Three Hierarchical Levels of Big-Data Market Model Over Multiple Data Sources for Internet of Things

Abstract

This paper proposes three hierarchical levels of a competitive big-data market model. We consider that a service provider gathers data from multiple data sources and provides valuable information from refined data as a service to its customers. Under our approach, a service provider determines optimal data procurement from multiple data sources within its budget constraint. The multiple data sources follow the service provider's action by independently submitting bidding prices to the service provider. Further, customers decide whether to subscribe or not based on the subscription fee, their willingness-to-pay, and the quality of the refined data. We study the economic benefits of such a market model by analyzing the hierarchical decision making procedures as a Stackelberg game. We show the existence and the uniqueness of the Nash equilibrium (NE), and the NE solution is given as a closed form. Finally, we reveal that the obtained unique equilibrium solution maximizes the payoff of all market participants.