Adaptive Deep Reinforcement Learning-Based Secure Transmission Mechanism for Underwater Wireless Sensor Networks

Le résumé fourni par la source

To address the security challenges of underwater wireless sensor networks (UWSNs) in complex and hostile environments, this paper investigates the physical-layer secrecy performance of the transmitter-receiver link, with particular emphasis on transmission optimization under eavesdropping threats. We formulate a non-linear, non-convex optimization problem that jointly allocates sub-channels and transmission power to maximize the secrecy rate while considering the inherent constraints of underwater acoustic communication. To efficiently solve this problem, we develop a deep reinforcement learning (DRL)-based resource allocation algorithm that integrates a prioritized experience replay mechanism, thereby enhancing learning efficiency and improving optimization performance. Extensive simulations are conducted to assess the convergence and adaptability of the proposed method under varying node densities and maximum transmission power levels. The results demonstrate that the proposed algorithm consistently achieves higher system security, greater stability, and faster convergence compared to several benchmark schemes.

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