A Data Transmission Scheme Based on Reinforcement-Learning-Aided Two-Stage Trust Evaluation for UASNs

Constructing underwater acoustic sensor networks (UASNs) for data collection has gradually become an effective ocean exploration and exploitation method. However, the interference of the underwater environment and the limited capacity of underwater communication equipment increase the difficulty of information interaction, posing a challenge to secure data transmission strategies for UASNs. Therefore, this study proposes a safe and reliable data transmission scheme based on reinforcement learning-aided two-stage trust evaluation (RLTST) to overcome the problems mentioned above. This article proposes a distinct self-trust concept, different from traditional trust mechanisms. A node self-trust evaluation method based on Q-learning is designed in the first stage, which defects compromised nodes actively. In the second stage, the trustworthiness of data is calculated based on the real data received, followed by backtracking the transmission path of untrustworthy data to identify malicious nodes. Finally, the results show that our proposed scheme is more effective in malicious node detection and improves data collection reliability.