  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.
