Pricing Fresh Data
Meng Zhang, Ahmed Arafa, Jianwei Huang, and H. Vincent Poor

TL;DR
This paper explores how to price data updates in a system where data freshness, measured by AoI, impacts costs and revenues, proposing and analyzing various pricing schemes to maximize source profit.
Contribution
It introduces a comprehensive framework for pricing fresh data using AoI, analyzing three pricing schemes and identifying the optimal strategies under different deadline scenarios.
Findings
Subscription-based pricing maximizes profit in both models.
Quantity-based pricing is optimal only with predictable deadlines.
Time-dependent pricing is asymptotically optimal with significant discounting.
Abstract
We introduce the concept of {\it 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 {\it age of information} (AoI) metric. Keeping data fresh relies on frequent data updates by the source, which motivates the source to {\it 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; 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 predictable-deadline and an unpredictable-deadline models. The key challenge of designing the optimal pricing scheme lies in the destination's time-interdependent valuations, due to the…
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Taxonomy
TopicsAge of Information Optimization
