  Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a
crowd of workers are recruited to perform sensing tasks collaboratively.
Although it has stimulated many applications, an open fundamental problem is
how to select among a massive number of workers to perform a given sensing task
under a limited budget. Nevertheless, due to the proliferation of smart devices
equipped with various sensors, it is very difficult to profile the workers in
terms of sensing ability. Although the uncertainties of the workers can be
addressed by standard Combinatorial Multi-Armed Bandit (CMAB) framework through
a trade-off between exploration and exploitation, we do not have sufficient
allowance to directly explore and exploit the workers under the limited budget.
Furthermore, since the sensor devices usually have quite limited resources, the
workers may have bounded capabilities to perform the sensing task for only few
times, which further restricts our opportunities to learn the uncertainty. To
address the above issues, we propose a Context-Aware Worker Selection (CAWS)
algorithm in this paper. By leveraging the correlation between the context
information of the workers and their sensing abilities, CAWS aims at maximizing
the expected total sensing revenue efficiently with both budget constraint and
capacity constraints respected, even when the number of the uncertain workers
is massive. The efficacy of CAWS can be verified by rigorous theoretical
analysis and extensive experiments.
