Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Jiangtao Wang, Feng Wang, Yasha Wang, Daqing Zhang, Leye Wang, Zhaopeng Qiu

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)


Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.

Original languageEnglish
Pages (from-to)1661 - 1673
Number of pages13
JournalIEEE Transactions on Mobile Computing
Issue number7
Early online date13 Aug 2018
Publication statusPublished - 1 Jul 2019
Externally publishedYes


  • Worker recruitment
  • mobile crowd sensing
  • social network
  • smart city


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