In a wide range of applications, large amounts of floating-point data are generated by Wireless Sensor
Networks (WSNs). This data is often transferred between several sensor nodes, in a multi-hop fashion,
before reaching its ultimate destination (the base station). It is well known that data communications is the
most energy-consuming task in sensor nodes 1. This can be a great concern when the nodes are
constrained in energy. Therefore, the amount of data to be transferred between nodes should be reduced to
save energy. In this paper, we investigate data compression for resource-constraint WSNs; we introduce
MAS as a novel adaptive lossless floating-point data compression algorithm for WSNs. MAS exploits the
disproportionality in energy consumption between data transmission and processing. Simulation results,
obtained from OMNeT++ and Atmel Studio, show that MAS surpasses other tested compression algorithms
in terms of compression ratio, compression speed, memory requirements and most importantly energy
savings.
%0 Journal Article
%1 noauthororeditor
%A Assi, Maher El
%A Ghaddar, Alia
%A Tawbi, Samar
%A Fadi, Ghaddar
%D 2013
%J International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC)
%K Compression Data Efficiency Energy Floating-point Lossless Networks Sensor Wireless
%N 5
%P 16
%R 10.5121/ijasuc.2013.4502
%T Resource-Efficient Floating-Point Data Compression Using MAS in WSN
%U http://aircconline.com/ijasuc/V4N5/4513ijasuc02.pdf
%V 4
%X In a wide range of applications, large amounts of floating-point data are generated by Wireless Sensor
Networks (WSNs). This data is often transferred between several sensor nodes, in a multi-hop fashion,
before reaching its ultimate destination (the base station). It is well known that data communications is the
most energy-consuming task in sensor nodes 1. This can be a great concern when the nodes are
constrained in energy. Therefore, the amount of data to be transferred between nodes should be reduced to
save energy. In this paper, we investigate data compression for resource-constraint WSNs; we introduce
MAS as a novel adaptive lossless floating-point data compression algorithm for WSNs. MAS exploits the
disproportionality in energy consumption between data transmission and processing. Simulation results,
obtained from OMNeT++ and Atmel Studio, show that MAS surpasses other tested compression algorithms
in terms of compression ratio, compression speed, memory requirements and most importantly energy
savings.
@article{noauthororeditor,
abstract = {In a wide range of applications, large amounts of floating-point data are generated by Wireless Sensor
Networks (WSNs). This data is often transferred between several sensor nodes, in a multi-hop fashion,
before reaching its ultimate destination (the base station). It is well known that data communications is the
most energy-consuming task in sensor nodes [1]. This can be a great concern when the nodes are
constrained in energy. Therefore, the amount of data to be transferred between nodes should be reduced to
save energy. In this paper, we investigate data compression for resource-constraint WSNs; we introduce
MAS as a novel adaptive lossless floating-point data compression algorithm for WSNs. MAS exploits the
disproportionality in energy consumption between data transmission and processing. Simulation results,
obtained from OMNeT++ and Atmel Studio, show that MAS surpasses other tested compression algorithms
in terms of compression ratio, compression speed, memory requirements and most importantly energy
savings.
},
added-at = {2019-09-17T11:36:15.000+0200},
author = {Assi, Maher El and Ghaddar, Alia and Tawbi, Samar and Fadi, Ghaddar},
biburl = {https://www.bibsonomy.org/bibtex/275785c1b93762090e37b34bd9bf4fd21/usmankhawaja},
doi = {10.5121/ijasuc.2013.4502},
interhash = {3210c8892fa4b7f6e2f86f37b928c69e},
intrahash = {75785c1b93762090e37b34bd9bf4fd21},
issn = {0976 - 1764},
journal = {International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) },
keywords = {Compression Data Efficiency Energy Floating-point Lossless Networks Sensor Wireless},
language = {English},
month = {october},
number = 5,
pages = 16,
timestamp = {2019-09-17T11:36:15.000+0200},
title = {Resource-Efficient Floating-Point Data Compression Using MAS in WSN },
url = {http://aircconline.com/ijasuc/V4N5/4513ijasuc02.pdf},
volume = 4,
year = 2013
}