Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing

Abstract

Purposes To address the imbalance between supply and demand in traditional single platform task assignment, Cross Online Matching (COM) has emerged as a novel solution that allows multiple similar platforms to establish cooperative relationships and send uncompleted tasks to other platforms, increasing the probability of task acceptance. However, current COM solutions only consider single-round matching processes, making it difficult to find optimal decision results in multi-platform competition. To settle these limitations, the Multi-Round Cross Online Matching problem (MRCOM) is studied and Greedy-based Multi-Round Cross Online Matching (G-MRCOM) and Game-Theoretic Multi-Round Cross Online Matching (GT-MRCOM) algorithms are proposed. Methods G-MRCOM improves task completion efficiency by forwarding and matching tasks in multiple rounds, with platforms greedily selecting high-reward tasks to accomplish. GT-MRCOM, on the other hand, establishes incentive mechanisms among algorithms cooperating platforms, calculates task assignment strategies that satisfy Nash Equilibrium, and enables the platform to find better strategies in competition, thereby enhancing overall performance. Findings Experimental results demonstrate that the proposed algorithms can increase the total revenue of platforms, showcasing the effectiveness and efficiency of this study.

Keywords

• spatial-temporal crowdsourcing
• task assignment
• online matching
• game theory
• greedy