ProphetRouterRL: A Reinforcement Learning-Enhanced Routing Protocol for V2X Delay-Tolerant Networks

Vehicle-to-Everything (V2X) networks may experience intermittent connectivity and frequent link disruption due to high node mobility and variable density. When an end-to-end path cannot be guaranteed, Delay-Tolerant Networking (DTN) enables message delivery through store-carry-forward communication. Among DTN routing protocols, PRoPHET exploits historical and transitive encounters to estimate destination-specific delivery predictability, enabling forwarding decisions without requiring a contemporaneous end-to-end path. However, its Greater Than (GRTR) forwarding rule has limited contextual awareness because relay selection relies on relative delivery predictability. This paper proposes ProphetRouterRL, a two-stage routing protocol that augments PRoPHET with Q-learning. PRoPHET first handles contacts, predictabilities, and direct delivery; a Q-learning agent then chooses SEND or HOLD for each message from a six-dimensional state (hop count, relay buffer space, relay node type, relay as destination, remaining time-to-live, and destination-encounter history). ProphetRouterRL was evaluated against conventional PRoPHET using the ONE Simulator in a map-based urban scenario of UKM Bangi, Malaysia, with 129 heterogeneous nodes. Across four 12-h runs with different movement seeds, ProphetRouterRL delivered 90.26% (SD 0.54%) against 46.40% (SD 0.20%) for PRoPHET, with a 36.8% lower overhead ratio and 39.5% lower latency (p<0.001). These results demonstrate the potential of context-aware learned forwarding decisions in opportunistic V2X environments.