An Online Pricing Mechanism for Mobile Crowdsensing Data Markets
Zhenzhe Zheng, Yanqing Peng, Fan Wu, Shaojie Tang, Guihai Chen · 2017
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, economic-robustness (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 arbitrage-freeness and a constant competitive ratio in terms of revenue maximization. We have evaluated ARETE on a real-world sensory 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.