  With the rapid development of Mobile Internet, spatial crowdsourcing is
gaining more and more attention from both academia and industry.
  In spatial crowdsourcing, spatial tasks are sent to workers based on their
locations.
  A wide kind of tasks in spatial crowdsourcing are specialty-aware, which are
complex and need to be completed by workers with different skills
collaboratively.
  Existing studies on specialty-aware spatial crowdsourcing assume that each
worker has a united charge when performing different tasks, no matter how many
skills of her/him are used to complete the task, which is not fair and
practical.
  In this paper, we study the problem of specialty-aware task assignment in
spatial crowdsourcing, where each worker has fine-grained charge for each of
their skills, and the goal is to maximize the total number of completed tasks
based on tasks' budget and requirements on particular skills.
  The problem is proven to be NP-hard. Thus, we propose two efficient
heuristics to solve the problem.
  Experiments on both synthetic and real datasets demonstrate the effectiveness
and efficiency of our solutions.
