Strategic Coalition for Data Pricing in IoT Data Markets
- URL: http://arxiv.org/abs/2206.07785v4
- Date: Tue, 29 Aug 2023 12:19:19 GMT
- Title: Strategic Coalition for Data Pricing in IoT Data Markets
- Authors: Shashi Raj Pandey, Pierre Pinson, Petar Popovski
- Abstract summary: This paper considers a market for trading Internet of Things (IoT) data that is used to train machine learning models.
The data is supplied to the market platform through a network and the price of such data is controlled based on the value it brings to the machine learning model.
- Score: 32.38170282930876
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper considers a market for trading Internet of Things (IoT) data that
is used to train machine learning models. The data, either raw or processed, is
supplied to the market platform through a network and the price of such data is
controlled based on the value it brings to the machine learning model. We
explore the correlation property of data in a game-theoretical setting to
eventually derive a simplified distributed solution for a data trading
mechanism that emphasizes the mutual benefit of devices and the market. The key
proposal is an efficient algorithm for markets that jointly addresses the
challenges of availability and heterogeneity in participation, as well as the
transfer of trust and the economic value of data exchange in IoT networks. The
proposed approach establishes the data market by reinforcing collaboration
opportunities between device with correlated data to avoid information leakage.
Therein, we develop a network-wide optimization problem that maximizes the
social value of coalition among the IoT devices of similar data types; at the
same time, it minimizes the cost due to network externalities, i.e., the impact
of information leakage due to data correlation, as well as the opportunity
costs. Finally, we reveal the structure of the formulated problem as a
distributed coalition game and solve it following the simplified
split-and-merge algorithm. Simulation results show the efficacy of our proposed
mechanism design toward a trusted IoT data market, with up to 32.72% gain in
the average payoff for each seller.
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