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Mobilized ad-hoc networks: A reinforcement learning approach

Research in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. Reinforcement learning methods can be used tocontrol both packet routing decisions and node mobility, dramaticallyimproving the connectivity of the network. We first motivate theproblem by presenting theoretical bounds for the connectivityimprovement of partially mobile networks and then present superiorempirical results under a variety of different scenarios in which themobile nodes in our ad-hoc network are embedded with adaptive routingpolicies and learned movement policies.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30437
Date04 December 2003
CreatorsChang, Yu-Han, Ho, Tracey, Kaelbling, Leslie Pack
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format9 p., 15523730 bytes, 577014 bytes, application/postscript, application/pdf
RelationMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory

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