Federated data acquisition market: Architecture and a mean-field based data pricing strategy

Abstract

With the increasing global mobile data traffic and daily user engagement, technologies, such as mobile crowdsensing, benefit hugely from the constant data flows from smartphone and IoT owners. However, the device users, as data owners, urgently require a secure and fair marketplace to negotiate with the data consumers. In this paper, we introduce a novel federated data acquisition market that consists of a group of local data aggregators (LDAs); a number of data owners; and, one data union to coordinate the data trade with the data consumers. Data consumers offer each data owner an individual price to stimulate participation. The mobile data owners naturally cooperate to gossip about individual prices with each other, which also leads to price fluctuation. It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario. Hence, we propose a data pricing strategy based on mean-field game (MFG) theory to model the data owners’ cost considering the price dynamics. We then investigate the interactions among the LDAs by using the distribution of price, namely the mean-field term. A numerical method is used to solve the proposed pricing strategy. The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme. The result further demonstrates that the influential LDAs determine the final price distribution. Last but not least, it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.

Keywords

Federated data acquisition market / Pricing strategy / Data trading

CRediT authorship contribution statement

Jiejun Hu-Bolz: Conceptualization, Methodology, Formal anal- ysis, Writing - original draft, Funding acquisition. Martin Reed: Conceptualization, Writing - original draft, Writing - review & editing, Supervision. Kai Zhang: Methodology, Validation, Formal analysis. Zelei Liu: Writing - review & editing, Visualization. Juncheng Hu: Validation, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported within the project TRACE-V2X, which has received funding from the European Union’s HORIZON-MSCA- 2022-SE-01-01 under grant agreement (101131204).

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