Trading Fresh IoT Data With Strategic Users

The immense value of IoT data in real-time applications has led to the rise of fresh IoT data trading. Existing research often neglects strategic users who optimally time their data purchases, significantly affecting market demand and revenue. This paper studies a fresh data market with strategic users arriving stochastically and having heterogeneous data valuations. Strategic users decide purchase timing based on data freshness and price, while the platform optimizes its data pricing policy to maximize profit. We first examine a dynamic pricing policy, offering a price menu to each arriving user. This analysis is technically challenging due to the varied integer programming problems faced by heterogeneous users, making direct price optimization infeasible. To address this, we adopt a mechanism design approach, analytically deriving the optimal dynamic pricing policy. To reduce implementation complexity, we also study two simpler pricing policies: single and two-price pricing. In a two-period refreshing model, we derive the optimal single and two-price pricing policies analytically. Our findings reveal that the optimal two-price policy significantly outperforms the single pricing policy, guaranteeing at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of the revenue achieved by the optimal dynamic pricing policy in a two-period refreshing model. Surprisingly, despite having more purchasing options, strategic users may be worse off than if they were myopic due to higher prices. The platform actually benefits from strategic users, generating up to five times more profit with strategic users than with myopic users, even while reducing data refresh frequency.