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TRANSCRIPT
A Survey on Data Compression Techniques in Wireless Sensor
Network
A.Alish Preethi1, Anjali Ramakrishnan
2
1(Department of Electronics and Communication, Sathyabama University, India)
2(Department of Electronics and Communication, Sathyabama University, India)
ABSTRACT: Compression is useful because it
helps us to reduce the resources use, such as data
storage capacity or transference capacity.
Compression methods have a many types. In this
survey explain a different simple efficient data
compression algorithm which suited to be used on
available many commercial nodes of a wireless
sensor network, where energy, memory and
computational resources are limited. Some
experimental results and comparisons of lossless
compression algorithm formerly proposed in the
literature to be inserted in sensor nodes. Hence
data compression methods are used to minimize the
number of bits to be transmitted by the
transmission module will significantly lessen the
energy requirement and maximize the life
expectancy of the sensor node. The present
explanation of the study contracted with the
sketching of systematic efficient data compression
algorithm, particularly suited for wireless sensor
network.
Keywords- Data compression, Lossless
compression, Sensor nodes Wireless sensor
networks.
1. INTRODUCTION
A wireless sensor network is a group of
exceptional unique transducers with a
communications infrastructure that uses radio for
monitoring and recording the physical or
environmental conditions at many and various
locations. Commonly monitored parameters are
temperature, airlessness, pressure, wind direction
and speed, illumination concentration, vibration
strength, sound potency, power-line voltage,
chemical concentrations, pollutant regulars and
vital body functions. A wireless sensor network
consists of three major components which are
classified as nodes, gateways, and software. The
spatially distributed calculation nodes interface
with sensors to observe assets or their surrounding
conditions. The obtained data wireless transmits to
the gateway, which can operate separately or attach
to a host system where you can assemble,
accumulate procedure, analyze, and submit your
calculation data using software. Routers are a
unique type of calculation node that you can use to
widen WSN distance and reliability. WSN
architectures unite various categories of nodes and
gateways to meet the specific needs of application.
Data compression is commonly referred to
as set of principles, where principles is said to be a
coding and it represented a data which satisfies a
certain need. Compression is the conversion of data
in such a format that requires few bits usually
formed to store and transmit the data easily and
efficiently. Compression is perfected to reduce
amount of data and needed to reproduce that data.
And the compression is done either to reduce the
volume of information in case of text, fax and
images or reduce bandwidth in case of speech,
audio and video.
Figure shows the compression method
with the help of following figure 1:
Fig 1: Diagrammatic representation of data
compression
A data compression is that it converts a
string of characters in another representation into a
new string which have the same data in small
length as much as possible. Data compression may
be viewed as the study of information theory in
which the main objective for the efficient coding
and to minimize the speed of transmission
bandwidth. The main purposes of this paper to
shows the variety of various lossless compression
techniques and their comparative study.
2. CLASSIFICATION OF
COMPRESSION METHODS
There are two types of data compression
methods:
2.1. LOSSLESS COMPRESSION
It is used to minimize the amount of
source information to be transmitted in such a way
that when compressed information is
decompressed, there is not any loss of information
2.2. LOSSY COMPRESSION
The aim of lossy compression is normally
not to replicate an exact copy of the information.
Fig 2: Tree representation of data compression
methods
But in this paper has concentrated only on
the lossless compression methods which are used
on the text data formatting and define them with
the help of some algorithms.
2. LITERATURE SURVEY
The survey of different data compression
techniques can be explained as below:
Peng Jiang and Sheng Qiang Li, (Oct
2010) has proposed an optimal order evaluation
type and dispersed set of principles can be
explained in spatial correlation and it is used for
supporting a one-dimensional signal. It gives the
maximum signal noise ratio (SNR) value. [1]
S.Shanmugasundaram, et, al., (Dec 2011)
are explained there are lots of data compression
concepts which are obtainable to compress set of
data in different formats. This paper explains a
survey of dissimilar basic lossless data
compression algorithms. The investigational
outcomes and collations of the lossless
compression method using statistical compression
method ion after decompression. In this case some
information is lost after decompression. And
dictionary based compression method were
performed on text data. [2]
C.Tharani and P.Vanaja Rajan, (2009) has
proposed the improved adaptive Huffman data
compression method based on distributed source
compression (DSC) for increased the complexity in
encoding and decoding. So this algorithm was not
suitable for wireless sensor node. It gives the low
compression ratio. [3]
Francesco Marcelloni, and Massimo
Vechhio, (Jun 2008) has proposed a basic
technique for data compression explained based on
dictionary compression algorithm which is said to
be Lempel Ziv Welch (S.LZW) for data storage
resources of sensors node but it gives the less
compression ratio. [4]
Jim Chou, et, al., (April 2003) has
proposed a dispersed and adaptive signal
processing proceed towards to reducing vitality
ingestion i.e. (energy consumption) in sensor
networks is based on orthogonal approach for
computing power is limited and its gives less
energy saving methods [5]
Sundeep Pattem, Ramesh Govindan, (Aug
2002) are explained distributed compression in a
dense micro-sensor network is explained based on
three algorithms such as Time Division Multiple
Access(TDMA), Frequency Division Multiple
Access(FDMA), Code Division Multiple
Access(CDMA) for decoding process and the
process of decoding is difficult in it. It gives the
parameter of achieved gain value. [6]
Piet M. T. Broerson, (Nov 2009) has
proposed the standard of delayed products and
autorevertive Yule–Walker Models as
autocorrelation which explains by the fast Fourier
transform algorithm. This algorithm needs
maximum number of data for compression and to
produce the results. The output value of the data
compression has low auto correlation. [7]
U.Khuranaand, A.Koul, (July 2012) are
explained a new compression algorithm that uses
referencing through two-byte numbers (indices) for
the cause of encoding has been presented. The
procedure of working is efficient in supplying high
compression ratios and faster search through the
text. [8]
. Ming-Bo Lin, et, al., (Sep 2006) has
proposed a lossless data compression and
decompression technique and its tools and
equipment structure explained by Parallel
dictionary Lempel Ziv Welch (PDLZW) technique.
