Learning for Robust Routing Based on Stochastic Game in Cognitive Radio Networks

This paper studies the problem of spectrum-aware routing in a multi-hop, multi-channel cognitive radio network when malicious nodes in the secondary network attempt to block the path with mixed attacks. Based on the location and time-variant path delay information, we model the path discovery process as a non-cooperative stochastic game. By exploiting the structure of the underlying Markov Decision Process, we decompose the stochastic routing game into a series of stage games. For each stage game, we propose a distributed strategy learning mechanism based on stochastic fictitious play to learn the equilibrium strategies of joint relay-channel selection in the condition of both limited information exchange and potential routing-toward-primary attacks. We also introduce a trustworthiness evaluation mechanism based on a multi-arm bandit process for normal users to avoid relaying to the sink-hole attackers. Simulation results show that without the need of information flooding, the proposed algorithm is efficient in bypassing the malicious nodes with mixed attacks.