DATA: A Double Auction Based Task Assignment Mechanism in Crowdsourcing Systems

With the increasing number of smartphone users, mobile phone sensing applications have been regarding as a promising paradigm which makes use of the smartphones to access the ubiquitous environment data. In this work, we study the sensing task auction problem where there are multiple tasks and smartphone users. The most significant challenge of this problem is how to design a truthful auction mechanisms, which is crucial for auction mechanism design. Thus, we address this challenge by proposing DATA, which is a truthful double auction mechanism for sensing tasks allocation. Different from the existing designs, we are the first to design double auction mechanism for solving mobile phone sensing problem. Besides, we further take the relationship between the utility of task demanders and the number of users that are assigned to do the tasks into consideration, and assign a set of smartphone users to a winning demander which can maximize the winning demander's utility. At last, we conduct extensive simulations to study the performances of the proposed auction mechanism, and the simulation results corroborate our theoretical analysis.