The resulting structure shows that it is not only to
reduce the tools and equipment cost significantly
and it gives the low data reduction. [9]
4. LITERATURE SURVEY TABLE To analyze the effects of different kinds of data compression in wireless sensor networks a survey
has been conducted and discussed in tabular form.
S
L.
N
o
Title Author Existing
Method Drawbacks Parameter
1
A data compression algorithm for
wireless sensor networks based
on an optimal order estimation
model and
distributed coding
Peng Jiang
and Sheng
Qiang Li,
(Oct 2010)
Spatial
Correlation
It is only support for
one-dimensional
signal
High SNR
value
Auto
correlation
2 A universal algorithm for
sequential data compression
Jacob Ziv, et,
al., (May
1997)
Optimal Fixed
Code
Susceptibility to error
propagation in the
event of a channel
error.
High
compressio
n ratio
Mean
square
value
3
Design of modified adaptive
Huffman data compression
algorithm for wireless sensor
network
C. Tharini
And P.Vanaja
Ranjan,
(2009)
Distributed
Source
Compression
(DSC) Algorithm
Increased the
complexity in
encoding and
decoding. So this
algorithm was not
suitable for Wireless
sensor node.
Low
compressio
n ratio
PSNR
value
4
A lossless data compression and
decompression algorithm and its
hardware architecture
Ming-Bo Lin,
et, al.,
(Sep 2006)
Parallel
Dictionary
LZW (PDLZW)
Algorithm
The resulting
architecture shows
that it is not only to
reduce the equipment
cost significantly
Data
reduction is
low
5
A simple algorithm for data
compression in wireless sensor
networks
Francesco
Marcelloni,
et, al., (June
2008)
Dictionary Based
Compression
Algorithm
(S.LZW)
Poor data storage
resources of sensors
node
Less
compressio
n ratio
Low auto
correlation
6
A distributed and adaptive signal
processing approach to reducing
energy consumption in sensor
networks
Jim Chou, et,
al., (April
2003)
Orthogonal
approach
Computing power is
limited
Less
energy can
be saved
7
Distributed
compression in a
dense micro-sensor
network
S. Sandeep
Pradhan, et,
al.,(Aug
2002)
TDMA, FDMA,
CDMA
Decoding process is
difficult
Achieved
gain value
Compressi
on ratio
8
GIST: Group-independent
spanning tree for data aggregation
in dense sensor
networks
Lujun Jia, et,
al., (2006 )
Constructing
Aggregation or
Dissemination
trees
There is a limit to
routing nodes
Time out
value is too
small
Correlation
coefficient
9
The impact of spatial correlation
on routing with compression in
wireless sensor networks
Sundeep
Pattem, et,
al.,
(April 2004 )
Distributed source
coding
Need space for
routing
Low
Correlation
coefficient
PSNR
10
The quality of lagged products
and autoregressive Yule–walker
models as autocorrelation
estimate
Piet M. T.
Broersen
(Nov 2009)
Fast Fourier
Transform
It needs maximum
data
Low Auto
correlation
Compressi
on ratio
5. CONCLUSIONS
Sensor nodes are usually battery powered
and thus how to conserve their energy is a primary
concern in WSNs. In this paper the survey is based
on data compression in wireless sensor network for
minimize the energy consumption and for
processing and transmission of data. Lossless data
compression schemes are mostly used due to their
simplicity of algorithms & better compression
efficiencies but lossy compression has high value
of compression ratio with some fraction of original
features lost which may not introduce a severe
case. The researchers keep on introducing their
improvisation in this field and try to make them
more efficient. Hence much more advancement is
being expected in future in the field of data
compression.
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