In the era of the Internet of Things (IoT), an immense amount of data is generated from numerous sensors and devices. Data as a service (DaaS) represents a new market whose time has come, and DaaS-based businesses are emerging quickly. Businesses across sectors begin seeing their data not only as fundamentally valuable but economically viable to distribute. Due to the exponential growth of the DaaS market, the current pricing models gradually become less suitable for the selling of data sets. A more sophisticated pricing strategy is needed to unlock the value of that data for the data vendor’s (DV’s) revenue growth and their customers’ benefits such as online service providers (SPs). In this article, we aim to maximize the DV’s profits by designing a mixed sales mechanism, which allows the DV to sell data sets separately or bundled. Particularly, we apply a multidimensional adverse selection model from contract theory to model the data set trading between DVs and SPs. The DV’s surplus maximization problem is solved in the single-product case first, then extended to the multiproduct case. Furthermore, the analysis of the solution of the pricing strategy in single-product and multiproduct cases is provided. Finally, the simulation results show that the proposed pricing model can improve the DV’s profits efficiently.

中文翻译：

---

混合捆绑策略的数据服务销售设计：多维逆向选择方法


在物联网（IoT）时代，大量传感器和设备产生大量数据。数据即服务 (DaaS) 代表了一个新市场，其时代已经到来，基于 DaaS 的业务正在迅速兴起。各行业的企业开始认为他们的数据不仅具有根本价值，而且在经济上可以进行分发。由于DaaS市场的指数级增长，当前的定价模型逐渐变得不太适合数据集的销售。需要更复杂的定价策略来释放数据的价值，以促进数据供应商 (DV) 的收入增长及其客户的利益，例如在线服务提供商 (SP)。在本文中，我们的目标是通过设计混合销售机制来最大化 DV 的利润，该机制允许 DV 单独或捆绑销售数据集。特别是，我们应用契约理论中的多维逆向选择模型来对 DV 和 SP 之间的数据集交易进行建模。 DV的剩余最大化问题首先在单产品情况下解决，然后扩展到多产品情况。此外，还分析了单产品和多产品情况下的定价策略解决方案。最后，仿真结果表明，所提出的定价模型能够有效提高DV的利润。