensory inputs and
can function as security alert when unusual sensor
inputs are detected.
artificial neural-networks 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
and data robustness. As a result of the dimensionality
reduction obtained simply from the outputs of the
neural-networks clustering algorithms, lower
communication costs and energy savings can also be
obtained.
In this paper we will present two 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 purposefully faulty sensors show
the data robustness of these architectures. The
proposed neural-networks classifiers have distributed
short and long-term memory of the sensory inputs and
can function as security alert when unusual sensor
inputs are detected.
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