Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning
This addresses secure data transmission in networks for applications like military or privacy-sensitive communications, but it is incremental as it builds on existing routing and RL methods.
The paper tackles covert routing in heterogeneous networks by developing a reinforcement learning algorithm that selects routes and modalities based on local feedback, achieving performance with negligible loss compared to optimal centralized routing.
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.