Optimal Pricing of Internet of Things: A Machine Learning Approach

• Published in: arXiv.org 2020-02
• Main Authors: Mohammad Abu Alsheikh, Dinh Thai Hoang, Niyato, Dusit, Leong, Derek, Wang, Ping, Zhu, Han
• Format: Article
• Language: English
• Subjects: Allocations; Bundling; Electronic devices; Game theory; Internet of Things; Machine learning; Pricing; Pricing policies
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2002.05929

Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.