Expertise-Aware Truth Analysis and Task Allocation in Mobile Crowdsourcing

In mobile crowdsourcing, 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 mobile 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, and hence causing two problems: low estimation accuracy in truth analysis and ineffective task allocation. To address these problems, we propose Expertise-aware Truth Analysis and Task Allocation (ETA(2)), which can effectively infer user expertise, and then estimate truth and allocate tasks based on the inferred expertise. ETA(2) relies on a novel semantic analysis method to identify the expertise, and an expertise-aware truth analysis method to find the truth. For expertise-aware task allocation in ETA(2), we formalize and solve two problems based on the optimization objectives: max-quality task allocation which maximizes the probability for tasks to be allocated to users with high expertise and min-cost task allocation which minimizes the cost of task allocation while ensuring high-quality data are collected. Experimental results based on two real-world datasets and one synthetic dataset demonstrate that ETA(2) significantly outperforms existing solutions.

Automated summary

Expertise-aware Truth Analysis and Task Allocation (ETA(2)) is proposed to address the issues of low estimation accuracy and ineffective task allocation in mobile crowdsourcing. By inferring user expertise and allocating tasks based on this expertise, ETA(2) significantly outperforms existing solutions in terms of collecting high-quality data.