As a significant business paradigm, data trading has attracted increasing attention. However, the study of data acquisition in data markets is still in its infancy. Mobile crowdsensing has been recognized as an efficient and scalable way to acquire large-scale data. Designing a practical data acquisition scheme for crowd-sensed data markets has to consider three major challenges: crowd-sensed data trading format determination, profit maximization with polynomial computational complexity, and payment minimization in strategic environments. In this paper, we jointly consider these design challenges, and propose VENUS, which is the first profit-driVEN data acqUiSition framework for crowd-sensed data markets. Specifically, VENUS consists of two complementary mechanisms: VENUS-PRO for profit maximization and VENUS-PAY for payment minimization. Given the expected payment for each of the data acquisition points, VENUS-PRO greedily selects the most "cost-efficient" data acquisition points to achieve a sub-optimal profit. To determine the minimum payment for each data acquisition point, we further design VENUS-PAY, which is a data procurement auction in Bayesian setting. Our theoretical analysis shows that VENUS-PAY can achieve both strategy-proofness and optimal expected payment. We evaluate VENUS on a public sensory data set, collected by Intel Research, Berkeley Laboratory. Our evaluation results show that VENUS-PRO approaches the optimal profit, and VENUS-PAY outperforms the canonical second-price reverse auction, in terms of total payment.
