Three hierarchical levels of Big-data Market Model over Multiple Data Sources for Internet of Things
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.
This study reveals that a bundling strategy can attract more customers and achieve higher revenue. Lately, by considering competitive market models among multiple market participants, which can be modeled using a non-cooperative market model, analytic research has been suggested in , . As a first approach to designing a non-cooperative data trading market model, Niyato et al. in  modeled a simple market form that consists of the data source, service provider, and service customers, and designs their interactions. They separate the role of service provider and data source; the service provider purchases raw data from the data source, and then extracts useful information from the data, and finally provides it to customers. They assume that all collected data is stored in a single data source. Thus, there is no competition among data source providers and willingness-to-pay (WTP) of the customers is assumed to be uniform. As a work to extend , an auction based market model was proposed by Jiao et al.  under the same market structure as that used in . In that study, customers bid a service fee to a service provider, and the service provider adopts an optimal bidding value to maximize its profit. In this process, the service provider uses Bayesian-optimal mechanisms to optimize the data size and the unit price of the data. All previous works on competitive data trading market models have adopted single data source and analyzed interaction between service provider under single data source and customers. However, as suggested in , to design a competitive data trading market and maximize revenue from valuable information, bundling from multiple data sources should be considered.
we addresses a data trading problem in which a service provider gathers data from multiple data sources and provides valuable information from refined data as a service to its customers. We consider that multiple data sources conduct a data bidding competition to maximize their own revenue by considering the budget suggested by the service provider. And, customers decide whether to subscribe or not based on the subscription fee, WTP, and the quality of the refined data. Finally, three hierarchical levels of multiple data sources, a service provider and customers are modeled based on Stackelberg game theory
In this paper, we have proposed a three hierarchical levels big-data market model that consists of multiple data sources, a service provider, and customers. We have designed a rigorous game theoretic model to derive the unique NE point of the market participants. We also designed realistic WTP by following LND, so the expected number of subscribers can be calculated practically. The analytic results have demonstrated that our proposed approach is guaranteed to have a unique equilibrium point that maximizes the payoff for all market participants. As our future work, multiple service providers can be considered to extend our model. Moreover, block chain technique can also be applied to design a fully decentralized structure where all components are either producers or consumers.
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