Cost-effective dynamic alliance pricing mechanism based on distributed edge intelligence

In beyond 5G (B5G) Internet of Things (IoT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of IoT services has attracted the attention of academia and industry. The pricing method based on noncooperative game allows edge service providers (ESPs) to compete fairly, effectively preventing edge nodes from malicious bidding. However, since only one winner can make a profit each time, it is easy to cause a large number of ESPs to lose money for a long time. To this end, a dynamic alliance pricing mechanism based on distributed edge intelligence is proposed. ESPs can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes but also makes full use of the advantages of mutual cooperation between nodes. According to the characteristics of edge nodes, various roles are reasonably divided. In order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance’s candidate solutions to improve pricing efficiency and edge benefit. The experimental results show that, compared with the traditional alliance game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%–61.37%. Meanwhile, the prediction accuracy was improved by 16.47%–38.86%, and the average prediction time was reduced by 42.81%–65.57%.