Open Access

ARTICLE

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

A. Arokiaraj Jovith¹, S. Rama Sree², Gudikandhula Narasimha Rao³, K. Vijaya Kumar⁴, Woong Cho⁵, Gyanendra Prasad Joshi⁶, Sung Won Kim^7,*
1 Department of Networking and Communications, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, India
2 Department of Computer Science Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, 533437, India
3 Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam, 530049, India
4 Department of Computer Science and Engineering, Vignan's Institute of Engineering for Women, Visakhapatnam, 530049, India
5 Department of Software Convergence, Daegu Catholic University, Gyeongsan, 38430, Korea
6 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea
7 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongbuk-do, 38541, Korea
* Corresponding Author: Sung Won Kim. Email:

Computers, Materials & Continua 2023, 74(3), 6429-6444. https://doi.org/10.32604/cmc.2023.031817

Received 27 April 2022; Accepted 29 May 2022; Issue published 28 December 2022

Abstract

The exponential growth of data necessitates an effective data storage scheme, which helps to effectively manage the large quantity of data. To accomplish this, Deoxyribonucleic Acid (DNA) digital data storage process can be employed, which encodes and decodes binary data to and from synthesized strands of DNA. Vector quantization (VQ) is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency. This article introduces a new DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems. The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage. Besides, the DNAC-WSAVQ model initially performs DNA encoding on the input images to generate a binary encoded form. In addition, a Water Strider algorithm with Linde-Buzo-Gray (WSA-LBG) model is applied for the compression process and thereby storage area can be considerably minimized. In order to generate optimal codebook for LBG, the WSA is applied to it. The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures. The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

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Keywords

DNA computing; data storage; image compression; vector quantization; ws algorithm; space saving

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