文章基本信息

标题：Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks
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作者：Kulkarni, Shrirang Ambaji ; Rao, G. Raghavendra
期刊名称：Journal of Computing and Information Technology
印刷版ISSN：1330-1136
电子版ISSN：1846-3908
出版年度：2012
卷号：20
期号：4
页码：247-256
DOI：10.2498/cit.1002080
出版社：SRCE - Sveučilišni računski centar
摘要：Mobile ad-hoc networks are dynamic networks which are decentralized and autonomous in nature. Many routing algorithms have been proposed for these dynamic networks. It is an important problem to model Quality of Service requirements on these types of algorithms which traditionally have certain limitations. To model this scenario we have considered a reinforcement learning algorithm SAMPLE. SAMPLE promises to deal effectively with congestion and under high traffic load. As it is natural for ad-hoc networks to move in groups, we have considered the various group mobility models. The Pursue Mobility Model with its superiormobilitymetrics exhibits better performance. At the data link layer we have considered IEEE 802.11e, a MAC layer which has provisions to support QoS. As mobile ad-hoc networks are constrained by resources like energy and bandwidth, it is imperative for them to cooperate in a reasonably selfish manner. Thus, in this paper we propose cooperation with a moderately punishing algorithm based on game theory. The proposed algorithm in synchronization with SAMPLE yields better results on IEEE 802.11e.
关键词：Quality of Service; reinforcement learning based routing protocol; group mobility models; routing metrics; game theory