Optimizing USV AoI for RIS‐Assisted UAV–USV MEC Network

ABSTRACT Aiming to promise timely data delivery and quantify the information freshness of unmanned surface vehicles (USVs), age of information (AoI) is proposed as a novel metric regarding exploring implementations for reconfigurable intelligent surface (RIS)–assisted unmanned aerial vehicle (UAV)–USV multiaccess edge computing network. In this paper, a set of RIS‐carried UAVs serves USVs via time division multiple access; each RIS is capable of delivering a single reflection per USV within its service duration. USV long‐term time‐averaged AoI (AAoI) minimization problem is investigated under USV service duration indicators, UAV‐mounted RIS phase shift vector, terrestrial base station (TBS) beamforming vector, and UAV trajectory constraints. To efficiently solve the formulated problem, Lyapunov framework is applied to decompose the original problem into an array of per‐slot problems, where each of which can be divided into numerous subproblems, for example, TBS beamforming vector subproblem, RIS phase shift subproblem, and the joint UAV trajectories and USV service duration indicator subproblem. Then, each subproblem can be solved using the proposed successive convex approximation, semidefinite relaxation method, and the enhanced differential evolution algorithm iteratively. Consequently, one can efficiently obtain the feasible solution. The proposed solution reduces USV AAoI by up to 55% while maintaining lower UAV power consumption compared with benchmarks. Also, the proposed solution can promise adequate network queue backlogs under typical USV task data size.