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Delay Tolerant Network Routing as a Machine Learning Classification ProblemThis paper discusses a machine learning-based approach to routing for delay tolerant networks (DTNs) [1]. DTNs are networks which experience frequent disconnections between nodes, uncertainty of an end-to-end path, long one-way trip times, and may have high error rates and asymmetric links. Such networks exist in deep space satellite networks, very rural environments, disaster areas and underwater environments. In this work, we use machine learning classifiers to predict a set of neighboring nodes which are the most likely to deliver a message to a desired location based on message history delivery information.We use the Common Open Research Emulator (CORE) [2] to emulate the DTN environment based on real-world location traces and collect network traffic statistics from the Bundle Protocol implementation IBR-DTN [3]. The software architecture for classification-based routing, analysis and preparation of the network history data and prediction results are discussed.
Document ID
Acquisition Source
Glenn Research Center
Document Type
Conference Paper
Dudukovich, Rachel
(NASA Glenn Research Center Cleveland, OH, United States)
Papachristou, Christos
(Case Western Reserve Univ. Cleveland, OH, United States)
Date Acquired
February 19, 2019
Publication Date
August 6, 2018
Subject Category
Space Communications, Spacecraft Communications, Command And Tracking
Report/Patent Number
Meeting Information
Meeting: 2018 NASA/ESA Conference on Adaptive Hardware and Systems
Location: Edinburgh
Country: United Kingdom
Start Date: August 6, 2018
End Date: August 9, 2018
Sponsors: NASA Headquarters, European Space Agency. European Space Research and Technology Center, ESTEC
Funding Number(s)
WBS: WBS 405034.
Distribution Limits
Public Use Permitted.
Delay Tolerant Networks
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