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

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd