We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Induced Redundancy based Lossy Data Compression Algorithm

by Kayiram Kavitha, Dhruv Sharma, Rahul Surana, R. Gururaj
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 16
Year of Publication: 2013
Authors: Kayiram Kavitha, Dhruv Sharma, Rahul Surana, R. Gururaj
10.5120/10164-4928

Kayiram Kavitha, Dhruv Sharma, Rahul Surana, R. Gururaj . Induced Redundancy based Lossy Data Compression Algorithm. International Journal of Computer Applications. 62, 16 ( January 2013), 16-21. DOI=10.5120/10164-4928

@article{ 10.5120/10164-4928,
author = { Kayiram Kavitha, Dhruv Sharma, Rahul Surana, R. Gururaj },
title = { Induced Redundancy based Lossy Data Compression Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 16 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number16/10164-4928/ },
doi = { 10.5120/10164-4928 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:58.871345+05:30
%A Kayiram Kavitha
%A Dhruv Sharma
%A Rahul Surana
%A R. Gururaj
%T Induced Redundancy based Lossy Data Compression Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 16
%P 16-21
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Wireless Sensor Network (WSN) is an increasingly important mechanism for enabling continuous monitoring and sensing of physical variables like temperature, humidity etc. The tiny sensor nodes are powered by low capacity batteries. As the WSNs are usually deployed in remote areas, battery replacement becomes difficult. To minimize the power consumption in WSN, the data compression schemes play a vital role. If applied appropriately, these data compression schemes can result in drastic increase in the lifetime of the network. In this paper, we present a scheme called Induced Redundancy based Lossy Data Compression Algorithm, (IR-LDCA), which is best suited for WSNs that sense data with higher correlation. Our algorithm induces certain amount of redundancy into the data set to achieve more effective data compression and also gives the user a flexibility to control the compression ratio and loss of data. Simulation results prove the effectiveness of the proposed scheme over the existing ones.

References
  1. Akylidiz, I. F. , Su, W. , Sankarasubramanium, Y. , Cayirci, E. : The survey on sensor networks,IEEE communications Magazine 40(8), 114-120(2002).
  2. Wang, F. , and Liu, J. : Duty-cycle-aware broadcast in wireless sensor networks, in Proceedings of the 28th IEEE Conference on Computer Communications (INFOCOM '09), pp. 468-476, April 2009.
  3. Krishnamachari, B. , Estrin, D. , and Wicker, S. : Impact of Data Aggregation in Wireless Sensor Networks, International Workshop on Distributed Event-Based Systems, (DEBS'02) Vienna, Austria, July 2002.
  4. Fasolo, E. , Rossi, M. , Widmer, J. , and Zorzi, M. : In-network aggregation techniques for wireless sensor networks: a survey, IEEE Transactions in Wireless Communications. , vol. 14, no. 2, 70–87, April 2007.
  5. Sadler, C. M. and Martonosi, M. : Data compression algorithms for energy-constrained devices in delay tolerant networks, 4th International Conference on Embedded networked sensor systems (SenSys '06), 265–278, 2006.
  6. Kusuma, J. , Doherty, L. , and Ramchandran, K. : Distributed compression for sensor networks, Proceedings of the International Conference on Image Processing (ICIP'01) Vol. 1 IEEE, Thessaloniki, Greece, 82–85, 2001.
  7. Francesco, M. , Vecchio, M. : A simple algorithm for data compression in wireless sensor networks. Communications Letters, IEEE, 12(6) :411–413, June 2008.
  8. Pamba, E. , Chichi, C. , Guyennet, H. and Friedt, J. -M. : K-RLE: A new data compression algorithm for wireless sensor network, proceedings of the Third International Conference on Sensor Technologies and Applications, SENSORCOMM, pp. 502–507, Athens/Glyfada, Greece, 2009
  9. Tharini, C. , and Ranjan, P. V. : Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network, Computer Science, vol. 5, no. 6, pp. 466–470, 2009.
  10. Salomon, D. , Data Compression: The Complete Reference, Second edition, 2004.
  11. Schoellhammer, T. , Greenstein, B. , Osterweil, E. , Wimbrow, M. , and Estrin, D. : Lightweight temporal compression of microclimate datasets, First IEEE Workshop on Embedded networked Sensors (EmNetS-I), Tampa, Florida, USA, November 2004.
  12. Intel Berkeley Research lab, http://db. csail. mit. edu/labdata/labdata. html
Index Terms

Computer Science
Information Sciences

Keywords

WSN correlated data lossy compression IR-LDCA

Powered by PhDFocusTM