Average age of information optimization in Mobile Edge Computing Networks

Abstract

Age of Information(AoI) is a novel metric to measure freshness of data in status update scenarios proposed by academia in recent years. Real-time applications need to transmit data packets for status update to the target node as soon as possible. However, due to the data density, the limited computing capacity of edge devices and the influence of the environment, the problems of intensive computation and high delay are caused. Mobile edge computing (MEC) is a new computing mode that extends cloud computing power closer to the user, where computing offloading and other technologies promise to solve those problems. We mainly studies the AoI optimization in MEC networks, in which data freshness and offloading strategy play an important role. Firstly, we propose the average AoI minimization problem for MEC network scenarios, and propose a multi-agent deep reinforcement learning(DRL) algorithm called Federated Multi-Agent Actor-Critic (Fed-MAAC). Federated learning is used to train agents to improve algorithm performance and data security. At the same time, we conducted experiments in gym, a popular simulation environment in reinforcement learning, and compared Fed-MAAC with baseline algorithm. The simulation results show that this algorithm is superior to other algorithms in average AoI optimization performance.