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| Content Provider | ACM Digital Library |
|---|---|
| Author | Peng, Yanqing Zheng, Zhenzhe Chen, Guihai Tang, Shaojie Wu, Fan |
| 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. 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. |
| Starting Page | 1 |
| Ending Page | 10 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450349123 |
| DOI | 10.1145/3084041.3084044 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-07-10 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Online pricing Mobile crowdsensing Data marketplace |
| Content Type | Text |
| Resource Type | Article |
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