Reinforcement Learning for Covert Heterogeneous Wireless Network Routing with a Threat Region

Multi-hop Heterogeneous wireless networks (HWNs) with multiple communication technologies have been extensively studied driven by the rising demand for enhanced resilience, coverage, and throughput. However, utilizing relays for wireless communication has the potential to heighten the risk of detection by an adversary. Furthermore, information about the adversary is usually unavailable in practice. Therefore, in this paper, we propose a covert routing using Q-learning for HWNs to maximize the detection error probability (DEP) with only limited knowledge about the adversary's general location, referred to as the threat region. To achieve this goal, we exploit fictitious adversaries that are randomly located inside the threat region to obtain estimated DEP. Then, we propose three different approaches to establish a route between the source and destination. Through simulations, we compare our proposed approaches to the conventional covert routing using Q-learning where the location of the adversary is assumed to be known. Our results show that our methods only experience a 10% reduction in DEP with fictitious adversaries. Furthermore, we investigate how the radius of the threat region affects the performance.