CADTR: Context-Aware Trust Routing Algorithm Based on Priority Sampling DDPG for UASNs

The underwater acoustic sensor network (UASN) is a pivotal paradigm within the underwater Internet of Things, where multi-hop forwarding-based underwater data routing is essential for information acquisition. However, the dynamic nature of underwater network topology and the instability of underwater acoustic communication pose significant challenges to achieving efficient and reliable data transmission. In light of unreliable underwater environments and potential malicious attacks, studying trusted routing strategies for UASNs is crucial. This study introduces a context-aware trust routing scheme (CADTR) based on deep reinforcement learning (DRL), which integrates real-time environmental state perception with AI-driven routing decisions, thereby enhancing the reliability and robustness of data routing in dynamic and potentially hostile underwater scenarios. First, a unified trust evidence framework is developed to strengthen the support of evidence experience for subsequent trust decisions by mapping multi-dimensional trust evidence to a unified scale. This framework is tightly coupled with the DRL agent, allowing the agent to evaluate and update trust levels based on real-time evidence. Second, a dynamic topology perception model and an underwater acoustic communication perception model are constructed to enable real-time perception of the interactive experience context. These models provide continuous input to the DRL agent, enabling it to adapt to topological changes and communication conditions dynamically. This facilitates priority experience sampling during the training process of the routing decision model, indirectly boosting model training efficiency and decision accuracy. Finally, the DRL agent learns optimal routing policies by interacting with the environment, leveraging the trust evidence and perception models to make informed decisions. Experimental results demonstrate that the proposed CADTR algorithm significantly improves the overall performance of the routing strategy in terms of packet delivery rate, energy utilization efficiency, and data transmission delay compared to the benchmark algorithms.