DocumentCode :
2711967
Title :
QoS Parameter Optimization Using Multi-Objective Genetic Algorithm in MANETs
Author :
Asraf, Noor M. ; Ainon, Raja N. ; Keong, Phang Keat
Author_Institution :
Fac. of Comput. Sci. & Inf. Technol., Univ. of Malaya, Kuala Lumpur, Malaysia
fYear :
2010
fDate :
26-28 May 2010
Firstpage :
138
Lastpage :
143
Abstract :
The increase in proliferation of mobile devices and wireless technologies in recent years has opened up new challenges in mobile ad hoc networks (MANET). This growth has also led to an increase in demand of applications such as streaming video, multi-player interactive games and financial services such as real-time stock quotes. Such applications impose a strict guarantee on quality of service (QoS), namely on end-to-end delay, bandwidth consumption and cost. But finding a path that satisfies multiple constraints is inherently hard. Further challenges occur with routing in a mobile environment where nodes are mobile, the data delivery path constantly changes and routing is constrained by battery levels. Multicast routing can improve network usage by sharing resources when sending messages to multiple destinations especially when multiple mobile nodes are located within transmission range of a node. We propose a multicast routing technique based on multi-objective genetic algorithm (MOGA) that optimizes multiple QoS parameters in MANET to find an optimal multicast tree. Simulation studies show that the GA is robust and scales well for a relatively large number of nodes.
Keywords :
Bandwidth; Batteries; Costs; Delay; Games; Genetic algorithms; Mobile ad hoc networks; Quality of service; Routing; Streaming media; Genetic Algorithm; QoS optimization; multicast routing; traffic engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on
Conference_Location :
Kota Kinabalu, Malaysia
Print_ISBN :
978-1-4244-7196-6
Type :
conf
DOI :
10.1109/AMS.2010.40
Filename :
5489640
Link To Document :
بازگشت