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Application of Machine Learning Techniques to Delay Tolerant Network RoutingThis dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.
Document ID
Document Type
Dudukovich, Rachel M.
(NASA Glenn Research Center Cleveland, OH, United States)
Date Acquired
May 6, 2019
Publication Date
January 1, 2019
Subject Category
Communications And Radar
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
Funding Number(s)
WBS: 277985.
Distribution Limits
Work of the US Gov. Public Use Permitted.
Technical Review
Single Expert
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