Competition and Cooperation: Global Task Assignment in Spatial Crowdsourcing

Boyang Li, Yurong Cheng*, Ye Yuan, Changsheng Li, Qianqian Jin, Guoren Wang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

5 引用 (Scopus)

摘要

Online spatial crowdsourcing platforms provide popular O2O services in people's daily. Users submit real-time tasks through the Internet and require the platform to immediately assign workers to serve them. However, the imbalance distribution of tasks and workers leads to the rejection of some tasks, which reduces the profit of the platform. In this paper, we propose that similar platforms can form an alliance to make full use of the global service supply through cooperation. We name the problem as Global Task Assignment (GTA), in which platforms are allowed to hire idle workers from other platforms to improve the profit of all the platforms together. Different from relevant works, the decision-makers in GTA are platforms rather than individual workers, which can better assign workers in all platforms and improve the overall profit. We design an auction-based incentive mechanism (AIM), to motivate platforms to rent idle workers to other platforms so that increase their own profit. Based on the mechanism, we propose a greedy-based assignment algorithm (BaseGTA), in which platforms greedily maximizes their current profit. We further propose a prediction-based assignment algorithm (ImpGTA), in which platforms make decisions based on the spatial-temporal distribution in the future time. Experimental results show that platforms using our algorithms can achieve higher profit than the existing studies.

源语言英语
页(从-至)9998-10010
页数13
期刊IEEE Transactions on Knowledge and Data Engineering
35
10
DOI
出版状态已出版 - 1 10月 2023

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