ARETE: On Designing Joint Online Pricing and Reward Sharing Mechanisms for Mobile Data Markets

Abstract

Although data has become an important kind of commercial goods, there are few appropriate online platforms to facilitate the trading of mobile crowd-sensed data so far. In this paper, we present the first architecture of mobile crowd-sensed data market, and conduct an in-depth study of the design problem of online data pricing and reward sharing. To build a practical mobile crowd-sensed data market, we have to consider four major design challenges: data uncertainty, economic-robustness (arbitrage-freeness in particular), profit maximization, and fair reward sharing. By jointly considering the design challenges, we propose an online query-b A sed c R owd-sens E d da T a pricing m E chanism, namely ARETE-PR, to determine the trading price of crowd-sensed data. Our theoretical analysis shows that ARETE-PR guarantees both arbitrage-freeness and a constant competitive ratio in terms of profit maximization. Based on some fairness criterions, we further design a reward sharing scheme, namely ARETE-SH, which is closely coupled with ARETE-PR, to incentivize data providers to contribute data. We have evaluated ARETE on a real-world sensory data set collected by Intel Berkeley lab. Evaluation results show that ARETE-PR outperforms the state-of-the-art pricing mechanisms, and achieves around 90 percent of the optimal revenue. ARETE-SH distributes the reward among data providers in a fair way.