Game-Based Multi-UAV Dynamic Collaborative With Energy-Efficient Hierarchical Information Sharing for Mobile Crowdsensing

The multiple Uncrewed Aerial Vehicles (multi-UAV) collaborative system significantly augments perception capabilities and coverage range of task environments through the establishment of a comprehensive three-dimensional monitoring network, emerging as an indispensable technological cornerstone for future Mobile Crowdsensing (MCS) systems. However, the co-existence of environmental dynamics and device heterogeneity induces non-trivial energy efficiency imbalances across the UAVs, posing a substantial challenge to achieving sustained and efficient multi-UAV exploration. Therefore, we propose an energy-efficient cluster cooperative exploration method that jointly optimizes information sharing and task allocation. To balance communication energy efficiency among UAVs, we introduce an energy-efficient hierarchical information sharing mechanism that dynamically adjusts relay nodes based on real-time attributes of UAVs. In order to improve the utilization of resources, a multi-UAV cooperative task allocation model was developed using cooperative game. It has also been proven that a fair task allocation strategy exists, which is acceptable to all UAVs. Furthermore, the approximate Shapley value of every UAV is calculated using the improved Monte Carlo sampling method combined with incremental update mechanism to ensure fair task allocation. Experimental results demonstrate that the maximum enhancement of task completion ratio is 12%, 26%, and 10%, respectively, at task-critical thresholds for systems utilizing 5, 10, and 15 UAVs. Moreover, the proposed method demonstrated superior performance in energy consumption ratio, synergy, and energy consumption difference compared to benchmarks.