New heuristic model for optimal CRC polynomial

Ahmed Salih Khirbeet, Ravie Chandren Muniyandi

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Cyclic Redundancy Codes (CRCs) are important for maintaining integrity in data transmissions. CRC performance is mainly affected by the polynomial chosen. Recent increases in data throughput require a foray into determining optimal polynomials through software or hardware implementations. Most CRC implementations in use, offer less than optimal performance or are inferior to their newer published counterparts. Classical approaches to determining optimal polynomials involve brute force based searching a population set of all possible polynomials in that set. This paper evaluates performance of CRC-polynomials generated with Genetic Algorithms. It then compares the resultant polynomials, both with and without encryption headers against a benchmark polynomial.

Original languageEnglish
Pages (from-to)521-525
Number of pages5
JournalInternational Journal of Electrical and Computer Engineering
Volume7
Issue number1
DOIs
Publication statusPublished - 2017

Fingerprint

Codes (standards)
Redundancy
Polynomials
Data communication systems
Cryptography
Genetic algorithms
Throughput
Hardware

Keywords

  • Cyclic redundancy codes
  • Data authentication
  • Data integrity
  • Genetic algorithms
  • Optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

New heuristic model for optimal CRC polynomial. / Khirbeet, Ahmed Salih; Muniyandi, Ravie Chandren.

In: International Journal of Electrical and Computer Engineering, Vol. 7, No. 1, 2017, p. 521-525.

Research output: Contribution to journalArticle

@article{6013891818f34ffbb9bf2bd9c986151e,
title = "New heuristic model for optimal CRC polynomial",
abstract = "Cyclic Redundancy Codes (CRCs) are important for maintaining integrity in data transmissions. CRC performance is mainly affected by the polynomial chosen. Recent increases in data throughput require a foray into determining optimal polynomials through software or hardware implementations. Most CRC implementations in use, offer less than optimal performance or are inferior to their newer published counterparts. Classical approaches to determining optimal polynomials involve brute force based searching a population set of all possible polynomials in that set. This paper evaluates performance of CRC-polynomials generated with Genetic Algorithms. It then compares the resultant polynomials, both with and without encryption headers against a benchmark polynomial.",
keywords = "Cyclic redundancy codes, Data authentication, Data integrity, Genetic algorithms, Optimization",
author = "Khirbeet, {Ahmed Salih} and Muniyandi, {Ravie Chandren}",
year = "2017",
doi = "10.11591/ijece.v7i1.pp521-525",
language = "English",
volume = "7",
pages = "521--525",
journal = "International Journal of Electrical and Computer Engineering",
issn = "2088-8708",
publisher = "Institute of Advanced Engineering and Science (IAES)",
number = "1",

}

TY - JOUR

T1 - New heuristic model for optimal CRC polynomial

AU - Khirbeet, Ahmed Salih

AU - Muniyandi, Ravie Chandren

PY - 2017

Y1 - 2017

N2 - Cyclic Redundancy Codes (CRCs) are important for maintaining integrity in data transmissions. CRC performance is mainly affected by the polynomial chosen. Recent increases in data throughput require a foray into determining optimal polynomials through software or hardware implementations. Most CRC implementations in use, offer less than optimal performance or are inferior to their newer published counterparts. Classical approaches to determining optimal polynomials involve brute force based searching a population set of all possible polynomials in that set. This paper evaluates performance of CRC-polynomials generated with Genetic Algorithms. It then compares the resultant polynomials, both with and without encryption headers against a benchmark polynomial.

AB - Cyclic Redundancy Codes (CRCs) are important for maintaining integrity in data transmissions. CRC performance is mainly affected by the polynomial chosen. Recent increases in data throughput require a foray into determining optimal polynomials through software or hardware implementations. Most CRC implementations in use, offer less than optimal performance or are inferior to their newer published counterparts. Classical approaches to determining optimal polynomials involve brute force based searching a population set of all possible polynomials in that set. This paper evaluates performance of CRC-polynomials generated with Genetic Algorithms. It then compares the resultant polynomials, both with and without encryption headers against a benchmark polynomial.

KW - Cyclic redundancy codes

KW - Data authentication

KW - Data integrity

KW - Genetic algorithms

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=85020922607&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020922607&partnerID=8YFLogxK

U2 - 10.11591/ijece.v7i1.pp521-525

DO - 10.11591/ijece.v7i1.pp521-525

M3 - Article

VL - 7

SP - 521

EP - 525

JO - International Journal of Electrical and Computer Engineering

JF - International Journal of Electrical and Computer Engineering

SN - 2088-8708

IS - 1

ER -