HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

Jiangtao Wang, Feng Wang, Yasha Wang, Leye Wang, Zhaopeng Qiu, Daqing Zhang, Bin Guo, Qin Lv

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers ) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers ) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.
Original languageEnglish
Article number8640066
Pages (from-to)598-611
Number of pages14
JournalIEEE Transactions on Mobile Computing
Issue number3
Early online date12 Feb 2019
Publication statusPublished - 1 Mar 2020
Externally publishedYes


  • Mobile crowd sensing
  • hybrid approach
  • task allocation

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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