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EFFECT OF MOBILITY MODELS ON REINFORCEMENT LEARNING BASED ROUTING ALGORITHM APPLIED FOR SCALABLE AD HOC NETWORK ENVIRONMENT

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Shrirang.Ambaji.Kulkarni , G.Raghavendra Rao, International Journal of Computer Networks & Communications (IJCNC
Dibuat : 2012-06-25, dengan 1 file

Keyword : Routing protocols, mobility models, scalability and reinforcement learning
Subjek : EFFECT OF MOBILITY MODELS ON REINFORCEMENT LEARNING BASED ROUTING ALGORITHM APPLIED FOR SCALABLE AD HOC NETWORK ENVIRONMENT
Url : http://airccse.org/journal/cnc/1110ijcnc04.pdf
Sumber pengambilan dokumen : Internet

Mobile Ad Hoc Network faces the greatest challenge for better performances in terms of mobility


characterization. The mobility of nodes and their underlying mobility models have a profound effect on the


performances of routing protocols which are central to the design of ad hoc networks. Most of the


traditional routing algorithms proposed for ad hoc networks do not scale well when the traffic variation


increases drastically. To model a solution to this problem we consider a reinforcement learning based


routing algorithm for ad hoc network known as SAMPLE. Most the scalability issues for ad hoc network


performance investigation have not considered the group mobility of nodes. In this paper we model


realistic group vehicular mobility model and analyze the robustness of a reinforcement learning based


routing algorithm under scalable conditions.

Deskripsi Alternatif :

Mobile Ad Hoc Network faces the greatest challenge for better performances in terms of mobility


characterization. The mobility of nodes and their underlying mobility models have a profound effect on the


performances of routing protocols which are central to the design of ad hoc networks. Most of the


traditional routing algorithms proposed for ad hoc networks do not scale well when the traffic variation


increases drastically. To model a solution to this problem we consider a reinforcement learning based


routing algorithm for ad hoc network known as SAMPLE. Most the scalability issues for ad hoc network


performance investigation have not considered the group mobility of nodes. In this paper we model


realistic group vehicular mobility model and analyze the robustness of a reinforcement learning based


routing algorithm under scalable conditions.

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