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Izvorni znanstveni članak
https://doi.org/10.2498/cit.1002080

Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks

Shrirang Ambaji Kulkarni   ORCID icon orcid.org/0000-0001-8392-1566 ; Gogte Institute of Technology, Karnataka, India
G. Raghavendra Rao ; National Institute of Engineering, Karnataka, India

Puni tekst: engleski, PDF (1 MB) str. 247-256 preuzimanja: 606* citiraj
APA 6th Edition
Kulkarni, S.A. i Rao, G.R. (2012). Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks. Journal of computing and information technology, 20 (4), 247-256. https://doi.org/10.2498/cit.1002080
MLA 8th Edition
Kulkarni, Shrirang Ambaji i G. Raghavendra Rao. "Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks." Journal of computing and information technology, vol. 20, br. 4, 2012, str. 247-256. https://doi.org/10.2498/cit.1002080. Citirano 18.06.2021.
Chicago 17th Edition
Kulkarni, Shrirang Ambaji i G. Raghavendra Rao. "Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks." Journal of computing and information technology 20, br. 4 (2012): 247-256. https://doi.org/10.2498/cit.1002080
Harvard
Kulkarni, S.A., i Rao, G.R. (2012). 'Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks', Journal of computing and information technology, 20(4), str. 247-256. https://doi.org/10.2498/cit.1002080
Vancouver
Kulkarni SA, Rao GR. Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks. Journal of computing and information technology [Internet]. 2012 [pristupljeno 18.06.2021.];20(4):247-256. https://doi.org/10.2498/cit.1002080
IEEE
S.A. Kulkarni i G.R. Rao, "Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks", Journal of computing and information technology, vol.20, br. 4, str. 247-256, 2012. [Online]. https://doi.org/10.2498/cit.1002080

Sažetak
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.

Ključne riječi
Quality of Service; reinforcement learning based routing protocol; group mobility models; routing metrics; game theory

Hrčak ID: 99479

URI
https://hrcak.srce.hr/99479

Posjeta: 849 *