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Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Research output: Contribution to journalJournal article

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  • Jiangtao Wang
  • Feng Wang
  • Yasha Wang
  • Daqing Zhang
  • Leye Wang
  • Zhaopeng Qiu
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<mark>Journal publication date</mark>1/07/2019
<mark>Journal</mark>IEEE Transactions on Mobile Computing
Issue number7
Volume18
Number of pages13
Pages (from-to)1661-1673
Publication statusPublished
Early online date11/08/18
Original languageEnglish

Abstract

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.

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©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.