Layered Trust-Aware Routing with Latency-Security Tradeoff Optimization in Maritime Networks

Abstract

Maritime emergency networks require routing policies that provide timely and secure packet delivery under sparse deployment, mobility-induced topology variation, heterogeneous node ownership, and potential adversarial interference. Existing approaches usually optimize communication efficiency or trust evaluation separately, offering limited support for mission-priority traffic under constrained link resources. This article proposes a layered trust-aware routing framework that couples task-hierarchical service control with security-aware deep reinforcement learning. The method first applies global pre-screening to remove infeasible or low-value relay candidates according to policy constraints, resource quotas, link load, energy, mobility, and geometric progress. It then uses a dynamic weighted fusion DQN to select the next hop from the screened candidates based on behavioral reputation, jurisdictional/policy attributes, geo-situational awareness, and network-utilization safety. Priority-aware scheduling, retransmission control, and task-aware reward shaping are further embedded into the learning loop. In the mobility-controlled performance evaluation at a node speed of 20 m/s, the proposed method improves throughput by 18.3% and reduces end-to-end delay by 22.7% compared with classical routing baselines. In the separate security-oriented evaluation, the method maintains a packet delivery ratio above 92.6% under a representative adversarial setting with 15% malicious nodes.