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A Machine Learning Concept for DTN RoutingThis 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 Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.
Document ID
Document Type
Conference Paper
Dudukovich, Rachel
(NASA Glenn Research Center Cleveland, OH, United States)
Hylton, Alan
(NASA Glenn Research Center Cleveland, OH, United States)
Papachristou, Christos
(Case Western Reserve Univ. Cleveland, OH, United States)
Date Acquired
October 31, 2017
Publication Date
October 10, 2017
Subject Category
Communications And Radar
Report/Patent Number
Meeting Information
Meeting: Annual IEEE International Conference on Wireless for Space and Extreme Environments (WISEE)
Location: Montreal, QC
Country: Canada
Start Date: October 10, 2017
End Date: October 12, 2017
Sponsors: Concordia Univ., Institute of Electrical and Electronics Engineers
Funding Number(s)
WBS: WBS 405034.
Distribution Limits
Public Use Permitted.
Delay Tolerant Networks
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