Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing, and communication with the emergence of bidirectional computation tasks. However, Quality-of-Service provisioning is still challenging since USVs are restricted with limited onboard resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)–USV mobile-edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase-shift vector, RUAV service selection indicator, and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase-shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicator subproblem, and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.

中文翻译：

---

RIS 辅助合作无人机-USV MEC 网络的能量最小化


随着双向计算任务的出现，无人水面航行器 （USV） 在实现集成传感、计算和通信方面变得越来越重要。然而，服务质量配置仍然具有挑战性，因为 USV 受到机载资源有限的限制，并且它们与岸基地面基站 （TBS） 之间的直接连接经常受阻。本文提出了一种新颖的可重构智能表面 （RIS） 辅助协同无人机 （UAV）-USV 移动边缘计算 （MEC） 网络架构，其中 RIS 安装的系留无人机 （TUAV） 和旋翼无人机 （RUAV） 协同用于为 USV 服务。RUAV 能量最小化是通过共同考虑 TUAV 悬停高度、RIS 相移向量、RUAV 服务选择指标和 RUAV 转折点来制定的。提出了一种启发式解决方案来解决所制定的问题，其中原始问题首先解耦为三个子问题，例如RIS相移向量和TUAV悬停高度子问题的联合优化、RUAVs服务选择指标子问题和RUAVs转折点子问题，每个子问题都由所提出的改进的乘子替代方向法（ADMM）算法求解。 提出的增强模拟退火 （ESA） 算法和提出的基于连续凸近似 （SCA） 的算法。通过这种方式，可以有效地迭代解决具有挑战性的问题。结果表明，与众多选定的高级算法相比，所提出的解决方案可以将 RUAV 的能耗降低近 29%。此外，还研究了所提出的解决方案在典型罚系数和 RIS 反射元件数量方面的性能。