Space-Air-Ground Integrated Network (SAGIN) emerges as an appealing framework for
implementing pervasive connections among diverse layers of space, air, and
ground. The scheme relies on satellite deployment for extensive space coverage,
Unmanned Aerial Vehicle (UAVs) acting as agile aerial relay stations, and ground
robots for performing the final task assignment. Nevertheless, integrating
heterogeneous nodes with varying mobility characteristics poses significant
challenges: satellite operations are restricted by orbital dynamics, ground
robot movements are restricted by topography constraints, whereas UAVs
demonstrate flexibility and can establish line-of-sight connections with both
satellites and ground robots. Current solutions usually adopt UAVs merely as
passive relay nodes, causing inefficient adaptation mechanisms and excessive
communication overhead when responding to dynamic perturbations or blocked
connections. To overcome the aforementioned challenge, this paper proposes a
novel fuzzy initiative-yielding coordination approach assigning two opposite
behaviors. Initiative is assigned to UAVs in order to facilitate coordinated
operations between layers, whereas Yielding refers to the adaptive response of
satellites and ground robots. Contrary to previous research where behavioral
assignments follow strict threshold rules, our fuzzy inference considers various
parameters such as link quality, node state, and task priority, providing
flexible behavioral assignments. Our predictive approach incorporates the
Exponentially Weighted Moving Average (EWMA) technique to perform proactive
failure avoidance. Based on five comprehensive experiments with 30 independent
runs each, our approach obtains similar task completion rates (84.4%), but
exhibits significantly lower communication overhead by up to 11 to 147 times
compared to baseline methods. Furthermore, the proposed behavioral assignments
avoid any coordination conflict (0 vs. 30.8), reduce completion time by 10.1%,
maintain robust performance under 20% rule reduction, scale to 150-node
mega-constellation scenarios, and exhibit higher fault tolerance under dynamic
environments (81.5% vs. 79.8% of CBBA under high stress conditions).