  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.
