Intelligent data aggregation in sensor networks using artificial neural-networks algorithms
Journal
Technical Proc. of the NSTI Nanotechnology Conference and Trade Show
Date Issued
2005
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, data robustness and autoclassification of sensor readings. 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 the paper we will propose three different kinds of
architectures for incorporating the ART and FuzzyART
artificial neural networks into the small Smart-It units’
network. We will also give some results of the
classifications of real-world data obtained with a sensor
network of 5 Smart-It units, each equipped with 6 different
types of sensors. We will also give results from the
simulations where we have purposefully made one of the
input sensors malfunctioning, giving zero or random signal,
in order to show the data robustness of our approach.
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, data robustness and autoclassification of sensor readings. 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 the paper we will propose three different kinds of
architectures for incorporating the ART and FuzzyART
artificial neural networks into the small Smart-It units’
network. We will also give some results of the
classifications of real-world data obtained with a sensor
network of 5 Smart-It units, each equipped with 6 different
types of sensors. We will also give results from the
simulations where we have purposefully made one of the
input sensors malfunctioning, giving zero or random signal,
in order to show the data robustness of our approach.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
Intelligent_data_aggregation_in_sensor_n20170520-3737-l61jtj-with-cover-page-v2.pdf
Size
833.94 KB
Format
Adobe PDF
Checksum
(MD5):eb5010ca032a67e84154af6c61db0a0e