TY - GEN
T1 - Expertise-Aware Truth Analysis and Task Allocation in Mobile Crowdsourcing
AU - Zhang, Xiaomei
AU - Wu, Yibo
AU - Huang, Lifu
AU - Ji, Heng
AU - Cao, Guohong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/13
Y1 - 2017/7/13
N2 - Mobile crowdsourcing has received considerable attention as it enables people to collect and share large volume of data through their mobile devices. Since the accuracy of the collected data is usually hard to ensure, researchers have proposed techniques to identify truth from noisy data by inferring and utilizing the reliability of users, and allocate tasks to users with higher reliability. However, they neglect the fact that a user may only have expertise on some problems (in some domains), but not others. Neglecting this expertise diversity may cause two problems: low estimation accuracy in truth analysis and ineffective task allocation. To address these problems, we propose an Expertise-aware Truth Analysis and Task Allocation (ETA2) approach, which can effectively infer user expertise and then allocate tasks and estimate truth based on the inferred expertise. ETA2 relies on a novel semantic analysis method to identify the expertise domains of the tasks and user expertise, an expertise-aware truth analysis solution to estimate truth and learn user expertise, and an expertise-aware task allocation method to maximize the probability that tasks are allocated to users with the right expertise while ensuring the work load does not exceed the processing capability at each user. Experimental results based on two real-world datasets demonstrate that ETA2 significantly outperforms existing solutions.
AB - Mobile crowdsourcing has received considerable attention as it enables people to collect and share large volume of data through their mobile devices. Since the accuracy of the collected data is usually hard to ensure, researchers have proposed techniques to identify truth from noisy data by inferring and utilizing the reliability of users, and allocate tasks to users with higher reliability. However, they neglect the fact that a user may only have expertise on some problems (in some domains), but not others. Neglecting this expertise diversity may cause two problems: low estimation accuracy in truth analysis and ineffective task allocation. To address these problems, we propose an Expertise-aware Truth Analysis and Task Allocation (ETA2) approach, which can effectively infer user expertise and then allocate tasks and estimate truth based on the inferred expertise. ETA2 relies on a novel semantic analysis method to identify the expertise domains of the tasks and user expertise, an expertise-aware truth analysis solution to estimate truth and learn user expertise, and an expertise-aware task allocation method to maximize the probability that tasks are allocated to users with the right expertise while ensuring the work load does not exceed the processing capability at each user. Experimental results based on two real-world datasets demonstrate that ETA2 significantly outperforms existing solutions.
UR - http://www.scopus.com/inward/record.url?scp=85027279680&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027279680&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2017.56
DO - 10.1109/ICDCS.2017.56
M3 - Conference contribution
AN - SCOPUS:85027279680
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 922
EP - 932
BT - Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
A2 - Lee, Kisung
A2 - Liu, Ling
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Y2 - 5 June 2017 through 8 June 2017
ER -