Data prediction in WSN using variable step size LMS algorithm
Journal
Proceedings of the 5th International Conference on Sensor Technologies and Applications
Date Issued
2011-08
Author(s)
Risteska Stojkoska, Biljana
Solev, Dimitar
Abstract
Wireless communication itself consumes the most
amount of energy in a given WSN, so the most logical way to
reduce the energy consumption is to reduce the number of
radio transmissions. To address this issue, there have been
developed data reduction strategies which reduce the amount of
sent data by predicting the measured values both at the source
and the sink, requiring transmission only if a certain reading
differ by a given margin from the predicted values. While these
strategies often provide great reduction in power consumption,
they need a-priori knowledge of the explored domain in order
to correctly model the expected values. Using a widely known
mathematical apparatus called the Least Mean Square
Algorithm (LMS), it is possible to get great energy savings
while eliminating the need of former knowledge or any kind of
modeling. In this paper with we use the Least Mean Square
Algorithm with variable step size (LMS-VSS) parameter. By
applying this algorithm on real-world data set with different
WSN topologies, we achieved maximum data reduction of over
95%, while retaining a reasonably high precision.
amount of energy in a given WSN, so the most logical way to
reduce the energy consumption is to reduce the number of
radio transmissions. To address this issue, there have been
developed data reduction strategies which reduce the amount of
sent data by predicting the measured values both at the source
and the sink, requiring transmission only if a certain reading
differ by a given margin from the predicted values. While these
strategies often provide great reduction in power consumption,
they need a-priori knowledge of the explored domain in order
to correctly model the expected values. Using a widely known
mathematical apparatus called the Least Mean Square
Algorithm (LMS), it is possible to get great energy savings
while eliminating the need of former knowledge or any kind of
modeling. In this paper with we use the Least Mean Square
Algorithm with variable step size (LMS-VSS) parameter. By
applying this algorithm on real-world data set with different
WSN topologies, we achieved maximum data reduction of over
95%, while retaining a reasonably high precision.
Subjects
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