Data Pricing via Uncertainty Quantification for Sparse Mobile CrowdSensing

Sparse Mobile CrowdSensing (MCS) is a cost-effective paradigm which recruits users to sense only a small amount of data and infer the rest ones. The existing works usually provide rewards for users to cover their sensing costs. Compared with the costs, we actually need to pay for the value of data, which is a new and valuable perspective of data pricing. However, in Sparse MCS, we can hardly estimate the value of data accurately, as it depends not only on the data itself but also on its contribution to inference. Moreover, the sensing data is naturally noisy and its spatiotemporal information may be invisible until we pay for it, which makes the data pricing more challenging. To this end, we investigate the value of sensing data, and propose a data pricing framework for Sparse MCS. We first present the sampling-based uncertainty quantification to evaluate the value of data, with the aim to pay for the intrinsic data values but not the indirect costs. As a byproduct, the noise can be dealt with naturally by such sampling-based methods. Further considering the availability of spatiotemporal information, we also study three cases of data pricing, i.e, paying for sensed data, spatiotemporal information, and next data. Extensive experiments on five real-world data sets verify the effectiveness of our proposals.