Pricing Fresh Data
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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 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; 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 nature of AoI and the infinite-dimensional and dynamic optimization. To this end, we consider three pricing schemes that exploit and understand the profitability of three different dimensions in designing pricing: 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; a subscription-based pricing scheme, in which the price for each update is flat-rate but the source charges an additional subscription fee. Our analysis reveals that the optimal subscription-based pricing maximizes the source's profit among all possible pricing schemes under both predictable deadline and unpredictable deadline models; the optimal quantity-based pricing scheme is only optimal with a predictable deadline; the time-dependent pricing scheme, under the unpredictable deadline, is asymptotically optimal under significant time discounting.
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