With the popularity of Internet of Things (IoT) and large-scale deployment of sensors, data has exploded. Big data is utilized to extract useful knowledge and information, and served as data services to consumers. While most of the current researches in the field of big data services focuses on developing and improving algorithms for data mining and information extraction, and rarely studies "big data" from an economic perspective. This paper thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then we build an economic model of data market, which consists of three parties: data vendor, service provider and service users. The data vendor gathers various raw data and sells them to service provider. The raw data is further processed into data services by service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of data market can achieve the maximum profit through the proposed pricing mechanism and economic model.

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal entities (TEs). This makes it a challenge to efficiently schedule computational tasks on ESs. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. This paper aims to investigate the dynamic tasks allocation problem between TEs and ESs and to meet the optimal benefits for both parties in MEC system. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. A bilateral game framework is applied to tackle the tasks allocation problem by modeling the problem as two noncooperative games: the supplier and customer side games. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Our simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoffs, as well as flattening the peak and off-peak loads.