Pricing for Revenue Maximization in IoT Data Markets: An Information Design Perspective

Abstract

Data is becoming an important kind of commercial good, and many online data marketplaces are set up to facilitate the trading of data. However, most existing data market models and the corresponding pricing mechanisms are simple, and fail to capture the unique economic properties of data. In this paper, we first characterize the distinctive features of IoT data as a commodity, and then present a new IoT data market model from an information design perspective. We further propose a family of data pricing mechanisms for revenue maximization under different market settings. Our MSimple mechanism extracts full surplus from the market for the model with one type of buyer. When multiple types of buyers coexist, our MGeneral mechanism optimally solves the problem of revenue maximization by formulating it as a polynomial size convex program. For a more practical setting where buyers have bounded rationality, we design MPractical mechanism with a tight logarithmic approximation ratio. We evaluate our pricing mechanisms on a real-world ambient sound dataset. Evaluation results show our pricing mechanisms achieve good performance and approach the revenue upper bound.