Decentralized Covert Routing in Heterogeneous Networks Using
  Reinforcement Learning
Justin Kong Terrence J. Moore Fikadu T. Dagefu
This letter investigates covert routing communications in a heterogeneous
network where a source transmits confidential data to a destination with the
aid of relaying nodes where each transmitter judiciously chooses one modality
among multiple communication modalities. We develop a novel reinforcement
learning-based covert routing algorithm that finds a route from the source to
the destination where each node identifies its next hop and modality only based
on the local feedback information received from its neighboring nodes. We show
based on numerical simulations that the proposed covert routing strategy has
only negligible performance loss compared to the optimal centralized routing
scheme.