Optimal Two-Sided Market Mechanism Design for Large-Scale Data Sharing and Trading in Massive IoT Networks

Abstract: The development of the Internet of Things (IoT) generates a significant amount of data that contains valuable knowledge for system operations and business opportunities. Since the data is the property of the IoT data owners, the access to the data requires permission from the data owners, which gives rise to a potential market opportunity for the IoT data sharing and trading to create economic values and market opportunities for both data owners and buyers. In this work, we leverage optimal mechanism design theory to develop a monopolist matching platform for data trading over massive IoT networks. The proposed mechanism is composed of a pair of matching and payment rules for each side of the market. We analyze the incentive compatibility of the market and characterize the optimal mechanism with a class of cut-off matching rules for both welfare-maximization and revenue-maximization mechanisms and study three matching behaviors including complete-matched, bottom-eliminated, and top-reserved.