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arXiv · 2310.12433

Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

Kun Li·Shengling Wang·Hongwei Shi·Xiuzhen Cheng·Minghui Xu
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Abstract

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.

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Kun Li, Shengling Wang, Hongwei Shi, Xiuzhen Cheng, Minghui Xu. 2023-10-19. Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity. https://arxiv.org/abs/2310.12433

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