  The recruitment of trustworthy and high-quality workers is an important
research issue for MCS. Previous studies either assume that the qualities of
workers are known in advance, or assume that the platform knows the qualities
of workers once it receives their collected data. In reality, to reduce costs
and thus maximize revenue, many strategic workers do not perform their sensing
tasks honestly and report fake data to the platform, which is called False data
attacks. And it is very hard for the platform to evaluate the authenticity of
the received data. In this paper, an incentive mechanism named Semi-supervision
based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to
solve the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem and design an UCB-based algorithm to separate the exploration and
exploitation, regarding the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We
theoretically prove that our SCMABA achieves truthfulness and individual
rationality and exhibits outstanding performances of the SCMABA mechanism
through in-depth simulations of real-world data traces.
