Cooperative IoT Data Sharing With Heterogeneity of Participants Based on Electricity Retail

Bohong Wang, Qinglai Guo, Tian Xia, Qiang Li, Di Liu, Feng Zhao

Abstract: With the development of the Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple scenarios. However, the isolation of IoT data among entities makes it difficult to optimally allocate data and convert them into economic value, thus it is necessary to introduce the IoT data sharing mode to drive data circulation. To enhance the accuracy and fairness of IoT data sharing, the heterogeneity of participants is sufficiently considered, and data valuation and profit allocation in IoT data sharing are improved based on electricity retail. Data valuation is supposed to be relevant to attributes of IoT data buyers, where risk preferences of electricity retailers are selected as characteristic attributes and data premium rates are proposed to depict their impacts. Profit allocation should measure the marginal profit shares of electricity retailers and data brokers (DBs) fairly, thus an asymmetric Nash bargaining model is used to guarantee that they receive reasonable profits based on their contributions to the coalition of IoT data sharing. Considering the heterogeneity of participants comprehensively, the proposed IoT data sharing fits a large coalition of IoT data sharing with multiple electricity retailers and DBs. To demonstrate the applications of IoT data sharing, case studies are utilized to validate the data value for electricity retailers with different risk preferences and the efficiency of profit allocation using the asymmetric Nash bargaining model. Finally, the proposed method can promote the efficiency of using IoT data and guide for potential large-scale IoT data transactions.