  Social sensing has emerged as a new sensing paradigm where humans (or devices
on their behalf) collectively report measurements about the physical world.
This paper focuses on a quality-cost-aware task allocation problem in
multi-attribute social sensing applications. The goal is to identify a task
allocation strategy (i.e., decide when and where to collect sensing data) to
achieve an optimized tradeoff between the data quality and the sensing cost.
While recent progress has been made to tackle similar problems, three important
challenges have not been well addressed: (i) "online task allocation": the task
allocation schemes need to respond quickly to the potentially large dynamics of
the measured variables in social sensing; (ii) "multi-attribute constrained
optimization": minimizing the overall sensing error given the dependencies and
constraints of multiple attributes of the measured variables is a non-trivial
problem to solve; (iii) "nonuniform task allocation cost": the task allocation
cost in social sensing often has a nonuniform distribution which adds
additional complexity to the optimized task allocation problem. This paper
develops a Quality-Cost-Aware Online Task Allocation (QCO-TA) scheme to address
the above challenges using a principled online reinforcement learning
framework. We evaluate the QCO-TA scheme through a real-world social sensing
application and the results show that our scheme significantly outperforms the
state-of-the-art baselines in terms of both sensing accuracy and cost.
