Simulation-Based IoT Stream Data Pricing Incorporating Seller Competition and Buyer Demands

Abstract

The concept of sensor clouds has been populated for utilizing data from massive amount of IoT devices. In the sensor cloud, a large number of sensors and users are connected and sensor data are traded among them. A number of market frameworks for such data ecosystems have been proposed so far, most of which assumes multiple stakeholders and coordinates their interests using techniques such as the traditional economic theory and game theory. However, because of the duplicability of IoT data, designing a natural pricing scheme based directly on market principles, such as the balance between seller competition and consumer demands, is still a challenge. In this paper, we propose a new pricing scheme for IoT stream data, where prices are determined by the balance between seller competition and consumer demand. Unlike conventional methods, our method is based on simulation. By simulating the market and sellers’ pricing behaviors on the broker’s platform, fair pricing is achieved without causing undesirable phenomena such as price wars. The evaluation results show that the proposed pricing method has desirable characteristics for an IoT data market.