An online pricing mechanism for mobile crowdsensing data markets

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. To build a practical mobile crowd-sensed data market, we have to consider three major design challenges: data uncertainty, economicrobustness (arbitrage-freeness in particular), revenue maximization. By jointly considering the design challenges, we propose a novel online query-bAsed cRowd-sensEd daTa pricing mEchanism, namely ARETE, to determine the trading price of crowd-sensed data. Our theoretical analysis shows that ARETE guarantees both arbitragefreeness and a constant competitive ratio in terms of revenue maximization. We have evaluated ARETE on a real-world sensoiy data set collected by Intel Berkeley lab. Evaluation results show that ARETE outperforms the state-of-the-art pricing mechanisms, and achieves around 90% of the optimal revenue.