Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm

An important issue in ad-hoc on-demand distance vector (AODV) routing protocols is route failure caused by node mobility in the MANETs. The AODV requires a new route discovery procedure whenever a route breaks and these frequent route discoveries increase transmission delays and routing overhead. Th...

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Main Authors: Ahmad Maleki, Mehdi Sadeghzadeh
Format: Article
Language:English
Published: Growing Science 2014-09-01
Series:Management Science Letters
Subjects:
Online Access:http://www.growingscience.com/msl/Vol4/msl_2014_234.pdf
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author Ahmad Maleki
Mehdi Sadeghzadeh
author_facet Ahmad Maleki
Mehdi Sadeghzadeh
author_sort Ahmad Maleki
collection DOAJ
description An important issue in ad-hoc on-demand distance vector (AODV) routing protocols is route failure caused by node mobility in the MANETs. The AODV requires a new route discovery procedure whenever a route breaks and these frequent route discoveries increase transmission delays and routing overhead. The present study proposes a new method for AODVs using a genetic algorithm to improve the route recovery mechanism. When failure occurs in a route, the proposed method (GAAODV) makes decisions regarding the QOS parameter to select source or local repair. The task of the genetic algorithm is to find an appropriate combination of weights to optimize end-to-end delay. This paper evaluates the metrics of routing overhead, average end-to-end delay, and packet delivery ratio. Comparison of the new algorithm and AODV (RFC 3561) using a NS-2 simulator shows that GAAODV obtains better results for the QOS parameters.
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institution Kabale University
issn 1923-2934
1923-9343
language English
publishDate 2014-09-01
publisher Growing Science
record_format Article
series Management Science Letters
spelling doaj-art-36db6070c74e46fd848ad3f37c4e1f612025-08-20T03:55:02ZengGrowing ScienceManagement Science Letters1923-29341923-93432014-09-01492171217610.5267/j.msl.2014.7.029Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithmAhmad Maleki Mehdi SadeghzadehAn important issue in ad-hoc on-demand distance vector (AODV) routing protocols is route failure caused by node mobility in the MANETs. The AODV requires a new route discovery procedure whenever a route breaks and these frequent route discoveries increase transmission delays and routing overhead. The present study proposes a new method for AODVs using a genetic algorithm to improve the route recovery mechanism. When failure occurs in a route, the proposed method (GAAODV) makes decisions regarding the QOS parameter to select source or local repair. The task of the genetic algorithm is to find an appropriate combination of weights to optimize end-to-end delay. This paper evaluates the metrics of routing overhead, average end-to-end delay, and packet delivery ratio. Comparison of the new algorithm and AODV (RFC 3561) using a NS-2 simulator shows that GAAODV obtains better results for the QOS parameters.http://www.growingscience.com/msl/Vol4/msl_2014_234.pdfMobile ad-hoc networksAODVRoute recovery mechanismsGenetic algorithmNS-2 simulator
spellingShingle Ahmad Maleki
Mehdi Sadeghzadeh
Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
Management Science Letters
Mobile ad-hoc networks
AODV
Route recovery mechanisms
Genetic algorithm
NS-2 simulator
title Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
title_full Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
title_fullStr Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
title_full_unstemmed Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
title_short Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
title_sort improved aodv route recovery in mobile ad hoc networks using a genetic algorithm
topic Mobile ad-hoc networks
AODV
Route recovery mechanisms
Genetic algorithm
NS-2 simulator
url http://www.growingscience.com/msl/Vol4/msl_2014_234.pdf
work_keys_str_mv AT ahmadmaleki improvedaodvrouterecoveryinmobileadhocnetworksusingageneticalgorithm
AT mehdisadeghzadeh improvedaodvrouterecoveryinmobileadhocnetworksusingageneticalgorithm