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With the prevalence of mobile devices and ubiquitous wireless networks,spatial crowdsourcing has attracted much attention from both academic andindustry communities. On spatial crowdsourcing platforms, task requesters canpublish spatial tasks and workers need to move to destinations to perform them.In this paper, we formally define the Skilled Task Assignment with Extra Budget(STAEB), which aims to maximize total platform revenue and achieve fairness forworkers and task requesters. In the STAEB problem, the complex task needs morethan one worker to satisfy its skill requirement and has the extra budget tosubsidize extra travel cost of workers to attract more workers. We prove thatthe STAEB problem is NP-complete. Therefore, two approximation algorithms areproposed to solve it, including a greedy approach and a game-theoreticapproach. Extensive experiments on both real and synthetic datasets demonstratethe efficiency and effectiveness of our proposed approaches.

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing