Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

TL;DR

This paper introduces a graph-based spatial crowdsourcing task allocation scheme that efficiently manages massive, heterogeneous spatial data by clustering and novel graph modeling to improve matching balance and computational performance.

Contribution

It proposes a new graph-based framework with clustering and non-crossing graph structures for optimized task-worker matching in spatial crowdsourcing.

Findings

Improved computational efficiency in large-scale data scenarios

Enhanced balance in task-worker matching

Effective handling of spatial heterogeneity

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.