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.

REFERENCES

[1] Peng Jiang and Sheng-Qiang Li, “A Data

Compression Algorithm for Wireless Sensor

Networks Based on an Optimal Order Estimation

Model and Distributed Coding” Academic Journal

of Sensors ,Vol. 10 Issue 10, p9065, October 2010.

[2] S.Shanmugasundaram and R.Lourdusamy,

“A Comparative Study of Text Compression

Algorithms” International Journal of Wisdom

Based Computing, Vol. 1 (3), December 2011.

[3] C.Tharini and P.V.Ranjan, “Design of

Modified Adaptive Huffman Data Compression

Algorithm for Wireless Sensor Network” Journal

of Computer Science Volume 5, Issue 6, Pages

466-470, 2009.

[4] Marcelloni, F. and Vecchio, M., “A

Simple Algorithm for Data Compression in

Wireless Sensor Networks”, Communications

Letters, IEEE (Volume:12 , Issue: 6 ), June 2008.

[5] Chou, J. Petrovic, D.Kannan

Ramachandran, “A distributed and adaptive signal

processing approach to reducing energy

consumption in sensor networks”, INFOCOM

2003.Twenty-Second Annual Joint Conferences of

the IEEE Computer and Communications. IEEE

Societies (Volume: 2) pgs 1054 - 1062, April 3

2003.

[6] Sundeep Pattem, Ramesh Govindan,

“Distributed compression in a dense micro sensor

network”, Signal Processing Magazine,

IEEE (Volume: 19 Issue: 2) pgs 51 – 60, 07

August 2002.

[7] Broersen, P.M.T. ,“The Quality of Lagged

Products and Autoregressive Yule–Walker Models

as Autocorrelation Estimates”, Instrumentation and

Measurement, IEEE Transactions on (Volume:58

, Issue: 11 ) pages 3867 – 3873, Nov. 2009.

[8] S.Kaur and V.S.Verma,” Design and

Implementation of LZW Data Compression

Algorithm”, International Journal of Information

Sciences and Techniques (IJIST) Vol.2, No.4, July

2012.

[9] Ming-Bo Lin Jang-Feng Lee ; Gene Eu

Jan, “A Lossless Data Compression and

Decompression Algorithm and Its Hardware

Architecture”, Very Large Scale Integration (VLSI)

Systems, IEEE Transactions on (Volume:14

, Issue: 9 ) Page(s):925 – 936, 23 October 2006.

[10] Rajinder Kaur and Mrs. Monica Goyal, “

A Survey on the different text data compression

techniques”, International Journal of Advanced

Research in Computer Engineering & Technology

(IJARCET) Volume 2, Issue 2, February 2013.

[11]. Manjeet Gupta Brijesh Kumar ,” Web

Page Compression using Huffman Coding

Technique,” International Conference on Recent

Advances and Future Trends in Information

Technology (iRAFIT2012) Proceedings published

in International Journal of Computer

Applications® (IJCA), 2012.

[12] I Made Agus,Dwi Suarjaya ,” A New

Algorithm for Data Compression Optimization,

“International Journal of Advanced Computer

Science and Applications (IJACSA), Vol. 3, No.8,

2012.

[13] Rupinder Singh Brar, Bikramjeet singh,

“A Survey on Different Compression Techniques

and Bit Reduction Algorithm for Compression of

Text/Lossless Data,” International Journal of

Advanced Research in Computer Science and

Software Engineering (IJARCSSE),Volume 3,

Issue 3, March 2013.

[14]. Rajinder Kaur ,Mrs. Monica Goyal ,”A

Survey on the different text data compression

techniques ,” International Journal of Advanced

Research in Computer Engineering & Technology

(IJARCET), Volume 2, Issue 2, February2013 .

[15]. Mr.Chandresh K Parmar, Prof.Kruti

Pancholi, “A Review on Image Compression

Techniques,” Journal of Information, Knowledge

and Research in Electrical Engineering, Volume –

02, Issue – 02, 2013.