Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks [Abdelgawad & Bayumi 2012-02-18].pdf

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Lecture Notes in Electrical Engineering
Volume 118
For further volumes:
http://www.springer.com/series/7818
Ahmed Abdelgawad
l
Magdy Bayoumi
Resource-Aware Data Fusion
Algorithms for Wireless
Sensor Networks
Ahmed Abdelgawad
54 Lavoie Drive
Essex Junction
VT 05452, USA
ama1916@cacs.louisiana.edu
Magdy Bayoumi
University of Louisiana
at Lafayette
Lafayette, Louisiana, USA
mab@cacs.louisiana.edu
ISSN 1876-1100
e-ISSN 1876-1119
ISBN 978-1-4614-1349-3
e-ISBN 978-1-4614-1350-9
DOI 10.1007/978-1-4614-1350-9
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2012930002
#
Springer Science+Business Media, LLC 2012
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The use in this publication of trade names, trademarks, service marks, and similar terms, even if
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Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
Preface
WSN (Wireless Sensors Networks) is intended to be deployed in environments
where sensors can be exposed to circumstances that might interfere with measure-
ments provided. Such circumstances include strong variations of pressure, temper-
ature, radiation, and electromagnetic noise. Thus, measurements may be imprecise
in such scenarios. Data fusion is used to overcome sensor failures, technological
limitations, and spatial and temporal coverage problems.
Not many books addressed the real life problem in WSN applications. In this
book, we are proposing real implementation of data fusion algorithms; taking into
consideration the resource constrains of WSN. In addition, we are introducing some
real applications, as case study, in the industry.
The data fusion can be implemented in both centralized and distributed systems.
In the centralized fusion case, we propose four algorithms to be implemented in
WSN. As a case study, we propose a remote monitoring framework for sand
production in pipelines. Our goal is to introduce a reliable and accurate sand
monitoring system. The framework combines two modules: a Wireless Sensor
Data Acquisition (WSDA) module and a Central Data Fusion (CDF) module.
The CDF module is implemented using four different proposed fusion methods;
Fuzzy Art (FA), Maximum Likelihood Estimator (MLE), Moving Average Filter
(MAF), and Kalman Filter (KF). All the fusion methods are evaluated throughout
simulation and experimental results. The results show that FA, MLE and MAF
methods are very optimistic, to be implemented in WSN, but Kalman filter algo-
rithm does not lend itself for easy implementation; this is because it involves many
matrix multiplications, divisions, and inversions. The computational complexity of
the centralized KF is not scalable in terms of the network size. Thus, we propose to
implement the Kalman filter in a distributed fashion. The proposed DKF is based on
a fast polynomial filter to accelerate distributed average consensus. The idea is to
apply a polynomial filter on the network matrix that will shape its spectrum in order
to increase the convergence rate by minimizing its second largest eigenvalue.
Fast convergence can contribute to significant energy savings. In order to imple-
ment the DKF in WSN, more power saving is needed. Since multiplication is the
v

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