Capitalize your data: Optimal selling mechanisms for IoT data exchange

Zun Li, Qinya Li, Zhenzhe Zheng, Fan Wu, Shaojie Tang, Zhao Zhang, Guihai Chen

Abstract:

More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand.
Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain IoT data
quality and inflexible pricing interface. To resolve these issues, potential solutions could be to launch data demonstrations and release
free sampling data to reduce the uncertainty about data quality, and to charge based on the volume of data actually used to enable
flexible pricing. However, there is still no clear understanding of economic benefits of these mechanisms. In this paper, we design the
optimal data selling mechanisms for IoT data exchange, and derive the following two results. First, whether to deploy a data
demonstration and how much free sampling data to release depend on the extent of data consumers’ inaccuracy perceptions for data
quality, which varies over a wide range in IoT applications. We found that the data vendor has no incentive to conduct these strategies if
data consumers extremely overestimate data quality. Second, although flexible data pricing mechanisms provide convenience for realtime and streaming IoT data exchange, it brings less economic benefits to the data vendor compared with the fixed pricing scheme,
which sells the whole data set with a fixed price. We evaluate the optimal selling mechanisms on a real-world Taxi GPS data set, and
evaluation results verify the insights derived from our theoretical analysis.