Optimal Two-Sided Market Mechanism Design for Large-Scale Data Sharing and Trading in Massive IoT Networks
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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 of the data requires the permission from the data owners, which gives rises to a potential market opportunity for the IoT data sharing and trading to create economic values and market opportunities for both data owners and consumers. 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 to optimize the payoffs under welfare-maximization and the profit maximization schemes. We characterize the optimal mechanism with a class of threshold matching rules under welfare and profit-maximization and study three matching behaviors including separating, bottom pooling, and top pooling. We use HealthyGo as a case study to illustrate the optimal threshold matching rules and corroborate the analytical results.
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