On Designing Market Model and Pricing Mechanisms for IoT Data Exchange

Zhenzhe Zheng, Weichao Mao, Yidan Xing, Fan Wu

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

External IDs:dblp:journals/tmc/ZhengMXW24