Online Data Quality Learning for Quality-Aware Crowdsensing

Crowdsensing has found a variety of applications (e.g., spectrum sensing, environmental monitoring) by leveraging the "wisdom" of a potentially large crowd of mobile users as "workers". The value of data collected in crowdsensing heavily depends on the quality of data provided by the workers participating in a crowdsensing task. In general, the quality of data varies for different workers. To fully exploit the potential of crowdsensing, it is important for the crowdsensing requester to know workers' data quality, based on which the requester allocates tasks to workers and aggregates data from workers. Such quality-aware crowdsensing can greatly improve the value and usefulness of data in crowdsensing. However, the quality of workers' data is often unknown to the requester (due to, e.g., workers' characteristics are unknown). In this paper, under a dynamic multi-task crowdsensing framework, we devise an online data quality learning algorithm that learns the data quality of workers from their data on the fly, while making use of the learned quality information to perform task allocation and data aggregation. Compared to prior online learning algorithms (such as those for the multi-armed bandit problems), our algorithm needs to overcome the challenge that the ground truth of the interested variable is unknown. We show that under some mild conditions, our algorithm converges to the offline optimal strategy over time, and have a regret in the order of O(logt) compared to the offline optimal strategy. We provide bounds on the regret for both the requester's utility and cost, and for both the simple average rule and the weighted average rule. We demonstrate the efficiency of the algorithm using simulation results.