A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Jianheng Tang; Kejia Fan; Wenxuan Xie; Luomin Zeng; Feijiang Han; Guosheng Huang; Tian Wang; Anfeng Liu; Shaobo Zhang

doi:10.1016/j.comcom.2023.04.030

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Comput Commun. 2023 Jun 1:206:85-100. doi: 10.1016/j.comcom.2023.04.030. Epub 2023 Apr 29.

Authors

Jianheng Tang¹, Kejia Fan¹, Wenxuan Xie¹, Luomin Zeng², Feijiang Han¹, Guosheng Huang³, Tian Wang⁴, Anfeng Liu¹, Shaobo Zhang⁵

Affiliations

¹ School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
² School of Civil Engineering, Central South University, Changsha, 410083, China.
³ School of computer Science and Engineering, Hunan First Normal University, Changsha, 410205, China.
⁴ Department of Artificial Intelligence and Future Networks, Beijing Normal University & UIC, Zhuhai, Guangdong, China.
⁵ School of Computer Science and Engineering of the Hunan University of Science and Technology, Xiangtan, 411201, China.

Abstract

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.

Keywords: Mobile crowdsensing; Multi-armed bandit; Reverse auction; Worker recruitment.