Secure and energy-efficient UAV-assisted maritime communication under multiple eavesdroppers with imperfect CSI and collusion

The maritime communication networks (MCNs) for industry require reliable, efficient, and wide coverage to deploy emerging intelligent marine applications. To this end, unmanned aerial vehicles (UAVs) can be integrated into MCNs to extend flexibility and mobility. However, harsh maritime environments and open line-of-sight (LoS) links increase the UAV onboard energy consumption and worsen communication security. In this work, we aim to propose a collaborative beamforming-based physical layer secure and energy-efficient communication method for UAV-assisted MCNs, in which the energy limitations of UAVs and the interference from both known and unknown eavesdroppers as well as the possible collusion among eavesdroppers are considered. Specifically, we formulate a multi-objective optimization problem (MOP) to improve the system confidentiality rate, reduce the level ratio of the UAV virtual antenna array, and decrease the energy consumption of the UAVs by jointly optimizing the UAV positions and synthesizing the UAV virtual antenna array. This MOP is non-convex and NP-hard, and thus we propose an enhanced non-dominated sorted whale optimization algorithm (ENSWOA) to solve the problem. In ENSWOA, the introduced chaotic solution initialization, adaptive weighting, and optimal position updating methods can increase the searching capability of the algorithm. Simulation results show that the proposed ENSWOA outperforms the baseline algorithms, and can effectively enhance the security and energy efficiency of the considered system.