A Machine Learning Concept for DTN Routing

被引：0
|
作者：
Dudukovich, Rachel [1 ]
Hylton, Alan [1 ]
Papachristou, Christos [2 ]
机构：
[1] NASA Glenn Res Ctr, Cleveland, OH 44135 USA
[2] Case Western Reserve Univ, Cleveland, OH 44106 USA
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON WIRELESS FOR SPACE AND EXTREME ENVIRONMENTS (WISEE) | 2017年
关键词：
D O I：
暂无
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
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 Naive 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.
引用
收藏
页码：110 / 115
页数：6
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