Optimal Fresh Data Sampling and Trading

Abstract

Data freshness, measured by Age of information (AoI), is becoming an increasingly significant metric for data valuation. However, most existing data trading markets ignore the impact of such a metric. In this paper, we study a fresh data market, where users with heterogeneous valuations for AoI stochastically arrive over time. The platform decides data sampling (which affects the AoI) and pricing policies (to the users), to maximize its profit. We consider three types of pricing policies with increasing flexibility, i.e., a uniform pricing policy, a dual pricing policy, and a dynamic pricing policy. The joint data sampling and pricing optimization is a non-smooth mixed integer programming problem, which is challenging to solve. Despite the difficulty, we derive the closed-form solutions of the optimal data sampling policies and pricing policies for all three cases. Our analysis yields several interesting practical insights. First, the optimal data prices decrease in the unit sampling cost and increase in the users’ arrival rate. Second, for all three pricing policies, the equal-spacing data sampling policy is optimal. Third, numerical results show that the optimal dual pricing policy significantly outperforms the optimal uniform pricing policy. Specifically, the optimal dual pricing policy produces up to 280% of the profit that is achieved by the optimal uniform pricing policy.