Underwater acoustic sensor networks (UASNs) have diverse applications in military and civilian domains but are vulnerable to various security threats due to their broadcast nature and challenging underwater environment. Trust mechanisms have emerged as effective solutions to enhance security and reliability in UASNs. However, existing trust models often lack efficient trust update mechanisms that can manage inevitable dynamic fluctuations in the underwater environment and various potential attacks. In this paper, an environment-aware Q-learning-based trust evaluation (EAQTE) scheme is presented in UASNs. EAQTE incorporates environmental features such as communication channel quality and node stability into the trust computation. Communication quality is assessed based on the variance in successful packet transmission probability, while node stability is measured through movement similarity. Each node collects three types of trust evidence — energy-based, data-based, and communication-based — by interacting with neighboring nodes. Energy-based evidence includes residual energy, current energy change rate, and the similarity of energy change sequences to normal patterns. Data-based evidence evaluates the consistency of collected data, and communication-based evidence considers successful and unsuccessful interactions. EAQTE uses a Q-learning algorithm with three trust states (belief, disbelief, uncertainty) to dynamically adapt trust levels. Simulation results demonstrate that EAQTE improves detection accuracy by 7.01% compared to TUMRL, ARTMM, and TMC based on simulation time. However, under attack mode switching scenarios, EAQTE’s detection accuracy is approximately 2.86% lower than TUMRL. Additionally, EAQTE reduces the false alarm rate by 19.65% relative to TUMRL when node speed varies, and by 11.8% compared to TUMRL under different node densities. Furthermore, EAQTE achieves higher energy efficiency and improves it by 5.19% over TUMRL when the percentage of compromised nodes increases, and by approximately 5.66% across varying node densities. These results indicate that EAQTE effectively balances adaptability, accuracy, and energy consumption in challenging underwater environments.
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