Abstract-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, Qrouting 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.
In REF , an architecture of a machine learning based router is proposed for delay tolerant space networks, and two machine learning methods are used to supplement the routing decisions of the Contact Graph Routing (CGR) algorithm, where Naive Bayesian classifier is used for the prediction of reliability of each candidate relay node and Q-learning routing is used for the estimation of the end-to-end delivery delay of routing.
A machine learning concept for DTN routing
2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)
2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)
Computer Science