Automatic Data Quality Enhancement with Expert Knowledge for Mobile Crowdsensing

Mobile crowdsensing (MCS) has recently found many applications in environmental monitoring and large-scale surveillance by recruiting crowd workers for data collection and labeling. The quality of labelled data from unknown crowd workers, however, is hard to guarantee. Therefore, it is critical to design a mechanism that can automatically make correct decisions from diverse and even conflicting labels from the crowd. To tackle the challenge, we propose a new algorithm, EFusion, which infuses knowledge from domain experts by asking them to check a small number of labels from the crowd. Taking advantage of cheaper but unreliable crowd workers as well as expensive but reliable experts, EFusion can greatly improve the accuracy in discovering the ground truth of classification-based mobile crowdsensing tasks. EFusion utilizes a probabilistic graphical model and the expectation maximization (EM) algorithm to infer the most likely expertise level for each crowd worker, the difficulty level of tasks, and the ground truth answers. EFusion has been evaluated using real-world case study as well as simulations. Evaluation results demonstrate that EFusion can return more accurate and stable classification results than the majority voting method and state-of-the-art methods.