Secrecy capacity maximization in autonomous underwater vehicle‐enabled underwater acoustic sensor networks

Abstract Data collection plays an essential role in underwater acoustic sensor networks (UASNs). To address the problem of underwater information collection, autonomous underwater vehicles (AUVs), which are dynamic and easy to reprogram, are expected to provide a feasible data‐gathering solution. In this study, we examined covert data collection in AUV‐assisted UASNs. Specifically, an AUV gathers covert information from all underwater sensor nodes (USNs) at the planned time, while an eavesdropper attempts to eavesdrop on this secret information. To improve the performance of UASNs, we formulate a complex optimization problem to maximize secrecy capacity under the constraints of the trajectory of the AUV, USN scheduling, connection outage probability, and secrecy outage probability. To solve the nonconvex problem, an efficient iterative optimization algorithm is proposed to optimize USN scheduling and AUV trajectories. Numerical results demonstrate the effectiveness of the proposed algorithm.