An Edge Intelligence framework with Reinforcement Learning for Digital Twins in Industrial Metaverse

With the rapid advancement of emerging technologies such as the metaverse and digital twins, the need for effective coordination among communication, computation, and storage in complex systems and edge computing environments has become more crucial than ever. This research presents a novel architecture for an industrial metaverse based on digital twins, which optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communications. The proposed architecture utilizes task offloading and storage on edge servers to reduce latency and meet the requirements of future metaverse systems in terms of reliability and latency minimization.The proposed method relies on reinforcement learning algorithms, including Deep Q-Network and its advanced variants, including Double Deep Q-Network (DDQN) and Dueling Deep Q-Network (Dueling DQN) to enable intelligent decision-making and adaptability in dynamic conditions. By enhancing adaptability in varying scenarios and making smarter decisions, and according to the obtained simulation results, the proposed method reduces latency by more than 10% on average compared to the best method available in the literature. The results show that this model not only reduces latency and energy consumption, but also enables optimal use of resources.