A Dynamic Data Trading Marketplace With Externalities

With huge amounts of data generated from Internet of Things (IoT) devices, data-driven technologies are increasingly applied by firms to improve their IoT-based services in real time. To facilitate efficient utilization of the collected data, the design of data trading markets becomes crucial. Two important practical concerns are: i) The data buyers arrive in a sequential and arbitrary manner; ii) A firm faces externalities when data is purchased by competing firms. In this paper, we design a data trading marketplace for data buyers arriving dynamically in real time where the early-arrived data buyers will exert negative externalities on the late arrivals within the same competition. Specifically, in market operations, when a data buyer arrives, it needs to submit a bid based on the price and the current externalities posted by the market operator. After receiving the bid, the market operator will announce the data allocation and payment as well as update the price. To construct the detailed market mechanism, we propose the allocation rule, payment rule, and price update method, which can be proven theoretically to guarantee the desirable properties, including incentive compatibility, individual rationality, revenue maximization, and computation efficiency. These theoretical conclusions are also validated via our numerical experiments.