RL-Recruiter+: Mobility-Predictability-Aware Participant Selection Learning for From-Scratch Mobile Crowdsensing

Yunfan Hu, Jiangtao Wang, Bo Wu, Sumi Helal

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Participant selection is a fundamental research issue in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long periods of candidate participants' historical mobility trajectories are available to model their patterns before the selection process, which is not realistic for some new MCS applications or platforms. The sparsity or even absence of mobility traces will incur inaccurate location prediction, thus undermining the deployment of new MCS applications. To this end, this paper investigates a novel problem called From-Scratch MCS (FS-MCS for short), in which we study how to intelligently select participants to minimize such cold-start effect. Specifically, we propose a novel framework based on reinforcement learning, named RL-Recruiter+. With the gradual accumulation of mobility trajectories over time, RL-Recruiter+ is able to make a good sequence of participant selection decisions for each sensing slot. Compared to its previous version, RL-Recruiter, Re-Recruiter+ jointly considers both the previous coverage and current mobility predictability when training the participant selection decision model. We evaluate our approach experimentally based on two real-world mobility datasets. The results demonstrate that RL-Recruiter+ outperforms the baseline approaches, including RL-Recruiter under various settings.

Original languageEnglish
Pages (from-to)4555-4568
Number of pages14
JournalIEEE Transactions on Mobile Computing
Issue number12
Early online date5 May 2021
Publication statusPublished - 1 Dec 2022

Bibliographical note

Publisher Copyright:

Copyright 2021 Elsevier B.V., All rights reserved.


  • Crowdsensing
  • Electronic mail
  • Reinforcement learning
  • Robot sensing systems
  • Sensors
  • Task analysis
  • Trajectory

ASJC Scopus subject areas

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


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