Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Kun Li, Shengling Wang, Hongwei Shi, Xiuzhen Cheng, Minghui Xu
AbstractSpatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings.