MAB-Based Reinforced Worker Selection Framework for Budgeted Spatial Crowdsensing

Spatial crowdsensing is a special kind of crowdsourcing which allocates tasks to workers in some special places where workers can sense data for them. Due to the lack of priori information about the quality of workers and the ground truth, selecting the most suitable workers, which can guarantee the quality of the sensing tasks, remains a great challenge. In this paper, we propose a novel framework which can choose the most reliable workers among available workers under constraint budget. We model the quality of workers through two factors, bias and variance, which describe the continuous feature of sensing tasks. Our framework first allocate some calibration tasks to calibrate the bias and then iteratively estimate the workers variance more and more accurately. To choose more reliable workers, we face the exploration and exploitation dilemma. Therefore, we design a novel Multi-Armed Bandit (MAB) algorithm which based on Upper Confidence Bounds (UCB) scheme and combined with a weighted data aggregation scheme to estimate a more accurate ground truth of a sensing task. Futhermore, a dynamic budget allocation algorithm is designed to achieve global optimization. Then, we prove the expected sensing error can be bounded according to the regret bound of the MAB. In simulation experiments, we compare our algorithm with several baselines with real-world data set and it shows the effectiveness in inferring the ground truth with limited budget.