Spatial 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.
@article{arxiv.2310.12433,
title = {Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity},
author = {Kun Li and Shengling Wang and Hongwei Shi and Xiuzhen Cheng and Minghui Xu},
journal= {arXiv preprint arXiv:2310.12433},
year = {2023}
}