Applying genetic algorithm for optimizing broadcasting process in ad-hoc network

Elaiwat, Said, Alazab, Ammar, Venkatraman, Sitalakshmi and Alazab, Mamoun 2010, Applying genetic algorithm for optimizing broadcasting process in ad-hoc network, International journal of recent trends in engineering and technology, vol. 4, no. 1, Issue on Computer Science, pp. 68-72.

Attached Files
Name Description MIMEType Size Downloads

Title Applying genetic algorithm for optimizing broadcasting process in ad-hoc network
Author(s) Elaiwat, Said
Alazab, Ammar
Venkatraman, Sitalakshmi
Alazab, Mamoun
Journal name International journal of recent trends in engineering and technology
Volume number 4
Issue number 1
Season Issue on Computer Science
Start page 68
End page 72
Total pages 5
Publisher The Association of Computer Electronics and Electrical Engineers (ACEEE)
Place of publication New York, N.Y.
Publication date 2010-11
ISSN 2158-5563
2158-5555
Keyword(s) MANET
network
genetic algorithm
optimisation
spanning tree
Summary Optimizing broadcasting process in mobile ad hoc network (MANET) is considered as a main challenge due to many problems, such as Broadcast Storm problem and high complexity in finding the optimal tree resulting in an NP-hard problem. Straight forward techniques like simple flooding give rise to Broadcast Storm problem with a high probability. In this work, genetic algorithm (GA) that searches over a population that represents a distinguishable ‘structure’ is adopted innovatively to suit MANETs. The novelty of the GA technique adopted here to provide the means to tackle this MANET problem lies mainly on the proposed method of searching for a structure of a suitable spanning tree that can be optimized, in order to meet the performance indices related to the broadcasting problem. In other words, the proposed genetic model (GM) evolves with the structure of random trees (individuals) ‘genetically’ generated using rules that are devised specifically to capture MANET behaviour in order to arrive at a minimal spanning tree that satisfies certain fitness function. Also, the model has the ability to give different solutions depending on the main factors specified such as, ‘time’ (or speed) in certain situations and ‘reachability’ in certain others.
Language eng
Field of Research 100503 Computer Communications Networks
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010 ACEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034520

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 170 Abstract Views, 11 File Downloads  -  Detailed Statistics
Created: Mon, 09 May 2011, 14:46:08 EST by Sandra Dunoon

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.