Stochastic-Stackelberg-Game-Based Edge Service Selection for Massive IoT Networks

Mobile edge computing is a promising technique to provide timely edge service for the Internet of Things. With a continuously expanding network scale, the number and types of devices in the IoT network are growing rapidly. Moreover, the total number of devices that need edge service actually in the network is uncertain because the devices are online or offline frequently. It is very critical to design an efficient service selection strategy for every device to access edge service providers (ESPs) in such a huge number of devices situation. To address the problem, this article first formulates the access of IoT devices as a stochastic Stackelberg game model. Then, for the edge service selection of devices, a Poisson game is utilized to model the coordinated selection of edge services, we propose a distributed iterative algorithm to solve this game and give the optimal edge service selection strategy for each device group. Next, for the noncooperative competition among ESPs with incomplete information, the service prices of other ESPs are first estimated through a Bayesian neural network (BNN). Then we model this stochastic noncooperative game as a partially observable Markov decision process (POMDP) model, and finally obtain the optimal long-term pricing strategy through Bayesian deep Q -learning network (BDQN) algorithm. The experimental simulation results show that the proposed strategy can significantly improve the utilities of both ESPs and devices and effectively decrease the average queuing probability of all devices.