TY - JOUR
T1 - Pricing Fresh Data
AU - Zhang, Meng
AU - Arafa, Ahmed
AU - Huang, Jianwei
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received July 8, 2020; revised December 13, 2020; accepted February 13, 2021. Date of publication March 17, 2021; date of current version April 16, 2021. This work was supported in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society, in part by the Presidential Fund from the Chinese University of Hong Kong, Shenzhen, and in part by the U.S. National Science Foundation under Grant CCF-0939370 and Grant CCF-1908308. This article was presented in part at the WiOpt 2019. (Corresponding author: Jianwei Huang.) Meng Zhang is with the Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208 USA (e-mail: meng.zhang@northwestern.edu).
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2021/5
Y1 - 2021/5
N2 - We introduce the concept of fresh data trading, in which a destination user requests, and pays for, fresh data updates from a source provider, and data freshness is captured by the age of information (AoI) metric. Keeping data fresh relies on costly frequent data updates by the source, which motivates the source to price fresh data. In this work, the destination incurs an age-related cost, modeled as a general increasing function of the AoI. The source designs a pricing mechanism to maximize its profit, while the destination chooses a data update schedule to trade off its payments to the source and its age-related cost. Depending on different real-time applications and scenarios, we study both a finite-horizon model and an infinite-horizon model with time discounting. The key challenge of designing the optimal pricing scheme lies in the destination's time-interdependent valuations, due to the nature of AoI, and the infinite-dimensional dynamic optimization. To this end, we exploit three different dimensions in designing pricing by studying three pricing schemes: a time-dependent pricing scheme, in which the price for each update depends on when it is requested; a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested; and a simple subscription-based pricing scheme, in which the price per update is constant but the source charges an additional subscription fee. Our analysis reveals that (1) the optimal subscription-based pricing maximizes the source's profit among all possible pricing schemes under both finite-horizon and infinite-horizon models; (2) the optimal quantity-based pricing scheme is only optimal with a finite horizon; and (3) the time-dependent pricing scheme, under the infinite-horizon model with significant time discounting, is asymptotically optimal. Numerical results show that the profit-maximizing pricing schemes can also lead to significant reductions in AoI and social costs, and that a moderate degree of time discounting is enough to achieve a close-to-optimal time-dependent pricing scheme.
AB - We introduce the concept of fresh data trading, in which a destination user requests, and pays for, fresh data updates from a source provider, and data freshness is captured by the age of information (AoI) metric. Keeping data fresh relies on costly frequent data updates by the source, which motivates the source to price fresh data. In this work, the destination incurs an age-related cost, modeled as a general increasing function of the AoI. The source designs a pricing mechanism to maximize its profit, while the destination chooses a data update schedule to trade off its payments to the source and its age-related cost. Depending on different real-time applications and scenarios, we study both a finite-horizon model and an infinite-horizon model with time discounting. The key challenge of designing the optimal pricing scheme lies in the destination's time-interdependent valuations, due to the nature of AoI, and the infinite-dimensional dynamic optimization. To this end, we exploit three different dimensions in designing pricing by studying three pricing schemes: a time-dependent pricing scheme, in which the price for each update depends on when it is requested; a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested; and a simple subscription-based pricing scheme, in which the price per update is constant but the source charges an additional subscription fee. Our analysis reveals that (1) the optimal subscription-based pricing maximizes the source's profit among all possible pricing schemes under both finite-horizon and infinite-horizon models; (2) the optimal quantity-based pricing scheme is only optimal with a finite horizon; and (3) the time-dependent pricing scheme, under the infinite-horizon model with significant time discounting, is asymptotically optimal. Numerical results show that the profit-maximizing pricing schemes can also lead to significant reductions in AoI and social costs, and that a moderate degree of time discounting is enough to achieve a close-to-optimal time-dependent pricing scheme.
KW - Pricing
KW - age-of-information
KW - game theory
KW - network economics
UR - http://www.scopus.com/inward/record.url?scp=85103206992&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103206992&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2021.3065088
DO - 10.1109/JSAC.2021.3065088
M3 - Article
AN - SCOPUS:85103206992
SN - 0733-8716
VL - 39
SP - 1211
EP - 1225
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 5
M1 - 9380905
ER -