Data is the cornerstone of intelligent algorithms such as deep learning, and the explosive development of mobile and wireless networks has prompted more devices to share data in time via the Internet. Meanwhile, data is highly time sensitive. It has been found that the value of data is becoming more and more critical to any application areas, significantly highlighting the importance of data pricing mechanisms in data transactions. Although traditional auction mechanisms for ordinary commodities are gradually becoming matures, they fail in the high timeliness data pricing market due to the following key challenges: Firstly, the value and price of the high timeliness data is ever changing with time, making existing mechanisms with fixed prices expired. Secondly, the price changing of such data is uncertain and dynamic, requiring the auction mechanisms to work stably under different price variations of the high timeliness data. To address these challenges, we for the first time innovatively propose an efficient auction mechanism for High Timeliness Data Pricing, namely HTDP. The newly proposed HTDP can maximize the profit of auctioneer in the high timeliness data transactions. And the key factor for HTDP's success is the consideration of the price changing in the high timeliness data, which fills the blank of traditional auction mechanisms in this area. We further evaluate the newly proposed HTDP on the overall auction profit, and compare the results with the benchmark. Experimental results demonstrate that HTDP not only achieves high profit under proper settings, but also is stable and efficient.
|Original language||English (US)|
|Title of host publication||2020 IEEE International Conference on Communications, ICC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Jun 2020|
|Event||2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland|
Duration: Jun 7 2020 → Jun 11 2020
|Name||IEEE International Conference on Communications|
|Conference||2020 IEEE International Conference on Communications, ICC 2020|
|Period||6/7/20 → 6/11/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was in part supported by National Science Foundation for Distinguished Young Scholars of China with No. 61825204, National Natural Science Foundation of China with No. 61932016, Beijing Outstanding Young Scientist Program with No. BJJWZYJH01201910003011 and Beijing National Research Center for Information Science and Technology (BNRist) with No. BNR2019RC01011.
- Data pricing
- High timeliness