In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts.

翻译：本文研究了动态聚类与调度下能效无人机群辅助移动边缘计算（MEC）技术。在考虑的系统模型中，无人机被划分为多个集群，每个集群包含一架领航无人机及若干跟随无人机，为终端用户提供计算服务。与现有研究不同，我们允许无人机以动态方式形成不同集群，即每架跟随无人机可根据时变的空间位置、更新的应用程序部署等因素动态更换其所属领航无人机。同时，无人机需要动态调度其能量补充、应用程序部署、轨迹规划及任务分配。为最大化无人机群辅助MEC系统的长期能效，本文构建了动态聚类与调度的联合优化问题。考虑智能无人机间的协同与竞争关系，我们进一步将该优化问题重构为一系列强耦合多智能体随机博弈的组合，并提出一种基于强化学习的无人机群动态协调（RLDC）算法以求解博弈均衡。仿真实验评估了RLDC算法的性能，并证实其相较现有方法的优越性。