TY - GEN
T1 - How to Price Fresh Data
AU - Zhang, Meng
AU - Arafa, Ahmed
AU - Huang, Jianwei
AU - Poor, H. Vincent
N1 - Funding Information:
M. Zhang and J. Huang are with Department of Information Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, E-mail: {zm015, jwhuang}@ie.cuhk.edu.hk; J. Huang is also with School of Engineering and Science, The Chinese University of Hong Kong, Shenzhen; A. Arafa and H. V. Poor are with the Department of Electrical Engineering, Princeton University, NJ, USA, E-mail: {aarafa, poor}@princeton.edu. This work was supported in part by the Global Scholarship Programme for Research Excellence from CUHK, in part by the Overseas Research Attachment Programme from School of Engineering at CUHK, in part by the General Research Fund CUHK 14219016 from Hong Kong UGC, 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 Grants CCF-0939370 and CCF-1513915.
Publisher Copyright:
© 2019 IFIP.
PY - 2019/6
Y1 - 2019/6
N2 - We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the age of information (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.
AB - We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the age of information (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.
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U2 - 10.23919/WiOPT47501.2019.9144091
DO - 10.23919/WiOPT47501.2019.9144091
M3 - Conference contribution
AN - SCOPUS:85069779707
T3 - Proceedings - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
BT - Proceedings - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
A2 - de Pelligrini, Francesco
A2 - de Pelligrini, Francesco
A2 - Saad, Walid
A2 - Tan, Chee Wei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
Y2 - 3 June 2019 through 7 June 2019
ER -