Two-Stage Incentive Mechanism Based on a Cooperative Mode: A Stackelberg Game Approach

With the explosive growth of mobile data, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection. The difficulty of data collection and the gaps in workers’ sensing capabilities are key factors to consider in worker recruitment and task assignment. To address these issues, we designed a two-stage cooperative incentive mechanism. In the first stage, a shelving level is introduced to assess task difficulty. Tasks are divided into high-quality and low-quality groups based on quality scores, while workers are categorized into high-ability and low-ability groups based on their historical performance. The Improved Chaotic Particle Swarm Optimization (ICPSO) algorithm is then applied to generate optimal task combinations. In the second stage, we address benefit distribution among cooperating workers by introducing time-dependent rewards and employing Stackelberg game theory to analyze the optimal completion time for both high-ability and low-ability workers. This analysis determines the optimal reward distribution and ability value allocation, with proof of the existence and uniqueness of a Nash equilibrium. Comparative experiments conducted on real-world datasets demonstrate that our cooperative task mechanism outperforms existing task bundling methods, validating its rationality and effectiveness.