  Collaborative Perception (CP) has been a promising solution to address
occlusions in the traffic environment by sharing sensor data among
collaborative vehicles (CoV) via vehicle-to-everything (V2X) network. With
limited wireless bandwidth, CP necessitates task-oriented and receiver-aware
sensor scheduling to prioritize important and complementary sensor data.
However, due to vehicular mobility, it is challenging and costly to obtain the
up-to-date perception topology, i.e., whether a combination of CoVs can jointly
detect an object. In this paper, we propose a combinatorial mobility-aware
sensor scheduling (C-MASS) framework for CP with minimal communication
overhead. Specifically, detections are replayed with sensor data from
individual CoVs and pairs of CoVs to maintain an empirical perception topology
up to the second order, which approximately represents the complete perception
topology. A hybrid greedy algorithm is then proposed to solve a variant of the
budgeted maximum coverage problem with a worst-case performance guarantee. The
C-MASS scheduling algorithm adapts the greedy algorithm by incorporating the
topological uncertainty and the unexplored time of CoVs to balance exploration
and exploitation, addressing the mobility challenge. Extensive numerical
experiments demonstrate the near-optimality of the proposed C-MASS framework in
both edge-assisted and distributed CP configurations. The weighted recall
improvements over object-level CP are 5.8% and 4.2%, respectively. Compared to
distance-based and area-based greedy heuristics, the gaps to the offline
optimal solutions are reduced by up to 75% and 71%, respectively.
