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

In Internet of Thing (ToT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of ToT services has attracted the attention of academia and industry. The pricing method based on non-cooperative game allows edge service providers to compete fairly, effectively preventing edge nodes from malicious bidding. However, in this scenario, a task can only be assigned to one edge vendor. Because only one winner can profit each time, it is easy to create a large number of edge service providers who cannot profit for a long time due to insufficient incentives. To this end, a dynamic alliance pricing mechanism based on distributed edge-intelligence is proposed. Edge service providers 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. Tn 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 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%.