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 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 {\it time-dependent} pricing scheme, in which the price for each update depends on when it is requested; a {\it quantity-based} pricing scheme, in which the price of each update depends on how many updates have been previously requested; a {\it 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.
翻译:我们引入了 ~ 新的数据交易} 的概念, 即目的地用户要求并支付来自来源提供者的最新数据更新, 以及数据更新由 ~ 信息年龄 } (AoI) 衡量。 保持数据更新依赖于源的频繁数据更新, 这促使源到 价格更新数据 。 在这项工作中, 目的地产生与年龄相关的成本, 其模式是AoI 的普遍增长功能。 源设计了一个定价机制, 以最大限度地增加其利润; 目的地选择了一个数据更新时间表, 将其付款与来源及其与年龄相关成本进行交换。 根据不同实时应用程序和情景, 我们研究一个可预测的- 死线和不可预测的死线模型。 设计最佳定价计划的关键挑战在于目的地的时间间估值, 由于 AoI 的性质以及无限的和动态优化。 为此, 我们考虑三种价格定价计划, 利用并理解其三个不同层面的利弊: ~ 时间基 ; 价格计划取决于 价格的每个要求的每个时间期限; 价格的每个期限都取决于 价格的每个期限; 价格的每个要求的每个期限; 价格的每个期限都取决于一个价格的每个期限; 价格的每个要求的每个期限。