A Learning-based Credible Participant Recruitment Strategy for Mobile Crowd Sensing

Hui Gao, Yu Xiao, Han Yan, Ye Tian, Danshi Wang, Wendong Wang

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
208 Downloads (Pure)


Mobile crowd sensing (MCS) acts as a key component of Internet of Things (IoT), which has attracted much attention. In an MCS system, participants play an important role, since all the data are collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high-quality data. In this article, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants' profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations in which relatively few data have so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict the quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality, and truthfulness. We evaluate the proposed scheme via simulation using three real data sets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with the other two methods.

Original languageEnglish
Article number9016107
Pages (from-to)5302-5314
Number of pages13
JournalIEEE Internet of Things Journal
Issue number6
Early online date2020
Publication statusPublished - Jun 2020
MoE publication typeA1 Journal article-refereed


  • Participant recruitment
  • deep reinforcement learning
  • mobile crowdsensing


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