A machine learning concept for DTN routing

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the c...

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Bibliographic Details
Published inIEEE International Conference on Wireless for Space and Extreme Environments conference digest (Online) pp. 110 - 115
Main Authors Dudukovich, Rachel, Hylton, Alan, Papachristou, Christos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Bayes methods
Delays
Learning (artificial intelligence)
Machine learning algorithms
Protocols
Routing
Space vehicles
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Summary:This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naïve Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN protocols is discussed. Finally, initial simulation setup and results are given.
ISSN:2380-7636
DOI:10.1109/WiSEE.2017.8124902