TY - GEN
T1 - CrowdRecruiter
T2 - 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
AU - Zhang, Daqing
AU - Xiong, Haoyi
AU - Wang, Leye
AU - Chen, Guanling
N1 - Publisher Copyright:
Copyright © 2014 by the Association for Computing Machinery, Inc. (ACM).
PY - 2014/1/1
Y1 - 2014/1/1
N2 - This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale realworld dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% - 73.5% fewer participants on average under the same probabilistic coverage constraint.
AB - This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale realworld dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% - 73.5% fewer participants on average under the same probabilistic coverage constraint.
KW - Participant selection for mobile crowdsensing
KW - Piggyback crowdsensing
UR - https://www.scopus.com/pages/publications/84908604900
U2 - 10.1145/2632048.2632059
DO - 10.1145/2632048.2632059
M3 - Conference contribution
AN - SCOPUS:84908604900
T3 - UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 703
EP - 714
BT - UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
Y2 - 13 September 2014 through 17 September 2014
ER -