  With the prevalence of mobile devices and ubiquitous wireless networks,
spatial crowdsourcing has attracted much attention from both academic and
industry communities. On spatial crowdsourcing platforms, task requesters can
publish 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 for
workers and task requesters. In the STAEB problem, the complex task needs more
than one worker to satisfy its skill requirement and has the extra budget to
subsidize extra travel cost of workers to attract more workers. We prove that
the STAEB problem is NP-complete. Therefore, two approximation algorithms are
proposed to solve it, including a greedy approach and a game-theoretic
approach. Extensive experiments on both real and synthetic datasets demonstrate
the efficiency and effectiveness of our proposed approaches.
