Optimal social welfare: A many-to-many data transaction mechanism based on double auctions

Abstract

With the development of Big Data and the Internet of Things (IoT), the data value is more significant in both academia and industry. Trading can achieve maximal data value and prepare data for smart city services. Due to data's unique characteristics, such as dispersion, heterogeneity and distributed storage, an unbiased platform is necessary for the data trading market with rational trading entities. Meanwhile, there are multiple buyers and sellers in a practical data trading market, and this makes it challenging to maximize social welfare. To solve these problems, this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism (SOMTM), which integrates three entities, a trading process and an algorithm named Many-to-Many Trading Algorithm (MMTA). Based on the market scale, market dominated-side and market fixed-side, simulations verify the convergency, economic properties and efficiency of SOMTM.